在通过环境声学声音区分海底类型的极限上
John Lipor1, John Gebbie2, Martin Siderius1
1Department of Electrical and Computer Engineering, Portland State University, 1900 Southwest 4th Avenue, Suite 160, Portland, Oregon 97201, USA.
The Journal of the Acoustical Society of America
|November 7, 2023
概括
这项研究表明如何使用环境声音最好地区分海洋环境. 库尔巴克-莱布勒分歧 (KLD) 有助于确定是否可以识别环境参数,改进了水下声学分析.
科学领域:
- 海洋声学 海洋声学
- 环境传感 环境传感
- 信号处理 信号处理
背景情况:
- 区分环境参数与海洋环境声音对于水下应用至关重要.
- 目前的方法包括参数估计和机器学习分类.
- 需要一个理论框架来实现最佳的环境歧视.
研究的目的:
- 开发一种理论分析,以优化区分环境参数与海洋环境声音.
- 用收到的声音快照来将分类问题作为假设测试的框架.
- 分析Kullback-Leibler分歧 (KLD) 以了解歧视能力.
主要方法:
- 将环境分类作为对环境声音快照的假设测试.
- 使用环境分布之间的库尔巴克-莱布勒分歧 (KLD).
- 分析KLD对信号与噪声比率,信号子空间和共变矩阵固有值的依赖.
主要成果:
- KLD分析揭示了成功测试假设的关键依赖关系.
- 提供了关于为什么某些环境参数难以区分的见解.
- 实验证实,某些沉积物类型在特定的阵列配置中是不可区分的.
结论:
- 理论分析提供了一种使用海洋环境声音进行最佳环境歧视的方法.
- KLD提供了用于预测数组处理性能的定量指标,超过了传统的底损失曲线.
- 该框架增强了对水下声学传感能力的理解.
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