DocumentCode :
539120
Title :
First-moment multi-object forward-backward smoothing
Author :
Clark, D.E.
Author_Institution :
Joint Res. Inst. in Signal & Image Process., Heriot-Watt Univ., Edinburgh, UK
fYear :
2010
fDate :
26-29 July 2010
Firstpage :
1
Lastpage :
6
Abstract :
The optimal solution to the problem of detecting, tracking and identifying multiple targets can be found through a direct generalisation of the Bayes filter to multi-object systems using Mahler´s Finite Set Statistics. Due to the inherent complexity of the multi-object Bayes filter, Mahler proposed to propagate the first-order multi-object moment density, known as the Probability Hypothesis Density (PHD), instead of the multi-object posterior. This was derived using the concept of the probability generating functional (p.g.fl.) from point process theory. In this paper, I derive multi-object first-moment smoothers for forward-backward smoothing through a new formulation of the p.g.fl. smoother which takes advantage of the p.g.fl. Bayes update. This formulation permits the straightforward derivation of first-moment multi-object smoothers, including the PHD smoother.
Keywords :
Bayes methods; probability; smoothing methods; statistics; Bayes filter; Mahler finite set statistics; PHD smoother; first-moment multiobject forward-backward smoothing; first-order multiobject moment density; point process theory; probability generating functional concept; probability hypothesis density; Equations; Joints; Markov processes; Probability; Smoothing methods; Target tracking; Time measurement; Finite Set Statistics (FISST); Multi-object estimation; Probability Hypothesis Density (PHD) filters; forward-backward smoothing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion (FUSION), 2010 13th Conference on
Conference_Location :
Edinburgh
Print_ISBN :
978-0-9824438-1-1
Type :
conf
DOI :
10.1109/ICIF.2010.5711921
Filename :
5711921
Link To Document :
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