Feature Extraction Methods for Underwater Acoustic Target Recognition of Divers

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 13; p. 4412
• Main Authors: Sun, Yuchen, Chen, Weiyi, Shuai, Changgeng, Zhang, Zhiqiang, Wang, Pingbo, Cheng, Guo, Yu, Wenjing
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.07.2024
• Subjects: Acoustics; Algorithms; diver; Energy; feature extraction; Fourier transforms; Methods; Neural networks; Signal processing; support vector machine; Target recognition; underwater acoustics; underwater acoustics; support vector machine; diver; feature extraction; target recognition
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24134412

The extraction of typical features of underwater target signals and excellent recognition algorithms are the keys to achieving underwater acoustic target recognition of divers. This paper proposes a feature extraction method for diver signals: frequency-domain multi-sub-band energy (FMSE), aiming to achieve accurate recognition of diver underwater acoustic targets by passive sonar. The impact of the presence or absence of targets, different numbers of targets, different signal-to-noise ratios, and different detection distances on this method was studied based on experimental data under different conditions, such as water pools and lakes. It was found that the FMSE method has the best robustness and performance compared with two other signal feature extraction methods: mel frequency cepstral coefficient filtering and gammatone frequency cepstral coefficient filtering. Combined with the commonly used recognition algorithm of support vector machines, the FMSE method can achieve a comprehensive recognition accuracy of over 94% for frogman underwater acoustic targets. This indicates that the FMSE method is suitable for underwater acoustic recognition of diver targets.