Proceedings Article | 3 June 2022
KEYWORDS: LIDAR, Monte Carlo methods, Atmospheric particles, Atmospheric modeling, Aerosols, Atmospheric optics, Backscatter, Raman spectroscopy, Electronics, Photons
LIght Detection And Ranging (LIDAR) systems are complex instruments whose performance is affected by a variety of atmospheric, system design, and geometric factors. To ensure performance parameters, such as maximum range and accuracy, are achieved it is imperative that statistically representative variations in these factors are considered to identify if the system will not meet performance goals under particular corner cases. To this end, a high fidelity, physics-based simulator was developed to aid in the LIDAR design process. This simulator is composed of four major libraries – atmosphere, LIDAR, electronics, and algorithms. The physics-based models in each of these libraries account for environmental, optical, electrical, and mechanical variations in the underlying components which ultimately affect LIDAR signals and data products. The goal of this simulator is to generate realistic LIDAR signals and LIDAR-derived data products to aid in performing trade studies, determine performance envelops, identify parameter tolerances, debug experimental data collections, and provide a testbed to evaluate different algorithms. The simulator has undergone validation and verification against other relevant models, such as NRL-MSISe00, LEEDR, MODTRAN, etc., as well as experimental data. Using this simulator, Monte Carlo techniques were utilized for aerosol (elastic), temperature (inelastic rotational Raman), and water vapor (inelastic vibrational-rotational Raman) LIDARs to determine their performance in a variety of representative atmospheres, and pointing angles. System performance is expressed as a cumulative distribution function of error between LIDAR-retrieved atmospheric quantities (aerosol extinction, temperature, and water vapor) and ‘truth’ atmospheric quantities input into the simulator.
