دانلود مقاله ISI انگلیسی شماره 65300
ترجمه فارسی عنوان مقاله

طراحی آشکارسازهای مبتنی بر رزونانس تصادفی

عنوان انگلیسی
Design of detectors based on stochastic resonance
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
65300 2003 20 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Signal Processing, Volume 83, Issue 6, June 2003, Pages 1193–1212

ترجمه کلمات کلیدی
رزونانس تصادفی؛ آستانه غیرخطی ؛ کوانتایزر؛ تشخیص تحت بهینه؛ تشخیص غیرفعال؛ نویز غیر گوسی؛ نویز دریایی
کلمات کلیدی انگلیسی
Stochastic resonance; Threshold nonlinearity; Quantizer; Suboptimal detector; Passive detection; Non-Gaussian noise; Marine noise
پیش نمایش مقاله
پیش نمایش مقاله  طراحی آشکارسازهای مبتنی بر رزونانس تصادفی

چکیده انگلیسی

This paper presents a study of the phenomenon of stochastic resonance in quantizers, and discusses the use of this phenomenon for the detection of weak sinusoidal signals in noise. Stochastic resonance in 2-level, symmetric 3-level, and symmetric multilevel quantizers is investigated. Expressions are derived for the signal-to-noise ratio (SNR) gain of the quantizers driven by a small amplitude sinsuoidal signal and i.i.d. noise. The gain depends on the probability density function (PDF) of the input noise, and for a given noise PDF, the gain can be maximized by a proper choice of the quantizer thresholds. The maximum gain GSR is less than unity if the input noise is Gaussian, but several non-Gaussian noise PDFs yield values of GSR exceeding unity. Thus, the quantizers provide an effective enhancement in the SNR, which can be utilized to design a nonlinear signal detector whose performance is better than that of the matched filter. The nonlinear detector in consideration consists of a stochastically resonating (SR) quantizer followed by a correlator. An asymptotic expression for the probability of detection of the SR detector is derived. It is shown that the detection performance of the SR detector is better than that of the matched filter for a large class of noise distributions belonging to the generalized Gaussian and the mixture-of-Gaussian families.