On the invariance property of one nonlinear GLR detector arising from fMRI
Digital Signal Processing
Local detectors for high-resolution spectral analysis: Algorithms and performance
Digital Signal Processing
Optimal statistical fault detection with nuisance parameters
Automatica (Journal of IFAC)
A Novel Approach for Target Detection and Classification Using Canonical Correlation Analysis
Journal of Signal Processing Systems
Non-Gaussian linear mixing models for hyperspectral images
Journal of Electrical and Computer Engineering - Special issue on Algorithms for Multispectral and Hyperspectral Image Analysis
Matched signal detection on graphs: theory and application to brain network classification
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Narrowband signal detection techniques in shallow ocean by acoustic vector sensor array
Digital Signal Processing
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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