针对传统粒子滤波存在的粒子枯竭问题,提出一种基于自适应差分进化的粒子滤波算法.利用自适应差分进化算法代替粒子滤波中的重采样策略来产生新粒子,使粒子向状态后验概率密度函数的高似然区移动,同时提高粒子的多样性.通过一种菲线性自适应调节策略自适应地调整变异因子和交叉因子,以提高改进粒子滤波中差分进化的寻优能力.应用于多径估计的仿真结果表明,该算法可克服粒子枯竭问题,与粒子滤波、扩展卡尔曼滤波和差分进化的粒子滤波算法相比,具有更好的多径估计性能.%Aiming at the problem of particle depletion in traditional Particle Filtering (PF),a PF algorithm based on Adaptive Differential Evolution (ADE) is proposed.The ADE algorithm instead of the re-sampling strategy is used to generate new particles in PF,which promotes the particles moving toward the region with high likelihood in the state posterior probability density function,and increases the diversity of the particles.A nonlinear adaptive control strategy is adopted to adjust the mutation factor and the crossover factor for improving the ability of optimization of DE in PF.Simulation results show that applied for multipath estimation,the proposed algorithm can overcome the problem of particle depletion.Compared with algorithms of PF,Extended Kalman Filtering(EKF) and Differential Evolution Particle Filtering (DE-FP),the proposed algorithm has better multipath estimation performance.
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