基于事件的摄像机建模用于大气流预测
Dor Mizrahi1,2, Daniel Brisk1, Yogev Mordechai1
1Applied Physics Division, Soreq Nuclear Research Center, Yavne 81800, Israel.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
被动的神经形态事件摄像头可以在没有活跃的发射器的情况下估计大气动荡 (Cn2). 这项技术为实时大气监测提供了一个紧的,低功耗的替代方案.
科学领域:
- 大气物理和光学.
- 神经形态工程的神经形态工程
- 机器学习应用程序 机器学习应用程序
背景情况:
- 大气动荡对光学系统和远程成像产生重大影响.
- 传统的流测量工具 (闪电计) 是重的,需要活跃的组件和精确的对齐.
- 需要被动,紧,低功耗的大气监测解决方案.
研究的目的:
- 评估被动神经形态事件摄像机在估计大气流中的有效性.
- 使用事件流数据来确定折射率结构参数 (Cn2).
- 为了比较事件摄像机的性能与地面真相闪仪.
主要方法:
- 在300米的路径上进行实地实验,使用事件摄像头和CMOS摄像头,用闪光计作为地面真相.
- 从事件流数据中提取19个统计特征,使用不同的整合时间 (2-50秒).
- 训练机器学习回归模型 (例如,XGBoost) 来从提取的特征中预测Cn2.
主要成果:
- 最好的模型 (XGBoost) 实现了高的皮尔森相关性 (0.93) 和平均绝对相对误差的35%在一个广泛的流范围 (10^-14到10^-12m^-2/3).
- 精度随着更长的整合时间和目标对比度更高的地区而提高.
- 量化了整合时间,目标对比度和特征稳定性对现场条件下估计准确性的影响.
结论:
- 被动神经形态事件摄像头可以在没有主动照明的情况下可靠地估计大气动荡 (Cn2).
- 这项技术是传统闪电仪的可行替代品,可实现紧且低功耗的实时大气监测.
- 事件驱动传感显示了先进环境监测应用的潜力.
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