粒子滤波方法是一种适合于非线性、非高斯系统状态的滤波方法,在目标跟踪等领域有着广泛应用。但传统粒子滤波方法的粒子之间缺乏交互性与合作意识,很可能在寻优过程中陷入局部极值。针对这一问题,提出一种混合蛙跳算法优化的粒子滤波方法。混合蛙跳算法速度快,全局搜索能力强,可以在局部间进行信息传递,使算法跳出局部极值。因此采用混合蛙跳算法优化传统粒子滤波方法,可以构建多种粒子子集的分布体系,把原本不具备智能行为的粒子分别赋予分群、选择、信息交互和进化等机制,使粒子群体表现出智能行为,从而使寻优搜索向着全局最优方向进行。最后采用仿真实验进行比较,优化后的方法在性能上明显优于传统粒子滤波方法,取得了较好效果。 The particle filter method is a filtering method suitable for the nonlinear and non-Gaussian system states and has been widely used in the field of target tracking. However, the traditional particle filter method is lack of interactivity and cooperation between the particles, which is likely to fall into the local extremum during the optimization process. To solve this problem, a hybrid particle frog filtering algorithm based on frog leaping algorithm is proposed. Mixed frog jump algorithm fast, global search ability, information can be transmitted between the local, the algorithm jump out of the local extremum. Therefore, the hybrid frog leaping algorithm is used to optimize the traditional particle filtering method, which can construct the distribution system of multiple particle subsets, assign the particles that do not have the intelligent behavior to the clustering, selection, information interaction and evolution mechanisms, Behavior, so that the optimal search toward the overall optimal direction. Finally, the simulation experiments are used to compare. The optimized method is obviously superior to the traditional particle filter in performance and has achieved good results.