A Deep-Learning-Based Distributed Compressive Sensing in UWB Soil Signals
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Various studies that address the distributed compressed sensing (DCS) problem are based on jointly sparse prior (Sarvotham et al Asilomar conference on signals, systems, and computers, pp 1537–1541 (2006), ). In this paper, we relax this condition and propose a data-driven method to reconstruct UWB soil echo signals from compressed sensing (CS) random measurements. To this end, we use a long short-term memory (LSTM) network architecture which takes in DCS measurements as input and outputs reconstruction signals. The proposed method is LSTM-DCS. On a dataset of UWB soil echo signals, we show that the LSTM-DCS significantly outperforms traditional DCS solvers.
KeywordsDistributed compressed sensing Long short-term memory Deep learning UWB signals
This work was supported by the National Natural Science Foundation of China (61671138, 61731006), and was partly supported by the 111 Project No. B17008.
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