Multiple model particle filter track-before-detect for range ambiguous radar

The middle pulse repetition frequency (MPRF) and high pulse repetition frequency (HPRF) modes are widely adopted in airborne pulse Doppler (PD) radar systems, which results in the problem that the range measurement of targets is ambiguous. The existing data processing based range ambiguity resolving methods work well on the condition that the signal-to-noise ratio (SNR) is high enough. In this paper, a multiple model particle filter (MMPF) based track-before-detect (TBD) method is proposed to address the problem of target detection and tracking with range ambiguous radar in low-SNR environment. By introducing a discrete variable that denotes whether a target is present or not and the discrete pulse interval number (PIN) as components of the target state vector, and modeling the incremental variable of the PIN as a three-state Markov chain, the proposed algorithm converts the problem of range ambiguity resolving into a hybrid state filtering problem. At last, the hybrid filtering problem is implemented by a MMPF-based TBD method in the Bayesian framework. Simulation results demonstrate that the proposed Bayesian approach can estimate target state as well as the PIN simultaneously, and succeeds in detecting and tracking weak targets with the range ambiguous radar. Simulation results also show that the performance of the proposed method is superior to that of the multiple hypothesis (MH) method in low-SNR environment.