Robust Multi-Bernoulli Filtering

被引：121

作者：

Vo, Ba-Tuong ^{[1
]}

Vo, Ba-Ngu ^{[1
]}

Hoseinnezhad, Reza ^{[2
]}

Mahler, Ronald P. S. ^{[3
]}

机构：

[1] Curtin Univ Technol, Dept Elect & Comp Engn, Bentley, WA 6102, Australia

[2] RMIT Univ, Sch Aerosp Mech & Mfg Engn, Carlton, Vic 3053, Australia

[3] Lockheed Martin Adv Technol Labs, Eagan, MN 55121 USA

来源：

IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING | 2013年 / 7卷 / 03期

关键词：

Finite set statistics; multi-Bernoulli filter; multi-target Bayes filter; multi-target tracking; online parameter estimation; robust filtering; PHD; CONVERGENCE; TRACKING;

D O I：

10.1109/JSTSP.2013.2252325

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In Bayesian multi-target filtering knowledge of parameters such as clutter intensity and detection probability profile are of critical importance. Significant mismatches in clutter and detection model parameters results in biased estimates. In this paper we propose a multi-target filtering solution that can accommodate non-linear target models and an unknown non-homogeneous clutter and detection profile. Our solution is based on the multi-target multi-Bernoulli filter that adaptively learns non-homogeneous clutter intensity and detection probability while filtering.

引用

页码：399 / 409

页数：11

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