Development of Wireless Magnetoresistive Sensor Network for Unexploded 
Ordnance and Landmine Detection 
 
by 
 
Houmin Li 
 
 
A thesis submitted to the Graduate Faculty of 
Auburn University 
in partial fulfillment of the 
requirements for the Degree of 
Master of Science 
 
Auburn, Alabama 
May 9, 2011 
 
 
 
Keywords: UXO detection; electromagnetic induction;  
magnetoresistive sensor; wireless sensor networks; distributed sensor nodes; 
 
 
Copyright 2011 by Houmin Li 
 
 
Approved by 
 
Lloyd S. Riggs, Chair, Professor of Electrical and Computer Engineering 
Gwynedd A. Thomas, Associate Professor of Polymer and Fiber Engineering 
Stuart M. Wentworth, Associate Professor of Electrical and Computer Engineering 
Michael E. Baginski, Associate Professor of Electrical and Computer Engineering 
 
  
ii 
 
 
 
 
 
Abstract 
 
This thesis summarizes the effort toward developing a passive/active 
magnetoresistive (MR) sensor array for unexploded ordnance (UXO) realized with 
wireless sensor network technology.  In recent years, the rapid development and 
research on wireless sensor networks (WSN) has enabled application of WSN technology 
in a variety of areas?This thesis analyzed the characteristics and advantages of the WSN 
approach in detail and discussed the feasibility of WSN implementation in a UXO and 
detection system.  An overall system design is given with the passive sensor array 
dedicated for large area magnetic field survey, and the active array for pin-point detection 
and target discrimination.   
The initial design utilizes off-the-shelf components and software to minimize 
engineering costs.  The design scheme of a distributed WSN node is introduced, with 
design of a WSN sensor platform based on the Silicon Labs C8051F912 microcontroller 
(MCU) and Si4431 radio frequency (RF) integrated circuit (IC) combination realized to 
operate on the 915MHz license free Industrial Scientific Medical (ISM) band.  The 
passive sensor node is realized with the HMC5843 digital magnetometer IC and the 
active node is realized with HMC1053 magnetic sensor (both HMC sensors from 
iii 
Honeywell) with customized analog circuits.  The WSN is designed with master-slave 
state machine with star topology that supports up to 255 nodes per master node.  The 
network protocol is comprised of a Si4431 physical layer, an EZMacPro media access 
control (MAC) layer, and a customized application layer.   
The fabricated passive and active sensor node prototypes are compact and light 
weight.  Lab testing demonstrates that the overall system performance goal has been met.  
A pilot production of the WSN nodes is proposed for field testing to further verify the 
effectiveness of the passive/active wireless MR sensor UXO/mine detection array.    
 
 
 
  
iv 
 
 
 
 
 
Acknowledgments 
 
Foremost I would like to thank God for giving me the strength and determination 
to achieve my graduate degree.  I would like to express deepest appreciation to Dr.  
Riggs for his continuous tutoring, support, and encouragement for every step in the 
process.  I would also like to thank Dr.  Thomas, Dr.  Baginski and Dr.  Wentworth 
for their contribution to this thesis by serving on my graduate committee.  To all of the 
professors and staff that ever helped me big or small, I am forever grateful.  To my 
research colleagues, Joshua Hamilton, Ashton Johnson, and David Branscomb, I am 
thankful for all your help throughout my research.  I also would like to express great 
thanks to my family, for their endless support, encouragement, and love.   
  
v 
 
 
 
 
 
Table of Contents 
 
Abstract ........................................................................................................................... ii 
Acknowledgments.............................................................................................................. iv 
List of Tables ................................................................................................................... vii 
List of Figures .................................................................................................................. viii 
List of Acronyms ............................................................................................................... xi 
Chapter 1. Preface................................................................................................................1 
1.1. Wireless Sensor Network Basics ...........................................................................2 
1.2. Objectives ............................................................................................................10 
1.3. Summary ..............................................................................................................10 
Chapter 2. Detection system and sensor node design ........................................................12 
2.1. UXO/mine detection techniques ..........................................................................12 
2.2. Overall system design ..........................................................................................14 
2.3. Magnetoresistance sensor basics..........................................................................15 
2.4. Sensor node design ..............................................................................................17 
2.5. Sensor node work flow ........................................................................................20 
2.6. Summary ..............................................................................................................22 
Chapter 3. Hardware design of wireless sensor platform ..................................................23 
3.1. MR Sensor selection ............................................................................................23 
3.2. Sensor node hardware realization ........................................................................26 
vi 
3.3. Sensor node performance .....................................................................................33 
Chapter 4. Magnetoresistance sensor module design ........................................................35 
4.1. Passive MR Sensor module design ......................................................................35 
4.2. Active MR Sensor module design .......................................................................46 
4.3. Preliminary results ...............................................................................................59 
4.4. Summary ..............................................................................................................61 
Chapter 5. Conclusion and future work .............................................................................62 
References ..........................................................................................................................64 
 
 
 
  
vii 
 
 
 
 
 
List of Tables 
 
Table 2-1 Data packet configuration ................................................................................. 19 
Table 3-1 Power consumption comparison of several low-power MCUs ........................ 24 
Table 3-2 Si4431 power consumption .............................................................................. 33 
Table 3-3 C8051F912 power consumption....................................................................... 33 
Table 3-4 HMC5843 power consumption ........................................................................ 33 
Table 4-1 Features of HMC5843 digital magnetometer IC .............................................. 39 
Table 4-2 Features of HMC1053 3-axis magnetic sensor ................................................ 52 
 
 
 
  
viii 
 
 
 
 
 
List of Figures 
 
Figure 1.1 WSN network structure ..................................................................................... 5 
Figure 1.2 Major components of a sensor node .................................................................. 6 
Figure 1.3 WSN protocol stack ........................................................................................... 7 
Figure 2.1 Overall UXO/mine detection system design ................................................... 15 
Figure 2.2 Linear and wheel line irrigation system .......................................................... 17 
Figure 2.3 Diagram of passive MR sensor array installation............................................ 18 
Figure 2.4 Passive sensor node design scheme ................................................................. 18 
Figure 2.5 Active sensor node design scheme .................................................................. 20 
Figure 2.6 Data gather....................................................................................................... 22 
Figure 2.7 Data send ......................................................................................................... 22 
Figure 2.8 Data receive ..................................................................................................... 22 
Figure 3.1 Design scheme of sensor node platform .......................................................... 27 
Figure 3.2 Reference design of Si4431 ............................................................................. 30 
Figure 3.3 Estimated battery life of passive WSN node ................................................... 34 
Figure 4.1 Earth magnetic field ........................................................................................ 35 
Figure 4.2 System diagram of passive MR sensing unit ................................................... 39 
Figure 4.3 HMC5843 I2C schematic and reference design .............................................. 40 
Figure 4.4 Passive WSN node scheme, MCU and MR sensor ......................................... 42 
Figure 4.5 Passive WSN node scheme, RF....................................................................... 43 
ix 
Figure 4.6 Passive node PCB layout ................................................................................. 45 
Figure 4.7 Fabricated passive node PCB .......................................................................... 45 
Figure 4.8 Assembled passive node .................................................................................. 45 
Figure 4.9 X-ray inspection of assembled passive node ................................................... 45 
Figure 4.10 X-ray integrated relief of passive node components ..................................... 46 
Figure 4.11 Typical EMI system setup ............................................................................. 47 
Figure 4.12 AMR Element [11] ........................................................................................ 47 
Figure 4.13 Linearization of the MR effect [11] ............................................................... 49 
Figure 4.14 Wheatstone bridge configuration [11] ........................................................... 49 
Figure 4.15 MR magnetic domain for set/reset [11] ......................................................... 50 
Figure 4.16 Typical MR characteristic curve [11] ............................................................ 51 
Figure 4.17 System diagram of active MR sensing unit ................................................... 52 
Figure 4.18 Reference design of amplification circuit...................................................... 53 
Figure 4.19 H-bridge Reference design of set/reset circuit .............................................. 54 
Figure 4.20 H-bridge set/reset current test ........................................................................ 54 
Figure 4.21 Active node scheme, MCU and analog circuit .............................................. 55 
Figure 4.22 Active node scheme, RF ................................................................................ 56 
Figure 4.23 Active node layout ......................................................................................... 57 
Figure 4.24 Fabricated active node PCB .......................................................................... 57 
Figure 4.25 Assembled active node .................................................................................. 58 
Figure 4.26 X-ray inspection of assembled active node ................................................... 58 
Figure 4.27 X-ray integrated relief of active node components ....................................... 58 
Figure 4.28 Response of a magnetic pen (10cm) .............................................................. 59 
x 
Figure 4.29 Response of a 60mm mortal shell (10cm) ..................................................... 60 
Figure 4.30 Response of a magnetic pen (20cm) .............................................................. 60 
Figure 4.31 Response of a 60mm mortal shell (20cm) ..................................................... 61 
xi 
 
 
 
 
 
