分布式压缩感知(Distributed Compressed Sensing,DCS)是在无线传感器网络(Wireless Sensor Network,WSN)中减少数据传输量、降低能量消耗的有效手段.该文面向分簇WSN,提出层次化分布式压缩感知(Hierarchical Distributed Compressed Sensing,HDCS).在利用簇内DCS消除簇内时间、空间冗余的基础上,利用簇间DCS消除簇间空间冗余,减少簇头的数据发送量.针对分簇WSN采集信号的结构化稀疏特性,建立块稀疏簇内联合稀疏模型与块稀疏簇间联合稀疏模型,提出HDCS观测方案与层次化联合重构算法.仿真结果表明,与普通DCS相比,HDCS在保证重建信号质量的同时,能够有效减轻簇头的通信负担,并显著降低Sink上的信号重构时间.%Distributed Compressed Sensing (DCS) is an effective means to reduce the amount of data transmission and energy consumption in Wireless Sensor Network (WSN).Hierarchical Distributed Compressed Sensing (HDCS) is proposed for clustering WSN.It eliminates the temporal-spatial redundancies among data collected by the cluster members with the intra-cluster DCS,and eliminates the spatial redundancies among clusters with the inter-cluster DCS.According to the signal's structured sparsity,a block-sparse intra-cluster joint sparsity model and a block-sparse inter-cluster joint sparsity model are constructed.Then,a hierarchical measurement scheme and a hierarchical joint reconstruction scheme are proposed for HDCS.Experimental results show that compared to general DCS,HDCS can relieve the transmission burden in the network effectively,without lowering the quality of the reconstructed signal.Moreover,it can reduce the signal reconstruction time at the Sink observably.
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