Sequential Monte Carlo and Probability Hypothesis Densities for Underwater Multitarget Tracking in Active Sonobuoy Systems
Pengfei Shao, Qing Li, Lei Wang · 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) · 2019
The problem of underwarter multitarget localization and tracking in active sonobuoy systems essentially belongs to the category of multi-sensor data fusion and parameter estimation. The random sets theory derived sequential monte carlo and probability hypothesis densities(SMC-PHD) filter, which can be used to obtain the approximate solution of bayesian posterior recursion for multisensor data fusion and multi-target tracking. In this paper, a clear mathematical theoretical framework and state estimation model has been constructed, and therefore the time-delay and doppler measurements obtained by each node in the active sonobuoy system are used as the input of SMC-PHD filter to output reliable estimation of unknown and time-varying number of underwater targets and their states under clutter environment. Simulation results show that the proposed method can achieve high precision tracking of time-varying number of targets in clutter environment.