基于围猎改进哈里斯鹰优化的粒子滤波方法

针对标准粒子滤波过程的权值退化和样本贫化问题，该文结合融入围猎策略的哈里斯鹰优化算法设计一种群智能优化粒子滤波方法(EHHOPF)。首先，引入围猎策略替代哈里斯鹰优化算法全局搜索策略以适配粒子滤波环境；其次，采用Sigmoid函数构建非线性猎物逃逸能量平衡算法的探索阶段和开发阶段；最后构建选择比例因子融合开发阶段捕猎策略并采用非线性猎物跳跃强度保证算法收敛效率。仿真结果表明，与标准粒子滤波以及磷虾算法、蝙蝠算法、布谷鸟算法、灰狼算法优化的粒子滤波方法相比，基于围猎改进哈里斯鹰优化的粒子滤波方法有效提升了系统状态估计精度、滤波稳定性和滤波实时性。

A Particle Filter Method Based on Harris Hawks Optimization Improved by Encircling Strategy

To deal with the weight degradation and sample impoverishment problems of particle filter, a Particle Filter based on Harris Hawks Optimization improved by Encircling strategy (EHHOPF) is designed. Firstly, the global search strategy in Harris Hawks Optimization is replaced by an encircling prey strategy to fit the filtering environment. Additionally, Sigmoid function is introduced to construct the nonlinear prey escaping energy to achieve the balance between exploration and exploitation. Lastly, the selection scale factor is proposed to simplify the selection mechanism of searching strategies and nonlinear dynamic prey jump strength is constructed to guarantee the convergence efficiency as well. The simulation results exhibited that the proposed particle filter can effectively improve the state estimation accuracy, filtering stability and real-time performance than the standard particle filter and particle filters optimized by krill herd algorithm, bat algorithm, cuckoo search algorithm and grey wolf optimizer.