Radar Probabilistic Data Association Filter with GPS Aiding for Target Selection and Relative Position Determination

Abstract

In this thesis, Global Positioning System (GPS) and radar measurements are utilized in a multi-sensor architecture to achieve a confident relative positioning solution between two vehicles. A GPS solution providing a three-dimensional positioning vector is deter- mined using pseudorange and carrier phase measurements. The carrier phase measurements make sub-meter level accuracy achievable. However, the carrier phase ambiguity must be re- solved before estimating the relative position vector. A Dynamic Base Real-Time Kinematic (DRTK) positioning algorithm using differential GPS methods is used to achieve highly precise relative positioning between the two GPS antennas. A comparison of the performance of the DRTK algorithm using either single frequency (L1 or L2 frequency only) or dual frequency (L1 and L2 frequency) measurements is introduced.

The radar measurements including range, range rate, and bearing will be utilized in a probabilistic data association filter (PDAF). The PDAF determines which of the radar channels’ solutions are considered valid, and the weighted mean of these solutions is used as the selected target measurements. The PDAF algorithm is discussed in great detail, and the performance of the PDAF algorithm using radar measurements and the performance of the DRTK solution are compared and presented demonstrating that the radar PDAF solution tracks the desired target with reasonable accuracy as long as the lead vehicle is in line of sight.

Finally, the DRTK algorithm is extended to incorporate the radar PDAF solution to increase solution availability, output rate, and reliability of the algorithm’s solution. The PDAF algorithm’s solution using the radar measurements can be utilized during GPS out- ages. The update rate of the radar measurements is ten times faster than the rate of the GPS receiver. The resultant combined system produces estimates at a much higher output rate. The integrated DRTK/PDAF system is implemented with three integration architectures including two “switch” methods and a sensor fusion Kalman filter. Analysis of the accuracy of the integrated systems is presented using experimental data collected on various test vehicles, and some conclusions can be made. The GPS measurements can assist the PDAF solution when the lead vehicle is not visible to the following vehicle. Also, the DRTK/PDAF integrated system produces a more robust relative positioning solution at a higher update rate than either sensor could produce individually.