Modeling and Characterization of LiDAR Echo-Waveforms in Fog With Experiment Validations

Light detection and ranging (LiDAR) generates undesirable clutter signals in fog, impeding target recognition ability. To identify fog clutter waveforms, a comprehensive LiDAR echo waveform generation model is established based on the mechanism of photon random movement, involving factors of environmental particle state, systematic parameters, and target characteristics. The emitted-and-returned photon bundle’s random motions are innovatively described as the superposition of multiple random scattering, and analytical formulations are deduced for photon bundle reception probability, guaranteeing both model accuracy and computational efficiency. Validation experiments are conducted in a large-scale fog chamber. The R-squared values between echo waveforms estimated from our model and measured data achieve $0.8307\sim 0.9754$ for fog clutter, and $0.9522\sim 0.9778$ for target, outperforming the contrasting models. In the simulation model and measurement, fog clutter waveforms exhibit right-skewed asymmetric patterns, enabling their easy differentiation from the Gaussian-distributed target echoes. The presented model and results can be expanded to analyze various atmospheric conditions, broadening the application scenarios of LiDAR.