Title :
AR Model Identification Using Higher Order Statistics
Author :
Al-Smadi, Adnan M.
Author_Institution :
Al Al-Bayt Univ., Al-Mafraq
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;
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
DOI :
10.1109/AICCSA.2007.370942
