A Particle Filter Track-before-detect Algorithm for Multi-Radar System

Current particle filter track-before-detect (PF-TBD) algorithms assume a single sensor system and a target being contained within the sensor detection coverage. In this paper, we develop PF-TBD for multiple asynchronous radar system. The radars in this system have different detection coverage, thus a target may move across the detection coverage of different radars (i.e. the target is not contained within the common detection coverage). For detecting dim target in this multi-radar system, a novel algorithm called classification PF-TBD (CPF-TBD) is proposed. It uses a classification criterion to divide the particles into two parts. This criterion is designed based on the detection coverage and the sampling rates of radars. According to the criterion, one part of the particles is used to estimate the target state, and the other part is used to preserve adequate particles in all radar detection coverage, which is conducive for next stage calculation. With this approach, the dim target can be centrally detected and tracked using all of the data, which is collected from asynchronous radars with different detection coverage. Simulation results show that CPF-TBD is able to produce higher accuracy compared with conventional PF-TBD.