Asymptotically Optimum Quadratic Detection in the Case of Subpixel Targets

Victor GOLIKOV; Olga LEBEDEVA; Andres CASTILLEJOS MORENO; Volodymyr PONOMARYOV

doi:10.1587/transfun.E94.A.1786

Asymptotically Optimum Quadratic Detection in the Case of Subpixel Targets

Victor GOLIKOV, Olga LEBEDEVA, Andres CASTILLEJOS MORENO, Volodymyr PONOMARYOV

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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.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E94-A No.9 pp.1786-1792

Publication Date: 2011/09/01

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Online ISSN: 1745-1337

DOI: 10.1587/transfun.E94.A.1786

Type of Manuscript: PAPER

Category: Digital Signal Processing

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Victor GOLIKOV, Olga LEBEDEVA, Andres CASTILLEJOS MORENO, Volodymyr PONOMARYOV, "Asymptotically Optimum Quadratic Detection in the Case of Subpixel Targets" in IEICE TRANSACTIONS on Fundamentals, vol. E94-A, no. 9, pp. 1786-1792, September 2011, doi: 10.1587/transfun.E94.A.1786.
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.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E94.A.1786/_p

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@ARTICLE{e94-a_9_1786,
author={Victor GOLIKOV, Olga LEBEDEVA, Andres CASTILLEJOS MORENO, Volodymyr PONOMARYOV, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Asymptotically Optimum Quadratic Detection in the Case of Subpixel Targets},
year={2011},
volume={E94-A},
number={9},
pages={1786-1792},
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.},
keywords={},
doi={10.1587/transfun.E94.A.1786},
ISSN={1745-1337},
month={September},}

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TY - JOUR
TI - Asymptotically Optimum Quadratic Detection in the Case of Subpixel Targets
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1786
EP - 1792
AU - Victor GOLIKOV
AU - Olga LEBEDEVA
AU - Andres CASTILLEJOS MORENO
AU - Volodymyr PONOMARYOV
PY - 2011
DO - 10.1587/transfun.E94.A.1786
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E94-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2011
AB - 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.
ER -