Asymptotically optimum quadratic detection in the case of subpixel targets

Victor Golikov, Olga Lebedeva, Castillejos Moreno, Volodymyr Ponomaryov

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

This work extends the optimum Neymann-Pearson methodology to detection of a subspace signal in the correlated additive Gaussian noise when the noise power may be different under the null (H0) and alternative (H1) hypotheses. Moreover, it is assumed that the noise covariance structure and power under the null hypothesis are known but under the alternative hypothesis the noise power can be unknown. This situation occurs when the presence of a small point (subpixel) target decreases the noise power. The conventional matched subspace detector (MSD) neglects this phenomenon and causes a consistent loss in the detection performance. We derive the generalized likelihood ratio test (GLRT) for such a detection problem comparing it against the conventional MSD. The designed detector is theoretically justified and numerically evaluated. Both the theoretical and computer simulation results have shown that the proposed detector outperforms the conventional MSD. As to the detection performance, it has been shown that the detectivity of the proposed detector depends on the additional adaptive corrective term in the threshold. This corrective term decreases the value of presumed threshold automatically and, therefore, increases the probability of detection. The influence of this corrective term on the detector performance has been evaluated for an example scenario. Copyright © 2011 The Institute of Electronics, Information and Communication Engineers.
Original languageAmerican English
Pages (from-to)1786-1792
Number of pages1606
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
DOIs
StatePublished - 1 Jan 2011

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Sub-pixel
Detector
Detectors
Target
Subspace
Term
Generalized Likelihood Ratio Test
Decrease
Probability of Detection
Alternatives
Covariance Structure
Gaussian Noise
Null hypothesis
Null
Electronic equipment
Computer Simulation
Electronics
Engineers
Unknown
Scenarios

Cite this

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Asymptotically optimum quadratic detection in the case of subpixel targets. / Golikov, Victor; Lebedeva, Olga; Moreno, Castillejos; Ponomaryov, Volodymyr.

In: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 01.01.2011, p. 1786-1792.

Research output: Contribution to journalArticleResearchpeer-review

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