Probability hypothesis density-based multitarget tracking with bistatic range and Doppler observations

Ronald Mahler's probability hypothesis density (PHD) provides a promising framework for the passive coherent location of targets observed via multiple bistatic radar measurements. A particle filter implementation of the Bayesian PHD filter is applied to target tracking using both range and Doppler measurements from a simple non-directional receiver that exploits noncooperative FM radio transmitters as its 'illuminators of opportunity'. Signal-to-noise ratios, probabilities of detection and false alarm and bistatic range and Doppler variances are incorporated into a realistic two-target scenario. Bistatic range cells are used in calculating the birth particle proposal density. The tracking results are compared to those obtained when the same tracker is used with range-only measurements. This is done for two different probabilities of false alarm. The PHD particle filter handles ghost targets well and has improved tracking performance when incorporating Doppler measurements along with the range measurements. This improved tracking performance, however, comes at the cost of requiring more particles and additional computation.