Sun Yuchen, Chen Weiyi, Shuai Changgeng, Zhang Zhiqiang, Wang Pingbo, Cheng Guo, Yu Wenjing
Institute of Noise and Vibration, Naval University of Engineering, Wuhan 430033, China.
National Key Laboratory on Ship Vibration and Noise, Wuhan 430033, China.
Sensors (Basel). 2024 Jul 8;24(13):4412. 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.
水下目标信号典型特征的提取以及优秀的识别算法是实现潜水员水下声学目标识别的关键。本文提出了一种潜水员信号特征提取方法：频域多子带能量（FMSE），旨在通过被动声纳实现对潜水员水下声学目标的准确识别。基于不同条件（如水池和湖泊）下的实验数据，研究了有无目标、不同目标数量、不同信噪比以及不同探测距离对该方法的影响。结果发现，与其他两种信号特征提取方法：梅尔频率倒谱系数滤波和伽马通频率倒谱系数滤波相比，FMSE方法具有最佳的鲁棒性和性能。结合常用的支持向量机识别算法，FMSE方法对蛙人水下声学目标的综合识别准确率可达94%以上。这表明FMSE方法适用于潜水员目标的水下声学识别。