List of Acronyms 
 
ADC analog-to-digital converter 
AMR anisotropic magnetoresistance 
CMR colossal magnetoresistance 
EM electromagnetic 
EMF electromotive force 
EMI  electromagnetic induction 
EMR extraordinary magnetoresistance 
ERW explosive remnants of war 
FD frequency domain 
FSK frequency shift keying 
GMR giant magnetoresistance 
GPS global positioning system 
IC integrated circuit 
IED  improvised explosive device 
ISM industrial, scientific and medical 
LOS line of sight 
MOSFET metal?oxide?semiconductor field-effect transistor 
OMR ordinary magnetoresistance 
OTS off-the-shelf 
xii 
P2P point-to-point 
PCB printed circuit board 
QFN quad flat no leads 
RF radio frequency 
RTC real-time clock 
SCL serial clock 
SDA serial data line 
SNR signal-to-noise ratio 
SPI serial peripheral interface 
SPS sample per second 
TD time domain 
UHF ultra high frequency 
USB universal serial bus 
UXO unexploded ordnance 
 
  
1 
 
 
 
 
 
Chapter 1. Preface 
 
Unexploded ordnance (UXO) and landmines are recognized as one of the world?s 
most serious and dangerous environmental problems.  Not only do the presence of UXO 
and landmines pose a life threatening situation to many people across the world, they also 
can significantly retard the reconstruction and development efforts in war-torn countries.  
The majority of the casualties caused by UXO and landmines are civilians and children, 
especially men who work in reconstruction areas which were once battlefields or military 
testing sites.  According to Landmine Monitor Report 2009[1], around two-thirds of the 
countries in the world (119 countries) are affected to some extent by the landmine/UXO 
problem.  Between 1999 and the end of 2008, Landmine Monitor collected information 
on 73,576 mines, explosive remnants of war (ERW) detonations, additionally improvised 
explosive devices (IED) were responsible for 17,867 death, 51,711 injuries, and 3,998 of 
unknown status, and the figure is most certainly under reported since it includes only 
recorded incidents.  The figure also does not include casualties during conflicts. 
The anti-personnel landmine is the weapon of choice for relatively 
underdeveloped countries, as the cost of a single landmine may be as low as US$3-10.  
Due to the cheap cost, there are two to five million landmines laid every year, while only 
100,000 are being removed at a cost of $300 to $1,000 per mine.  As for the UXO case, 
for the U.S. alone, there are currently more than 10 million acres of land on around 1400 
sites that are thought to contain UXO [2].  With the danger involved with lingering 
2 
UXO, the land cannot be utilized for any productive activity.  It is estimated that it 
would cost tens of billions of dollars to check and clear all of the possibly affected land in 
the U.S. alone.   
 There is currently a significant amount of research being performed in an 
attempt to develop methods to detect and clear these sites quickly and easily.  To clear a 
site of UXO, the explosive must be detected first.  This is typically done by performing 
a geophysical survey.  A geophysical survey provides a complete map of any detectable 
geophysical anomalies across a wide area.  Data collected can then is used to subdivide 
the area into smaller regions with high threat potential.  Sensors of various types are 
currently used to detect metal objects on or below the ground.  Since explosives 
normally contain metal, the explosives can be located by detecting, for example, 
perturbation in the earth magnetic field due to mine/UXO with ferrous materials.   
A Wireless Sensor Network may be rapidly deployed over a wide geological area.  
With appropriate sensors integration they can be powerful tools in locating UXO, 
landmines, and other metallic objects.   
1.1. Wireless Sensor Network Basics 
1.1.1. Definitions 
A wireless sensor network (WSN) is a novel type of self-organized network that 
bloomed in the late 1990s.  It can be formed by a large number of compact and low cost 
sensor nodes with integrated sensing, processing, and wireless communication capability.  
The purpose of a WSN is to cooperatively monitor, sense, and gather any information 
about the environment or object under test and process this information, convey it to end 
users through multi-hop wireless channels.  With WSN, the conventional data 
3 
acquisition method has evolved from single wired sensor mode to highly integrated, 
miniaturized, automated, and interconnected ?smart? network mode.  WSNs can also be 
easily deployed to areas hard to reach or even to hazardous environments.   It represents 
a novel information acquisition and processing technology and has drawn much attention 
from the research community. 
1.1.2. Characteristics of Wireless Sensor Network 
WSN can be deployed using air cargo to the area of interest to form a 
self-organized network.  The sensor nodes cooperatively gather, process, and 
communicate information.  Due to the requirements on dimensions, cost, and power, 
each sensor node only has access to very limited physical resources.  The characteristics 
of a WSN can be summarized as:  
Large Scale: To acquire precise real-time information, it is necessary to deploy a 
large number of sensor nodes to the area of interest, the number of nodes can be 
thousands or more.  For example, with the nodes loaded on and deployed by aircraft, 
WSN can be deployed over thousands of acres of land for geological survey or oceanic 
environmental survey.   
Energy Limitation: Every node in the network has access to limited energy.  
WSN typically functions in rural areas with a scarce population or even in a hazardous 
environment.  Sometimes it is impossible to change the battery at a node and ultra-low 
power consumption is required to prolong the operation life of the network. 
Self-organization: Self-organization including functions of auto network 
association, node and terminal identification, and hack prevention.  Compared to a fixed 
4 
point wireless network, the configuration of a WSN is similar to an ad hoc network, with 
the prerequisite of a suitable communication protocol for unmanned operation.   
Self-management 
In WSN, data processing is done within the nodes in order to reduce the data rate 
on the wireless medium, so that only the data related to other nodes are sent over the RF 
link.  Data centric is another feature of WSN.  Since the positions and deployment of 
the nodes are not pre-planned, it is possible that some nodes will become out of service 
due to errors or accidents during deployment.   To ensure undisturbed services, 
redundant nodes must be included in WSN design.  Moreover, data sharing and 
cooperation between nodes must be implemented to guarantee complete monitoring of 
the subject. 
Different from ad hoc networks: WSN is similar to ad hoc network on the 
feature of distributed network.  However, there are many differences between them.  
Generally speaking, WSN nodes have greater limited access to resources compared to 
handheld devices, but the computing requirement is only optional.  When a 
computational task is needed, if cost of communication is low, it is possible to transfer 
the computing task to the sink node to improve overall performance. 
1.1.3. WSN System Structure 
WSN Networking Structure: WSN typically consist of sensor node, sink node, 
and management node.  A great number of sensor nodes randomly scattered inside or 
around the sensing field, forms a network using the self-organize capability, real-time 
monitor and capture information of objects, and send data back to the sink node through 
multi-hopping or other mechanisms.  The data can be processed during transmission, 
5 
and then passed to the internet or via satellite to the management node.  The end user 
can configure and manage the whole sensor network remotely, setting monitor tasks or 
change objectives ?on the fly?.  A basic WSN network structure is shown in Figure 1.1. 
Sensor nodes are usually micro embedded systems, have very limited processing 
power, memory storage, and communication capability.  They are usually equipped with 
a small battery, a radio transceiver, and microprocessor used to control and process 
wireless data packages.  Hardware and software at the sensor nodes are usually the key 
research area addressed when developing a specific WSN.  On the other hand, sink 
nodes usually have better processing power, memory storage, and communication 
capability, and may have connectivity to an external network such as the internet, 
implementing interconnectivity between different protocols, disseminating tasks to the 
sensor nodes, and conveying the gathered data to an external network. 
 
Figure 1.1 WSN network structure 
WSN Sensor Node Structure: WSN has emerged as an independent computer 
network, and its basic unit is the node.  Every node has its own sensor unit, processing 
unit and communication module.  The functions of each node are to sense data, process 
6 
data, and transmit data.  A sensor node can have up to five major components as shown 
in Figure 1.2. 
 
Figure 1.2 Major components of a sensor node 
The microprocessor or microcontroller controls all processes and the behavior of 
the device and execute preprogramed codes.  The storage unit stores the gathered and 
relayed data.  Normally program and data uses different memory allocations.  Sensors 
are the units used to gather information from the environment or data on a specific object.  
Communication modules are used to convey the data out to the external network and end 
users.  The power system is usually a battery, with or without energy harvest devices.   
1.1.4. WSN Protocol Stack 
The protocol stack of a WSN utilizes simplified open system interface (OSI) 
model, it usually consists of Physical Layer, Data Link Layer, Network Layer, Transport 
Layer, and Application Layer as shown in Figure 1.3.  Besides, the stack should also 
include power management platform, mobility management platform, and task 
management platform.  These platforms enable the sensor node to work efficiently and 
utilize the least energy, thus prolonging system work life. 
7 
 
