Distributed Compressed Sensing Reconstruction Algorithm Based on Attention Mechanism and GRU

The purpose of this paper is to solve the reconstruction problem of sparse vectors in distributed compressed sensing with Multiple Measurement Vectors (MMV). Many traditional methods are based on certain sparse conditions, such as assuming that signals of different channels are joint sparse. This paper relaxes this restriction and assumes some correlation between the signals of different channels. We use Gated Recurrent Units (GRU) to capture this correlation and reconstruct the signal. Meanwhile, the previous reconstruction algorithms treat the signals of different channels equally, but the influence of different channels on the reconstruction results may be different. To solve this problem, we design an attention mechanism that adaptively learns and adjusts the influence factors of different channel signals, and optimizes the reconstruction results. The method proposed in this paper is only for the decoder, without any additional requirements for the encoder, so the encoder can use the conventional compression sampling method. Extensive experiments indicate the efficiency and robustness of the proposed method in the case of simulated data and real data.