一种考虑先验估计误差相关性的快速DUIF 算法

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.

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

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.