An Improved PHD Filter Based on Dynamic Programming
Traditional PHD filter for detecting and tracking weak targets does not work well in the case of low detection probability. In this paper, an improvement of PHD filtering based on dynamic programming is proposed. The method takes advantage of the correlation among the multi-frame data. The result of dynamic programming is applied to PHD filter for getting stable detecting and tracking effect. Monte Carlo simulation results show that the improved method is superior to the PHD filter under low detection probability.
KeywordsWeak targets PHD filter Dynamic programming
The work is supported by NSFC (No. 61771028 and No. 61673146).
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