用符合预测的到达方向估计的不确定性量化
Ishan D Khurjekar1, Peter Gerstoft1
1Scripps Institute of Oceanography, University of California San Diego, La Jolla, California 92093, USA.
The Journal of the Acoustical Society of America
|August 15, 2023
概括
使用合规预测量化的不确定性量化为基于深度学习的声学估计提供了严格的置信区间. 这提高了到达方向估计系统的现实应用性.
科学领域:
- 声学 声学 在声学方面
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 深度学习 (DL) 方法越来越多地用于声学估计任务.
- 对DL预测建立信心对于现实世界部署至关重要.
- 不确定性量化 (UQ) 对于评估这些系统的可靠性至关重要.
研究的目的:
- 在基于深度学习的到达方向 (DOA) 估计中提出和评估UQ的合规预测 (CP).
- 为了证明CP在提供统计严格的置信区间方面的有效性.
- 提高DL用于声学传感的实际应用.
主要方法:
- 在DOA估计中,对UQ应用了符合性预测 (CP).
- CP使用用户定义的得分数量计算置信区间.
- 该方法在环境不确定性下的各种DL模型中进行了测试.
主要成果:
- 拟议的CP方法为DOA估计提供了统计学上合理的置信区间.
- 在不确定的环境中,CP提高了基于DL的声学估计的可靠性.
- 该方法可以适应不同的DL架构和分数函数.
结论:
- 符合性预测为基于DL的声学估计中UQ提供了一个强大的框架.
- 这种技术提高了DOA估计系统的可靠性和实时适用性.
- 该研究强调了CP在推进声学传感技术方面的潜力.
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