Abstract
Resampling is an inevitable process in the standard particle filter, but it also can lead to particles vanish diversity and degenerate the performance. In order to solve this problem, an elite genetic resampling particle filter is proposed in this paper. The global optimization of the genetic algorithm could keep particles move towards real state probability density function. The state estimate is corresponding to the maximum fitness state after several evolution generations. As the maximum fitness of every generation of the algorithm constitutes a non-negative bounded sub-martingale, this algorithm theoretically converges to the optimal global solution with probability 1. The estimate expression of absolute error is also concluded. The simulation demonstrates that this algorithm outperforming the particle filter using genetic operation in resampling could improve the estimation accuracy of high-speed flying targets tracking in the non-Gaussian background.
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Acknowledgements
This research was funded by the National Natural Science Foundations of China (NSFC) under Grant 61801141 and the Doctoral Fund Project of LongDong university under Grant XYBY202001.
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Nie, L., Yang, X., He, J., Mu, Y., Wang, L. (2021). Research on the Elite Genetic Particle Filter Algorithm and Application on High-Speed Flying Target Tracking. In: Liang, Q., Wang, W., Liu, X., Na, Z., Li, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2020. Lecture Notes in Electrical Engineering, vol 654. Springer, Singapore. https://doi.org/10.1007/978-981-15-8411-4_105
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DOI: https://doi.org/10.1007/978-981-15-8411-4_105
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