Noise-Resistant Feature Extraction from Measured Data of a Passive Sonar

Vahid Bagheri, Vahid Izadi, Kamran Davoodi

Abstract


In this paper, two different methods for the classification of passive sonar data based on time-frequency methods are studied. In the first step, two passive sonar signal classifier systems are implemented using the Short-Time Fourier Transformation (STFT) approach and Short-Time Fractional Fourier Transformation (STFrFT). The performance of the proposed classifier for passive sonars in the presence of an increased amount of noise is investigated in this study. The results showed that the classification system based on STFT has better efficiency in classifying the original signals. The method based on STFT showed more resistance to noisy signal classification so that the accuracy of the classification system was reduced by a smoother slope than the STFT classification system. The loss of accuracy in the STFrFT-based method for increasing the noise level is -0.15, while for STFT-based method is equal to -0.37.


Keywords


Feature extraction, Short-Time Fourier Transformation (STFT), Short-Time Fractional Fourier Transformation (STFrFT), Sonar Data ClassificationBagheri, V., Jahromi, M. S., Keshavarz, A., & Rostami, H. (2014). Time-Frequency Signal Processing Based on Fract

Full Text:

Abstract PDF

References


Bagheri, V., Jahromi, M. S., Keshavarz, A., & Rostami, H. (2014). Time-Frequency Signal Processing Based on Fractional Fourier Transform in Passive Sonar Classification. Int. J. Electron. Commun. Comput. Eng, 5(6), 1366-1370.

De Seixas, J. M., & De Moura, N. N. (2011). Preprocessing passive sonar signals for neural classification. IET radar, sonar & navigation, 5(6), 605-612.

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

Farrokhrooz, M., & Karimi, M. (2005, June). Ship noise classification using probabilistic neural network and AR model coefficients. In Europe Oceans 2005 (Vol. 2, pp. 1107-1110). IEEE.

Izadi, V., & Ghasemi, A. (2019). Determination of roles and interaction modes in a haptic shared control framework. In Proceedings of the ASMA Dynamic Systems and Control Conference in Park City, Utah (pp. 1-8).

Izadi, V., Abedi, M., Bolandi, H., & Vaghei, G. (2014). Reaction wheel functional modeling based on internal component. In 17th Iranian Conference on Electrical and Electronics Engineering in (pp. 123-127).

Izadi, V., Abedi, M., & Bolandi, H. (2017). Supervisory algorithm based on reaction wheel modelling and spectrum analysis for detection and classification of electromechanical faults. IET Science, Measurement & Technology, 11(8), 1085-1093.

Izadi, V., Abedi, M., & Bolandi, H. (2016, January). Verification of reaction wheel functional model in HIL test-bed. In 2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA) (pp. 155-160). IEEE.

Izadi, V., Shahri, P. K., & Ahani, H. (2019). A compressed-sensing-based compressor for ECG. Biomedical engineering letters.

Liu, Z., Meng, L., Zhao, W., & Yu, F. (2010, May). Application of ANN in food safety early warning. In 2010 2nd International Conference on Future Computer and Communication (Vol. 3, pp. V3-677). IEEE.

Mitra, D., Zanddizari, H., & Rajan, S. (2019). Investigation of kronecker-based recovery of compressed ecg signal. IEEE Transactions on Instrumentation and Measurement.

Moavenian M., Ashtiani B., Ahani H., Nazari F. (2012) Applying Neural Network for the Identification of Multiple Cracks in Beams Using Genetic Algorithm. ICMEAT2012

Ozaktas, H. M., & Mendlovic, D. (1993). Fractional Fourier transforms and their optical implementation. II. JOSA A, 10(12), 2522-2531.

Rajagopal, R., Sankaranarayanan, B., & Rao, P. R. (1990, April). Target classification in a passive sonar-an expert system approach. In International Conference on Acoustics, Speech, and Signal Processing (pp. 2911-2914). IEEE.

Rana, P., Mishra, V., & Pachauri, R. (2013). Filtering in time-frequency domain using STFrFT. International Journal of Computer Applications, 69(26), 5-9.

Shahri, P. K., Shindgikar, S. C., HomChaudhuri, B., & Ghasemi, A. (2019). Optimal Lane Management in Heterogeneous Traffic Network. In Proceedings of the ASMA Dynamic Systems and Control Conference in Park City, Utah.

Taremi, R. S., Shahri, P. K., & Kalareh, A. Y. (2019). Design a Tracking Control Law for the Nonlinear Continuous Time Fuzzy Polynomial Systems. Journal of Soft Computing and Decision Support Systems, 6(6), 21-27.

Ward, M. K., & Stevenson, M. (2000, May). Sonar signal detection and classification using artificial neural networks. In 2000 Canadian Conference on Electrical and Computer Engineering. Conference Proceedings. Navigating to a New Era (Cat. No. 00TH8492) (Vol. 2, pp. 717-721). IEEE.

Zanddizari, H., S. Rajan, and Houman Zarrabi. "Increasing the quality of reconstructed signal in compressive sensing utilizing Kronecker technique." Biomedical engineering letters 8.2 (2018): 239-247.

Zeng, X. Y., & Wang, S. G. (2013). Bark-wavelet Analysis and Hilbert–Huang Transform for Underwater Target Recognition. Defence Technology, 9(2), 115-120.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright © 2014 Penerbit UTM Press. Universiti Teknologi Malaysia. All rights reserved.

Mailing Address: Penerbit UTM Press, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia.