Mathematical Problems in Engineering
Volume 2 (1996), Issue 5, Pages 367-392

Nonlinear filtering for LIDAR signal processing

D. G. Lainiotis,1 Paraskevas Papaparaskeva,2 and Kostas Plataniotis3

1Intelligent Systems Technology, 3207 S. Hwy. A1A, Melbourne Beach, FL 32951, USA
2Stanford Telecom 1221 Crossman Av. Sunnyvoli CA 94089 Tel: 408-730-5062. Email:, USA
3Department of Electrical Engineering, University of Toronto, Toronto, Canada, 24 Wellesley St. West, Apt. 402, Toronto M4Y 1G, USA

Received 11 July 1995

Copyright © 1996 D. G. Lainiotis et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.