Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84493
Title: Design and performance analysis of a signal detector based on suprathreshold stochastic resonance
Authors: Hari, V. N.
Anand, G. V.
Premkumar, A. B.
Madhukumar, A. S.
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Hari, V. N., Anand, G. V., Premkumar, A. B., & Madhukumar, A. S. (2012). Design and performance analysis of a signal detector based on suprathreshold stochastic resonance. Signal Processing, 92(7), 1745-1757.
Series/Report no.: Signal Processing
Abstract: This paper presents the design and performance analysis of a detector based on suprathreshold stochastic resonance (SSR) for the detection of deterministic signals in heavy-tailed non-Gaussian noise. The detector consists of a matched filter preceded by an SSR system which acts as a preprocessor. The SSR system is composed of an array of 2-level quantizers with independent and identically distributed (i.i.d) noise added to the input of each quantizer. The standard deviation σ of quantizer noise is chosen to maximize the detection probability for a given false alarm probability. In the case of a weak signal, the optimum σ also minimizes the mean-square difference between the output of the quantizer array and the output of the nonlinear transformation of the locally optimum detector. The optimum σ depends only on the probability density functions (pdfs) of input noise and quantizer noise for weak signals, and also on the signal amplitude and the false alarm probability for non-weak signals. Improvement in detector performance stems primarily from quantization and to a lesser extent from the optimization of quantizer noise. For most input noise pdfs, the performance of the SSR detector is very close to that of the optimum detector.
URI: https://hdl.handle.net/10356/84493
http://hdl.handle.net/10220/12030
ISSN: 0165-1684
DOI: http://dx.doi.org/10.1016/j.sigpro.2012.01.013
Rights: © 2012 Elsevier B.V.
metadata.item.grantfulltext: none
metadata.item.fulltext: No Fulltext
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