提出量化被动声呐纹理的新指标，提升模型对复杂噪声的识别能力。
Quantitative Measures for Passive Sonar Texture Analysis
- 设计两种新指标，量化声呐信号的统计与结构纹理特征。
- 实测数据表明，传统CNN在统计纹理信号上表现差，改进后性能显著提升。
- 适合声学信号处理、水下目标识别方向的研究者参考。
被动声呐信号包含复杂的特性，常由环境噪声、船舶机械及传播效应引起。尽管卷积神经网络（CNN）在被动声呐分类任务中表现良好，但在数据中的统计变化面前可能表现不佳。为此，生成了以幅度和周期变化为中心的合成水下声学数据集。提出两种度量方法，用于量化并验证被动声呐在统计与结构纹理方面的特性。这些度量应用于真实世界被动声呐数据集，评估信号中的纹理信息，并关联模型性能。结果表明，CNN在具有统计纹理的信号上表现欠佳，而引入显式的统计纹理建模可带来一致的性能提升。研究强调了量化纹理信息对被动声呐分类的重要性。
原文摘要 · Abstract (English)
Passive sonar signals contain complex characteristics often arising from environmental noise, vessel machinery, and propagation effects. While convolutional neural networks (CNNs) perform well on passive sonar classification tasks, they can struggle with statistical variations that occur in the data. To investigate this limitation, synthetic underwater acoustic datasets are generated that centered on amplitude and period variations. Two metrics are proposed to quantify and validate these characteristics in the context of statistical and structural texture for passive sonar. These measures are applied to real-world passive sonar datasets to assess texture information in the signals and correlate the performances of the models. Results show that CNNs underperform on statistically textured signals, but incorporating explicit statistical texture modeling yields consistent improvements. These findings highlight the importance of quantifying texture information for passive sonar classification.
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