Robust Multi-Bernoulli Filtering

In Bayesian multi-target filtering knowledge of parameters such as clutter intensity and detection probability profile are of critical importance. Significant mismatches in clutter and detection model parameters results in biased estimates. In this paper we propose a multi-target filtering solution...

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Bibliographic Details
Published in:IEEE journal of selected topics in signal processing 2013-06, Vol.7 (3), p.399-409
Main Authors: Ba-Tuong Vo, Ba-Ngu Vo, Hoseinnezhad, R., Mahler, R. P. S.
Format: Article
Language:English
Subjects:
Bayes methods
Clutter
Filtering
Finite set statistics
multi-Bernoulli filter
multi-target Bayes filter
multi-target tracking
online parameter estimation
Parameter estimation
robust filtering
Statistics
Target tracking
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Summary:In Bayesian multi-target filtering knowledge of parameters such as clutter intensity and detection probability profile are of critical importance. Significant mismatches in clutter and detection model parameters results in biased estimates. In this paper we propose a multi-target filtering solution that can accommodate non-linear target models and an unknown non-homogeneous clutter and detection profile. Our solution is based on the multi-target multi-Bernoulli filter that adaptively learns non-homogeneous clutter intensity and detection probability while filtering.
ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2013.2252325