摘要： 为更好地解决无线传感网分布式测量中有效数据估计问题,提出一种新的分布式压缩估计算法。通过在一个压缩维度上完成未知参数变量的分布式估计,并采用自适应随机梯度递归方法更新测量矩阵,将分布式压缩估计与测量矩阵优化相结合,实现收敛速度及估计误差精度的最优化。仿真结果表明,与dNLMS、DCE算法相比,该算法具有更快的收敛速度及更高的估计误差精度。

关键词: 无线传感测量, 分布式压缩估计, 梯度递归, 测量矩阵优化, 系统模型

Abstract: In order to solve the problem of effective data estimation in distributed measurement of Wireless Sensor Network(WSN),a Distributed Compression Estimation Algorithm for Measurement Matrix Optimization(MMDCE) is proposed.It performs the distributed estimation of unknown parameter variables on a compressed dimension,and updates the measurement matrix by using adaptive random gradient recursion.The DCE is combined with the measurement matrix optimization,the optimization of convergence rate and estimated error accuracy are achieved.Simulation results show that compared with dNLMS algorithm and DCE algorithm,the algorithm has better convergence speed and higher precision of estimation error.

Key words: wireless sensor measurement, Distributed Compression Estimation(DCE), gradient recursion, measurement matrix optimization, system model

中图分类号: 

• TP391