Advertisement

Compressive Sensing in UWB Echoes

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 463)

Abstract

Ultra-wide band (UWB) signals have very wide bandwidths. On the basis of Nyquist sampling theory, it is too difficult for A/D converters to sample such signals. This issue confused people for a long time until compressive sensing (CS) was proposed. CS in UWB was brought into focus once it appeared because CS can get useful information with abandoning a lot of redundant data and UWB signals are easily sparse. Hence it is usually used in UWB to deal with sampling problems. This paper mainly describes the principles of CS and its mathematical theory, and then compares the different transform matrices, measurement matrices and reconstruction algorithms based on UWB echoes. Finally, find a best method to recovery UWB echoes with Gaussian white noise.

Keywords

Ultra-wide band signals Compressive sensing Sparse representation Reconstruction algorithms 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61671138), the Fundamental Research Funds for the Central Universities Project No. ZYGX2015J021, and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.

References

  1. 1.
    Win, M.Z., Dardari, D., Molisch, A.F., et al.: History and applications of UWB. Proc. IEEE 97(2), 198–204 (2009)CrossRefGoogle Scholar
  2. 2.
    Yang, L., Giannakis, G.B.: Ultra-wideband communications: an idea whose time has come. IEEE Signal Proces. Mag. 21(6), 26–54 (2004)CrossRefGoogle Scholar
  3. 3.
    Zhang, P., Hu, Z., Qiu, R.C., et al.: A compressed sensing based ultra-wideband communication system. In: IEEE International Conference on Communications, pp. 1–5 (2009)Google Scholar
  4. 4.
    Zhao, Y.: Study of Effective Sparse Representation Method of Ultra-wide Band Signal. Harbin Institute of Technology (2015)Google Scholar
  5. 5.
    Qu, Y.: The Design of UWB Pulse Shapes on Gaussian Polynomials. LanZhou University (2007)Google Scholar
  6. 6.
    Baraniuk, R.G.: Compressive sensing. IEEE Signal Process. Mag. 24(4), 118–124 (2007)CrossRefGoogle Scholar
  7. 7.
    Chen, S.S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20, 33–61 (1998)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Donoho, D.L.: De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41, 613–627 (1995)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Electronic EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

Personalised recommendations