Figure 1.3 WSN protocol stack 
Physical Layer: The Physical Layer consists of the basic hardware transmission 
technologies of a network such as signal modulation/demodulation, transmission and 
reception.  The main functions of Physical Layer include frequency selection, signal 
modulation, and data encryption.  For long range telecommunication, it is a critical 
component in terms of implementation complexity and energy consumption.   
Data Link Layer: Data Link Layer is focused on solving multiple access 
problems.  It provides the functions and procedures to transfer data between network 
entities without contention and might provide the means to detect and correct errors that 
may occur in the Physical Layer. 
Network Layer: Network Layer takes care of the data routing from the 
Transportation Layer.  With many sensor nodes scattered around the observation area, a 
routing protocol is necessary to be used for the communication between sensor nodes and 
sink node. 
Transportation Layer: To ensure quality of service (QoS) of the WSN, 
Transportation Layer is utilized to maintain data stream throughout the WSN.  Referring 
8 
to the protocol diagram in Figure 1.3, when WSN needs to be connected to another type 
of network, the Transportation Layer allows the sink node to communicate with the task 
management node with conventional TCP or UDP protocols.   However, since the 
sensor nodes have very limited power and memory resource, the WSN itself needs a 
resource efficient protocol. 
Application Layer: Based on the task requirement of the WSN, different 
applications can be added to the Application Layer.   It can include a series of 
application software for monitoring purposes.   Because of their limited computing 
power, memory storage and communication capability, each individual sensor node can 
only acquire a very limited topology of the network, the protocol cannot be overly 
complicated.  Meanwhile, WSN topology may be constantly changing, i.e. wireless 
mobile sensor network, with a requirement of dynamic networking resource reallocation, 
thus places even a higher requirement of the WSN protocol.  The WSN protocol must 
also ensure each individual node forms a multi-hop data transportation network, and the 
current key research areas are Network Layer and Data Link Layer protocols.   
1.1.5. Wireless Sensor Network Applications 
WSN nodes have the capability of continuously gathering data, detecting events, 
and marking events, monitoring position, and controlling other nodes.  These features 
plus the wireless communication capability ensures broad applications of WSNs.  They 
can be applied in environmental monitoring and prediction, geological survey, health 
monitoring, smart homes, building monitoring, control of complex machinery, 
metropolitan traffic control, space exploration, garage management, industrial park safety, 
etc.  A very important feature of WSN nodes is that they can be deployed to any 
9 
designated area for different humanitarian missions including UXO and mine clearance 
missions.  As the research and application of WSN deepens, WSN will have great 
impact on every aspect of human lives.   
1.1.6. Current Research on Wireless Sensor Networks  
WSN technology bloomed in the late 1990s, and due to great application potential, 
it has become one of the most widely pursued research areas in the academic and 
industrial world. 
The earliest WSN research effort was funded by NSF and DARPA, and was 
carried out by the University of California and Cornell University.  Starting in the year 
2000, many industrial researches on WSN have been reported including works from Intel 
[3] and Microsoft [4].   
Nowadays, WSN research has gone through two stages of developments.  The 
first stage is focused on the utilization of micro-electro-mechanism system (MEMS) 
technology and miniaturization of the nodes.  The representing works including 
Wireless integrated network sensors [5] (WINS), and Smart Dust [6].   The second 
stage if research is mostly about the network itself and has become an active research 
area.  From the network architecture point of view, every layer has to be studied 
separately to optimize the overall performance of the WSN.  Current research is mainly 
focused on the Network Layer and Link Layer with special interests in establishing 
robust point-to-point (P2P) or meshed network through different MAC protocols.   
10 
1.2. Objectives 
The ultimate goal of this research is to develop a distributed, mobile 
passive/active WSN array for UXO/mine detection.  The highly sensitive magnetic 
sensor nodes are to form a sensor array to survey large areas with adequate resolution.  
The gathered data together with the GPS coordinate of the sensor node can be sent to the 
user terminal for target discrimination and mapping, thus providing critical data for 
further clearance efforts.   
The first objective of this research is to develop a passive magnetoresistance 
sensor node by using a digital magnetometer sensor.  The digital magnetometers have 
relatively small bandwidth and are mainly used to measure the earth magnetic field (DC) 
anomaly to detect the location of a UXO/mine.  They are easier to build than an 
electromagnetic induction (EMI) system but do not have the frequency bandwidth 
necessary to discriminate among targets. 
The second objective is to develop an active magnetic field sensor which can be 
used as an EMI system to provide additional information about the buried target. Over 
what is available using a simple magnetometers array, the additional information can 
enhance the ability to discriminate mines from clutter and other uninterested geologic 
anomalies.  
1.3. Summary 
This research is focused on the development of UXO/mine detection WSN using 
passive/active magnetic sensors and that provides an efficient way to locate and 
discriminate among potentially hazardous buried anomalies.   
11 
The overall structure of this thesis is arranged as follows: 
Chapter 1 briefly describes the UXO/mine hazard, the background of WSN, and 
overall research objectives.  Chapter 2 introduces the overall distributed detection 
system design, compares different detection technologies, and gives sensor node design 
and a work flow diagram.  Chapter 3 explains the details of the hardware design of the 
sensor node, including schematics and PCB layout.  Chapter 4 introduces sensor unit 
design for passive and active sensing using the magnetoresistance method.  Chapter 5 
gives preliminary test results prove the feasibility of WSN application in UXO and mine 
detection.  Chapter 6 summarizes the research and gives suggestions for future work. 
 
 
12 
 
 
 
 
 
Chapter 2. Detection system and sensor node design 
 
To take full advantage of a WSN, the distributed nodes network architecture is 
utilized.  This approach distributes a task to an individual node, and the task is 
completed locally.  For the passive system, due to the large size of the network, each 
sensor node gathers magnetic field data at its own rate (10sps typical) and sends the data 
to the sink node through multi-hop scheme.  For the active EMI system, a central 
synchronization signal needs to be sent out by the sink node to reset/set the magnetic 
sensors for best sensitivity and resolution.  Thus the active WSN array is relatively 
small in size but requires much higher sampling rate (50ksps) and consumes more power.   
2.1. UXO/mine detection techniques 
The development of UXO/mine detection techniques largely depends on the 
advances in various sensor systems.  The current most popular technologies used are 
magnetometry, ground penetrating radar, and EMI.  Among these, the prominent 
technologies are magnetometry and EMI sensors.   
2.1.1. Magnetometry 
Magnetometry is the technique of measuring and mapping patterns of magnetic 
field using magnetometers.  A magnetometer is a passive sensor which uses the earth?s 
magnetic field as an excitation source to measure an object?s magnetic susceptibility.  
Magnetic sensor or magnetometers are widely used in geophysical survey research.  
13 
They can detect the disturbances in the surrounding static magnetic field i.e. earth 
magnetic field.  Magnetic surveys have been one of the most successful techniques used 
for locating buried UXO/mines.  When the sensor passes over ferrous metal objects such 
as UXO or mine, the field disturbance of the earth?s magnetic field can be recorded by 
the sensor leading to target detection.  Although magnetometers are easiest to use, since 
the magnetic sensor only measures passive field disturbances, they are prone to geologic 
noises and inefficient at determine target shapes.   
2.1.2. EMI system 
An EMI sensor is an active device that carries an excitation source and measures 
magnetic susceptibility and electrical conductivity depending on the frequency of 
excitation.  The EMI sensor is relatively immune to geologic noise and has the 
capability to determine target shape, size and orientation.  However, it is sensitive to 
sensor orientation and its false alarm rate is high due to metal fragments.   
Conventional EMI systems are classified into time domain (TD) and frequency 
domain (FD) sensors based on the type of excitation.  Time domain sensors use short 
duration current pulses to excite the transmitter coil and are referred to as pulsed EMI 
systems.  The current pulses are usually increased to a certain level then abruptly shut 
off to induce an EMF in the buried object.  TD detectors are usually preferred over FD 
detector for UXO/mine detection since the former method provides somewhat higher 
signal-to-noise ratio (SNR). The reason for this is that once the transmitter current in a 
TD is turned off only the signal from the buried object remains; whereas with FD system 
the transmitter is always on and its energy competes with the usually weak return from 
the buried object. 
14 
2.1.3. Ground penetrating radar 
Ground Penetrating Radar (GPR) techniques have been highly developed in the 
past decade [7, 8] and have resulted in several commercial devices.  It has been proven 
to reduce false alarm when used with other sensor technologies.  Although it has the 
ability to determine target depth and shape, it performs poorly in any medium that has a 
high conductivity, such as wet and/or clay rich soils.  Also, since the electromagnetic 
wave absorption rate of soil increases with frequency, GPR performance degrades when 
high frequency is necessary to achieve adequate resolution of small objects.  In the case 
of UXO/mine detection, GPR can suffer from poor detection statistics due to clutter.  
Moreover, the GPR technique is not proven to be a standalone UXO sensor and must be 
used together with other sensor technologies.  For more information on GPR, refer to 
reference [7, 8]. 
2.2. Overall system design 
The proposed UXO detection system is comprised of many wirelessly 
interconnected nodes with passive and active magnetic field sensors.  The passive 
system is designed to achieve simultaneous large area survey, while the active system 
works together with an EMI source to provide detailed target shape and depth 
information.  This setup takes advantage of WSN features for maximized efficiency and 
accuracy, and provides maximum safety for the bomb disposal team.  The overall 
system diagram is shown in Figure 2.1.   
15 
 
