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中文摘要：
针对标准粒子滤波算法中存在的粒子权值退化和计算量大的缺陷,提出了基于MKLD准则的粒子群优化粒子滤波算法。该方法将粒子群优化算法嵌入到粒子滤波算法的重要性采样过程中,对采样过程进行了优化,提高了粒子集的优良性的同时保证了粒子滤波状态估计的性能。同时,为了降低计算量,算法设计时基于MKLD准则自适应地选择粒子群优化算法所要优化的粒子及粒子群优化算法实施的时刻。大量的数值实验和GPS/DR组合导航仿真实验的结果验证了新方法的有效性。
英文摘要：
Considering the degeneracy of particle weight and the large amount of calculation existing in the standard particle filtering algorithm,a particle swarm optimization particle filtering method based on the criteria of MKLD is brought forward in this paper.This method embeds the particle swarm optimization algorithm into the important sampling process of the particle filtering method,to optimize the sampling process and improve the fine collection of particles while maintaining the state estimation performance of the particle filtering method.At the same time,in order to reduce the computational complexity,the new algorithm adaptively selects the optimized particles and the implementation moment of the particle swarm optimization based on the criteria of MKLD.The results of a large amount of computational experiments and the GPS/DR integrated navigation simulation experiment show the effectiveness of the novel method proposed in this paper.
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