Rao-Blackwellised approximate conditional mean probability hypothesis density filtering
N. Nandakumaran, S. Sutharsan, Ratnasingham Tharmarasa, T. Lang, Thia Kirubarajan · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
In this paper, a new state estimation algorithm for estimating the states of targets that are separable into linear and nonlinear subsets with non-Gaussian observation noise distributed according to a mixture of Gaussian functions is proposed. The approach involves modeling the collection of targets and measurements as random finite sets and applying a new Rao-Blackwellised Approximate Conditional Mean Probability Hypothesis Density (RB-ACM-PHD) recursion to propagate the posterior density. The RB-ACM-PHD filter jointly estimates the time-varying number of targets and the observation sets in the presence of data association uncertainty, detection uncertainty, noise and false alarms. The proposed algorithm approximates a mixture Gaussian distribution with a moment-matched Gaussian in the weight update phase of the filtering recursion. A two dimensional maneuvering target tracking example is used to evaluate the merits of the proposed algorithm. The RB-ACM-PHD filter results in a significant reduction in computation time while maintaining filter accuracies similar to the standard sequential Monte Carlo PHD implementation.