Multipath-Aided Probabilistic Multi-Hypothesis Tracker for the SFN-Based Passive Radar

Passive radar (PR) system can provide many benefits and advantages in urban area surveillance. Many PRs have been deployed in the single frequency network (SFN). For multitarget tracking in these scenarios, one of the key issue is the measurement-to-target-and-illuminator association. Moreover, since in the urban scenario, the multipath effect is another difficulty should be handled. An new algorithm is proposed by extending the Probabilistic Multi-Hypothesis Tracker (PMHT) to handle the triple uncertainties of association among measurements, illuminators, propagation paths and target states efficiently. The numerical simulation in an urban Long-Term Evolution (LTE)-based PR scenario to track two small unmanned aerial vehicles (UAVs) illustrates the improved performance of the proposed algorithm over the existed algorithms.