Abstract
Algorithms for Robust Fault Detection and Identification (RFDI) in dynamic systems strive to be sensitive to faults in plant components, while being insensitive to plant and noise model uncertainties. In the field of signal detection with applications such as radar and medical images, sensitivity to signals of interest is also an objective, while robustness is concerned with undesirable interferences residing in unknown or partially known subspaces, and uncertainties in the signal and learned interference models. In this review article, we present the similarities and differences in the game theoretic, stochastic, and geometric subspace formulations and solution approaches to robust detection in dynamic systems and in signal processing. We illustrate how innovative formulations are successfully addressing challenging problems by briefly describing results from two examples, one in functional Magnetic Resonance Imaging (f MRI) of the brain, and the other in noninvasive image based classification of stem cells.
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