DocumentCode :
867411
Title :
Separability and axial symmetry of M-D polynomials and maximum-entropy M-D random Processes
Author :
Choi, ByoungSeon
Author_Institution :
Dept. of Appl. Stat., Yonsei Univ., Seoul, South Korea
Volume :
11
Issue :
2
fYear :
2004
Firstpage :
193
Lastpage :
196
Abstract :
In multidimensional system and signal analysis it is often assumed that an M-dimensional (M-D) polynomial is separable. The following properties are presented to show rationales of the separability assumption. First, if an M-D polynomial is separable, it is axially symmetric. Second, if the autocorrelation generating function of an M-D deterministic sequence with the support in the first 2M-rant is axially symmetric, it is separable. Third, if a wide-sense stationary and 2M-rant causal M-D stochastic process has the maximum entropy subject to marginal autocovariance constraints, it is separable.
Keywords :
autoregressive moving average processes; axial symmetry; correlation theory; maximum entropy methods; multidimensional signal processing; multidimensional systems; polynomials; random processes; sequences; 2M-rant causal multidimensional stochastic process; ARMA process; autocorrelation generating function; autoregressive moving-average process; marginal autocovariance constraint; maximum-entropy multidimensional random process; multidimensional deterministic sequence; multidimensional polynomial axial symmetry; multidimensional polynomial separability; multidimensional signal processing; multidimensional system; signal analysis; wide-sense stationary process; Autocorrelation; Entropy; Filters; Multidimensional signal processing; Multidimensional systems; Polynomials; Random processes; Signal analysis; Statistics; Stochastic processes;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2003.821690
Filename :
1261977
Link To Document :
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