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- W4313463865 abstract "Under low signal-to-noise ratio (SNR) target tracking, poor target information and high clutter limit the tracking effect. Extended targets potentially generate more than one measurement per time step. Multiple extended targets tracking is therefore can be used to improve tracking performance with low SNR, due to the expanded data than point targets tracking. Based on the classical probability hypothesis density (PHD) filter, the extended target PHD (ET- PHD) filter is proposed to track multiple extended targets. The main contribution of this paper is the improvement of the classical extended target Gaussian-mixture probability hypothesis density (ET-GM-PHD) filter. A method based on the ET-GM-PHD filter is proposed for decreasing false alarms and improving measurement set partition performance under low SNR cases. The optimized method is shown a better tracking performance in estimation accuracy of the targets number and targets state in comparison with a point PHD filter." @default.
- W4313463865 created "2023-01-06" @default.
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- W4313463865 date "2022-11-03" @default.
- W4313463865 modified "2023-09-30" @default.
- W4313463865 title "Optimization Tracking Algorithm Based on Extended Target Gaussian Mixture PHD Filter" @default.
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- W4313463865 doi "https://doi.org/10.1145/3571662.3571687" @default.
- W4313463865 hasPublicationYear "2022" @default.
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