An automated approach to passive sonar classification using binary image features

Abstract

This paper proposes a new method for ship recognition and classification using sound produced and radiated underwater. To do so, a three-step procedure is proposed. First, the preprocessing operations are utilized to reduce noise effects and provide signal for feature extraction. Second, a binary image, made from frequency spectrum of signal segmentation, is formed to extract effective features. Third, a neural classifier is designed to classify the signals. Two approaches, the proposed method and the fractal-based method are compared and tested on real data. The comparative results indicated better recognition ability and more robust performance of the proposed method than the fractal-based method. Therefore, the proposed method could improve the recognition accuracy of underwater acoustic targets.

Keywords

binary image / passive sonar / neural classifier / ship recognition / short-time Fourier transform / fractal-based method

References

[1] Bao F, Li C, Wang X, Wang Q, Du S. Ship classification using nonlinear features of radiated sound: An approach based on empirical mode decomposition. The Journal of the Acoustical Society of America, 2010, 128(1): 206-214

[2] Becchetti C, Ricotti LP. Speech recognition, 1999, John Wiley: New York, 1-67

[3] Chen C, Lee J, Lin M. Classification of under-water signals using neural network. Tamkang Journal of Science and Engineering, 2000, 3(1): 31-48

[4] Diamant R, Lampe L. Underwater localization with time synchronization and propagation speed uncertainties. IEEE Transactions on Mobile Computing, 2013, 12(7): 1257-1269

[5] Duda RO, Hart PE, Stork DG. Pattern classification, 2000, John Wiley: New York, 282-320

[6] Eickstedt D, Schmidt H. A low-frequency sonar for sensor adaptive, multistatic, detection and classification of underwater targets with AUVs. Proceedings of the OCEANS, 2003, San Diego, CA: USA, 1440-1447

[7] Farrokhrooz M, Karimi M. Ship noise classification using probabilistic neural network and AR model coefficients. Proceedings of the OCEANS, 2005, Washington, DC: USA, 1107-1110

[8] Farrokhrooz M, Karimi M. Marine vessels acoustic radiated noise classification in passive sonar using probabilistic neural network and spectral features. Intelligent Automation and Soft Computing, 2011, 17(3): 369-383

[9] He X, Cheng J, He G. Application of BP neural network and higher order spectrum for ship-radiated noise classification. Proceedings of the 2nd International Conference on Future Computer and Communication, 2010, Wuhan: China, 712-716

[10] Howell B, Wood S. Passive sonar recognition and analysis using hybrid neural networks. Proceedings of the OCEANS, 2003, San Diego: USA, 1917-1924

[11] Lennartsson R, Dalberg E, Levonen M, Lindgren D, Persson L. Fused classification of surface ships based on hydroacoustic and electromagnetic signatures. Proceedings of the OCEANS, 2006, 1-5

[12] Li Q, Wang J, Wei W. An application of expert system in recognition of radiated noise of underwater target. Proceedings of the OCEANS, 1995, San Diego, CA: USA, 404-408

[13] Lourens J. Classification of ships using underwater radiated noise. Proceedings of the Conference on Communications and Signal Processing, 1988, Pretoria: South Africa, 130-134

[14] Luo H, Wu K, Guo Z, Gu L, Ni L. Ship detection with wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 2012, 23(7): 1336-1343

[15] Oppenheim A, Schafer R. Discrete-time signal processing. Prentice-Hall, 1989, Upper Saddle River: USA, 541-628

[16] Orfanidis S. Optimum signal processing: An introduction, 1996, McGraw-Hill: New York, 234-290

[17] Rajagopal R, Sankaranarayanan B, Ramakrishna RP. Target classification in a passive sonar—an expert system approach. Proceedings of the Acoustics, Speech, and Signal Processing, 1990, Albuquerque: USA, 2911-2914

[18] Rogoyski A, Dawe F, Robinson M. Passive sonar data processing. Proceedings of the 6th Undersea Defense Technology Conference, 1994, London: UK, 310-313

[19] Sadjadi F, Chun C. Passive polarimetric IR target classification. IEEE Transactions on Aerospace and Electronic Systems, 2001, 37(2): 740-751

[20] Shi GZ, Hu JC. Ship noise demodulation line spectrum fusion feature extraction based on the wavelet packet. Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, 2007, Beijing: China, 846-850

[21] Soares-Filho W, Seixas J, Pereira C L. Averaging spectra to improve the classification of the noise radiated by ships using neural networks. Proceedings of the Sixth Brazilian Symposium Neural Networks, 2000, Rio de Janeiro: Brazil, 156-161

[22] Soares-Filho W, Seixas J, Pereira C L. Principal component analysis for classifying passive sonar signals. Proceedings of the IEEE International Symposium on Circuits and Systems, 2001, Sydney: Australia, 592-595

[23] Stoica P, Moses R. Spectral analysis of signals. Pearson Education, Prentice Hall, 2005, Upper Saddle River: USA, 144-198

[24] Urick R J. Principles of underwater sound, 2008, McGraw-Hill: New York, 237-291

[25] Ward M, Stevenson M. Sonar signal detection and classification using artificial neural networks. Proceedings of the Canadian Conference on Electrical and Computer Engineering, 2000, Halifax: Canada, 717-721

[26] Yang S, Li Z. Classification of ship-radiated signals via chaotic features. Electronics Letters, 2003, 39(4): 395-397

[27] Yang S, Li Z, Wang X. Vessel radiated noise recognition with fractal features. Electronics Letters, 2000, 36(10): 923-925

[28] Yang S, Li Z, Wang X. Ship recognition via its radiated sound: The fractal based approaches. The Journal of the Acoustical Society of America, 2002, 112(1): 172-177

[29] Zak A. Ships classification basing on acoustic signatures. WSEAS Transactions on Signal Processing, 2008, 4(4): 137-149

[30] Zimmer WMX, Harwood J, Tyack PL, Johnson MP, Madsen PT. Passive acoustic detection of deep-diving beaked whales. The Journal of the Acoustical Society of America, 2008, 124(5): 2823-2832