二值传感器网络的分布式稀疏LMS算法

在基于无线传感器网络的参数估计中，每个节点在数据采集、存储、处理和传输等方面的能力是有限的。二值传感器网络中的每个节点只能提供低精度1比特测量值，与能够提供模拟测量值（无限精度）的传感器相比，二值传感器有较低的使用成本。如何利用低成本二值传感器网络获得较好的参数估计性能近些年已引起广泛关注，基于该二值传感器网络，论文提出了一种分布式稀疏参数估计的自适应最小均方（LMS）算法。该算法采用稀疏惩罚最大似然优化，并结合期望最大化和LMS方法，获得稀疏信号的在线估计。仿真实验表明，尽管只采用1比特测量，提出的算法仍具有较好的收敛性，并且稳定状态的估计误差接近于非1比特测量的同类算法。 

Distributed Sparse LMS Algorithm over Binary Sensor Network

In the parameter estimation over wireless sensor networks（WSNs）, each node's ability in data acquisition, storage, processing and transmission is limited. In a binary sensor network, each node only can provide low-precision One-bit observations. Compared with the measurement value sensors that can provide analog measurements (infinite accuracy), the binary sensors have lower cost. How to use low-cost binary sensor networks to obtain better parameter estimation performance have attracted extensive attention in recent years. Based on this binary sensor network, an adaptive least mean square (LMS) algorithm for distributed sparse parameter estimation is proposed. The algorithm adopts sparse penalty maximum likelihood optimization, combined with expectation maximization (EM) and Least Mean Square (LMS) method, to obtain the online estimation of sparse signal. Simulation experiment results show that the proposed algorithm, though only using 1-bit measurements, has good convergence, is comparable to the existing algorithms based on analog measurements (infinite accuracy).