关键词: 粒子滤波, 鲸鱼优化算法, 自适应调整, Ｌｅｖｙ飞行, ＳＬＡＭ

Abstract: Aiming at the problem that the resampling of the traditional particle filter algorithm leads to particle scarcity and the need to increase the number of particles to improve estimation accuracy,this paper proposes a recombined particle filter algorithm based on multi-strategy whale algorithm optimization.First of all,through the bubble net feeding mechanism of the whale algorithm,the optimal particle guides the particle set to move to the high likelihood region so as to improve the estimation accuracy.Secondly,the particle density near the optimal particle is calculated in real time.When the density is greater than the set random search threshold,the Levy flight strategy is introduced to expand the searching space.When the threshold is greater than the maximum density,the iteration number is adjusted adaptively.Finally,the resampling stage recombines the retained particles after screening and the remaining particles into new particles to increase particle diversity.The simulation experiments are conducted to verify the performance of the improved algorithm in simultaneous localization and mapping (SLAM).The results show that the proposed algorithm has a higher accuracy and better robustness than the standard algorithm does.