Matched field localization based on CS-MUSIC algorithm

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• 作者：Shuangle Guo ; Ruichun Tang ; Linhui Peng…
• 关键词：matched field processing ; compressed sensing ; CS MUSIC
• 刊名：Journal of Ocean University of China
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：15
• 期：2
• 页码：254-260
• 全文大小：674 KB
• 参考文献：Baggeroer, A. B., Kuperman, W. A., and Mikhalevsky, P. N., 1993. An overview of matched field methods in ocean acoustics. Oceanic Engineering, 18(4): 401–424.CrossRef
  Bucker, H. P., 1976. Use of calculated sound fields and matchedfield detection to locate sound sources in shallow water. Journal of the Acoustical Society of America, 59(2): 368–373.CrossRef
  Candès, E. J., and Wakin, M. B., 2008. An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2): 21–30.CrossRef
  Capon, J., 1969. High-resolution frequency-wave number spectrum analysis. Proceedings of the IEEE, 57(8): 1408–1418.CrossRef
  Chen, J., and Huo, X., 2006. Theoretical results on sparse representations of multiple-measurement vectors. IEEE Transactions on Signal Processing, 54(12): 4634–4643.CrossRef
  Devaney, A. J., 2000. Super-resolution processing of multi-static data using time reversal and MUSIC. Journal of the Acoustical Society of America, 123(5): 1–25.
  Donoho, D. L., 2006. Compressed sensing. IEEE Transactions on Information Theory, 52(4): 1289–1306.CrossRef
  Foucart, S., and Rauhut, H., 2013. Restricted Isometry Property. A Mathematical Introduction to Compressive Sensing. Springer, New York, 133–174.
  Kim, J. M., Lee, O. K., and Ye, J. C., 2012. Compressive MUSIC: Revisiting the link between compressive sensing and array signal processing. IEEE Transactions on Information Theory, 58(1): 278–301.CrossRef
  Pan, C., and Chen, J., 2014. Performance study of the MVDR beamformer as a function of the source incidence angle. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 22(1): 67–79.CrossRef
  Porter, M. B., and Tolstoy, A., 1994. The matched field processing benchmark problems. Journal of Computational Acoustics, 2(3): 161–185.CrossRef
  Schmidt, H., Baggeroer, A. B., Kuperman, W. A., and Scheer, E. K., 1990. Environmentally tolerant beamforming for highresolution matched field processing: Deterministic mismatch. Journal of the Acoustical Society of America, 88(4): 1851–1862.CrossRef
  Schmidt, R. O., 1986. Multiple emitter location and signal parameter estimation. IEEE Transactions on Antennas and Propagation, 34(3): 276–280.CrossRef
  Shi, H. H., 2013. Sparse-reconstruction-based high resolution matched-field source localization. Master thesis. Zhejiang University.
  Tolstoy, A., 1993. Matched Field Processing for Underwater Acoustic. World Scientific Publishing Co. Pte. Ltd., Singapore, 8–15.
  Vaidyanathan, C., and Buckley, K. M., 1995. Performance analysis of the MVDR spatial spectrum estimator. IEEE Transactions on Signal Processing, 43(6): 1427–1437.CrossRef
  Wu, X. C., Deng, W. B., and Yang, Q., 2013. DOA estimation method based on CS-MUSIC algorithm. System Engineering and Electronics, 35(9): 1821–1824 (in Chinese with English abstract).
  
• 作者单位：Shuangle Guo (1) (2)
  Ruichun Tang (1)
  Linhui Peng (1)
  Xiaopeng Ji (1)
  
  1. College of Information Science and Engineering, Ocean University of China, Qingdao, 266100, P. R. China
  2. College of Information Engineering, Binzhou University, Binzhou, 256600, P. R. China
  
• 刊物主题：Oceanography; Meteorology;
• 出版者：Springer Berlin Heidelberg
• ISSN：1993-5021

文摘

The problem caused by shortness or excessiveness of snapshots and by coherent sources in underwater acoustic positioning is considered. A matched field localization algorithm based on CS-MUSIC (Compressive Sensing Multiple Signal Classification) is proposed based on the sparse mathematical model of the underwater positioning. The signal matrix is calculated through the SVD (Singular Value Decomposition) of the observation matrix. The observation matrix in the sparse mathematical model is replaced by the signal matrix, and a new concise sparse mathematical model is obtained, which means not only the scale of the localization problem but also the noise level is reduced; then the new sparse mathematical model is solved by the CS-MUSIC algorithm which is a combination of CS (Compressive Sensing) method and MUSIC (Multiple Signal Classification) method. The algorithm proposed in this paper can overcome effectively the difficulties caused by correlated sources and shortness of snapshots, and it can also reduce the time complexity and noise level of the localization problem by using the SVD of the observation matrix when the number of snapshots is large, which will be proved in this paper. Keywords matched field processing compressed sensing CS MUSIC