Using SVM Classifier and Micro-Doppler Signature for Automatic Recognition of Sonar Targets

• Autorzy: Saffari Abbas , Zahiri Seye Hamid , Khozein Ghanad Navid

Identyfikatory

DOI

10.24425/aoa.2022.142909

• Języki publikacji: EN

Abstrakty

EN

In this paper, we propose using a propeller modulation on the transmitted signal (called sonar micro-Doppler) and different support vector machine (SVM) kernels for automatic recognition of moving sonar targets. In general, the main challenge for researchers and craftsmen working in the field of sonar target recognition is the lack of access to a valid and comprehensive database. Therefore, using a comprehensive mathematical model to simulate the signal received from the target can respond to this challenge. The mathematical model used in this paper simulates the return signal of moving sonar targets well. The resulting signals have unique properties and are known as frequency signatures. However, to reduce the complexity of the model, the 128-point fast Fourier transform (FFT) is used. The selected SVM classification is the most popular machine learning algorithm with three main kernel functions: RBF kernel, linear kernel, and polynomial kernel tested. The accuracy of correctly recognizing targets for different signal-to-noise ratios (SNR) and different viewing angles was assessed. Accuracy detection of targets for different SNRs (−20, −15, −10, −5, 0, 5, 10, 15, 20) and different viewing angles (10, 20, 30, 40, 50, 60, 70, 80) is evaluated. For a more fair comparison, multilayer perceptron neural network with two back-propagation (MLP-BP) training methods and gray wolf optimization (MLP-GWO) algorithm were used. But unfortunately, considering the number of classes, its performance was not satisfactory. The results showed that the RBF kernel is more capable for high SNRs (SNR = 20, viewing angle = 10) with an accuracy of 98.528%.

Słowa kluczowe

EN

sonar micro-Doppler; automatic recognition; SVM; RBF kernel; linear kernel; polynomial kernel

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2023
• Tom: Vol. 48, No. 1
• Strony: 49--61
• Opis fizyczny: Bibliogr. 42 poz., rys., tab., wykr.

