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A Deep-Learning-Based Distributed Compressive Sensing in UWB Soil Signals

  • Chenkai ZhaoEmail author
  • Jing Liang
  • Qin Tang
Conference paper
  • 16 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 571)

Abstract

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), [1]). 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.

Keywords

Distributed compressed sensing Long short-term memory Deep learning UWB signals 

Notes

Acknowledgements

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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