Paper Info
Title
An unbiased and computationally efficient LS estimation method for identifying parameters of 2D noncausal SAR models
Abstract
An unbiased and computationally efficient modified least squares (LS) estimation method for identifying parameters of two-dimensional noncausal simultaneous autoregressive models is presented. Some intuitive and mathematical proof of the unbiasedness of the method are given, and a recursive in-order fast algorithm to implement it is introduced. Computer simulation results are given to sustain the theoretical analysis. Both the theoretical analysis and the computer simulation show that the method possesses much higher estimation accuracy and lower computational complexity than the conventional LS estimation method. Compared to the approximate maximum-likelihood method of Kashyap and Chellappa (1983), the scheme is much faster, has the same estimation accuracy, and is parallelizable
Year
DOI
Venue
1993
10.1109/78.193222
IEEE Transactions on Signal Processing
Keywords
Field
DocType
two-dimensional noncausal simultaneous autoregressive models,image processing,parameter estimation,statistical analysis,theoretical analysis,computationally efficient ls estimation method,computational complexity,least squares approximations,recursive in-order fast algorithm,modified least squares method,unbiasedness,estimation accuracy,parameter identification,computer simulation,parallelizable scheme,least square,maximum likelihood estimation,predictive models,image analysis,algorithm design and analysis,autoregressive model,computational modeling,least squares approximation,maximum likelihood method
Least squares,Signal processing,Autoregressive model,Mathematical optimization,Algorithm design,Recursion (computer science),Estimation theory,Recursion,Mathematics,Computational complexity theory
Journal
Volume
Issue
ISSN
41
2
1053-587X
Citations 
PageRank 
References 
7
0.56
3
Authors
2
Name
Order
Citations
PageRank
Ping-Ya Zhao181.26
Dao-Rong Yu270.56