Mitigating atmospheric phase-errors in SAL data using model-based reconstruction

Abstract

Synthetic Aperture Ladar (SAL) is an emerging ladar remote sensing technology capable of providing fine-resolution imagery of an illuminated region-of-interest similar to Synthetic Aperture Radar (SAR). A SAL sensor operates at optical as opposed RF wavelengths making the sensing modality attractive as a queued sensor (shorter collection times, smaller illuminated region-of-interest, low probability-of-detection, etc.). However, with the drastic decrease in wavelength comes an increase in susceptibility to atmospheric turbulence. We evaluate the performance of three model error correction algorithms to mitigate atmospheric blurring in reconstructed imagery. We show that a spatially-invariant model error correction does not mitigate atmospheric blurring, rather a spatially-variant model error correction is necessary to improve reconstructed image quality. We introduce the novel model-based atmospheric phase correction algorithm that utilizes well-established atmospheric basis sets to improve mitigation of atmospheric blurring. We compare the three model error correction algorithms using an atmospheric ray trace simulation and show that the model-based atmospheric phase correction algorithm offers the highest quality reconstructed imagery.