【摘要】 在粒子滤波算法中,重采样的引入有效地改善粒子退化现象,但同时也导致了粒子多样性减弱问题的产生.本文给出了一种基于进化采样的改进粒子滤波算法.该算法在重采样过程后,首先根据马尔可夫链蒙特卡罗(Markov-Chain-Monte-Carlo,MCMC)技术和遗传算法中的模拟二进制交叉原理生成候选粒子,并利用适应度函数完成对于其权重的度量.然后结合当前时刻的重采样粒子构建候选粒子集,进而提升了重采样后粒子的多样性,最终依据粒子自身的权重实现粒子的优选.仿真结果表明:该算法可有效地提高对于非线性系统状态的估计精度.更多还原

【Abstract】 In particle filter algorithm,the re-sampling step effectively solves the problem of particles degeneracy,however, it reduces the particle variety.An improved particle filtering algorithm is given based on the evolution sampling.In the process of re-sampling,this algorithm generates candidate particles based on the Markov-Chain-Monte-Carlo(MCMC) technique and the analog binary crossover principle,and then,weighs the sampling particles against their importance according to the fitness function.The current re-sampling particles are then associated in constructing the candidate particle set to enhance the variety of re-sampling particles.Finally,the optimizing selection of particles is realized based on the particle weigh.Simulation results show the method can effectively improve the state estimation precision.更多还原