Track-before-detect for Infrared Maneuvering Dim Multi-target via MM-PHD |
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Authors: | LONG Yunli XU Hui AN Wei LIU Li |
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Institution: | LONG Yunli, XU Hui, AN Wei*, LIU Li College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China |
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Abstract: | In this paper, we present a novel and efficient track-before-detect (TBD) algorithm based on multiple-model probability hypothesis density (MM-PHD) for tracking infrared maneuvering dim multi-target. Firstly, the standard sequential Monte Carlo probability hypothesis density (SMC-PHD) TBD-based algorithm is introduced and sequentially improved by the adaptive process noise and the importance re-sampling on particle likelihood, which result in the improvement in the algorithm robustness and convergence speed. Secondly, backward recursion of SMC-PHD is derived in order to ameliorate the tracking performance especially at the time of the multi-target arising. Finally, SMC-PHD is extended with multiple-model to track maneuvering dim multi-target. Extensive experiments have proved the efficiency of the presented algorithm in tracking infrared maneuvering dim multi-target, which produces better performance in track detection and tracking than other TBD-based algorithms including SMC-PHD, multiple-model particle filter (MM-PF), histogram probability multi-hypothesis tracking (H-PMHT) and Viterbi-like. |
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Keywords: | target tracking probability hypothesis density Monte Carlo track-before-detect importance re-sampling |
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