为有效评价量测不确定下的粒子权重,提出了一种基于粒子权重优化的粒子滤波算法.首先,通过置信距离和置信矩阵的构建及求解实现粒子间蕴含的冗余和互补信息的充分提取,给出了一种度量粒子间相互支持程度的一致性权重,并利用权重平衡因子实现代价评估粒子滤波中粒子权重和一致性权重的优化组合,进而实现粒子权重的合理优化.新算法既充分利用了当前时刻粒子集中的信息,又避免了量测噪声先验统计信息的偏差的不利影响,从而提升了粒子权重度量结果稳定性和可靠性.理论分析和仿真实验验证了所提算法的有效性.

In order to effectively measure the particle weight in uncertain measurement,a novel particle filter algorithm based on particle weight optimization is proposed.In this algorithm,first,the redundancy and complementary information among particles is fully extracted by constructing and solving the confidence distance and the confidence matrix,and a new consistency weight to measure the mutual support degree among particles is presented.Then,the weight balance factor is used to combine the cost reference weight with the consistency weight for reasonably optimizing the particle weight.The proposed algorithm not only makes full use of the information of the particles set at the current time,but also avoids the adverse effect due to the error of prior statistical information,which improves the stability and reliability of particle weight measurement.Theoretical and simulated results show that the proposed algorithm is effective.