Design of detectors based on stochastic resonance
Signal Processing
Principles of Signal Detection and Parameter Estimation
Principles of Signal Detection and Parameter Estimation
Feature Extraction & Image Processing, Second Edition
Feature Extraction & Image Processing, Second Edition
Analysis of area under the ROC curve of energy detection
IEEE Transactions on Wireless Communications
On optimal threshold and structure in threshold system based detector
Signal Processing
An adaptive stochastic-resonance-based detector and its application in watermark extraction
WSEAS Transactions on Signal Processing
Detection in incompletely characterized colored non-Gaussian noisevia parametric modeling
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Locally optimum nonlinearities for DCT watermark detection
IEEE Transactions on Image Processing
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In this paper, we propose a threshold-system-based detector (TD) for detecting a known deterministic signal in independent non-Gaussian noise whose probability density function (pdf) is unknown but is symmetric and unimodal. The optimality of the proposed TD is proved under the assumptions of white noise, small signal, and a large number of samples. While previous TD designs need accurate information of the noise pdf, the proposed TD is independent of the noise pdf, and thus is robust to the noise pdf. The detection probability and the receiver operating characteristic (ROC) of the proposed TD are analyzed both theoretically and numerically. It is shown that even without knowing the noise pdf, the proposed TD has close performance to the optimal detector designed with the noise pdf information. It also performs significantly better than the matched filter (MF) when the noise pdf has heavy tails. The practical implementation, robustness to both the noise pdf and the signal, and region of validity of the proposed TD are also investigated.