Design and performance analysis of a signal detector based on suprathreshold stochastic resonance

  • Authors:

  • V. N. Hari;G. V. Anand;A. B. Premkumar;A. S. Madhukumar

  • Affiliations:

  • CeMNet Annex, Block N4, B2b-05, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore;Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore 560012, India;CeMNet Annex, Block N4, B2b-05, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore;CeMNet Annex, Block N4, B2b-05, School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore

  • Venue:
  • Signal Processing
  • Year:
  • 2012

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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 @s 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 @s 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 @s 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.