Abstract: We address automatic classification of active sonar signals using the Wigner Ville transform (WVT), the wavelet transform (WT), and the scalogram. Features are extracted by integrating over regions in time frequency (TF) distribution and are classified by a decision tree. Experimental results show classification and detection rates of up to 92% at $MIN@4 dB of SNR. The WT outperforms the WVT and the scalogram particularly at high noise levels; this can be partially attributed to the absence of cross terms in the WT. !7
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机译：摘要：我们使用Wigner Ville变换（WVT），小波变换（WT）和比例尺解决有源声纳信号的自动分类问题。通过在时间频率（TF）分布中对区域进行积分来提取特征，并通过决策树对其进行分类。实验结果表明，在$ MIN @ 4 dB的SNR下，分类和检测率高达92％。 WT的性能优于WVT和比例尺，尤其是在高噪声水平下。这可以部分归因于WT中没有交叉项。 ！7
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