Sonar target classification using a decision fusion method based on a fuzzy learning automata

Mahmoudikhah, Sajjad; Zahiri, Seyed Hamid; Behravan, Iman

doi:10.22061/jecei.2025.11966.846

تعداد نشریات	11
تعداد شماره‌ها	234
تعداد مقالات	2,381
تعداد مشاهده مقاله	3,802,090
تعداد دریافت فایل اصل مقاله	2,763,590

	Sonar target classification using a decision fusion method based on a fuzzy learning automata
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 16، دوره 14، شماره 1، مهر 2026، صفحه 211-226 اصل مقاله (1.6 M)
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2025.11966.846
نویسندگان
Sajjad Mahmoudikhah¹؛ Seyed Hamid Zahiri^* ¹؛ Iman Behravan²
¹Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
²Department of Electrical Engineering, University of Birjand, Birjand, Iran.
تاریخ دریافت: 29 خرداد 1404، تاریخ بازنگری: 04 مهر 1404، تاریخ پذیرش: 15 مهر 1404
چکیده
Background and Objectives: Sonar data processing helps in identifying and tracking targets with unstable echoes, which conventional tracking methods often misidentify. Recently, RLA has significantly improved the accuracy of undersea target detection compared to traditional sonar object recognition techniques that tend to lack robustness and precision. Methods: This research utilizes a combination of classifiers to improve the accuracy of Sonar data classification for complex tasks like identifying marine targets. Each classifier creates its own data pattern and maintains a model. Ultimately, a weighted voting process is carried out by the fuzzy learning automata algorithm among these classifiers, with the one receiving the highest votes being the most impactful on performance improvement. Results: We compared the performance of SVM, RF, DT, XGBoost, ensemble methods, R-EFMD, T-EFMD, R-LFMD, T-LFMD, ANN, CNN, TIFR-DCNN+SA, and joint models against the proposed model. Given the differences in objectives and databases, we focused on benchmarking the average detection rate. This comparison examined key parameters including Precision, Recall, F1_Score, and Accuracy to highlight the superior performance of the proposed method compared to the others. Conclusion: The results obtained with the analytical parameters Precision, Recall, F1_Score and Accuracy have been examined and compared with the latest similar research and the values of 88.6%, 90.2%, 89.02% and 88.6% have been obtained for each of these parameters in the proposed method, respectively. Also, in this research, the impressive performance of the new method compared to the Sonar data fusion by the conventional learning automata method is evident.
کلیدواژه‌ها
Sonar Classification؛ Fuzzy Learning Automata؛ Performance Parameters؛ Data Fusion

