没有分布的预测间隔,对声学估计进行符合性预测
Ishan 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
|October 18, 2024
概括
在声学参数估计中量化不确定性至关重要. 本研究引入了合规预测 (CP),为声学模型提供可靠的不确定性间隔,即使没有训练数据.
科学领域:
- 信号处理 信号处理
- 声学 声学 在声学方面
- 机器学习 机器学习
背景情况:
- 声学参数估计在各个领域都至关重要.
- 现有的方法缺乏对估计的信心指标,阻碍了现实世界的应用.
- 外部不确定性,如噪音和传感器错误影响估计性能.
研究的目的:
- 在声学参数估计中引入一种统计学上有效的不确定性量化方法.
- 为了适应缺乏训练数据或使用分析方法的模型的合规预测 (CP).
- 为了应对CP中有限的校准数据分布的挑战.
主要方法:
- 使用合规预测 (CP) 来产生不确定性间隔.
- 将CP应用于数据驱动和分析声学模型.
- 验证到达方向和源定位任务的性能.
主要成果:
- 合规预测 (CP) 成功生成了统计学上有效的不确定性间隔.
- 证明了CP的适用于各种不确定性来源 (噪音,干扰,传感器位置) 的声学参数估计.
- 展示了CP与数据驱动和传统传播模型的集成.
结论:
- 符合性预测 (CP) 为声学参数估计中的不确定性量化提供了一个强大的解决方案.
- CP提供了可靠部署所必需的统计学上有效的置信区间.
- 即使校准数据与测试时间数据分布不同时,该方法也有效.
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