Browsing by Subject "Binary composites"

Now showing 1 - 3 of 3

Open Access
Effects of additional independent noise in binary composite hypothesis-testing problems
(IEEE, 2009-09) Bayram, Suat; Gezici, Sinan
Performance of some suboptimal detectors can be improved by adding independent noise to their observations. In this paper, the effects of adding independent noise to observations of a detector are investigated for binary composite hypothesistesting problems in a generalized Neyman-Pearson framework. Sufficient conditions are derived to determine when performance of a detector can or cannot be improved via additional independent noise. Also, upper and lower limits are derived on the performance of a detector in the presence of additional noise, and statistical characterization of optimal additional noise is provided. In addition, two optimization techniques are proposed to calculate the optimal additional noise. Finally, simulation results are presented to investigate the theoretical results. © 2009 IEEE.
Open Access
Noise enhanced detection in restricted Neyman-Pearson framework
(IEEE, 2012-06) Bayram, S.; Gültekin, San; Gezici, Sinan
Noise enhanced detection is studied for binary composite hypothesis-testing problems in the presence of prior information uncertainty. The restricted Neyman-Pearson (NP) framework is considered, and a formulation is obtained for the optimal additive noise that maximizes the average detection probability under constraints on worst-case detection and false-alarm probabilities. In addition, sufficient conditions are provided to specify when the use of additive noise can or cannot improve performance of a given detector according to the restricted NP criterion. A numerical example is presented to illustrate the improvements obtained via additive noise. © 2012 IEEE.
Open Access
Stochastic resonance in binary composite hypothesis-testing problems in the Neyman-Pearson framework
(Elsevier, 2012-02-20) Bayram, S.; Gezici, Sinan
Performance of some suboptimal detectors can be enhanced by adding independent noise to their inputs via the stochastic resonance (SR) effect. In this paper, the effects of SR are studied for binary composite hypothesis-testing problems. A Neyman-Pearson framework is considered, and the maximization of detection performance under a constraint on the maximum probability of false-alarm is studied. The detection performance is quantified in terms of the sum, the minimum, and the maximum of the detection probabilities corresponding to possible parameter values under the alternative hypothesis. Sufficient conditions under which detection performance can or cannot be improved are derived for each case. Also, statistical characterization of optimal additive noise is provided, and the resulting false-alarm probabilities and bounds on detection performance are investigated. In addition, optimization theoretic approaches to obtaining the probability distribution of optimal additive noise are discussed. Finally, a detection example is presented to investigate the theoretical results.