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

被引:255
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
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
收藏
页码:1664 / 1687
页数:24
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