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.
Omer Shubi
PhD Candidate Data Science
My research revolves around decoding our cognitive state in language comprehension, with a focus on utilizing eye movements in reading, and neural activity while listening.