Adaptive filter solution for processing lidar returns: optical parameter estimation.

Area of Science:

• Atmospheric optics
• Remote sensing
• Signal processing

Background:

• Accurate retrieval of atmospheric optical properties like extinction and backscatter profiles is crucial for understanding atmospheric phenomena.
• Traditional methods for profile retrieval from elastic-backscatter lidar data often rely on simplified models or assumptions.
• Developing advanced algorithms is necessary to improve the accuracy and robustness of lidar data inversion.

Purpose of the Study:

• To develop and validate an extended Kalman filter (EKF) for the joint estimation of extinction and backscatter profiles from elastic-backscatter lidar signals.
• To investigate the theoretical framework, simulation performance, and real-world applicability of the proposed EKF approach.
• To compare the EKF method with existing non-memory algorithms for lidar data inversion.

Main Methods:

• Formulation of an extended Kalman filter (EKF) for joint profile estimation.
• Development of an atmospheric stochastic model incorporating temporal and spatial correlations.
• Testing the EKF through extensive simulations under simplified conditions and a first real-world application.
• Comparison with Klett's method and other exponential-curve fitting algorithms.

Main Results:

• The EKF successfully performs joint estimation of extinction and backscatter profiles.
• Inversion errors are shown to be strongly dependent on atmospheric conditions (visibility) and signal-to-noise ratio.
• The filter's performance is robust despite potential modeling errors in assumed statistical properties.
• The EKF demonstrates adaptive behavior and provides accurate inversion results.

Conclusions:

• The extended Kalman filter (EKF) offers a promising and successful alternative to current non-memory algorithms for lidar data inversion.
• The EKF approach enhances the accuracy of retrieving atmospheric optical parameters.
• The method's reliance on atmospheric conditions and signal-to-noise ratio highlights key factors for successful lidar profiling.