无线传感器网络中分布式量化航迹稳健融合

针对无线传感器网络（wireless sensor network, WSN）中的通信带宽和节点能量约束，提出了一种新的分布式量化航迹稳健融合框架。首先,对局部状态估计的方差阵进行压缩处理，取其对角上确界矩阵；再对压缩后的方差阵和状态估计向量进行K 均值矢量量化,送往融合中心（fusion center, FC）。其次，针对局部估计的未知或者不完整相关性，提出了不依赖于相关性的稳健航迹融合方法--内椭球逼近法（inner ellipsoidal approximation, IEA）用于簇首（cluster head, CH）的融合估计。仿真结果证明所提出算法的有效性：跟踪精度方面非常接近已有文献中的结果，而所需通信带宽远低于已有方法；通信能量方面，相对于随机选取激活节点策略，采用目标导向的动态分簇策略节省最高可达42%的能量。

Distributed robust fusion of quantized track for target tracking in wireless sensor networks

Considering the limited communicational bandwidth and energy supply in wireless sensor networks (WSNs), a novel framework for quantized track fusion in a robust and distributed way is proposed. Firstly, the local covariance matrices are compressed to find the diagonal lower bound of the matrix. Then the compressed covariance matrix and the target state estimate are quantized using K means, and sent to the fusion center (FC), ie., cluster header (CH). Secondly, a correlation independent robust tracking fusion algorithm, inner ellipsoidal approximation, is proposed in order to attack the unknown or incomplete correlations among local estimates. Finally, an example is included to illustrate the effectiveness of the proposed algorithm. It performs very closely to the existing results while requires much less bandwidth; on the other hand, compared with the random selection of node activation, the target oriented dynamic clustering approach saves energy consumption up to 42%.