This course presents the basics of estimation and detection theory that are commonly Solutions will be provided. While I do.
—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)
Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and the publisher was aware of a trademark claim, the designations have been printed with initial capital letters or in all capitals. The author and publisher have taken care in the preparation of… (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  which dealt solely with time delays and amplitudes, and extended by Habboosh and Vaccaro  to include Doppler scale. In this paper, an algorithm is presented for… (More)
—An expression for the Cramer–Rao lower bound (CRB) on the covariance of unbiased estimators of a constrained complex parameter vector is derived. The application and usefulness of the result is demonstrated through its use in the context of a semiblind channel estimation problem.
—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)