Twórcy

autor

Saffari Abbas

• University of Birjand Birjand, Iran

• https://orcid.org/0000-0001-6679-7225

autor

Zahiri Seye Hamid

• University of Birjand Birjand, Iran

• https://orcid.org/0000-0002-1280-8133

autor

Khozein Ghanad Navid

• Sajjad University of Mashhad Mashhad, Iran

• https://orcid.org/0000-0002-5849-2832

Bibliografia

• 1. Anderson S.J. (2004), Target classification, recognition and identification with HF radar, [in:] RTO SET Symposium on Target Identification and Recognition Using RF Systems, pp. 1-20.
• 2. Berthold T., Leichter A., Rosenhahn B., Berkhahn V., Valerius J. (2018), Seabed sediment classification of side-scan sonar data using convolutional neural networks, [in:] 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1-8, doi: 10.1109/SSCI.2017.8285220.
• 3. Bhanu B. (1986), Automatic target recognition: State of the art survey, IEEE Transactions on Aerospace and Electronic Systems, 22(4): 364-379, doi: 10.1109/TAES.1986.310772.
• 4. Cai X. et al. (2021), Dynamically controlling terahertz wavefronts with cascaded metasurfaces, Advanced Photonics, 3(3): 036003, doi: 10.1117/1.AP.3.3.036003.
• 5. Chen B. et al. (2022), DPM-LES investigation on flow field dynamic and acoustic characteristics of a twin-fluid nozzle by multi-field coupling method, International Journal of Heat and Mass Transfer, 192: 122927, doi: 10.1016/j.ijheatmasstransfer.2022.122927.
• 6. Chen H., Li S. (2022), Collinear nonlinear mixed-frequency ultrasound with FEM and experimental method for structural health prognosis, Processes, 10(4): 656, doi: 10.3390/pr10040656.
• 7. Chen V.C., Tahmoush D., Miceli W.J. (2014), Radar Micro-Doppler Signature: Processing and Applications, Institution of Engineering and Technology.
• 8. Clemente C., Balleri A., Woodbridge K., Soraghan J.J. (2013), Developments in target micro-Doppler signatures analysis: Radar imaging, ultrasound and through-the-wall radar, EURASIP Journal on Advances in Signal Processing, 2013: 47, doi: 10.1186/1687-6180-2013-47.
• 9. Dong J., Deng R., Quanying Z., Cai J., Ding Y., Li M. (2021), Research on recognition of gas saturation in sandstone reservoir based on capture mode, Applied Radiation and Isotopes, 178: 109939, doi: 10.1016/j.apradiso.2021.109939.
• 10. Ehrman L.M., Lanterman A.D. (2020), Incorporation of aircraft orientation into automatic target recognition using passive radar, IET Radar, Sonar & Navigation, 14(7): 1079-1087, doi: 10.1049/ietrsn.2020.0010.
• 11. Foued C., Ammar M., Arezki Y. (2017), Detection and classification of ground targets using a Doppler RADAR, [in:] 2017 Seminar on Detection Systems Architectures and Technologies (DAT), pp. 1-8, doi: 10.1109/DAT.2017.7889168.
• 12. Gaye B., Zhang D., Wulamu A. (2021), Improvement of support vector machine algorithm in big data background, Mathematical Problems in Engineering, 2021: 5594899, doi: 10.1155/2021/5594899.
• 13. Glover F., Laguna M. (2008), Principles of Tabu Search, Springer Science.
• 14. Jin C. et al. (2020), Development and evaluation of an artificial intelligence system for COVID-19 diagnosis, Nature Communications, 11: 5088, doi: 10.1038/s41467-020-18685-1.
• 15. Kaveh M., Khishe M., Mosavi M.R. (2019), Design and implementation of a neighborhood search biogeography-based optimization trainer for classifying sonar dataset using multi-layer perceptron neural network, Analog Integrated Circuits and Signal Processing, 100(2): 405-428, doi: 10.1007/s10470-018-1366-3.
• 16. Kavzoglu T., Colkesen I. (2009), A kernel functions analysis for support vector machines for land cover classification, International Journal of Applied Earth Observation and Geoinformation, 11(5): 352-359, doi: 10.1016/j.jag.2009.06.002.
• 17. Kazimierski W., Zaniewicz G. (2021), Determination of process noise for underwater target tracking with forward looking sonar, Remote Sensing, 13(5): 1014, doi: 10.3390/rs13051014.
• 18. Khishe M., Mohammadi H. (2019), Passive sonar target classification using multi-layer perceptron trained by salp swarm algorithm, Ocean Engineering, 181: 98-108, doi: 10.1016/j.oceaneng.2019.04.013.
• 19. Khishe M., Mosavi M.R. (2019), Improved whale trainer for sonar datasets classification using neural network, Applied Acoustics, 154: 176-192, doi: 10.1016/j.apacoust.2019.05.006.
• 20. Khishe M., Safari A. (2019), Classification of sonar targets using an MLP Neural Network trained by Dragonfly Algorithm, Wireless Personal Communications, 108: 2241-2260, doi: 10.1007/s11277-019-06520-w.
• 21. Koturwar P., Girase S., Mukhopadhyay D. (2015), A survey of classification techniques in the area of big data, Computer Science, pp. 1-7.
