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Robust Multi-Bernoulli Filtering
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DOI：10.1109/JSTSP.2013.2252325.png)
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Abstract
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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.
Keywords:
Finite set statistics
multi-Bernoulli filter
multi-target Bayes filter
multi-target tracking
online parameter estimation
robust filtering
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