ASSESSMENT AND ROBUST RECONSTRUCTION OF LASER-RADAR SIGNALS

In direct-detection imaging, laser radar speckle noise, drop-outs, and outliers have to be considered in order to ensure high accuracy and reliability of measurement data. The most common approach for the stabilization of laser radar data is temporal averaging over several shots. This, however, is not in all cases the best method for the reconstruction of noisy image data. It is shown that principal component reconstruction can yield a remarkable improvement of accuracy and robustness of range data.