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Time series identification and Kalman filtering techniques for Doppler lidar velocity estimation
Applied Optics
|June 16, 2010
Summary
This study explores Kalman filter algorithms for Doppler lidar data. A random walk model effectively identifies time series system models, enabling real-time data filtering and outlier rejection.
Area of Science:
- Atmospheric Science
- Signal Processing
- Data Analysis
Background:
- Doppler lidar systems generate complex time series data.
- Accurate modeling and filtering are crucial for reliable atmospheric measurements.
- Recursive techniques offer potential for efficient data processing.
Purpose of the Study:
- To investigate recursive techniques using Kalman filters for Doppler lidar data.
- To identify appropriate time series system models for lidar returns.
- To enhance data filtering, smoothing, and outlier rejection.
Main Methods:
- Review of stochastic system models for time series.
- Application of Kalman filter algorithms.
- Utilizing reiterative maximum likelihood and innovation spectral tests for model identification.
Main Results:
- A random walk model was found to be adequate for Doppler lidar returns.
- The proposed method facilitates real-time data processing.
- Effective on-line outlier rejection was demonstrated.
Conclusions:
- Kalman filter-based recursive techniques are effective for Doppler lidar data.
- The random walk model provides a suitable framework for lidar return analysis.
- The methodology supports advanced real-time atmospheric data analysis.
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