nonlinear filtering for lidar signal processing

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ID: 250418
1996
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Abstract
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.
Reference Key
lainiotis1996mathematicalnonlinear Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;D. G. Lainiotis;Paraskevas Papaparaskeva;Kostas Plataniotis
Journal journal of power sources
Year 1996
DOI
10.1155/S1024123X96000397
URL
http://dx.doi.org/10.1155/S1024123X96000397
Keywords
civil engineering (general)mathematics engineering (general)

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