Abstract

We have considered various Kalman filters¹ that can recursively filter and smooth, in an optimal way, the observations of a return that is varying in time. A basic problem for any lidar filter is estimation of return power. For coherent lidar, raw power measurements are corrupted by speckle that is characterized by high variance and asymmetrical exponential statistics. Both problems are mitigated if speckle diversity techniques are used, so data taken over a time short compared with the time scale of return power fluctuations can be preprocessed by averaging. Here we consider interfacing small samples of return power data, observed with and without such preprocessing, to the Kalman filter used subsequently.

© 1991 Optical Society of America