多传感器量测下权重优化粒子滤波算法  

Weights Optimization Particle Filter Algorithm in Multi-sensor Measurement

作　　者：胡振涛[1] 刘宇[1] 杨树军[2] 

机构地区：[1]河南大学图像处理与模式识别研究所,开封475004 [2]江南计算技术研究所,无锡240083

出　　处：《计算机科学》2013年第12期152-155,159,共5页Computer Science

基　　金：国家自然科学基金项目(61300214;U1204611);河南省高校科技创新团队支持计划(13IRTSTHN021);河南省基础与前沿技术研究计划(132300410148);河南省教育厅科学技术研究重点项目(13A413066);河南大学教学改革重点项目(HDXJJG2013-07)的资助

摘　　要：针对粒子滤波在多传感器量测系统状态估计问题中的有效实现,提出一种多传感器量测下的权重优化粒子滤波算法。首先,依据提议分布的具体形式设计用于度量当前时刻粒子的权重的量测似然函数,并利用单个滤波周期内的全部量测分别计算每个粒子权重;其次,考虑到不同传感器精度存在的差异性,结合传感器精度等先验信息,通过加权融合处理方式实现对单个粒子在多传感器量测下权重度量结果的优化;进而在减小粒子权重方差的基础上改善滤波的精度。理论分析和仿真实验结果验证了算法的可行性和有效性。Aiming at the effective realization of particle filter in multi-sensor measurement system state estimation, a no- vel particle filter algorithm based on weights optimization in multi-sensor measurement was proposed in this paper. In the new algorithm, the measurements likelihood function is firstly constructed on the basis of the concrete form of pro- posal distribution,and all measurement in single filter period are used to calculate the every particle weights, respective- ly. Secondly, given the otherness of different sensors precision, combining with priori information of sensors precision, the weighting fusion method is used to optim^e every particle weights in multi-sensor measurement. Finally, the filter precision is improved by decreasing the variance of particle weights. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

关 键 词：多源信息融合 多传感器量测 粒子滤波 权重优化 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]