DocumentCode
2798729
Title
AR Model Identification Using Higher Order Statistics
Author
Al-Smadi, Adnan M.
Author_Institution
Al Al-Bayt Univ., Al-Mafraq
fYear
2007
fDate
13-16 May 2007
Firstpage
588
Lastpage
591
Abstract
This paper presents a new approach to identify the parameters of autoregressive (AR) model from the third order statistics of the output sequence. The observed signal may be corrupted by additive colored Gaussian or non-Gaussian noise. The system is driven by a zero-mean independent and identically distributed (i.i.d) non- Gaussian sequence. The simulation results confirm the good numerical conditioning of the algorithm and the improvement in performance with respect to existing methods.
Keywords
Gaussian noise; autoregressive processes; higher order statistics; parameter estimation; sequences; AR model parameter identification; additive colored Gaussian noise; additive colored nonGaussian noise; autoregressive model; higher order statistics; output sequence; third order cumulants; zero-mean iid nonGaussian sequence; Additive noise; Autoregressive processes; Biomedical measurements; Cost function; Data processing; Filters; Gaussian noise; Higher order statistics; Iterative algorithms; Parameter estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Systems and Applications, 2007. AICCSA '07. IEEE/ACS International Conference on
Conference_Location
Amman
Print_ISBN
1-4244-1030-4
Electronic_ISBN
1-4244-1031-2
Type
conf
DOI
10.1109/AICCSA.2007.370942
Filename
4231017
Link To Document