title: Cubature Information SMC-PHD for Multi-Target Tracking.
abstract: In multi-target tracking, the key problem lies in estimating the number and states of individual targets, in which the challenge is the time-varying multi-target numbers and states. Recently, several multi-target tracking approaches, based on the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter, have been presented to solve such a problem. However, most of these approaches select the transition density as the importance sampling (IS) function, which is inefficient in a nonlinear scenario. To enhance the performance of the conventional SMC-PHD filter, we propose in this paper two approaches using the cubature information filter (CIF) for multi-target tracking. More specifically, we first apply the posterior intensity as the IS function. Then, we propose to utilize the CIF algorithm with a gating method to calculate the IS function, namely CISMC-PHD approach. Meanwhile, a fast implementation of the CISMC-PHD approach is proposed, which clusters the particles into several groups according to the Gaussian mixture components. With the constructed components, the IS function is approximated instead of particles. As a result, the computational complexity of the CISMC-PHD approach can be significantly reduced. The simulation results demonstrate the effectiveness of our approaches.
authors.affiliation: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China. liuzhe201@buaa.edu.cn.|School of Information and Communication Engineering, North University of China, Taiyuan 030051, China. liuzhe201@buaa.edu.cn.
authors.forename: Zhe
authors.initials: Z
authors.lastname: Liu
authors.affiliation: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China. wzulin@buaa.edu.cn.|Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China. wzulin@buaa.edu.cn.
authors.forename: Zulin
authors.initials: Z
authors.lastname: Wang
authors.affiliation: School of Electronic and Information Engineering, Beihang University, Beijing 100191, China. maixu@buaa.edu.cn.
authors.forename: Mai
authors.initials: M
authors.lastname: Xu
keywords: Gaussian mixture; J0101; Sequential monte carlo; cubature information filter; importance sampling; probability hypothesis density