TY  - JOUR
AU  - WEN, Shangsheng
AU  - QIU, Zhiqiang
AU  - XU, Hanming
AU  - CHEN, Xiandong
PY  - 2023
TI  - Self-Reset Particle Filter Method Optimized Based on Differential Evolution Algorithm
JO  - Journal of South China University of Technology (Natural Science Edition)
SN  - 1000-565X
SP  - 133
EP  - 145
VL  - 51
IS  - 3
AB  - As a commonly used non-Gaussian nonlinear filtering method, particle filter has been successfully applied in various engineering fields. However, the traditional resampling method leads to the problem of particle depletion, which seriously reduces the accuracy and robustness of the filter estimation. This paper proposed a self-reset particle filter method that combines tracking failure detection and enhanced differential evolution optimization. Firstly, the filter estimation value is preliminarily checked by the tracking failure identification method, and the optimization strategy is not enabled during normal tracking, and the algorithm performance is consistent with the standard particle filter. When the tracking fails, the particle set is reset by differential optimization. During the reset process, the upper and lower bounds of particle confidence interval are set to prevent the particles from being over-concentrated, and the multiple optimization of the particles is avoided by combining the test indication value to reduce the estimation time of the algorithm. The simulation results show that the proposed algorithm inherits the advantages of standard particle filter and differential evolution particle filter through dynamic adjustment, and it effectively improves the robustness and estimation accuracy of the filter estimation. It can avoid using the optimization strategy to reduce the overall time complexity of the algorithm when the filter is successful, and enable the differential optimization strategy to self-reset when the filter fails. In addition, under the same positioning accuracy, the number of particles required by the algorithm is lower than that of standard particle filter, and the overall time consumption is lower than differential evolution particle filter, which also works well when modeling is uncertain.
UR  - https://doi.org/10.12141/j.issn.1000-565X.220368
DO  - 10.12141/j.issn.1000-565X.220368