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Abstract：
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.
摘要：
水下目标信号典型特征的提取和优秀的识别算法是实现潜水员水声目标识别的关键。本文提出了一种针对潜水员信号的特征提取方法:频域多子带能量(FMSE),目的是实现被动声纳对潜水员水声目标的准确识别。存在或不存在目标的影响，不同数量的目标，不同的信噪比，根据不同条件下的实验数据,研究了该方法的不同检测距离,如水池和湖泊。发现与其他两种信号特征提取方法相比，FMSE方法具有最佳的鲁棒性和性能：mel频率倒谱系数滤波和gammatone频率倒谱系数滤波。结合常用的支持向量机识别算法,FMSE方法对蛙人水声目标的综合识别准确率达到94%以上。这表明FMSE方法适用于潜水员目标的水声识别。
