Compressed Sensing in Soil Ultra-Wideband Signals

  • Chenkai ZhaoEmail author
  • Jing Liang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


This paper investigated the compressed sensing (CS) of ultra-wideband (UWB) soil echo signals. When CS is used in the transmission of UWB signals, sampling rate can be effectively reduced and sparse signals can be reconstructed from fewer observations. Therefore, how to apply CS into UWB soil echo signals is of great importance. The proposed approach reveals that UWB signals can be expressed by linear combinations of many atoms from a proper dictionary. In this paper, K-singular value decomposition (KSVD) dictionary and three types of Gaussian pulse dictionaries are designed, and the probability of successful reconstruction can reach 0.95. It is shown that Gaussian first-order derivative dictionary is the most suitable; the root-mean-square error (RMSE) of UWB signals and reconstructing signals is lower than 0.12.


Compressed sensing Sparse dictionary 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.


  1. 1.
    Liang J, Liu X, Liao K. Soil moisture retrieval using UWB echoes via fuzzy logic and machine learning. IEEE Internet Things J (Early Access); 2017 Oct 9.Google Scholar
  2. 2.
    Reed JH. An introduction to ultra wideband communication systems. Prentice Hall Communications Engineering and Emerging Technologies Series, NJ: Prentice-Hall; 2005.Google Scholar
  3. 3.
    Donoho DL. Compressed sensing. IEEE Trans Inf Theor. 2006.Google Scholar
  4. 4.
    Pati YC, Rezaiifar R, Krishnaprasad PS. Orthogonal matching pursuit-recursive function approximation with applications to wavelet decomposition. In: Proceedings of annual asilomar conference signals, systems, and computers; 1993 Nov; Pacific Grove.Google Scholar
  5. 5.
    Tropp JA, Gilbert AC. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theor. 2007;53(12):4655–66.MathSciNetCrossRefGoogle Scholar
  6. 6.
    Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm fordesigning overcomplete dictionaries for sparse representation. IEEE Trans Signal Process. 2006;54(11):4311.CrossRefGoogle Scholar
  7. 7.
    Paredes JL, Arce GR, Wang Z. Ultra-wideband compressed sensing: channel estimation. IEEE J Sel Top Signal Process. 2007;1(3):383–95.CrossRefGoogle Scholar

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© 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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