适应性采样用于在声场重建中优化传感器放置
Yiming Han1, Fanqin Hong1, Dongcai Wang1
1Key Laboratory of Modern Acoustics, Institute of Acoustics, Nanjing University, Nanjing 210093, China.
The Journal of the Acoustical Society of America
|January 21, 2026
概括
适应采样 (AS) 改进了用于声场重建的传感器放置. 这种新方法对非静止场更有效,使用的传感器比传统的非自适应技术更少.
科学领域:
- 声学和信号处理
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 声场重建旨在从离散测量中创建一个连续的声学地图.
- 传统的传感器放置方法往往不适应,适合静态声场,但对动态声场无效.
研究的目的:
- 开发和评估一种适应性采样 (AS) 策略,用于在声场重建中高效地放置传感器.
- 提高传感器效率,特别是对于非静止的声学环境.
主要方法:
- 在贝叶斯/高斯过程框架内分析非适应性抽样标准.
- 建议采用适应性采样 (AS) 策略,将利用的交叉验证和探测的基于波长的间隔结合起来.
- 基于模拟的AS与非适应性方法在静止和非静止声场上的比较.
主要成果:
- 适应性采样 (AS) 与静态场上的非适应性方法相匹配.
- 在非静止场上,AS表现出显著提高的效率,使用大约一半的传感器来达到同等的准确性.
- 关联系统战略有效地平衡了有针对性的数据采集 (利用) 与广泛的空间覆盖 (探索).
结论:
- 适应性采样 (AS) 在声场重建的传感器放置效率上提供了显著的改进,特别是在动态场景中.
- AS为声学中的顺序测量工作流提供了实用和高效的解决方案.
- 这些发现表明,在复杂的声学环境中,需要转向适应性策略,以实现最佳的传感器网络设计.
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