Improving Multiple Surface Range Estimation of a 3-Dimensional FLASH LADAR in the Presence of Atmospheric Turbulence.

摘要

Laser Radar sensors can be designed to provide two-dimensional and three-dimensional (3-D) images of a scene from a single laser pulse. Currently, there are various data recording and presentation techniques being developed for 3-D sensors. While the technology is still being proven, many applications are being explored and suggested. As technological advancements are coupled with enhanced signal processing algorithms, it is possible that this technology will present exciting new military capabilities for sensor users. The goal of this work is to develop an algorithm to enhance the utility of 3-D Laser Radar sensors through accurate ranging to multiple surfaces per image pixel while minimizing the effects of diffraction. Via a new 3-D blind deconvolution algorithm, it will be possible to realize numerous enhancements over both traditional Gaussian mixture modeling and single surface range estimation. While traditional Gaussian mixture modeling can effectively model the received pulse, we know that its shape is likely altered due to optical aberrations from the imaging system and the medium through which it is imaging. Simulation examples show that the multi-surface ranging algorithm derived in this work improves range estimation over standard Gaussian mixture modeling and frame-by-frame deconvolution by up to 89% and 85% respectively.