可靠的水下多目标到达方向估计与最佳运输使用深度模型
Zehui Yang1,2, Weihang Nie1,2, Lingxuan Ye1,2
1Speech and Intelligent Information Processing Laboratory, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
The Journal of the Acoustical Society of America
|October 3, 2024
概括
本研究介绍了学习到达方向的最佳运输 (LOT) 方法,用于在声纳中准确的多目标到达方向 (DoA) 估计. 在复杂的场景中,LOT利用最佳的运输损失来提高DoA的准确性和稳定性.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 声学 声学 在声学上
背景情况:
- 多目标到达方向 (DoA) 估计在声纳信号处理中至关重要但具有挑战性.
- 现有的方法在复杂的声学环境中往往难以获得准确性和稳定性.
研究的目的:
- 开发一种新的深度学习方法,用于使用单一模型准确的多目标DoA估计.
- 引入最佳运输 (OT) 损失,以更好地处理DoA估计中角数据的连续性.
主要方法:
- 提出了最佳运输 (LOT) 方法的到达学习方向,将DoA估计模型作为多标签分类任务.
- 引入了OT损失与自定义成本矩阵来捕获角格子属性,提高预测准确度.
- 开发了一种轻量级的通道罩数据增强模块,用于基于共变矩阵的深度模型.
主要成果:
- 与基线方法相比,LOT方法在DoA估计中显示出更高的准确性.
- 提出的方法在各种实验场景和测量中显示出有效性和稳定性.
- 在SwellEx-96数据上的实验证实了LOT方法的实用性和现实应用性.
结论:
- 该LOT方法提供了一个强大的和准确的解决方案,用于多目标DoA估计在声纳.
- 建议的最佳运输损失和数据增强模块提高了用于DoA估计的深度学习模型的性能.
- 开发的技术可以在不同的网络架构中移植,并显示出对实际声纳应用的希望.
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