CPHD filtering with unknown clutter rate and detection profile

CPHD filtering with unknown clutter rate and detection profile

In Bayesian multi-target filtering we have to contend with two notable sources of uncertainty, clutter and detection. Knowledge of parameters such as clutter rate and detection profile are of critical importance in multi-target filters such as the probability hypothesis density (PHD) and Cardinalize...

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Bibliographic Details
Published in:14th International Conference on Information Fusion pp. 1 - 8
Main Authors: Mahler, R. P. S., Ba Tuong Vo, Ba Ngu Vo
Format: Conference Proceeding
Language:English
Published: IEEE 01-07-2011
Subjects:
Approximation methods
Clutter
CPHD
Filtering
finite set statistics
Generators
Markov processes
multi-target tracking
parameter estimation
PHD
Probability
robust filtering
Time measurement
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Summary:In Bayesian multi-target filtering we have to contend with two notable sources of uncertainty, clutter and detection. Knowledge of parameters such as clutter rate and detection profile are of critical importance in multi-target filters such as the probability hypothesis density (PHD) and Cardinalized PHD (CPHD) filters. Naive application of the CPHD (and PHD) filter with mismatches in clutter and detection model parameters results in biased estimates. In this paper we show how to use the CPHD (and PHD) filter in unknown clutter rate and detection profile.
ISBN:9781457702679
1457702673