标准粒子滤波存在粒子退化问题，处理非线性问题需要大量粒子以达到合适估计精度，降低了算法的综合性能。本文提出一种结合Levy飞行策略、差分进化与成功历史策略的混合多策略优化的粒子滤波方法，Levy飞行策略丰富样本集的基本框架，并通过差分进化算法优化低权重的无效粒子，成功-历史策略进行参数自适应调整以动态调节算法在全局搜索与局部搜索之间的平衡，避免粒子运动尺度过大陷入局部最优。仿真结果表明，本文算法有效提高了粒子多样性与滤波精度，改善了在低测量噪声下的粒子退化问题，降低了对非线性系统进行状态估计的粒子数量。

The standard particle filter has long problems referred as sample degeneracy and impoverishment. Requiring large number of samples to achieve suitable estimation accuracy, which reduces the comprehensive performance of the algorithm. This paper proposes a hybrid multi-strategy optimization particle filter method based on Levy flight strategy, differential evolution and success-history strategy. The Levy flight strategy enricifies the basic framework of the sample set, ineffective particles with low-weight are optimized through the differential evolution algorithm, and successful history strategy is used to adjust the parameters to achieve a balance between the global search and the local search, so as to prevent particles from falling into the local optimum when the motion scale is too large. Experiments show that the proposed algorithm can effectively improve the particle diversity, accuracy and sample degeneracy under low measurement noise, reducing the number of particles needed to estimate nonlinear systems.