Publication | Open Access
Matched subspace detectors
933
Citations
4
References
1994
Year
EngineeringFeature DetectionSpectrum EstimationDetection TechniqueSubspace SignalsStatistical Signal ProcessingImage AnalysisPattern RecognitionNatural InvariancesSignal DetectionEstimation TheoryStatisticsMachine VisionAutomatic Target RecognitionObject DetectionMatched Subspace DetectorsComputer ScienceSignal ProcessingComputer VisionSubspace InterferenceStatistical Inference
We formulate a general class of problems for detecting subspace signals in subspace interference and broadband noise. We derive the generalized likelihood ratio (GLR) for each problem in the class. We then establish the invariances for the GLR and argue that these are the natural invariances for the problem. In each case, the GLR is a maximal invariant statistic, and the distribution of the maximal invariant statistic is monotone. This means that the GLR test (GLRT) is the uniformly most powerful invariant detector. We illustrate the utility of this finding by solving a number of problems for detecting subspace signals in subspace interference and broadband noise. In each case we give the distribution for the detector and compute performance curves.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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