Figure 2.1 Overall UXO/mine detection system design 
2.3. Magnetoresistance sensor basics 
Magnetoresistance (MR) is the property of a material to change the value of its 
electrical resistance when an external magnetic field is applied.  MR is measured as 
magneto-resistance ratio, and magnetic reluctivity defined as: 
g2015 g3404 g1844g3009 g3397g1844g2868g1844
g2868
 
Where g1844g3009 is the resistance when magnetic field H interacts with the material, 
and g1844g2868 the resistance when no magnetic field is applied.  Depending on the angle 
between the applied field and current (0uni00B0 when parallel, 90uni00B0 when perpendicular), the 
MR is called longitudinal and transverse MR effect for parallel and perpendicular cases 
respectively.   Based on the mechanism of MR, it can be categorized into two types, 
ordinary magnetoresistance (OMR), and extraordinary magnetoresistance (EMR).   
16 
OMR is caused by the enlarged electron scattering area due to introduction of 
external magnetic field.  The effect is the same on ferrous and non-ferrous materials.  
For non-magnetic metals, MR effects at low fields are very small, although the effect can 
become quite large for high fields.   
On the other hand, EMR is a unique property of ferromagnetic materials.  The 
physical origin of the magnetoresistance effect lies in spin orbit coupling.  The electron 
cloud about each nucleus deforms slightly as the direction of the magnetization rotates, 
and this deformation changes the amount of scattering undergone by the conduction 
electrons when traversing the lattice [9].  A simplified explanation can be given: when 
the materials is under magnetization saturation status, if the field and magnetization are 
oriented transverse to the current, then the electronic orbits are in the plane of the current, 
and there is a small cross-section for scattering, giving a low resistance state; if the field 
is parallel to the current, the electronic orbits are oriented perpendicular to the current, 
and the cross-section for scattering is increased, giving a high resistance state.   
EMR can be further classified as anisotropic magnetoresistance (AMR), giant 
magnetoresistance (GMR), and colossal magnetoresistance (CMR).  The materials and 
mechanisms for these three types of magnetoresistance are also distinctly different.  The 
AMR is of particular interest in this research.   
The ferromagnetic material utilized for AMR heads is NiFe (permalloy).  This is 
due to the relatively large effect at room temperature (?g2025/g2025 ~ 2%), and the low 
saturation fields (g1834g3046 ~ 5-10 Oe) required to obtain the AMR effect [9].  In order to 
properly utilize the AMR effect, the operating point (zero applied field) of the sensor has 
to be either on the increasing or decreasing slope of the AMR response (biasing).  
17 
Moreover, an external field may cause the operating point to drift, and cause ?latch up?, 
thus frequency reset/set pulses must be implemented to guarantee accuracy.   
2.4. Sensor node design 
2.4.1. Passive MR Sensor node 
The passive sensors can be installed on a linear or wheel line irrigation system, as 
shown in Figure 2.2 or similar structures, to form a passive magnetometer array and 
sweep a large area at once.  This solution uses commercially available products and can 
provide a novel way to acquire large area magnetic field data with minimal modification 
of the carrier structures, as depicted in Figure 2.3.   
  
Figure 2.2 Linear and wheel line irrigation system 
18 
 
Figure 2.3 Diagram of passive MR sensor array installation 
 
The passive sensor node is designed to be ultra-portable, ultralow-power, and 
with adequate communication range.  Battery operation and compact size, as well as 
robustness are the major considerations.  The sensor node design scheme is shown in 
Figure 2.4. 
 
Figure 2.4 Passive sensor node design scheme 
19 
An off the shelf (OTS) digital MR sensor module is utilized in the passive sensor 
node.  The digital MR sensor module should have amplification, set/reset/offset strap 
drive control, ADC, and digital communication, functions built in, so that no external 
analog circuit need be implemented.  The passive MR sensing requires a lower sampling 
rate compared of the active system, thus on-chip ADC and control MCU on the digital 
sensor is acceptable.  Since the sampling rate is relatively low (10sps typical), it is 
affordable to send all the data back to the sink node through the RF link (128kbps typical) 
for data processing.  Assuming 16 bits (2bytes) data reading for each axis, the RF link 
can support up to g2869g2870g2876g3038g2872g2876?g2869g2868 ? 266 sensor nodes.  The packet configuration of each data 
package is shown in Table 2-1. 
Table 2-1 Data packet configuration  
Preamble Sync 
pattern 
Payload 
length 
Payload 
 3-axis MR reading 
CRC 
AA AA AA AA AA 2D D4 xx xx xx xx xx 
5 bytes 2 bytes 1 byte 6 bytes 1 byte 
 
The detailed realization of the passive sensor node is shown in Chapter 3. 
2.4.2. Active MR Sensor node 
Active MR Sensors are more challenging than the passive ones.  To acquire 
detailed target information such as shape, depth, and material, EMI system must be used.  
Compatibility issues between active MR Sensor and the EMI system must be taken into 
consideration. 
Due to the unique requirements of the active system, OTS components are not 
available.  A specially designed analog circuit must be implemented to address this issue.  
The signal from the MR sensor needs to be amplified and sampled at much higher rate 
20 
(50ksps) in order to capture the EMI signature of the target.  The sample rate also makes 
it impossible to send every data reading back to the sink node for processing, so that 
on-sensor processing capability must be implemented.  The design scheme of the active 
sensor node is shown in Figure 2.5. 
 
Figure 2.5 Active sensor node design scheme 
 
The MR sensor used in the active node is a 3-axis AMR sensor.  The analog 
signal gathered will be carefully conditioned before passed into the on-chip ADC of the 
MCU.  A Set/Reset circuit is carefully designed to synchronize the Set/Reset impulse 
with the EMI pulses.  An offset compensation circuit is also implemented for 
maximized system sensitivity and resolution.  The data will be gathered by the MCU for 
exponential factor extraction.  The extracted data is then sent back to the sink node for 
further processing. 
2.5. Sensor node work flow 
The WSN is design to work with master-slave architecture with sensor nodes as 
slaves and sink node as master.  For the passive system, the sensor node is to achieve 
21 
the solo function of gathering earth magnetic field information.  Their small sizes and 
ultralow-power features make sure they can be easily deployed over a broad area or form 
a large array for UXO/mine survey purposes.  While for the active system, due to the 
limited range of the EMI source, relatively high data processing requirements, and 
synchronization requirements, a smaller array is desired which provides better accuracy 
and discrimination capability.  Both sensor systems can communicate with the same 
sink node.  The sink node takes care of positioning, routing, network management and 
other complicated functions, and will be realized with off-the-shelf (OTS) USB 
transmitter and a laptop computer.  The design of a passive and active sensor node is the 
main focus of this research.   
In order to support network expandability, efficient node management, and low 
power consumption, a Contention Sensing Multiple Access (CSMA) scheme with 
automated query and acknowledge, is used.  Multi-hop transmission is also supported 
for extended range.  The overall work flow diagram of the CSMA scheme is shown in 
Figure 2.6 through Figure 2.8.   
 
22 
   
Figure 2.6 Data gather Figure 2.7 Data send  Figure 2.8 Data receive 
2.6. Summary 
In this chapter, the overall detection system design is briefly introduced.  
Different UXO/mine detection techniques are introduced and compared.  Based on 
current detection technology, hybrid techniques are preferred to take advantage of 
different technologies.  Then, the distributed passive/active WSN arrangement is 
introduced with detailed sensor node design schemes.  Finally the sensor node work 
flow chart is given. 
 
  
23 
 
 
 
 
 
