Abstract
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.
© 1989 Optical Society of America
Full Article |
PDF Article
More Like This
Cited By
You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription
Figures (4)
You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription
Tables (6)
You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription
Equations (35)
You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.
Contact your librarian or system administrator
or
Login to access Optica Member Subscription