Improved MM-CBMeMBer Filter for Maneuvering Multi-target Tracking

被引：0

作者：

Li, Bo ^{[1
]}

Zhan, Shanshan ^{[1
]}

机构：

[1] Liaoning Univ Technol, Sch Elect & Informat Engn, Jinzhou 121001, Peoples R China

来源：

2018 37TH CHINESE CONTROL CONFERENCE (CCC) | 2018年

基金：

中国国家自然科学基金;

关键词：

MM-CBMeMBer filter; Particle weight; Motion model; Clutter; BLACKWELLIZED PARTICLE FILTER; BERNOULLI FILTER;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To solve the problems of unstable state estimation and long running time of existing multi-model cardinality-balanced multi-target multi-Bernoulli (MM-CBMeMBer) filters, we present an improved MM-CBMeMBer filter in this paper. Firstly, the velocity component of targets is estimated based on the Kalman filter (KF). By using small covariance, the position component of targets is achieved in low dimensional state space. Besides, the multi target tracks are systematically merged based on the identifying label. As a result, the running time is saved owing to half of sampling particles. The numeric simulation indicated that the proposed algorithm can track multi target in clutter circumstance with satisfactory efficiency and reliability.

引用

页码：4259 / 4264

页数：6

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