基于卡尔曼一致滤波的分布式稀疏信号重构优化算法

An Optimized Algorithm of Consensus based Kalman Filtering with Distributed Sparse Signal Reconstruction

针对无线传感器网络稀疏信号重构问题,提出了一种基于嵌入伪测量的平方根无迹卡尔曼一致滤波(SRUKCF)的分布式稀疏非线性信号重构算法。融合来自无线传感器网络中不同节点的随机线性测量值,使各滤波器对稀疏非线性信号的估计达到一致。仿真结果验证了该算法的有效性。

In order to deal with the problem of sparse signal reconstruction for wireless sensor networks(WSNs),an distributed sparse signal reconstruction Optimized algorithm based on square root unscented kalman consensus filter(SRUKCF)with embedded pseudo-measurement is proposed in this paper.By fusing the random linear measurements from different nodes in the WSNs,such that all filters can reach a consensus on the estimate of sparse nonlinear signals.The simulation results show that the Optimized algorithm is effect.