期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 59, 期 8, 页码 3497-3513出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2011.2128316
关键词
CPHD; Finite set statistics; multi-target tracking; parameter estimation; PHD; robust filtering
资金
- Australian Research Council [DP0989007, DP0880553]
- Australian Research Council [DP0880553, DP0989007] Funding Source: Australian Research Council
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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