Chapter 3. Hardware design of wireless sensor platform 
 
3.1. MR Sensor selection 
There are generally five design parameters for WSN node development, low cost, 
proper microprocessor selection, ultra-low power, interference resistance, and 
miniaturization.    
3.1.1. Low cost 
Since WSN is typically comprised of large number of sensor nodes, and under 
many circumstances, the sensor nodes are one-time use only, the cost of sensor node 
directly impact the overall implementation of the WSN.  As a result, lowering 
fabrication cost is the most important design parameter in designing WSN sensor nodes.   
3.1.2. Microprocessor Selection 
Due to the limitation on dimension, cost, and power, it is generally preferred to 
use microcontrollers (MCU) for system control and data processing.  The data 
processing task on sensor nodes are typically minimal, microprocessors with powerful 
data processing capability are typically not suitable for sensor nodes.  To choose a 
proper MCU, several factors have to be taken into consideration: 
Interface performance: When deployed, the sensor node?s MCU need to carry out 
many tasks simultaneously, including node device control, task management, energy 
24 
calculation, function management, and etc., a suitable number of interfaces is required to 
accomplish all these tasks.  The more tasks that need to be accomplished, the more 
interfaces will be required.  Before purchasing the MCU, work flow of the sensor node 
need to be examined and planned carefully.  One may calculate the total number of ports 
and interfaces based on the tasks, and select the MCU with proper size and functions.  
There are many OTS MCUs in the market that are suitable for WSN applications, for 
example, C8051F series from Silicon Labs, MSP430 from TI, R8C family from Renesas, 
and AVR series from Atmel.   
Storage size and I/O ports: Storage size largely determines the size of program 
and data a MCU is capable of processing.  System upgradability and expandability are 
important design factors for WSN, since not all the functions can be anticipated that need 
to integrate into the system.  Upgradable firmware and expandable storage size 
guarantees the system can be improved over time. 
Processor speed: For the passive sensor node, the processing power requirement is 
minimal.  However, the active sensor node requires real-time sampling and data 
processing, thus the MCU must be able to process adequate data.   
Power consumption: The method of deployment of the passive sensor requires 
ultralow-power features.  A low sleep mode current is desirable for the design.  Table 
3-1 shows typical power consumption of several different MCUs. 
Table 3-1 Power consumption comparison of several low-power MCUs 
 C8051F912 ATtiny10 TI MSP 
Wake up time (us) 2 N/A 5 
Sleep mode current (uA) 0.01 0.1 3 
Active mode current (uA/MHz) 160 (25MHz) 200 (12MHz) 45 (4MHz) 
Supply voltage (V) 0.9-3.6 1.8-5.5 1.5-1.65V 
g3
25 
3.1.3. Ultra-low Power and Energy Harvest Design 
The widely distributed sensor nodes are required to gather data while transmitting 
data through the RF link.  In general, the on board battery is the only energy source of 
the sensor, thus ultralow-power consumption is a very important technology for these 
sensor nodes.  It is required that the power consumption of the node to be as low as 
possible.  Toward this end, the components of choice must be low power, especially the 
sensor, MCU, and RF IC.  The components are also desired to have low operation 
voltages.  For constant current, lower supply voltage means lower power consumption.  
A proper battery is also an important factor and OTS batteries are the best of choice for 
reliability and availability.  The different components must have a sleep mode so when 
not in use by the system, they can be switched to sleep mode independently.   
3.1.4. Anti-interference Design 
The signal to noise (SNR) ratio largely determines the sensitivity and 
performance of the detection system.  There are two major sources of noise, radiated 
emission from external sources and conducted emission from the sensor itself.  Radiated 
emission comes from external sources such as lightening, radiation from space, and other 
high power RF devices.  The frequency spectrum expands from DC to UHF and higher.  
The radiated emission induces currents in the sensor circuit boards, leads, and sensors 
and disrupts the sensitivity.  Self-interference comes from common mode interference, 
Electrostatic coupling interference, conducted interference coupling and other noises 
from internal sources.   
To reduce system noise, several precautions must be taken into consideration 
including shielding and digital filtering.  Metallic films can effectively radiation at 
26 
1000Hz and above.  Metallic shields are transparent to magnetic field signals, and thus 
are applicable to this detection system.  The metal shield also needs to be grounded 
properly to reduce coupled interferences.  It is preferred to enclose the sensor node 
circuits inside a metal casing and of course leave the antenna outside the enclosure.  
Digital filtering is another effective method to improve the SNR of the system on the RF 
side.  A built-in digital filter feature of the RF IC is desirable for the sensor node.   
3.1.5. Miniaturization 
Miniaturization is another important feature of WSN.  The size of the sensor 
node largely depends on the MCU of choice.  A high performance MCU with Small 
Outline Package (SOP), integrated ADC, and other digital peripherals is desired.   
Sensor node design is a complicated and involves many design tradeoffs.  
Engineering principles and trade-offs is necessary to balance the above requirements and 
make the final product a success. 
3.2. Sensor node hardware realization 
Based on the above guidelines, a WSN node platform is design for both passive 
and active MR sensors.  The platform utilizes MCU as central processing module, and 
includes sensor module, wireless communication module, power module, and other 
peripherals.  A detailed explanation is given for each component of the passive and 
active sensor node.  The overall platform scheme is shown in Figure 3.1. 
27 
 
Figure 3.1 Design scheme of sensor node platform 
The hardware of the sensor node platform uses a modular design.  The MCU 
takes care of sensor operation, task management, function coordination, and data storage 
and processing.  The sensor module measures the external magnetic field and generates 
a proportional voltage signal.  The wireless communication module is responsible for 
data transmission between nodes and the power module provides power to all 
components.  A Silicon Labs C2 interface is used as in-circuit-debugging interface for 
firmware update. A detailed component description is provided below. 
3.2.1. Microprocessor Module Design 
The MCU is the core of the sensor node.  It controls and coordinates all 
components of the sensor node.  Although there are many microprocessors in the market, 
only a few of them are suitable for use in WSN sensor node applications.  The 
requirement of MCU include small size, high integration, ultralow-power, high 
processing speed, low cost, and support sleep mode.  The microprocessors in the market 
can be categorized as microcontroller (MCU), Digital Signal Processor (DSP), 
28 
Programmable Logic Controller (PLC), Advanced RISC Machine (ARM), and other 
specialty microprocessors.  ARM processors have very powerful processing power and 
speed, but they are larger in size and require much higher power.  DSPs are not designed 
as controllers and are generally used with other processors.  MCUs are low power, small 
size, specialty in control, and are most suitable for WSN application.  The MCU of 
choice is the high performance, ultralow-power C8051F912 MCU from Silicon Labs.   
The C8051F912 is optimized for battery operated operations.  It can operate as 
low as 0.9V supply, which make is possible to design a sensor node running on a single 
AA or AAA battery.  It is capable of providing up to 25 million instructions per second 
(MIPS) throughput with 25 MHz clock, 768 bytes of on-chip RAM, and 16kB in-system 
programmable flash.  It features 2g2020s wake-up time, 160g2020A/MHz active mode current, 
and as low as 10nA sleep mode current.  It also has a built-in temperature sensor, 12-bit 
ADC, DC-to-DC converter, 16 port I/O (including I2C and UART), four general purpose 
16-bit counter/timers, and Real-time clock (RTC).  With advanced pipelined 
architecture, it is capable of processing adequate amount of data with common digital 
signal processing algorithm such as the Fourier transformation. 
3.2.2. Wireless Communication Module Design 
There are many short/medium range wireless technologies available for WSN 
such as Wi-Fi, Bluetooth, ZigBee, and sub-GHz industrial, scientific and medical (ISM) 
band technology.  The priorities for choosing the most suitable wireless technology are 
range, power consumption, data rates, antenna size and interoperability.  Compared with 
the most popular 2.4GHz technologies, sub-GHz wireless systems offer several 
29 
advantages, including longer range, reduced power consumption, and lower deployment 
and operating costs. 
A simple calculation according to the Friis Equation compares the path loss of 
900MHz and 2.4GHz is given 
g1842g1853g1872?uni0009g1838g1867g1871g1871 g3404 20?log4g2024g1856g2019  
Where d is the distance and g2019 is the wavelength.  The frequency difference can 
be translated as 8.5dB higher loss for 2.4GHz technology.  Thus with same operation 
power, sub-GHz has roughly 2.7 times the range compared to 2.4GHz technology.  Plus, 
sub-GHz provides superior penetration, slower fade, and less blocking effect. 
For data rate consideration, although 2.4GHz provides higher data rate than 
sub-GHz (54Mbps vs. 128kbps), the excess data rate is not a necessary for either passive 
or active systems.  The passive system only requires 480bps per node, the sub-GHz can 
very well support up to 200 nodes, which is more than sufficient.  As for the active 
system, 2.4Mbps per node is required to send every data point through the RF link to the 
sink node for data processing.  The 54Mbps bandwidth can only support up to 20 nodes, 
a large scale network is not economically acceptable. 
30 
 
