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—This paper develops the mathematical framework to analyze the stochastic resonance (SR) effect in binary hypothesis testing problems. The mechanism for SR noise enhanced signal detection is explored. The detection performance of a noise modified detector is derived in terms of the probability of detection D and the probability of false alarm FA.(More)
—The problem of reducing the probability of decision error of an existing binary receiver that is suboptimal using the ideas of stochastic resonance is solved. The optimal probability density function of the random variable that should be added to the input is found to be a Dirac delta function, and hence, the optimal random variable is a constant. The(More)
—Estimation of signals with nonlinear as well as linear parameters in noise is studied. Maximum likelihood estimation has been shown to perform the best among all the methods. In such problems, joint maximum likelihood estimation of the unknown parameters reduces to a separable optimization problem, where first, the nonlinear parameters are estimated via a(More)
This paper considers a method for estimating time delays, amplitudes, and Doppler scales of a multipath signal. The method is an extension of work previously reported by Man-ickam and Vaccaro [1] which dealt solely with time delays and amplitudes, and extended by Habboosh and Vaccaro [2] to include Doppler scale. In this paper, an algorithm is presented for(More)
The problem of the parameter estimation of chirp signals is addressed. Several closely related estimators are proposed whose main characteristics are simplicity, accuracy, and ease of on-line or off-line implementation. For moderately high signal-to-noise ratios they are unbiased and attain the Cramer-Rao bound. Monte Carlo simulations verify the expected(More)