【摘要】 文章研究了在传感器网络下的分布式一致性滤波问题.在传感器网络中,各传感器之间存在最短路径链接通信以获得彼此的信息.文章提出了一种具有多重融合的分布式一致滤波算法.在每一时刻,每个传感器节点基于自身的观测进行局部滤波,然后将估值发给邻居节点的同时接收邻居节点传来的估值.采用线性无偏最小方差最优矩阵加权融合算法,每个传感器节点基于自己和收到的邻居节点的估值进行融合估计.经过多次信息交换与融合使得所有传感器节点的估计结果趋于一致.所提出的算法要求节点之间互协方差阵的计算,存在较大的计算负担.为了避免各节点之间互协方差阵的计算,文章又提出了基于并行协方差交叉融合(PCI)的分布式次优一致性滤波器.并对所提出的算法进行了精度和计算量的比较分析.仿真例子验证了所提算法的有效性.更多还原

【Abstract】 This paper studies the distributed consensus filtering problem in sensor networks.In the sensor network,there is the shortest path link communication between sensors to obtain information of each other.This paper presents a distributed consensus filtering algorithm with a two-stage filtering structure.At each moment,each sensor node performs local filtering based on its observations,and then sends its estimate to neighbor nodes and receives estimates from neighbor nodes.By employing the optimal matrix-weighted fusion algorithm in the linear unbiased minimum variance sense,each sensor node performs the fusion estimate based on the estimates of its and neighbor nodes.After many times of information exchanges and fusions,the estimation accuracy of all sensor nodes trends to consensus.The proposed algorithm needs to calculate cross-covariance matrices between nodes,which brings heavy computational burden.To avoid the calculation of cross-covariance matrices between nodes,a distributed suboptimal consensus filter is also proposed based on the parallel covariance intersection(PCI) fusion algorithm.The accuracy and computational amount of the proposed algorithms are compared and analyzed.Simulation results demonstrate the effectiveness of the proposed algorithms.更多还原