Self-Localization of Target nodes using Opportunistic communication with reference nodes, Statistical Time-Of-Arrival, Grid Method, Linear Matrix Inequality and Center-Of-Gravity
Type of DegreeMaster's Thesis
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Self-localization of mobile nodes is an important problem because many useful mobile applications will become feasible once accurate position information is available. Generally, existing methods for obtaining accurate location information require either sophisticated hardware (e.g. Global Positioning System (GPS), Ultra-Wideband (UWB), ultrasounds transceiver) or dedicated infrastructures (e.g. GSM, WLAN). We address this problem using a different approach that requires no special hardware or infrastructure support. The main concept adapted to solve this problem is: localization can be performed and improved by means of cooperative and opportunistic data exchanges among mobile nodes. Consider a GPS-denied target node with no position information that communicates opportunistically with a number of in-range mobile peer nodes with some positioning capabilities. The data exchanges between the target node and the peer nodes will then be used by the target node to refine its position estimation using a combination of these algorithms: Statistical Time-of-Arrival (TOA), Linear Matrix Inequalities (LMI), barycentric algorithms, Grid Method and Center of Gravity (COG) techniques. Approximate ranging using Statistical TOA method using Bhattacharyya Distance enables the LMI, Grid Method, barycentric algorithms and Center of Gravity(COG) technique to improve position accuracy. To investigate the performance of such an opportunistic localization algorithm, we define a simple model that describes the opportunistic interactions between nodes and then we run several computer simulations to analyze the effect of the ranging error on the positioning of the target node. Along with the simulation model we have conducted the experiments with real-world data to measure the performance of the techniques in the real-world. The results generated from both simulation and real-world data show that opportunistic interactions can actually improve self-localization accuracy, where the position estimation error is about 2 m or less in many different scenarios.