基于状态预测自适应一致滤波器的分布式估计融合算法

Distributed Estimation Fusion Using the State Prediction-based Adaptive Consensus Filter

应用自适应滤波算法改进了基于一致滤波器的估计融合算法以加快节点估计的一致收敛速度,提出了一种基于状态预测的自适应一致滤波器.在此算法中,节点采用状态预测值作为自适应滤波器的参考信号,应用自适应算法修正一致滤波器的加权矩阵.仿真结果表明,本文提出的算法不仅能够加快节点估计的一致收敛速度,还能减小收敛过程中节点的估计误差.

This paper utilizes the adaptive filter to improve the consensus filter-based estimation fusion algorithm for accelerating the convergence of the nodes＇ estimates, and proposes the state prediction-based adaptive consensus filter. In this algorithm, each node uses the predicted state as a reference signal for the adaptive filter, and modifies the weighted matrix of the consensus filter according to the algorithm of the adaptive filter. The simulation results demonstrate that the proposed algorithm can not only accelerate the convergence of the nodes＇ estimates, but also decrease the estimation error before the convergence is reached.