The forward-backward Probability Hypothesis Density smoother

A forward-backward Probability Hypothesis Density (PHD) smoother involving forward filtering followed by backward smoothing is derived. The forward filtering is performed by Mahler's PHD recursion. The PHD backward smoothing recursion is derived using Finite Set Statistics (FISST) and standard...

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Bibliographic Details
Published in2010 13th International Conference on Information Fusion pp. 1 - 8
Main Authors Mahler, R P S, Ba-Ngu Vo, Ba-Tuong Vo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2010
Subjects
Clutter
Filtering
finite set statistics
PHD
point processes
random sets
Random variables
Smoothing
Smoothing methods
Target tracking
tracking
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Summary:A forward-backward Probability Hypothesis Density (PHD) smoother involving forward filtering followed by backward smoothing is derived. The forward filtering is performed by Mahler's PHD recursion. The PHD backward smoothing recursion is derived using Finite Set Statistics (FISST) and standard point process theory. Unlike the forward PHD recursion, the proposed backward PHD recursion is exact and does not require the previous iterate to be Poisson.
DOI:10.1109/ICIF.2010.5711920