Abstract

This work documents the performance of a recently proposed generalized likelihood ratio test (GLRT) algorithm in detecting thermal point-source targets against a sky background. A calibrated source is placed above the horizon at various ranges and then imaged using a mid-wave infrared camera. The proposed algorithm combines a so-called “shrinkage” estimator of the background covariance matrix and an iterative maximum likelihood estimator of the point-source parameters to produce the GLRT statistic. It is clearly shown that the proposed approach results in better detection performance than either standard energy detection or previous implementations of the GLRT detector.

Background first- and second-order modeling for point target detection

Laure Genin, Frédéric Champagnat, and Guy Le Besnerais
Appl. Opt. 51(31) 7701-7713 (2012)

Generalized likelihood ratio test change detection with optical theorem constraint

Jing Tu and Edwin A. Marengo
J. Opt. Soc. Am. A 33(11) 2225-2236 (2016)

Improved robustness of target location in nonhomogeneous backgrounds by use of the maximum-likelihood ratio test location algorithm

Vincent Pagé, François Goudail, and Ph. Réfrégier
Opt. Lett. 24(20) 1383-1385 (1999)

Methods to detect objects in photon-limited images

Ahmad Abu-Naser, Nikolas P. Galatsanos, and Miles N. Wernick
J. Opt. Soc. Am. A 23(2) 272-278 (2006)

Point target detection and subpixel position estimation in optical imagery

Vincent Samson, Frédéric Champagnat, and Jean-François Giovannelli
Appl. Opt. 43(2) 257-263 (2004)