Adaptive determination of eigenvalues and eigenvectors from perturbed autocorrelation matrices for automatic target recognition

P. Ragothaman; W. B. Mikhael; R. Muise; A. Mahalanobis; T. Yang

doi:10.1117/12.665750

18 May 2006 Adaptive determination of eigenvalues and eigenvectors from perturbed autocorrelation matrices for automatic target recognition

P. Ragothaman, W. B. Mikhael, R. Muise, A. Mahalanobis, T. Yang

Author Affiliations +

Proceedings Volume 6234, Automatic Target Recognition XVI; 62340F (2006) https://doi.org/10.1117/12.665750
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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.

Citation Download Citation

P. Ragothaman, W. B. Mikhael, R. Muise, A. Mahalanobis, and T. Yang "Adaptive determination of eigenvalues and eigenvectors from perturbed autocorrelation matrices for automatic target recognition", Proc. SPIE 6234, Automatic Target Recognition XVI, 62340F (18 May 2006); https://doi.org/10.1117/12.665750

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