Performance evaluations of multipath multitarget tracking using PCRLB

In this paper, we study the performance of the multipath-assisted multitarget tracking using multiframe assignment for initiating and tracking multiple targets by employing one or more transmitters and receivers. The basis of the technique is to use the posterior Cramer-Rao lower bound (PCRLB) to quantify the optimal achievable accuracy of target state estimation. When resolved multipath signals are present at the sensors, if proper measures are not taken, multiple tracks will be formed for a single target. In typical radar systems, these spurious tracks are removed from tracking, and therefore the information carried in such target return tracks are wasted. In multipath environment, in every scan the number of sensor measurements from a target is equal to the number of resolved signals received by different propagation modes. The data association becomes more complex as this is in contrary to the standard data association problem whereas the total number of sensor measurements from a target is equal to at most one. This leads to a challenging problem of fusing the direct and multipath measurements from the same target. We showed in our evaluations that incorporating multipath information improves the performance of the algorithm significantly in terms of estimation error. Simulation results are presented to show the effectiveness of the proposed method.