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An underwater acoustic data compression method based on compressed sensing

  • Mechanical Engineering, Control Science and Information Engineering
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Abstract

The use of underwater acoustic data has rapidly expanded with the application of multichannel, large-aperture underwater detection arrays. This study presents an underwater acoustic data compression method that is based on compressed sensing. Underwater acoustic signals are transformed into the sparse domain for data storage at a receiving terminal, and the improved orthogonal matching pursuit (IOMP) algorithm is used to reconstruct the original underwater acoustic signals at a data processing terminal. When an increase in sidelobe level occasionally causes a direction of arrival estimation error, the proposed compression method can achieve a 10 times stronger compression for narrowband signals and a 5 times stronger compression for wideband signals than the orthogonal matching pursuit (OMP) algorithm. The IOMP algorithm also reduces the computing time by about 20% more than the original OMP algorithm. The simulation and experimental results are discussed.

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References

  1. SAYOOD K. Introduction to data compression [M]. United States: Newnes Press, 2006.

    MATH  Google Scholar 

  2. JAIN A K. A fast Karhunen-Loeve transform for a random processes [M]. Chicago: IEEE Computer Society, 1974, 24: 1023–1029.

    Google Scholar 

  3. TURCZA P, DUPLAGA M. Low-Power image compression for wireless capsule endoscopy [C]// IEEE International Workshop on Imaging Systems and Techniques-IST. Krakow, Poland, 2007: 1–4.

    Google Scholar 

  4. MO Y B, QIU Y B, LIU J Z, LING Y X. A data compression algorithm baseed on adaptive huffman code for wireless sensor networks [C]// Intelligent Computation Technology and Automation (ICICTA). 2011: 3–6.

    Google Scholar 

  5. SHEN Yan-chun, GUAN Yu-jun, WANG Fang, LUN Zhi-xin. The investigation of image compress coding based on wavelet transformation [C]// International Conference on Future Information Technology and Management Engineering. 2010: 324–326.

    Google Scholar 

  6. BERGER C R, ZHOU S L, PREISIG J C. Sparse channel estimation for multicarrier underwater acoustic communication: From subspace methods to compressed sensing [J]. IEEE Transactions on Signal Processing, 2010, 58(3): 1708–1721.

    Article  MathSciNet  Google Scholar 

  7. DONOHO D L. Compressed sensing [J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306.

    Article  MathSciNet  MATH  Google Scholar 

  8. TROPP J A, GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit [J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655–4666.

    Article  MathSciNet  MATH  Google Scholar 

  9. BOASHASH B, O'SHEA P. A methodology for detection and classification of some underwater acoustic signals using timefrequency analysis techniques [J]. IEEE Transactions on Acoustics, Speech and Signal Processing, 1990, 38(11): 1829–1841.

    Article  Google Scholar 

  10. CANDES E J, TAO T. Near-optimal signal recovery from random projections: universal encoding strategies [J]. IEEE Transactions on Information Theory, 2006, 52(12): 5406–5425.

    Article  MathSciNet  MATH  Google Scholar 

  11. DAVENPORT M A, WAKIN M B. Analysis of orthogonal matching pursuit using the restricted isometry property [J]. IEEE Transactions on Information Theory, 2010, 56(9): 4395–4401.

    Article  MathSciNet  Google Scholar 

  12. ZHANG Ge-sen, JIAO Shu-hong, XU Xiao-li, WANG Lan. Compressed sensing and reconstruction with bernoulli matrices [C]// IEEE International Conference on Information and Automation. 2010: 455–460. (in Chinese)

    Google Scholar 

  13. GAN Lu, LI Ke-zhi, LING Cong. Golay meets hadamard: Golaypaired hadamard matrices for fast compressed sensing [C]// IEEE Information Theory Workshop. 2012: 637–641.

    Google Scholar 

  14. TANG Gui-lin, QIU Yun-ming. Improved least square method apply in ship performance analysis [C]// International Conference on Advanced Computer Theory and Engineering (ICACTE). 2010: 594–596.

    Google Scholar 

  15. MARQUARDT D W. An algorithm for last-squares estimation of nonlinear parameters [J]. Journal of the Society for Industrial and Applied Mathematics, 1963, 11(2): 431–441.

    Article  MathSciNet  MATH  Google Scholar 

  16. LI Ling-zhi, ZOU Bei-ji, ZHU Cheng-zhang. Improved nonconvex optimization model for low-rank matrix recovery [J]. Journal of Central South University, 2015, 22(3): 984–991.

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, 710072, China

    Xiao-le Guo  (郭晓乐), Kun-de Yang  (杨坤德), Yang Shi  (史阳) & Rui Duan  (段睿)

  2. Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi’an, 710072, China

    Xiao-le Guo  (郭晓乐), Kun-de Yang  (杨坤德), Yang Shi  (史阳) & Rui Duan  (段睿)

Authors
  1. Xiao-le Guo  (郭晓乐)
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  2. Kun-de Yang  (杨坤德)
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  3. Yang Shi  (史阳)
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  4. Rui Duan  (段睿)
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Corresponding author

Correspondence to Kun-de Yang  (杨坤德).

Additional information

Foundation item: Project(11174235) supported by the National Natural Science Foundation of China; Project(3102014JC02010301) supported by the Fundamental Research Funds for the Central Universities, China

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Guo, Xl., Yang, Kd., Shi, Y. et al. An underwater acoustic data compression method based on compressed sensing. J. Cent. South Univ. 23, 1981–1989 (2016). https://doi.org/10.1007/s11771-016-3255-1

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  • DOI: https://doi.org/10.1007/s11771-016-3255-1

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