• 22. Lakhwani K. (2020), Big data classification techniques: A systematic literature, Journal of Natural Remedies, 21(2).
• 23. Liu J., Zhou Z., Zeng X. (2019), Multi-static active sonar target recognition method based on bionic signal, [in:] 2019 IEEE 2nd International Conference on Information Communication and Signal Processing (ICICSP), pp. 285-289, doi: 10.1109/ICICSP48821.2019.8958532.
• 24. Mamgain R., Jain R., Deb D. (2018), Study and Simulation of Radar Targets’ Micro-Doppler Signature, [in:] 2018 International Conference on Radar (RADAR), pp. 1-5, doi: 10.1109/RADAR.2018.8557264.
• 25. Molchanov P.O., Astola J.T., Egiazarian K.O., Totsky A.V. (2013), Classification of ground moving targets using bicepstrum-based features extracted from Micro-Doppler radar signatures, EURASIP Journal on Advances in Signal Processing, 2013(1): 1-13, doi: 10.1186/1687-6180-2013-61.
• 26. Qiao W., Khishe M., Ravakhah S. (2021), Underwater targets classification using local wavelet acoustic pattern and Multi-Layer Perceptron neural network optimized by modified Whale Optimization Algorithm, Ocean Engineering, 219: 108415, doi: 10.1016/j.oceaneng.2020.108415.
• 27. Saffari A., Khishe M., Zahiri S. (2022a), Fuzzy-ChOA: An improved chimp optimization algorithm for marine mammal classification using artificial neural network, Analog Integrated Circuits and Signal Processing, 111: 403-417, doi: 10.1007/s10470-022-02014-1.
• 28. Saffari A., Zahiri S., Khishe M. (2022b), Automatic recognition of sonar targets using feature selection in micro-Doppler signature, Defence Technology, doi: 10.1016/j.dt.2022.05.007.
• 29. Saffari A., Zahiri S.H., Khishe M. (2022c), Fuzzy grasshopper optimization algorithm: A hybrid technique for tuning the control parameters of GOA using Fuzzy System for big data sonar classification, Iranian Journal of Electrical and Electronic Engineering, 18(1): 1-13, doi: 10.22068/IJEEE.18.1.2131.
• 30. Saffari A., Zahiri S.H., Khishe M. (2022d), Fuzzy whale optimisation algorithm: A new hybrid approach for automatic sonar target recognition, Journal of Experimental & Theoretical Artificial Intelligence, doi: 10.1080/0952813X.2021.1960639.
• 31. Sclavounos P.D., Ma Y. (2018), Artificial intelligence machine learning in marine hydrodynamics, [in:] Proceedings of the ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. Volume 9: Offshore Geotechnics; Honoring Symposium for Professor Bernard Molin on Marine and Offshore Hydrodynamics, doi: 10.1115/OMAE2018-77599.
• 32. Smith G.E., Woodbridge K., Baker C.J. (2007), Multiperspective micro-Doppler signature classification, [in:] IET International Conference on Radar Systems 2007, doi: 10.1049/cp:20070522.
• 33. Tahmoush D. (2015), Review of micro-Doppler signatures, IET Radar, Sonar & Navigation, 9(9): 1140-1146, doi: 10.1049/iet-rsn.2015.0118.
• 34. Uddin S., Khan A., Hossain M.E., Moni M.A. (2019), Comparing different supervised machine learning algorithms for disease prediction, BMC Medical Informatics and Decision Making, 19: 281, doi: 10.1186/s12911-019-1004-8.
• 35. Waite A.D. (2002), Sonar for Practising Engineers, Wiley.
• 36. Wawrzyniak N., Stateczny A. (2017), MSIS image postioning in port areas with the aid of comparative navigation methods, Polish Maritime Research, 24(1): 32-41, doi: 10.1515/pomr-2017-0004.
• 37. Wu C., Khishe M., Mohammadi M., Taher Karim S.H., Rashid T.A. (2021), Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images, Soft Computing, doi: 10.1007/s00500-021-05839-6.
• 38. Xu H., Zhou J., Asteris P.G., Armaghani D.J., Tahir M.M. (2019), Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate, Applied Sciences, 9(18): 3715, doi: 10.3390/app9183715.
• 39. Yang Y., Lei J., Zhang W., Lu C. (2006), Target classification and pattern recognition using micro-Doppler radar signatures, [in:] Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD’06), pp. 213-217, doi: 10.1109/SNPDSAWN2006.68.
• 40. Zhang T., Liu S., He X., Huang H., Hao K. (2020), Underwater target tracking using forward-looking sonar for autonomous underwater vehicles, Sensors, 20(1): 102, doi: 10.3390/s20010102.
• 41. Zhong T., Cheng M., Lu S., Dong X., Li Y. (2022), RCEN: A deep-learning-based background noise suppression method for DAS-VSP records, [in:] IEEE Geoscience and Remote Sensing Letters, 19: 3004905, doi: 10.1109/LGRS.2021.3127637.
• 42. Zhou J., Bai J., Liu Y. (2022), Fabrication and modeling of matching system for air-coupled transducer, Micromachines, 13(5): 781, doi: 10.3390/mi13050781

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023). (PL).