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Improved Iterated-corrector PHD with Gaussian mixture implementation
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DOI：10.1016/j.sigpro.2015.01.007.png)
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摘要
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Many filter algorithms based on the probability hypothesis density (PHD) filter have been proposed to solve the multi-target tracking (MTT) problem. Most of them are applied to single-sensor case. As a simple and feasible multi-sensor filter algorithm, the Iterated-PHD filter is influenced by the order of the sensor updates and the probability of detection. In this paper, an improved algorithm with a modified update formula is proposed to deal with the above problems. In this algorithm, the original detection probability is divided into two parts: the improved miss-detection probability and the improved detection probability, which take the order of the sensor updates and the original detection probability of each sensor into consideration simultaneously. The effectiveness of the proposed algorithm is verified by the simulation results. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Multi-target tracking
Random finite set
Multi-sensor PHD filter
Iterated-PHD
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