D. G. Lainiotis, Paraskevas Papaparaskeva, Kostas Plataniotis, "Nonlinear filtering for LIDAR signal processing", Mathematical Problems in Engineering, vol. 2, Article ID 203751, 26 pages, 1996. https://doi.org/10.1155/S1024123X96000397
Nonlinear filtering for LIDAR signal processing
LIDAR (Laser Integrated Radar) is an engineering problem of great practical importance in environmental monitoring sciences. Signal processing for LIDAR applications involves highly nonlinear models and consequently nonlinear filtering. Optimal nonlinear filters, however, are practically unrealizable. In this paper, the Lainiotis's multi-model partitioning methodology and the related approximate but effective nonlinear filtering algorithms are reviewed and applied to LIDAR signal processing. Extensive simulation and performance evaluation of the multi-model partitioning approach and its application to LIDAR signal processing shows that the nonlinear partitioning methods are very effective and significantly superior to the nonlinear extended Kalman filter (EKF), which has been the standard nonlinear filter in past engineering applications.
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