Target Detection Using Nonsingular Approximations for a Singular Covariance Matrix
Joint Authors
Gorelik, Nir
Rotman, Stanley R.
Blumberg, Dan G.
Borghys, Dirk
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-07-31
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Engineering Sciences and Information Technology
Information Technology and Computer Science
Abstract EN
Accurate covariance matrix estimation for high-dimensional data can be a difficult problem.
A good approximation of the covariance matrix needs in most cases a prohibitively large number of pixels, that is, pixels from a stationary section of the image whose number is greater than several times the number of bands.
Estimating the covariance matrix with a number of pixels that is on the order of the number of bands or less will cause not only a bad estimation of the covariance matrix but also a singular covariance matrix which cannot be inverted.
In this paper we will investigate two methods to give a sufficient approximation for the covariance matrix while only using a small number of neighboring pixels.
The first is the quasilocal covariance matrix (QLRX) that uses the variance of the global covariance instead of the variances that are too small and cause a singular covariance.
The second method is sparse matrix transform (SMT) that performs a set of K-givens rotations to estimate the covariance matrix.
We will compare results from target acquisition that are based on both of these methods.
An improvement for the SMT algorithm is suggested.
American Psychological Association (APA)
Gorelik, Nir& Blumberg, Dan G.& Rotman, Stanley R.& Borghys, Dirk. 2012. Target Detection Using Nonsingular Approximations for a Singular Covariance Matrix. Journal of Electrical and Computer Engineering،Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-486393
Modern Language Association (MLA)
Gorelik, Nir…[et al.]. Target Detection Using Nonsingular Approximations for a Singular Covariance Matrix. Journal of Electrical and Computer Engineering No. 2012 (2012), pp.1-7.
https://search.emarefa.net/detail/BIM-486393
American Medical Association (AMA)
Gorelik, Nir& Blumberg, Dan G.& Rotman, Stanley R.& Borghys, Dirk. Target Detection Using Nonsingular Approximations for a Singular Covariance Matrix. Journal of Electrical and Computer Engineering. 2012. Vol. 2012, no. 2012, pp.1-7.
https://search.emarefa.net/detail/BIM-486393
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-486393