Figure 3.2 Reference design of Si4431g3
 
With all design considerations and to avoid compatibility issues, a sub-GHz RF 
IC Si4431 from Silicon Labs is chosen to be paired with the C8051F912 MCU as the RF 
module.  It features small size (20-pin QFN package), ultralow-power (18.5 mA receive, 
31 
28 mA @ +13 dBm transmit), low voltage (1.8-3.6V), high sensitivity (?118 dBm), and 
moderate data rate (128kbps).  In this design, the Si4431 is configured to work on 
915MHz band, with FSK modulation, and 128kbps data rate.  The MCU configures the 
Si4431 registers through SPI interface to control the state machine and determine the 
state of the device (idle, receive, or transmit).    
Another major concern using this Silicon Labs combination setup is that Silicon 
Labs provides the EZMacPro library as a generic PHY and MAC layer protocol solution 
for their IC users.  The EZMacPro fully utilize the built-in RF hardware of the Si4431, 
including digital modem, FIFO, fast frequency hopping and other capabilities.  A 
reference design of the Si4431 is shown in Figure 3.2. 
3.2.3. Sensor Module Design 
Due to the different assignment of the passive and active method, different MR 
sensor implementations are used.  The passive sensor module has an amplification 
circuit and other peripheral built in, while the active sensor requires a carefully designed 
analog circuit.  The design details of the passive and active sensor modules are 
described in Chapter 4.     
3.2.4. Power Management Module 
Power source is a critical component of the sensor node.  For reliable operation, 
it is required that each component of the sensor node is supplied with adequate and stable 
current.  Battery or other power source is not able to achieve this, thus a voltage 
regulation circuit is required.  The C8051F912 MCU has a built in DC-DC converting 
circuit, which allow operation from a single cell battery with a supply voltage as low as 
32 
0.9 V.  Other specifications of the on-chip DC-DC converter include fully 
programmable output voltage range of 1.8 to 3.3 V, typical power output of 110mW max, 
and can be used for powering other devices in the system.  These features allow the 
flexibility when interfacing to sensors and other components which typically require a 
higher supply voltage than a single-cell battery can provide.  The DC-DC converter also 
supports programmable bypass mode for the ease of use with solar cells.  During normal 
operation, if the dc-dc converter input voltage exceeds the programmed output voltage, 
the converter will stop switching and the external source (solar cell) will be used directly 
to power the circuit and charge the battery.  Based on these features, a rechargeable 
AAA battery is used as the primary power source with energy harvest interface designed 
in case of prolonged operation requirement. 
3.2.5. Other peripherals 
Due to the time sensitive nature of the MR measurements, it is desired to have a 
real-time clock to track time for the sensor nodes to sleep and wake up and thereby save 
power.  The C8051F912 has a built-in ultralow power 32-bit SmaRTClock (Real Time 
Clock) with alarm.  The SmaRTClock has a dedicated 32 kHz oscillator that can be 
configured for use with or without a crystal.  The SmaRTClock can operate directly 
from a 0.9?3.6 V battery voltage and remains operational even when the device goes into 
its lowest power down mode.  The SmaRTClock output can be buffered and routed to a 
GPIO pin to provide an accurate, low frequency clock to other devices while the MCU is 
in its lowest power down mode.   
I2C interface: The C8051F912 MCU also has built-in I2C capability.  The I2C 
I/O interface is a two-wire, bi-directional serial bus.  Data can be transferred at up to 
33 
100kbps as a master or slave.  The interface makes it possible to connect the MCU with 
the HMC5843 digital MR sensor on chip level.   
3.3. Sensor node performance 
3.3.1. Power consumption 
The power consumption of a WSN node is comprised of RF, MCU, and sensor 
power consumptions.  The power consumption of the RF IC is shown in Table 3-2.   
Table 3-2 Si4431 power consumption 
Frequency 915MHz 
RF Status Transmit 
(+1dBm) 
Transmit 
(+13dBm) 
Receive Sleep 
Current 18mA 28mA 18.5mA 800nA 
The power consumption of the C8051F912 MCU is determined by the operation 
voltage and system clock frequency.  The power consumption of C8051F912 under 
different modes is shown in Table 3-3. 
Table 3-3 C8051F912 power consumption 
Condition VDD=1.8V 
MCU 
Status 
Active 
F=24.5MHz 
Active 
F=1MHz 
Idle 
F=24.5MHz 
Sleep 
with RTC 
Current 4mA 265uA 2.1mA 0.6uA 
The power consumption of the digital magnetometer HMC5843 is determined by 
supply sources, and measure modes.  The detailed power consumption is shown in 
Table 3-4. 
Table 3-4 HMC5843 power consumption 
Condition AVDD = 2.5 volts 
Sensor status Measure mode Idle mode Sleep mode 
Current 0.9mA 340uA 110uA 
34 
3.3.2. Battery life 
Based on the power consumption, the maximum battery life can be calculated 
using Silicon Labs Battery Life Estimator.  The simulated results are shown in Figure 
3.3. 
0 2 4 6 8 10
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
 
 
Vo
lta
ge
 (V
)
Time (Days)
 Voltage (V), Single Cell AAA
 
Figure 3.3 Estimated battery life of passive WSN node 
3.3.3. Summary 
In this chapter, hardware implementation of the WSN node is described.  The 
overall system utilized modular design, and a detailed explanation is given for each 
module.  The power consumption and estimated battery life is given for the passive 
system.  It is shown that the overall system design conform to the WSN requirements. 
 
35 
 
 
 
 
 
Chapter 4. Magnetoresistance sensor module design 
 
4.1. Passive MR Sensor module design 
4.1.1. Earth magnetic field characteristic and field detection application 
The earth's magnetic field (and the surface magnetic field) is approximately a 
magnetic dipole, with the magnetic field South Pole near the Earth's geographic North 
Pole and the other magnetic field North Pole near the Earth's geographic South Pole.  
This makes the compass usable for navigation and other application such as 
magnetometry for UXO and mine detection.  A simple dipole model of earth magnetic 
field is shown in Figure 4.1 [10].   
 
Figure 4.1 Earth magnetic field  
 
36 
Like any other magnetic field, earth magnetic field can be measured in magnetic 
field strength H or its vector components.  The earth magnetic field is mainly generated 
inside the earth.  The field strength is typically within 30000nT~60000nT and is almost 
constant within a few kilometers on earth surface, where large ferrous object can 
significant disturb the field strength.  In geophysics, it is conventional to use nT as the 
unit for the earth?s magnetic field strength measurement.  The relationship between 
Tesla and Gauss, is 1g1846 g3404 g2869g3024g3029g3040g3118 g3404 10g2872uni0009g1833(g1859g1853g1873g1871g1871) g3404 10g2877g1866g1846 .  For field anomaly 
application such as UXO/mine detection, the sensor should be able to detect magnetic 
field in the nT range.    
Due to the development of advanced magnetic field sensors, the microprocessor, 
and filtering technology, the combination of MR sensor with WSN is possible.  With the 
advent of GMR and AMR sensors, there have been several interests in measuring the 
earth?s magnetic field and perturbation thereof due to ferrous objects like mines and 
UXO.  
4.1.2. Magnetic field sensor options 
Magnetic field sensors, generally called magnetometers, are manufactured based 
on different physic principles.  A brief review on different magnetic sensing principles 
is given here to provide a baseline for choosing different technology for UXO detection 
solution.   
4.1.2.1. Coil sensors 
Coils are the oldest means to measure variations of magnetic fields.  According 
to Faraday?s law, any change in the magnetic environment of a coil of wire will cause a 
37 
voltage (EMF) to be induced in the coil.  Thus by placing a coupled coil in a time 
varying magnetic field, the variation of the field can be recorded by the variation in the 
EMF generated by the coil.   
Coils can only be used to measure time varying magnetic field.  There are many 
implementations using the coil, such as the traffic light control sensor.  The coils buried 
underneath the roadbed can sense the passing by vehicles.  However, due to the large 
size and relatively high cost, coils are not ideally suited for use with WSN.    
4.1.2.2. Fluxgate magnetometers 
A fluxgate is a magnetometer developed based on Faraday?s Law.  A fluxgate 
magnetometer consists of a small, magnetically susceptible, core wrapped by two coils of 
wire.  An alternating current is passed through one coil, driving the core through an 
alternating cycle of magnetic saturation.  This alternating magnetic field induces an 
electrical current in the second coil, and this output current is measured by a detector.  
The fluxgate magnetometer has been around since WII, and their typical sensitivity 
nowadays are on the order of 0.5nT to 1nT.  However, due to the coils and magnetic 
core involved, their sizes are relatively large, and costs from $5,000 to $10,000 are 
typical.  For time spatial sampling of the magnetic field, the fluxgate magnetometers, 
because of its large size, is not ideal.  
4.1.2.3. Hall Effect sensors 
The most common magnetic sensing devices are solid-state Hall Effect sensors.  
A Hall Effect sensor is a transducer that varies its output voltage in response to changes 
in magnetic field.  Hall Effect sensors are typically used to measure 10Oe to 1000Oe 
38 
Strong fields.  Hall Effect can exist in both metals and semiconductors.  The most 
common Hall Effect sensors are silicon based Hall Effect ICs with signal conditioning 
circuit build in the sensor.  The typical sensitivity of silicon based Hall Effect sensors 
are around 5mV/V/Oe, and the minimum detectable magnetizing field is around 10Oe, 
which is much larger than earth?s magnetic field (0.5Oe max).   
4.1.2.4. Magnetoresistance sensors 
Magnetoresistive (MR) sensors make use of the magnetoresistive effect, the 
property of a current-carrying magnetic material to change its resistivity in the presence 
of an external magnetic field.  The MR sensors are typically made of film strips of 
ferromagnetic material called permalloy (19% Fe, 81% Ni) deposited on silicon 
substrates using a semiconductor fabrication process.  The films are typically 150 to 
500A (Angstrom) thick, 10-50um wide, and 500-1000um long.  Their miniatures sizes, 
high sensitivity, and near immunity to EM noises make them ideal for use in a WSN.   
As a result, the sensor of choice for our UXO/mine detection WSN is the MR 
sensor.g3
4.1.3. Passive MR Sensor module design 
Many highly sensitive sensors capable of sensing small variation in Earth 
magnetic field are developed in recent years based on the MR mechanisms.  Based on 
these sensors, it is possible to develop a UXO detection system with excellent spatial 
resolution. Another very desirable attribute of the MR sensor is its ability to read all three 
vector components of the earth?s magnetic field, leading to improved discrimination 
capability over a single axis measure. 
39 
 
MR sensors can be used to detect the magnetic field anomalies caused by ferrous 
materials and these estimate the location of the object.  The data gathered from a 3-axis 
magnetometer can be processed to extract complete magnetic field vector information.   
For our passive MR sensor array, we used the HMC5843 3-axis digital 
magnetometer IC.  A summary of HMC5843 features is listed in Table 4-1 and the 
system diagram of MR sensing unit is shown in Figure 4.2.   
Table 4-1 Features of HMC5843 digital magnetometer IC 
Ultra-low power Low Voltage (2.5 to 3.3V), active mode current 0.9mA 
Small Size 4.0x4.0x1.3mm surface mount LCC package 
High resolution 120 g2020g1859g1853g1873g1871g1871  
High sensitivity 1.0 mV/V/gauss 
Integrated peripherals Built-in ASIC circuits with amplification, strap drivers, 
offset cancellation, 12-bit ADC and an I2C interface 
 
Figure 4.2 System diagram of passive MR sensing unit 
40 
Although the HMC5843 has amplification, strap drivers, offset cancellation, 
12-bit ADC and an I2C serial bus interface built in, a few supportive components such as 
pull up resistors and reservoir capacitors have to be added for proper operation.  The 
I2C interface with the host MCU must also be designed.  The HMC5843 requires the 
I2C bus to be interfaced with the host MCU, as shown in Figure 4.3.   
  
