Various algorithms on multi-target multisensor tracking have been developed to provide reliable performance, in terms of tracking accuracy and computational efficiency. Propagating full multi-target posterior of the states at every time step of estimation process would certainly not be a suitable option due to its computational costs. To alleviate this problem, Random Finite Sets (RFSs) approach which leads to the implementation of Probability Hypothesis Density (PHD) filter offers more effective method. Based on the theory of Finite Set Statistics (FISST), RFSs represents the multi-target states and multisensor observations as a single meta-state and a single meta-observation, respectively. And the system propagates only the first moment, or PHD, associated with multi-target posterior in every recursion time step. This paper is evaluating the performance of this approach using simulation on a nonlinear range and bearing tracking problem, which is employed to track multi-target using several sensors to get the observations. Simulation results show that the algorithm successfully tracks the targets over the surveillance region, with slightly decreasing performance when the level of noise is higher and the clutter density is denser. © 2012 IEEE.

Bayesian filtering,Computational costs,Estimation process,Finite set statistics,First moments,Multi sensor,Multi-sensor observations,Multi-sensor tracking,Multitarget,Nonlinear ranges,Particle filter,Probability hypothesis density filter,Random finite sets,Recursions,Reliable performance,Time step,Tracking accuracy,Tracking problem

multi-target multisensor tracking,particle filter,Probability Hypothesis Density filter,Random Finite Sets

https://doi.org/10.1109/TSSA.2012.6366071