CPHD Filtering With Unknown Clutter Rate and Detection Profile
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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. Significant mismatches in clutter and detection model parameters result in biased estimates. In practice, these model parameters are often manually tuned or estimated offline from training data. In this paper we propose PHD/CPHD filters that can accommodate model mismatch in clutter rate and detection profile. In particular we devise versions of the PHD/CPHD filters that can adaptively learn the clutter rate and detection profile while filtering. Moreover, closed-form solutions to these filtering recursions are derived using Beta and Gaussian mixtures. Simulations are presented to verify the proposed solutions.
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Mahler, R.; Vo, Ba Tuong; Vo, Ba-Ngu (2011)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 ...
Mahler, Ronald (2014)The "background-agnostic" CPHD filter was introduced at the 2010 SPIE Defense, Security and Sensing Symposium in 2010. It is a CPHD filter that is capable of operation when both the clutter background and the target-detection ...
Mahler, Ronald (2014)© 2014 IEEE. In previous publications the author introduced CPHD filters designed to detect and track multiple targets in unknown, dynamically changing clutter backgrounds. The first such filters employed Poisson clutter ...