Title
Efficient Adaptive Subspace Tracking Algorithm For Automatic Target Recognition
Abstract
Automatic target recognition using quadratic correlation filters has been reported recently. It requires the eigenvalue decomposition (EVD) of a large matrix computed using the autocorrelation matrices of target and clutter training images. In practice, situations arise where new images need to be incorporated, which perturbs the EVD. Proposed is a novel computationally efficient method to obtain the new EVD adaptively. Sample results using an infrared dataset illustrate the effectiveness of the technique. © The Institution of Engineering and Technology 2006.
Publication Date
10-9-2006
Publication Title
Electronics Letters
Volume
42
Issue
20
Number of Pages
1183-1184
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1049/el:20061641
Copyright Status
Unknown
Socpus ID
33749330147 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/33749330147
STARS Citation
Ragothaman, P.; Yang, T.; Mikhael, W. B.; Muise, R. R.; and Mahalanobis, A., "Efficient Adaptive Subspace Tracking Algorithm For Automatic Target Recognition" (2006). Scopus Export 2000s. 7901.
https://stars.library.ucf.edu/scopus2000/7901