مراجع
[1] W. Tang, "Highlight model of echoes from sonar targets," Acta Acustica, 19(2): 92-100, 1994. [2] C. Erbe, S. A. Marley, R. P. Schoeman, J. N. Smith, L. E. Trigg, C. B. Embling, "The effects of ship noise on marine mammals—a review," Front. Mar. Sci., 6: 606, 2019. [3] X. Luo, Y. Feng, "An underwater acoustic target recognition method based on restricted Boltzmann machine," Sensors, 20(18): 5399, 2020. [4] L. Zhang, D. Wu, X. Han, Z. Zhu, "Feature extraction of underwater target signal using mel frequency cepstrum coefficients based on acoustic vector sensor," J. Sensors, 2016(1): 7864213, 2016. [5] J. Abeßer, "A review of deep learning based methods for acoustic scene classification," Appl. Sci. 10(6), 2020. [6] I. J. Ananya, S. Suad, S. H. Choudhury, M. A. Khan, "A comparative study on approaches to acoustic scene classification using CNNs," in Proc. Mexican International Conference on Artificial Intelligence: 81-91, 2021. [7] M. Yu et al., "Spatiotemporal event detection: A review," Int. J. Digital Earth, 13(12): 1339-1365, 2020. [8] Z. Bai, X.-L. Zhang, "Speaker recognition based on deep learning: An overview," Neural Networks, 140: 65-99, 2021. [9] R. M. Hanifa, K. Isa, S. Mohamad, "A review on speaker recognition: Technology and challenges," Comput. Electr. Eng., 90: 107005, 2021. [10] S. Shen, H. Yang, X. Yao, J. Li, G. Xu, M. Sheng, "Ship type classification by convolutional neural networks with auditory-like mechanisms," Sensors, 20(1): 253, 2020. [11] M. Hassaballah, A. I. Awad, Deep learning in computer vision: principles and applications. crc Press, 2020. [12] W. Xiong, X. Gu, Y. Cui, "Tracking and data association based on reinforcement learning," Electron., 12(11): 2388, 2023. [13] M. Valdenegro-Toro, "Best practices in convolutional networks for forward-looking sonar image recognition," in Proc. OCEANS 2017-aberdeen: 1-9, 2017. [14] E. L. Ferguson, R. Ramakrishnan, S. B. Williams, C. T. Jin, "Convolutional neural networks for passive monitoring of a shallow water environment using a single sensor," in Proc. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): 2657-2661, 2017. [15] G. Huo, Z. Wu, J. Li, "Underwater object classification in sidescan sonar images using deep transfer learning and semisynthetic training data," IEEE Access, 8: 47407-47418, 2020. [16] F. Mousavipour, M. R. Mosavi, "Sonar data classification using neural network trained by hybrid dragonfly and chimp optimization algorithms," Wireless Pers. Commun., 129(1): 191-208, 2023. [17] B. N. K. Reddy, C. A. Vaithilingam, S. Kamalakkannan, "SONAR based under water mine detection using machine learning algorithms," in Proc. 2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM): 1-4, 2024. [18] S. Mahmoudikhah, S. Zahiri, I. Behravan, "Fusion of classifiers using learning automata algorithm," J. Electr. Comput. Eng. Innovations, 13(1): 65-80, 2025. [19] Q. Wang, S. Du, W. Zhang, F. Wang, "Active sonar target recognition method based on multi‐domain transformations and attention‐based fusion network," IET Radar Sonar Navig., 18(10): 1814-1828, 2024. [20] S. Z. Tian, D. B. Chen, Y. Fu, J. L. Zhou, "Joint learning model for underwater acoustic target recognition," Knowledge-Based Syst., 260: 110119, 2023. [21] H. Yang, Y. Huang, Y. Liu, "Spatial attention deep convolution neural network for call recognition of marine mammal," in Proc. International Conference on Autonomous Unmanned Systems: 2725-2733, 2022. [22] F. Ahmad, M. Z. Ansari, R. Anwar, B. Shahzad, A. Ikram, "Deep learning based classification of underwater acoustic signals," Procedia Comput. Sci., 235: 1115-1124, 2024. [23] A. K. Bathla, S. Bansal, M. Kumar, "OKC classifier: an efficient approach for classification of imbalanced dataset using hybrid methodology," Soft Comput., 26(21): 11497-11503, 2022. [24] K. Taunk, S. De, S. Verma, A. Swetapadma, "A brief review of nearest neighbor algorithm for learning and classification," in Proc. 2019 International Conference on Intelligent Computing and Control Systems (ICCS): 1255-1260, 2019. [25] D. Wettschereck, T. Dietterich, "Locally adaptive nearest neighbor algorithms," Adv. Neural Inf. Process. Syst., 6, 1993. [26] D. A. Berry, D. A. Berry, Statistics: a Bayesian perspective. Duxbury Press Belmont, CA, 1996. [27] D. Berrar, "Bayes' theorem and naive Bayes classifier," 2019. [28] D. Berrar, "An empirical evaluation of ranking measures with respect to robustness to noise," J. Artif. Intell. Res., 49: 241-267, 2014. [29] D. Berrar, P. Flach, "Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them)," Briefings Bioinf., 13(1): 83-97, 2012. [30] T. Hastie, R. Tibshirani, J. H. Friedman, J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction. Springer, 2009. [31] Y. Y. Song, L. Ying, "Decision tree methods: Applications for classification and prediction," Shanghai Arch. Psychiatry, 27(2): 130, 2015. [32] J. R. Quinlan, "Learning decision tree classifiers," ACM Comput. Surv. (CSUR), 28(1): 71-72, 1996. [33] F. J. Yang, "An extended idea about decision trees," in Proc. 2019 International Conference on Computational Science and Computational Intelligence (CSCI): 349-354, 2019. [34] J. Liang, Z. Qin, S. Xiao, L. Ou, X. Lin, "Efficient and secure decision tree classification for cloud-assisted online diagnosis services," IEEE Trans. Dependable Secure Comput., 18(4): 1632-1644, 2019. [35] A. Brifcani, A. Issa, "Intrusion detection and attack classifier based on three techniques: A comparative study," Eng. Tech. J., 29(2): 368-412, 2011. [36] A. S. Eesa, Z. Orman, A. M. A. Brifcani, "A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems," Expert Syst. Appl., 42(5): 2670-2679, 2015. [37] A. Suresh, R. Udendhran, M. Balamurgan, "Hybridized neural network and decision tree based classifier for prognostic decision making in breast cancers," Soft Comput., 24(11): 7947-7953, 2020. [38] Priyanka, D. Kumar, "Decision tree classifier: A detailed survey," Int. J. Inf. Decis. Sci., 12(3): 246-269, 2020. [39] A. S. Eesa, A. M. Abdulazeez, Z. Orman, "A dids based on the combination of cuttlefish algorithm and Decision Tree," Sci. J. Univ. Zakho, 5(4): 313-318, 2017. [40] C. Z. Janikow, "Fuzzy decision trees: Issues and methods," IEEE Trans. Syst., Man Cybern. Part B Cybern., 28(1): 1-14, 1998. [41] C. M. Bishop, Neural networks for pattern recognition. Oxford university press, 1995. [42] M. A. Thathachar, P. S. Sastry, Networks of learning automata: Techniques for online stochastic optimization. Springer Science & Business Media, 2011. [43] Q. Wu, H. Liao, "Function optimisation by learning automata," Inf. Sci., 220: 379-398, 2013. [44] N. S. Shahraki, S. H. Zahiri, "DRLA: Dimensionality ranking in learning automata and its application on designing analog active filters," Knowledge-Based Syst., 219: 106886, 2021. [45] I. Ahmed, I. Sultana, S. K. Paul, A. Azeem, "Employee performance evaluation: A fuzzy approach," Int. J. prod. Perform. Manag., 62(7): 718-734, 2013.
آمار تعداد مشاهده مقاله: 178 تعداد دریافت فایل اصل مقاله: 39

سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب

پیوندهای مفید

آمار

Sonar target classification using a decision fusion method based on a fuzzy learning automata