logo

SCIENCE CHINA Information Sciences, Volume 59, Issue 12: 122308(2016) https://doi.org/10.1007/s11432-016-5585-x

Underwater sonar target imaging via compressed sensing with M sequences

More info
  • ReceivedMar 24, 2016
  • AcceptedApr 27, 2016
  • PublishedAug 9, 2016

Abstract

Due to the low sound propagation speed, the tradeoff between high azimuth resolution and wide imaging swath has severely limited the application of sonar underwater target imaging. However, based on compressed sensing (CS) technique, it is feasible to image targets with merely one pulse and thus avoid the above tradeoff. To investigate the possible waveforms for CS-based underwater imaging, the deterministic M sequences widely used in sonar applications are introduced in this paper. By analyzing the compressive matrix constructed from M sequences, the coherence parameter and the restricted isometry property (RIP) of the matrix are derived. Also, the feasibility and advances of M sequence are demonstrated by being compared with the existing Alltop sequence in underwater CS imaging framework. Finally, the results of numerical simulations and a real experiment are provided to reveal the effectiveness of the proposed signal.


Acknowledgment

Acknowledgments

This work was supported in part by National Natural Science Foundation of China (Grant No. 61271391). 111 Project of China Ministry of Education (MOE) (Grant No. B14010), New Century Excellent Talents Supporting Plan of China MOE (Grant No. NCET-13-0049), Ministry Research Foundation (Grant No. 9140A21050114HT05338) and Outstanding Youth Teacher Training Plan of BIT (Grant No. BIT-JC-201205). The authors thank Professor Zhang Mingmin, and Doctor Lu Jianbin of Electronic Engineering College, Naval University of Engineering, Wuhan, China for their great supports in conducting the experiment.


References

[1] Gilmour G A. Synthetic Aperture Side-Looking Sonar System. U.S. Patent 4088978, May 1978. Google Scholar

[2] Griffiths H D. Synthetic aperture imaging with sonar and radar: a comparison. In: Proceedings of the 5th World Congress on Ultrasonics, Paris, 2003. 511--518. Google Scholar

[3] Hayes P, Gough T. Synthetic aperture sonar: a review of current status. IEEE J Oceanic Eng, 2009, 34: 207-224 CrossRef Google Scholar

[4] Franceschetti G, Lanari R. Synthetic Aperture Radar Processing. Boca Raton: CRC Press, 1999. Google Scholar

[5] Cutrona L J. Comparison of sonar system performance achievable using synthetic aperture techniques with the performance achievable with more conventional means. J Acoust Soc America, 1975, 58: 336-348 CrossRef Google Scholar

[6] Cutrona L J. Additional characteristics of synthetic-aperture sonar systems and a further comparison with nonsynthetic-aperture sonar systems. J Acoust Soc America, 1977, 61: 1213-1217 CrossRef Google Scholar

[7] Yu M, Xu J, Peng Y. Joint Doppler parameters estimation for squint-looking SAR. IET Radar Sonar Nav, 2007, 1: 207-212 CrossRef Google Scholar

[8] Sutton T J, Griffiths H D, Hetet A, et al. Experimental validation of algorithms for high-resolution imaging of the seabed using synthetic aperture sonar. IET Radar Sonar Nav, 2003, 150: 78-83 CrossRef Google Scholar

[9] Donoho D. Compressed sensing. IEEE Trans Inf Theory, 2006, 52: 1289-1306 CrossRef Google Scholar

[10] Candes E, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory, 2006, 52: 489-509 CrossRef Google Scholar

[11] Eldar Y, Kutyniok G. Compressed Sensing: Theory and Applications. New York: Cambridge University Press, 2012. Google Scholar

[12] Yan H C, Xu J, Xia X-G, et al. Wideband underwater sonar imaging via compressed sensing with scaling effect compensation. Sci China Inf Sci, 2015, 58: 020306-509 Google Scholar

[13] Baraniuk R, Steeghs P. Compressive radar imaging. In: Proceedings of IEEE Radar Conference, Boston, 2007. 128--133. Google Scholar

[14] Alonso T, Lòpez-Dekker P, Mallorquì J. A novel strategy for radar imaging based on compressive sensing. IEEE Trans Geosci Rem Sens, 2010, 48: 4285-4295 CrossRef Google Scholar

[15] Yan H, Xu J, Peng S, et al. A compressed sensing method for a wider swath in synthetic aperture imaging. In: Proceedings of IET International Radar Conference, Xi'an, 2013. 1--6. Google Scholar

[16] Patel V M, Easley G R, Healy D M, et al. Compressed synthetic aperture radar. IEEE J Sel Top Signal Process, 2010, 4: 244-254 CrossRef Google Scholar

[17] Zhu X, Bamler R. Tomographic SAR inversion by L1-norm regularization--the compressive sensing approach. IEEE Trans Geosci Rem Sens, 2010, 48: 3839-3846 CrossRef Google Scholar

[18] Gurbuz A, McClellan J, Scott W. A compressive sensing data acquisition and imaging method for stepped frequency GPRs. IEEE Trans Signal Process, 2009, 57: 2640-2650 CrossRef Google Scholar

[19] Li G, Burkholder R J. Hybrid matching pursuit for distributed through-wall radar imaging. IEEE Trans Antennas Propag, 2015, 63: 1701-1711 CrossRef Google Scholar

[20] Li G, Varshney P K. Micro-Doppler parameter estimation via parametric sparse representation and pruned orthogonal matching pursuit. IEEE J Sel Top in Appl Earth Observ and Rem Sens, 2014, 7: 4937-4948 CrossRef Google Scholar

[21] Herman M, Strohmer T. High-resolution radar via compressed sensing. IEEE Trans Signal Process, 2009, 57: 2275-2284 CrossRef Google Scholar

[22] Baraniuk R, Davenport M, DeVore R, et al. A simple proof of the restricted isometry property for random matrices. Constr Approx, 2008, 28: 253-263 CrossRef Google Scholar

[23] Mendelson S, Pajor A, Tomczak-Jaegermann N. Uniform uncertainty principle for Bernoulli and subgaussian ensembles. Constr Approx, 2008, 28: 277-289 CrossRef Google Scholar

[24] Tropp J A, Wakin M B, Duarte M F, et al. Random filters for compressive sampling and reconstruction. In: Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Toulouse, 2006. 14--19. Google Scholar

[25] Rauhut H, Romberg J, Tropp J. Restricted isometries for partial random circulant matrices. Appli Comput Harmon Anal, 2012, 32: 242-254 CrossRef Google Scholar

[26] Xu J, Pi Y, Cao Z. Optimized projection matrix for compressive sensing. EURASIP J Advances in Signal Process, 2010, 43: 1-8 Google Scholar

[27] Cleju N. Optimized projections for compressed sensing via rank-constrained nearest correlation matrix. Appli Comput Harmon Anal, 2014, 36: 495-507 CrossRef Google Scholar

[28] Tropp J. Greed is good: algorithmic results for sparse approximation. IEEE Trans Inf Theory, 2004, 50: 2231-2242 CrossRef Google Scholar

[29] Donoho D, Elad M. On the stability of the basis pursuit in the presence of noise. EURASIP Signal Process J, 2006, 86: 511-532 CrossRef Google Scholar

[30] Li K, Gan L, Ling C. Convolutional compressed sensing using deterministic sequences. IEEE Trans Signal Process, 2013, 61: 740-752 CrossRef Google Scholar

[31] Welch L. Lower bounds on the maximum cross correlation of signals. IEEE Trans Inf Theory, 1974, 20: 397-399 CrossRef Google Scholar

[32] Dinan E, Jabbari B. Spreading codes for direct sequence CDMA and wideband CDMA cellular networks. IEEE Commun Mag, 1998, 36: 48-54 Google Scholar

Copyright 2019 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备18024590号-1