Title :
Model order estimation of 2D autoregressive processes
Author :
Djuric, P.M. ; Kay, Steven M.
Author_Institution :
Dept. of Electr. Eng., State Univ. of New York, Stony Brook, NY, USA
Abstract :
The work on model order estimation by Bayesian predictive densities of 1-D real autoregressive processes is extended to 2-D complex autoregressive processes. According to the procedure, the best model is the one which most accurately predicts the data yet to be observed and whose parameters are estimated from the data already observed. The derivation steps of the algorithm are demonstrated and verified by computer simulations. The computer simulations show that the algorithm based on this approach yields good results
Keywords :
filtering and prediction theory; parameter estimation; signal processing; 2-D complex autoregressive processes; 2D signal processing; Bayesian predictive densities; algorithm; computer simulations; model order estimation; parameter estimation; Autoregressive processes; Bayesian methods; Image processing; Phase estimation; Phased arrays; Predictive models; Radar imaging; Radar signal processing; Radio astronomy; Signal processing;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on
Conference_Location :
Toronto, Ont.
Print_ISBN :
0-7803-0003-3
DOI :
10.1109/ICASSP.1991.150185