The Gaussian Particle Multi-target Multi-Bernoulli Filter

摘要：

Multi-target multi-Bernoulli (MeMBer) filter is a new attractive approach to tracking an unknown and time-varying number of targets. In this paper, we present a new implementation of the MeMBer recursion—the Gaussian particle MeMBer (GP-MeMBer) filter—for nonlinear models. The probability density in the multi-Bernoulli is approximated by a weighted sum of Gaussians, as in the existed Gaussian mixture (GM-MeMBer) filter, but the target dynamics or observation can be nonlinear. Monte Carlo integration is applied for approximating the prediction and posterior densities in the multi-Bernoulli and the multi-Bernoulli existence probability. The simulation results verify the effectiveness of the proposed GP-MeMBer filter.

关键词： signal processing Gaussian particle multi-target multi-Bernoulli (GS-MeMBer) simulation random finite sets (RFSs) nonlinear tracking

作者: Jianjun Yin Jianqiu Zhang Jin Zhao

作者单位: Electronic Engineering Department Fudan University Shanghai, China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 556-560

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）