PHD Filter of Multi-target Tracking With Passive Radar Observations

Ronald Mahler's probability hypothesis density (PHD) provides a framework for the passive coherent location of targets observed via a T/R-R type passive radar measurements. We apply a particle filter implementation of the Bayesian PHD filter to target tracking using bearing and velocity measurements from a transmit/receive antenna and receiver pair (T/R-R). The tracking results are compared to those obtained when the same tracker is used with bearing-only measurements. The PHD particle filter handles ghost targets well and has improved tracking performance when incorporating velocity along with the bearing measurements