Figure 4.3 HMC5843 I2C schematic and reference design 
 
For the MCU and RF module we used a Silicon Labs C8051F912 MCU and a 
Si4431 radio.  This combination features ultralow power and voltage operation.  A 
single AAA battery can support the module in full active mode (24.5 MHz clock) for 
over 7 days (refer to 3.3.2).  The C8051F912 has an on-chip dc-dc converter to allow 
operation from a single cell battery with a supply voltage as low as 0.9 V.  The DC-DC 
converter is configured to enable bypass mode when VBAT is greater than VDD for use 
with a solar panel.  A power harvest interface is utilized for solar panel connection.  
41 
When the voltage supplied by the solar cell/battery combination reaches the designed 
output voltage, the DC-DC converter will bypass itself to operate at maximum efficiency.  
Since the DC-DC convertor is flexible with input voltage range, this allows for different 
combinations of batteries and solar cells for the system if other power source options are 
desired. 
The Si4431 RF IC is connected to the MCU through serial peripheral interface 
(SPI) and is controlled by Silicon Labs EZMacPro library software.  A PCB meander 
line antenna from Silicon Labs EZRadioPro reference design is used together with the 
Si4431 radio.  The MCU also has a C2 interface for debugging and firmware update.   
The HMC5843 digital magnetometer is connected to the MCU I/O ports P0.0 as 
serial data line (SDA) and P0.1 as serial clock (SCL) using I2C bus configuration.  The 
I2C bus operates at 100kbps data rate with the C8051F912 MCU configured as master 
and HMC5843 configured as slave.  A breadboard test is successfully carried out before 
the PCB design to verify the compatibility between the MCU and HMC5843.  The final 
schematic of the passive WSN node is shown in Figure 4.4 and Figure 4.5.   
 
42 
 
Figure 4.4 Passive WSN node scheme, MCU and MR sensor  
43 
 
Figure 4.5 Passive WSN node scheme, RF 
44 
In order to keep the board to a minimal size as well as reduce in circuit 
interferences, we chose to use leadless Quad Flat No leads (QFN) based ICs and surface 
mount passive components that are sized down to 0402 based packages.  An advantage 
to using standard surface mount components is that the board can be assembled on a 
larger scale using standard manufacture assembly lines for electronics because they are 
easily handled using automated ?pick and place? machines.  Using components this 
small, hand-soldering is less than ideal for populating the board; therefore, we chose to 
use the reflow soldering technique.  This method requires that a solder paste to be 
applied to each of the component pads on the circuit board.  To place the correct solder 
paste in the right location, we chose to have a solder paste stencil made of 3mm thick 
clear Mylar for relatively low cost at Pololu Robotics and Electronics.  Upon placing the 
components, the circuit board is place in an oven to flow the solder, which will harden 
when cooled.  The final layout is shown in Figure 4.6.  An unpopulated circuit board is 
shown in Figure 4.7 and a fully assembled board is shown in Figure 4.8. A X-ray 
inspection of the fully assembled board is also carried out using a phoenix PCBA 
inspector, the overall X-ray image and integrated relief are shown in Figure 4.9 and 
Figure 4.10 
45 
 
Figure 4.6 Passive node PCB layout 
 
Figure 4.7 Fabricated passive node PCB 
 
Figure 4.8 Assembled passive node 
 
Figure 4.9 X-ray inspection of assembled passive node 
46 
 
Figure 4.10 X-ray integrated relief of passive node components 
The X-ray results shown a few minor soldering defects possibly introduced during 
assembly process and will be improved in further prototyping iterations. 
4.2. Active MR Sensor module design 
4.2.1. EMI system  
Time-varying electric currents produce time-varying magnetic fields, and 
according to Faraday?s law, this field will induce eddy currents in any nearby conductive 
and/or permeable object.  The eddy currents in turn generate a secondary time-varying 
magnetic field which can be detected using a magnetic field sensor.  The received signal 
may be used to acquire information regarding the UXO/mine target and ultimately used 
for discrimination purposes.  The above electromagnetic interaction may be referred to 
as electromagnetic induction (EMI) phenomenon.  Typical components of an EMI 
system are shown in Figure 4.11 and include a transmitter and receiver and buried object 
or objects. 
47 
 
Figure 4.11 Typical EMI system setup 
As shown in the figure, the typical EMI system implementations use coils as 
transmitter and receiver.  However, as stated in 4.1.2.1, coils are costly and bulky and 
not ideally suitable for WSN applications, thus MR sensors are used to replace the 
receiver coils in our design.  
4.2.2. The magnetoresistive effect 
In sensors employing the MR effect, the resistance of the sensor changes with 
g1855g1867g1871g2870g2016 relationship under the influence of a magnetic field: 
 
Figure 4.12 AMR Element [11] 
g1844 g3404 g1844g3016 g3397uni0394g1844g3016g1855g1867g1871g2870g2016 (4-1) 
It can be shown that 
48 
sing2870uni0009g2016 g3404 g3009g3118g3009
g3264g3118
 for g1834 ? g1834g3016 (4-2) 
and  
sing2870uni0009g2016 g3404 1 for g1834 > g1834g3016 (4-3) 
where Ho can be regarded as a material constant comprising the demagnetizing 
and anisotropic fields and g2016 is the angle between the magnetic moment (M) vector and 
the current flow (I).  Figure 4.12 [11] shows the Permalloy element with field and 
current applied. 
Applying equations (4-2) and (4-3) to equation (4-1) leads to: 
g1844 g3404 g1844g3016 g3397uni0394g1844g3016 g46721? g3009g3118g3009
g3264g3118
g4673 foruni0009uni0009g1834 ? g1834g3016 (4-4) 
and 
g1844 g3404 g1844g3016 for g1834 > g1834g3016 (4-5) 
which clearly shows the non-linear nature of the MR effect. 
4.2.2.1. Linearization 
In order to be used for UXO detection application, the AMR sensors need to be 
configured with in a linear range.  The MR effect can be linearized by depositing 
aluminum stripes (Barber poles) on top of the permalloy strip at an angle of 45? to the 
strip axis as shown in Figure 4.13.  As aluminum has a much higher conductivity than 
permalloy, the effect of the Barber poles is to rotate the current direction through 45?, 
forming a saw-tooth shaped current flow, and effectively changing the rotation angle of 
the magnetization relative to the current from g2009 to g2009 ?uni000945uni00B0. 
49 
 
Figure 4.13 Linearization of the MR effect [11] 
A Wheatstone bridge configuration is also used for linearized applications as 
shown in Figure 4.14.  In one pair of diagonally opposed elements, the Barber poles are 
at +45? to the strip axis, while in another pair they are at ?45?.  A resistance increase in 
one pair of elements due to an external magnetic field is thus matched by a decrease in 
resistance of equal magnitude in the other pair.  The resulting bridge imbalance is then a 
linear function of the amplitude of the external magnetic field in the plane of the 
permalloy strips, normal to the strip axis. 
  
Figure 4.14 Wheatstone bridge configuration [11] 
 
50 
4.2.2.2. Set/reset function 
One undesirable characteristic of the wheatstone bridge MR sensor is that strong 
magnetic fields can disrupt the magnetic domains of the film particles from a smooth 
factory orientation to arbitrary directions thus causing a non-linear responses.  Accuracy 
and resolution of these sensors will suffer until the film magnetic domains are reset to 
recreate a uniform orientation.  Figure 4.15 shows three examples of magnetic 
orientation of the film domain structure. 
 
