Cross Evaluation of Detection Schemes for Sparse Signals
Sinduja Seethapathy *
Department of Electrical Engineering, University of Colorado Denver, Denver, Colorado 80217, USA.
A. T. Burrell
Department of Computer Science, Oklahoma State University, Stillwater, Oklahoma 74078, USA.
P. Papantoni-Kazakos
Department of Electrical Engineering, University of Colorado Denver, Denver, Colorado 80217, USA.
*Author to whom correspondence should be addressed.
Abstract
We consider environments where sparse signals are embedded in additive white noise. We consider specific signal models and cross-evaluate previously derived parametrically optimal, robust and tree-search policies for the detection of signal presence, in terms of the a posteriori probabilities of correct detection they induce. We specifically present numerical results for the case of a constant signal embedded in additive white Gaussian noise and the signal presence per observation being generated independently by a Bernoulli variable, in both the presence and the absence of data outliers.
Keywords: Sparse signals, detection of signal presence, parametrically optimal, robust and tree-search detection, white noise.