Distributed compressed sensing based on differential signal sparse model
基于差值信号稀疏模型的分布式压缩感知

This paper proposed a differential signal sparse model by exploiting inter-signal correlation structures. The model was appropriate for the WSNs applications in which multi-node were used to monitor the same physical phenomena or events. Based on differential signal sparse model, this paper proposed a distributed compressed sensingDCS algorithm for the model. The proposed algorithm could encode differential signal without inter-node communications. Simulations indicate that, compared with separately reconstruction, the proposed algorithm can joint reconstruct multiple signals with high probability by using significantly fewer measurements per sensor and accommodates requirements of WSNs applications in energy efficient way.