Synthetic aperture ladar is an emerging sensor technology providing
high-resolution imagery of targets from long standoff ranges.
Atmospheric turbulence corrupts the collected phase history data with
spatially variant phase perturbations, impacting resolution and
contrast of reconstructed imagery. We explore the efficacy of
model-based reconstruction algorithms with model error corrections to
mitigate the deleterious effects of atmospheric turbulence and restore
image quality. We present results from model error correction
techniques utilizing spatially invariant, spatially variant, and a
model-based atmospheric phase error correction. We quantify the
performance of all algorithms using an atmospheric ray-trace
simulation.