Monte Carlo simulations as a tool to optimize target detection by AUV/ROV laser line scanners

Abstract

The widespread use of laser line scanners (LLS) aboard autonomous underwater vehicles (AUV) and remotely operated vehicles (ROV) in the last decade has opened a unique window to a series of homeland security applications. Numerical experiments were performed to calculate the target signal and the effect of background medium (bottom, water) signals on target identification of fan-type LLS (Real-time Ocean Bottom Optical Topographer, ROBOT). Several 2-D Monte Carlo simulations were run with various bottom albedos, optical properties of the water, laser wavelengths, target distances, and source-detector angles. A forward 1-D Monte Carlo model was validated using Hydrolight based on upwelling and downwelling irradiance values computed at different depths. Signal/noise values (S/N) at the ROBOT detector were obtained by dividing the target peak by the path-radiance peak for each line-spread function. Since bottom-target reflectance was assumed Lambertian, target contribution was symmetrical with respect to the center of the target. Conversely, background contributions evidenced a bulge on the path radiance side of the target center, which was more apparent at higher turbidities. As expected, S/N values were higher when ROBOT was closer to the target. For daylight simulations, system noise includes both LLS path radiance and environmental path and target radiances because they reduce the laser-line contrast. The Hybrid marine optical model (HyMOM) provided the environmental radiance field. Optimum target detection based on laser wavelength and source-detector angle will depend on chosen ambient light conditions and AUV-ROVs altitude settings.