LIDAR ECHO WAVEFORM MODELLING AND VALIDATION IN RAIN AND FOG

Light Detection and Ranging (LiDAR) encounters three-dimensional imaging degradation and noise generation in adverse weathers. To investigate its mechanism, an echo waveform model of LiDAR is developed in rain and fog, based on estimation of photons-return reception probability by semi-analytic Monte Carlo method. By innovatively combining of phase function for scattering and reflection, the proposed model can effectively depict both clutter and target waveforms and accurately estimated waveform parameters. Massive experiments are conducted to validate the method using significance testing (T-test), involving clutter and target waveforms in three weathers (i.e. heavy rain, heavy fog and light fog) and four key parameters (i.e. peak value, peak time, pulse width and skewness). Results show highly consistency between simulated and measured waveforms, as their statistical significances are well above typical significance value. The proposed model can further assist noise suppression, performance evaluation, and system design of LiDAR in complex weathers.