Time series identification and Kalman filtering techniques for Doppler lidar velocity estimation

The use of recursive techniques based on Kalman filter algorithms for identification of time series system models for Doppler lidar returns and the subsequent filtering and smoothing of measured data is explored. The form of possible stochastic system models is reviewed, and reiterative maximum likelihood and innovation spectral tests are used for identification. It is found that a random walk model is adequate for the returns here, and possible explanations for this are considered. Examples are given to illustrate the extension of our method to real-time applications and on-line outlier rejection.