Adaptive Kalman-Bucy filter for differential absorption lidar time series data

Area of Science:

• Atmospheric Science
• Optical Remote Sensing
• Signal Processing

Background:

• Accurate estimation of path-integrated concentration is crucial for atmospheric monitoring.
• Multiwavelength differential absorption lidar (DIAL) provides valuable data for concentration retrieval.
• Traditional Kalman-Bucy filters require accurate system model covariance, which can be challenging to obtain.

Purpose of the Study:

• To develop and evaluate an extended Kalman-Bucy algorithm for on-line estimation of multimaterial path-integrated concentration.
• To introduce adaptive estimation of the system model covariance using input lidar data.
• To compare the performance of the adaptive filter against a nonadaptive Kalman-Bucy filter.

Main Methods:

• Extension of the Kalman-Bucy algorithm.
• On-line estimation of multimaterial path-integrated concentration.
• Adaptive estimation of system model covariance from multiwavelength differential absorption lidar time series data.
• Performance comparison using synthetic and actual lidar data.

Main Results:

• The adaptive Kalman-Bucy filter demonstrated improved performance in estimating multimaterial concentration.
• Adaptive estimation of system model covariance enhanced filter accuracy.
• Validation with both synthetic and real lidar datasets confirmed the filter's effectiveness.

Conclusions:

• The proposed adaptive Kalman-Bucy filter offers a robust approach for on-line concentration estimation from DIAL data.
• Adaptive covariance estimation is a key improvement for enhancing filter performance in lidar applications.
• This method provides a valuable tool for atmospheric composition monitoring.