Title
Adaptive Determination Of Eigenvalues And Eigenvectors From Perturbed Autocorrelation Matrices For Automatic Target Recognition
Keywords
Adaptive Eigendecomposition; Automatic Target Recognition; Quadratic Correlation Filters
Abstract
The Modified Eigenvalue problem arises in many applications such as Array Processing, Automatic Target Recognition (ATR), etc. These applications usually involve the Eigenvalue Decomposition (EVD) of matrices that are time varying. It is desirable to have methods that eliminate the need to perform an EVD every time the matrix changes but instead update the EVD adaptively, starting from the initial EVD. In this paper, we propose a novel Optimal Adaptive Algorithm for the Modified EVD problem (OAMEVD). Sample results are presented for an ATR application, which uses Rayleigh Quotient Quadratic Correlation filters (RQQCF). Using a Infrared (IR) dataset, the effectiveness of this new technique as well as its advantages are illustrated.
Publication Date
9-18-2006
Publication Title
Proceedings of SPIE - The International Society for Optical Engineering
Volume
6234
Number of Pages
-
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1117/12.665750
Copyright Status
Unknown
Socpus ID
33748550031 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/33748550031
STARS Citation
Ragothaman, P.; Mikhael, W. B.; Muise, R.; Mahalanobis, A.; and Yang, T., "Adaptive Determination Of Eigenvalues And Eigenvectors From Perturbed Autocorrelation Matrices For Automatic Target Recognition" (2006). Scopus Export 2000s. 8181.
https://stars.library.ucf.edu/scopus2000/8181