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Mahmoudikhah, Sajjad, Zahiri, Seyed Hamid, Behravan, Iman. (1404). Sonar target classification using a decision fusion method based on a fuzzy learning automata. فناوری آموزش, (), -. doi: 10.22061/jecei.2025.11966.846
Sajjad Mahmoudikhah; Seyed Hamid Zahiri; Iman Behravan. "Sonar target classification using a decision fusion method based on a fuzzy learning automata". فناوری آموزش, , , 1404, -. doi: 10.22061/jecei.2025.11966.846
Mahmoudikhah, Sajjad, Zahiri, Seyed Hamid, Behravan, Iman. (1404). 'Sonar target classification using a decision fusion method based on a fuzzy learning automata', فناوری آموزش, (), pp. -. doi: 10.22061/jecei.2025.11966.846
Mahmoudikhah, Sajjad, Zahiri, Seyed Hamid, Behravan, Iman. Sonar target classification using a decision fusion method based on a fuzzy learning automata. فناوری آموزش, 1404; (): -. doi: 10.22061/jecei.2025.11966.846

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

Journal of Electrical and Computer Engineering Innovations (JECEI)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 20 مهر 1404
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
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