Figure 4.15 MR magnetic domain for set/reset [11] 
The alignment of the magnetic domains is up along the easy axis of the material 
while the sensitive axis is perpendicular to the easy axis direction which is the driving 
function of the MR characteristic curve as shown in Figure 4.16. 
51 
 
Figure 4.16 Typical MR characteristic curve [11] 
Due to the large current used in the active detection system, especially the EMI 
source, a set/reset circuit must be implemented to acquire accurate and sensitive 
secondary magnetic field data. 
4.2.3. Active MR Sensor module design 
In the active MR sensor design, we used a HMC1053 3-axis magnetic sensor.  
The HMC1053 is a MR sensor designed for low field magnetic sensing.  The advantage 
of the HMC1053 sensor is in the near-perfectly orthogonal sensors on a single chip with 
shared set/reset and offset coils/straps included.  The HMC1053 allows for a much 
faster sampling rate than is available with the HMC5843 (50sps max).  The HMC1053 
is designed to be sampled with the built-in ADC of the C8051F912 MCU, which is 
capable of sampling at 75ksps and this should provide adequate resolution for target 
discrimination.  To function properly, the HMC1053 also requires auxiliary signal 
conditioning, amplification, and a set/reset circuit.  A summary of HMC1053 features is 
listed in Table 4-2 and the system diagram of MR sensing unit is shown in Figure 4.17.   
 
 
52 
 
Table 4-2 Features of HMC1053 3-axis magnetic sensor 
Ultra-low power Low Voltage (1.8V to 20V), active mode current 0.5mA 
Wide bandwidth DC to 5MHz 
High resolution 120 g2020g1859g1853g1873g1871g1871  
High sensitivity 1.0 mV/V/gauss 
Low noise 50 nV/sqrt Hz @ 1kHz 
 
 
Figure 4.17 System diagram of active MR sensing unit 
Although the set/reset and offset straps are built-in, unlike the HMC5843 digital 
magnetometer, the HMC1053 requires an external analog circuit for amplification, signal 
conditioning, set/reset drive, and offset drive circuit.   
The offset strap allows for several modes of operation when a direct current is 
driven through it, including: 1) Subtraction (bucking) of an unwanted external magnetic 
field, 2) nulling of the bridge offset voltage, 3) Closed loop field cancellation, and 4) 
Auto-calibration of bridge gain. 
53 
The set/reset strap can be pulsed with high currents for the following benefits: 1) 
Enable the sensor to perform high sensitivity measurements, 2) Flip the polarity of the 
bridge output voltage, and 3) Periodically used to improve linearity, lower cross-axis 
effects, and temperature effects. 
A reference design of the amplification circuit is shown in Figure 4.18. 
 
Figure 4.18 Reference design of amplification circuit 
In our design, the LMV358 Op-Amps are replaced with a four channel LMV934 
low voltage Op-Amp.  Three out of the four channels are used to amplify the 3-axis 
signal from the HMC1053.  The output from the Op-Amp is connected to three I/O ports 
of the MCU for 12-bit ADC.   
An H-bridge set/reset circuit is implemented in this design.  A reference design 
is shown in Figure 4.19.  A SOT-353 inverter and a pair of IRF7507 MOSFET are used 
instead of the components shown in the reference design. 
54 
 
Figure 4.19 H-bridge Reference design of set/reset circuit 
A breadboard test of the analog circuit has been implemented, and shows 
expected performance of the components.  The set/reset circuit can provide up to 0.6A 
on each set/reset strap which is sufficient to align the sensor to the desired easy axis, as 
shown in Figure 4.20. 
-0.000015 -0.000010 -0.000005 0.000000 0.000005 0.000010 0.000015
-700
-600
-500
-400
-300
-200
-100
0
100
 
 
J (
mA
)
t (sec)
 Single Set/Reset Strap
 Two Set/Rest Straps
 
Figure 4.20 H-bridge set/reset current test 
Based on the reference designs and breadboard implementation, the circuit 
scheme design is shown in Figure 4.21 and Figure 4.22. 
55 
 
Figure 4.21 Active node scheme, MCU and analog circuit 
56 
 
Figure 4.22 Active node scheme, RF 
57 
Following the same reflow procedures as the passive WSN node, the designed 
active WSN node solder paste stencil is ordered from Pololu Robotics and Electronics.  
The PCB board layout shown in Figure 4.23 is ordered from Advanced Circuits.   
 
Figure 4.23 Active node layout 
An unpopulated circuit board is shown in Figure 4.24 and a fully assembled board 
is shown in Figure 4.25. An X-ray overall inspection is shown in Figure 4.26 and an 
integrated relief of MCU, RFIC, MR sensor, and antenna is shown in Figure 4.27. 
 
Figure 4.24 Fabricated active node PCB 
 
58 
 
Figure 4.25 Assembled active node 
 
Figure 4.26 X-ray inspection of assembled active node 
 
Figure 4.27 X-ray integrated relief of active node components 
The X-ray result shows a few defects including several shorts and mistakes in 
antenna layout and will be fixed in further prototyping iterations. 
59 
4.3. Preliminary results 
Preliminary results of a magnetic pen and a 60mm mortal shell with a breadboard 
prototype of the HMC1051 MR WSN node are taken. MR sensor can detect the magnetic 
field disturbance when ferrous objects enter its vicinity.  The disturbance is measured in 
term of voltage variation across the Wheatstone bridge and converted into digital signal 
by the on-chip ADC.  The magnitude of signal depends on the distance between the 
object and the sensor, the closer the object, the stronger the signal.   
408 410 412 414 416 418
1700
1800
1900
2000
2100
2200
 
 
Se
ns
or1
 (c
t)
Time (s)
 ADC count
 
Figure 4.28 Response of a magnetic pen (10cm) 
60 
424 426 428 430 432 434
1700
1800
1900
2000
2100
2200
 
 
Se
ns
or1
 (c
t)
Time (s)
 ADC count
 
Figure 4.29 Response of a 60mm mortal shell (10cm) 
As shown in Figure 4.28 and Figure 4.29, the response of a magnetic pen and a 
60mm mortal shell 10 cm away from the sensor can have as strong as 150-200 ADC 
count, with significant different signal signatures such as amplitudes and pulse shapes.  
15 16 17 18 19 20
1800
1850
1900
1950
2000
2050
2100
2150
2200
 
 
Se
ns
or1
 (c
t)
Time (s)
 ADC count
 
Figure 4.30 Response of a magnetic pen (20cm) 
61 
35 36 37 38 39 40
1800
1850
1900
1950
2000
2050
2100
2150
2200
 
 
Se
ns
or1
 (c
t)
Time (s)
 ADC count
 
Figure 4.31 Response of a 60mm mortal shell (20cm) 
Also shown in Figure 4.30 and Figure 4.31, the response of a magnetic pen and a 
60mm mortal shell 20 cm away from the sensor have only about 50 ADC counts, which 
suggests the signal strength decays significantly with increased distance. 
4.4. Summary 
In this chapter, detailed design schemes of active and passive WSN node are 
given.  Different MR sensors are chosen for active and passive detection systems.  A 
detailed explanation of the corresponding circuits and components for the passive and 
active sensor nodes is also provided.  Some preliminary data gathered with the wireless 
MR sensor is also presented which verifies the feasibility of the system design. 
  
62 
 
 
 
 
 
Chapter 5. Conclusion and future work 
 
In this thesis, based on our understanding of WSN technology, a wireless 
magnetoresistive sensor network is designed to be used for UXO and landmine detection.   
Chapter 1 and 2 provided a brief background and introduction to UXO mine detection 
and WSN technologies followed by Chapter 3 that provided an overall system design of 
the passive/active WSN detection system as well as a detailed design of the wireless 
sensor node platform.  Chapter 4 focused on the design and realization of the sensor 
node hardware.   
This thesis also summarizes efforts toward hardware development for the active 
and passive WSN UXO detection system.  A wireless sensor array is designed that is 
comprised of identical sensor nodes. The nodes utilize multi-hopping and are CSMA 
enabled and overall the system satisfied design criterion for UXO/mine detection system.  
The detailed overall system design and implementation plan is provided.  The sensor 
node employs a modular design scheme.  The detailed design requirement and 
realization of each module is given.  A sensor platform based on the C8051F912 and 
Si4431 combination is presented.  With the customized active or passive sensor module 
circuit, a passive/active WSN can be formed that cooperatively detects magnetic field 
anomalies caused by UXO or mines.   
To the best of our knowledge, this work is the first effort utilizing WSN 
technology for UXO and mine detection.  All the components used are off-the-shelf 
63 
products.  Due to time constraints, the project is still under development, additional 
testing is planned as a future activity.  
The communication range of the sensor node platform is much less than 
handbook data.  Improvements on antenna design, antenna matching network, and 
assembly technique are required to address this issue.  With the sensor nodes assembled 
at the moment, it is impossible to test the robustness of the RF link of the entire WSN.  
Pilot production of the sensor nodes together with large scale WSN test bed are required 
to verify the effectiveness of the entire network.  The WSN protocol has much room for 
improvement.  As the scale of the WSN grows, it is possible that the current MAC 
protocol stack may not be able to provide sufficient system capacity (255 nodes per sink 
node).  Clustered topology may be investigated and employed to solve this problem.  
Field testing of the passive/active UXO and mine detection system is required to verify 
the overall effectiveness of the system. 
 
  
64 
 
 
 
 
 
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