Imbedded Cubature Particle PHD Filter Multitarget Tracking Algorithm

Considering the low accuracy, filter divergence and poor timeliness of nonlinear multitarget tracking based on probability hypothesis density (PHD), a new filter named imbedded cubature particle PHD(ICPPHD) is proposed. ICPPHD implements particle sampling with Halton points sets, and generates infinite integral points based on the thirddegree imbedded cubature rule to perform particle filtering for the purpose of matching the important density function. As a result of the welldistributed particles obtained with Halton sets, ICPPHD can avoid the phenomenon of particle aggregation. Besides, ICPPHD can deal with the contradictions between time and accuracy well because of the few integral points and high accuracy. Simulation was made and it showed that ICPPHD could be able to track multiple targets effectively. Moreover, ICPPHD spent less time compared with Gauss Hermite, and performed better in targets number estimation and state estimation comparing with cubature particle PHD(CPPHD).