A particle filter based track-before-detect procedure for towed passive array sonar system

Passive sonar has been widely researched and used in underwater target tracking for its high covertness. In this paper, the detection and tracking of an unknown and time-varying number of targets for a towed sonar system is considered. To enhance the detection and tracking performance, a track-before-detect (TBD) based signal processing method is devised. Specifically, the raw bearing measurements generated after acoustic signal beamforming are directly used as the inputs of the proposed TBD processor. To handle the nonlinearity between target states and the raw bearing measurements, particle filter is employed to compute the joint multitarget probability density (JMPD) recursively through a Bayesian framework. Besides, the target initiation and termination steps are integrated into the particle filtering process as well to deal with the random births and deaths of multiple targets. The effectiveness of the proposed method are demonstrated by numerical experiment.