通过将飞机跟踪数据与飞机跟踪数据融合,探索不同飞机类型的声学特征
Xinxiang Zhang1, Chris Hayward1, Sarah McComas2
1Roy M. Huffington Department of Earth Sciences, Southern Methodist University, 3225 Daniel Avenue, Suite 260, Dallas, Texas 75205, USA.
The Journal of the Acoustical Society of America
|May 30, 2023
概括
这项研究将声信号与自动依赖监视广播 (ADS-B) 数据融合在一起,以创建标记的飞机声音数据集. 这种方法有效地描述了固定翼单发动机,固定翼多发动机和旋翼飞机之间的声学差异.
科学领域:
- 声学 声学 在声学方面
- 航空航天工程 航空航天工程
- 数据科学数据科学数据科学
背景情况:
- 传统的飞机声学分析需要昂贵的地面真相数据.
- 自动依赖监视广播 (ADS-B) 提供开源的飞机监视数据.
- 整合不同的数据源可以增强声信号分析.
研究的目的:
- 开发一种使用ADS-B数据标记飞机声信号的新方法.
- 为分析飞机声学特征创建一个标记的数据集.
- 评估不同类别的飞机的声学特性.
主要方法:
- 声信号被分配给相应的ADS-B数据.
- 一个融合框架结合了来自声学传感器和ADS-B接收器的数据.
- 该方法在一个大型城市地区 (德克萨斯州达拉斯南方卫理会大学) 进行了评估.
主要成果:
- 在没有昂贵的实验的情况下,成功生成了飞机声学信号的标记数据集.
- 较小的固定翼单发动机 (FWSE) 飞机在5000米以内被检测到.
- 大型固定翼多发动机 (FWME) 飞机在7500米以上被检测到.
- 确定了中位源频率:FWSE为100 Hz,FWME为80 Hz,旋翼飞机为30-100 Hz.
结论:
- 将声学和ADS-B数据结合起来,有利于创建标记数据集.
- 该方法提供了对不同类型飞机的声学特征的洞察.
- 这种方法提供了一种有效的方式来研究飞机的声学.
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