A Distributed Compressed Sensing-based Algorithm for the Joint Recovery of Signal Ensemble

Abstract

This paper considers sparsity-aware adaptive compressed sensing acquisition and the joint reconstruction of intra- and inter-correlated signals in the wireless sensor networks via distributed compressed sensing. textcolor{red}{ Due to the different sparsity order of the finite-length signals, we develop an adaptive sensing framework based on the sparsity order, in which sensor readings are sampled according to its own sparsity order measure.} On the decoder side, utilizing a distributed compressive sensing scheme, a joint reconstruction method is proposed to recover signal ensemble even in imperfect data communication. textcolor{red}{Moreover, we explore that by adapting the sampling rate of the sensed signals, not only the whole required number of measurements is reduced, but also the reconstruction performance is significantly improved. Numerical experiments verify that our proposed algorithm achieves higher reconstruction accuracy with a smaller number of required transmission, and with lower complexity as compared to those of the state of the art CS methods.

Keywords

• Sparsity measure
• sparsity-aware distributed compressed sensing
• compressive sensing