基于信息矩阵加权一致策略的分布式Kalman滤波器

Distributed Kalman Filter with Information Matrix Weighted Consensus Strategies

  • 摘要: 本文提出采用信息矩阵加权的方法改进基于一致性策略的分布式Kalman滤波算法,提高其在传感器网络估计融合应用中的性能.此方法中,节点根据其自身及其邻居节点估计的不确定信息融合估计值.在此基础上,文中还讨论了通过优化一致加权系数进一步提高算法性能的方法.仿真实验表明,本文所提算法不仅改进了节点状态估计的精确度,而且显著提高了各个节点状态估计的一致性.

     

    Abstract: An information matrix weighted consensus strategy is proposed to improve the consensus based distributed Kalman filter(DKF) algorithm in the estimation fusion of sensor networks.In this method,each node fuses the estimates from its neighbors according to the uncertainty of the estimates.Based on the proposed method,the consensus weights optimization problem is also discussed to achieve the better performance.The simulation results demonstrate that the proposed algorithms not only improve the accuracy of the state estimate,but also largely enhance the consistency of the state estimate achieved by each node.

     

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