一种考虑先验估计误差相关性的快速DUIF算法  

A fast distributed unscented information filter algorithm taking into account the correlation among prior estimate errors

作　　者：汤文俊[1,2] 张国良[1] 曾静[3] 徐君 姚二亮 

机构地区：[1]第二炮兵工程大学三系 [2]中国人民解放军96164部队 [3]第二炮兵工程大学理学院

出　　处：《控制与决策》2016年第9期1663-1670,共8页Control and Decision

基　　金：中国工程科技中长期发展战略研究项目(中国工程院与国家自然科学基金委联合资助项目)(2014-zcq-10)

摘　　要：针对稀疏无线传感器网络(WSN)中加权平均一致分布式无迹信息滤波(DUIF)算法估计次优和滤波效率较低的问题,提出一种考虑先验估计误差相关性的快速DUIF算法.采用加权统计线性回归(WSLR)方法线性化观测模型,以节点共享信息作为平均一致性算法输入,从而在极大后验估计中引入先验估计交互协方差信息;设计最优通信连接边权值并自适应修正状态加权矩阵,提高平均一致性算法收敛速率.仿真实验结果表明,所提出的算法能够有效应用于稀疏WSN目标跟踪.The distributed unscented information filter（DUIF） based on weighted average consensus has the problems of suboptimal estimation accuracy and low filter efficiency in the sparse wireless sensor network（WSN）, therefore, a fast DUIF algorithm taking into account the correlation among prior estimate errors is proposed. The observation model is linearized by using the weighted statistical linear regression（WSLR） method. And the mutual information is taken as the input of the average consensus algorithm, so that the information of the prior estimate cross-covariance can be introduced into the results of the maximum posteriori estimation. Meanwhile, by designing the optimal weights of the communication edges and modifying the state weighted matrices adaptively, the convergence rate of the average consensus algorithm can be improved.The simulation results show that the proposed DUIF algorithm can efficiently track the target in the sparse WSN.

关 键 词：先验估计误差相关性 稀疏通信拓扑 节点度差异 平均一致性算法 分布式无迹信息滤波算法 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]