Bernoulli Forward-Backward Smoothing for Joint Target Detection and Tracking

被引：73

作者：

Vo, Ba-Tuong ^{[1
]}

Clark, Daniel ^{[2
]}

Vo, Ba-Ngu ^{[1
]}

Ristic, Branko ^{[3
]}

机构：

[1] Univ Western Australia, Sch Elect Elect & Comp Engn, Crawley, WA 6009, Australia

[2] Heriot Watt Univ, Sch Engn & Phys Sci, Joint Res Inst Signal & Image Proc, Edinburgh EH14 4AS, Midlothian, Scotland

[3] Def Sci & Technol Org, ISR Div, Port Melbourne, Vic 3207, Australia

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2011年 / 59卷 / 09期

基金：

英国工程与自然科学研究理事会; 澳大利亚研究理事会;

关键词：

Detection; estimation; filtering; smoothing; tracking;

D O I：

10.1109/TSP.2011.2158427

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 this correspondence, we derive a forward-backward smoother for joint target detection and estimation and propose a sequential Monte Carlo implementation. We model the target by a Bernoulli random finite set since the target can be in one of two "present" or "absent" modes. Finite set statistics is used to derive the smoothing recursion. Our results indicate that smoothing has two distinct advantages over just using filtering: First, we are able to more accurately identify the appearance and disappearance of a target in the scene, and second, we can provide improved state estimates when the target exists.

引用

页码：4473 / 4477

页数：5

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