Marginal Multi-Bernoulli Filters: RFS Derivation of MHT, JIPDA, and Association-Based MeMBer

被引：240

作者：

Williams, Jason L. ^{[1
]}

机构：

[1] Def Sci & Technol Org, Natl Secur & ISR Div, Data & Informat Fus Grp, Edinburgh, SA 5111, Australia

来源：

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS | 2015年 / 51卷 / 03期

关键词：

PROBABILISTIC DATA ASSOCIATION; RANDOM FINITE SETS; MULTITARGET TRACKING; EFFICIENT; ALGORITHM; TARGETS; CLUTTER;

D O I：

10.1109/TAES.2015.130550

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

Recent developments in random finite sets (RFSs) have yielded a variety of tracking methods that avoid data association. This paper derives a form of the full Bayes RFS filter and observes that data association is implicitly present, in a data structure similar to multiple hypothesis tracking (MHT). Subsequently, algorithms are obtained by approximating the distribution of associations. Two algorithms result: one nearly identical to joint integrated probabilistic data association (JIPDA), and another related to the multiple target multi-Bernoulli (MeMBer) filter. Both improve performance in challenging environments.

引用

页码：1664 / 1687

页数：24

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