Efficient Adaptive Subspace Tracking Algorithm for Automatic Target Recognition

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

doi:10.1049/el:20061641

Efficient Adaptive Subspace Tracking Algorithm for Automatic Target Recognition

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

Electrical Engineering and Computer Science

University of Central Florida
Lockheed-Martin Corporation

Research output: Contribution to journal › Article › peer-review

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.

Original language	American English
Journal	IET Electronics Letters
Volume	42
DOIs	https://doi.org/10.1049/el:20061641
State	Published - Oct 9 2006

Disciplines

Signal Processing

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10.1049/el:20061641License: CC BY

Cite this

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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. {\textcopyright} The Institution of Engineering and Technology 2006.",

author = "P. Ragothaman and T. Yang and W. Mikhael and R. Muise and A. Mahalanobis",

year = "2006",

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AU - Ragothaman, P.

AU - Yang, T.

AU - Mikhael, W.

AU - Muise, R.

AU - Mahalanobis, A.

PY - 2006/10/9

Y1 - 2006/10/9

N2 - 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.

AB - 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.

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U2 - 10.1049/el:20061641

DO - 10.1049/el:20061641

M3 - Article

SN - 1350-911X

VL - 42

JO - IET Electronics Letters

JF - IET Electronics Letters

ER -

Efficient Adaptive Subspace Tracking Algorithm for Automatic Target Recognition

Abstract

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