Supervised Learning Calibration of an Atmospheric Lidar | IEEE Conference Publication | IEEE Xplore

Supervised Learning Calibration of an Atmospheric Lidar


Abstract:

Calibration of an atmospheric lidar is often required due to variations in the electro-optical system. Rayleigh fitting commonly performed may fail under various conditio...Show More

Abstract:

Calibration of an atmospheric lidar is often required due to variations in the electro-optical system. Rayleigh fitting commonly performed may fail under various conditions. Temporal and spatial variations both affect lidar signals. We hence opt for spatiotemporal analysis. We present a novel deep-learning (DL) lidar calibration model based on convolutional neural networks (CNN). We demonstrate our method on simulated data that mimics natural ground-based pulsed time-of-flight lidar signals. Such an approach can better address measurements with a poor signal-to-noise ratio (SNR) and provide a more frequent calibration.
Date of Conference: 17-22 July 2022
Date Added to IEEE Xplore: 28 September 2022
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Conference Location: Kuala Lumpur, Malaysia

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