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Parametrically Optimal, Robust and Tree-Search Detection of Sparse Signals
Oklahoma State University, Computer Science Department, Stillwater, USA
- 1 Oklahoma State University, Computer Science Department, Stillwater, USA
- 2
Journal of Signal and Information Processing·Volume 04 (2013)·Pages 336–342·Published 30 July 2013·DOI10.4236/jsip.2013.43042
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Abstract
We consider sparse signals embedded in additive white noise. We study parametrically optimal as well as tree-search sub-optimal signal detection policies. As a special case, we consider a constant signal and Gaussian noise, with and without data outliers present. In the presence of outliers, we study outlier resistant robust detection techniques. We compare the studied policies in terms of error performance, complexity and resistance to outliers.
KeywordsSparse SignalsDetectionRobustnessOutlier ResistanceTree Search
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