一种适用于稀疏无线传感器网络的改进分布式UIF算法

发布时间：2018-04-17 06:10

本文选题：稀疏无线传感器网络 + 无效节点　；参考：《自动化学报》2014年11期

【摘要】：分布式无迹信息滤波(Distributed unscented information filter,DUIF)算法是一种有效的非线性分布式状态估计多源信息融合方法,然而当将该算法应用于稀疏无线传感器网络(Wireless sensor networks,WSN)时,稀疏WSN中存在的无效节点会引起使滤波趋于发散的平均一致误差.针对该问题,本文提出一种改进DUIF算法.该算法不改变DUIF算法的级联结构,而是将其底层和上层滤波器分别改进为局部无迹信息滤波器(Local unscented information filter,LUIF)和加权平均一致性滤波器.LUIF对每个节点的局部多源观测信息进行局部融合,得到局部的后验估计信息向量和矩阵,进而将它们作为加权平均一致性滤波器的输入,最终得到不包含平均一致误差的分布式后验估计结果.其中,加权平均一致性滤波器是通过对由LUIF输出的局部后验估计信息向量和矩阵分别进行平均一致性滤波而得以在改进DUIF算法框架下实现的.同时,在此过程中,相邻节点之间的状态估计互相关信息也被引入改进DUIF算法的输出结果中,进一步增强了滤波的可靠性.仿真实验结果表明,改进DUIF算法能够在稀疏WSN中对机动目标进行有效跟踪,在估计精度和抑制滤波发散方面明显优于标准DUIF算法.
[Abstract]:Distributed unscented information filter (DUIFA) algorithm is an effective multi-source information fusion method for nonlinear distributed state estimation. However, when the algorithm is applied to sparse wireless sensor networks (WSNs),The existence of invalid nodes in sparse WSN results in an average consistent error which tends to diverge the filtering.To solve this problem, an improved DUIF algorithm is proposed in this paper.The algorithm does not change the cascade structure of the DUIF algorithm.Instead, the local unscented information filter and the weighted average consistency filter. LUIF are improved to the local unscented information filter and the local multi-source observation information of each node, respectively.The local posterior estimation information vector and matrix are obtained, which are used as input of weighted average consistency filter. Finally, the distributed posteriori estimation results without average consistency error are obtained.The weighted average consistency filter is implemented under the framework of the improved DUIF algorithm by using the average consistency filtering of the local posteriori estimation information vector and the matrix output by LUIF.At the same time, the cross-correlation information of state estimation between adjacent nodes is also introduced into the output of the improved DUIF algorithm, which further enhances the reliability of the filter.The simulation results show that the improved DUIF algorithm can effectively track maneuvering targets in sparse WSN and is superior to the standard DUIF algorithm in estimation accuracy and suppression of filtering divergence.
【作者单位】：第二炮兵工程大学;
【基金】：陕西省基金项目(2012K06-45)资助~~
【分类号】：TP212.9;TN929.5

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