DocumentCode :
1497566
Title :
Estimation of continuous-time AR process parameters from discrete-time data
Author :
Fan, H.Howard ; Söderström, Torsten ; Mossberg, Magnus ; Carlsson, Bengt ; Zou, Yuanjie
Author_Institution :
Dept. of Electr. & Comput. Eng., Cincinnati Univ., OH, USA
Volume :
47
Issue :
5
fYear :
1999
fDate :
5/1/1999 12:00:00 AM
Firstpage :
1232
Lastpage :
1244
Abstract :
The problem of estimating continuous-time autoregressive process parameters from discrete-time data is considered. The basic approach used here is based on replacing the derivatives in the model by discrete-time differences, forming a linear regression, and using the least squares method. Such a procedure is simple to apply, computationally flexible and efficient, and may have good numerical properties. It is known, however, that all standard approximations of the highest order derivative, such as repeated use of the delta operator, gives a biased least squares estimate, even as the sampling interval tends to zero. Some of our previous approaches to overcome this problem are reviewed. Then. two new methods, which avoid the shift in our previous results, are presented. One of them, which is termed bias compensation, is computationally very efficient. Finally, the relationship of the above least squares approaches with an instrumental variable method is investigated. Comparative simulation results are also presented
Keywords :
autoregressive processes; continuous time systems; discrete time systems; least squares approximations; parameter estimation; signal processing; bias compensation; biased least squares estimate; computationally efficient method; continuous-time AR process; continuous-time autoregressive process; delta operator; discrete-time data; discrete-time differences; highest order derivative; instrumental variable method; least squares method; linear regression; numerical properties; parameter estimation; sampling interval; signal processing; simulation results; standard approximations; Astrophysics; Autoregressive processes; Least squares approximation; Least squares methods; Linear regression; Medical control systems; Microeconomics; Optimization methods; Sampling methods; Signal processing;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.757211
Filename :
757211
Link To Document :
بازگشت