基于最佳联合稀疏表示的分布式压缩感知算法

通过探索无线传感器网络节点感知数据的时空相关性，可以构建适用于不同应用情形的联合稀疏模型。利用联合稀疏模型，提出了一种适用于无线传感器网络的分布式压缩感知算法。该算法采用联合编码联合解码的方式，充分利用了信号内部和信号之间的相关性，从而可以用更少的观测值实现信号群的精确重构。与单独编码单独解码相比，采用联合编码联合解码的方法，在保证信息可靠传输的前提下，减少了整个网络的数据流量，节约了宝贵的能量资源，以能量有效的方式满足了传感器网络的应用。

Distributed Compressed Sensing Algorithm Based on the Most Compact Joint Sparsity Representation

Joint sparsity models(JSM) that applied in different situations could be constructed by exploiting both intra-and inter-signal correlation structures of multi-signal ensembles,such as wireless sensor network(WSN).Based on JSM,a distributed compressed sensing(DCS) algorithm that applied in WSN was proposed.The proposed algorithm in which multiple signals are encoded jointly and decoded jointly can reconstruct multiple signals with high probability by using significantly fewer measurements per sensor.In the premise of reliability of message delivery,it makes significant reduction in the network load and saves precious energy resources.Compared with the traditional method that encodes separately and decodes separately,the proposed method accommodated the requirements of WSN applications in energy efficient way.