White noise

About: White noise is a research topic. Over the lifetime, 16496 publications have been published within this topic receiving 318633 citations.

Performance analysis of Root-Music

Abstract: The authors analyze the performance of Root-Music, a variation of the MUSIC algorithm, for estimating the direction of arrival (DOA) of plane waves in white noise in the case of a linear equispaced sensor array The performance of the method is analyzed by examining the perturbation in the roots of the polynomial formed in the intermediate step of Root-Music In particular, asymptotic results for the mean squared error in the estimates of the direction of arrival are derived Simplified expressions are presented for the one- and two-source case and compared to those obtained for least-squares ESPRIT Computer simulations are also presented, and they are in close agreement with the theory An important outcome of this analysis is the fact that the error in the signal zeros has a largely radial component This provides an explanation as to why the Root-Music is superior to the spectral MUSIC algorithm >

A family of suboptimum detectors for coherent multiuser communications

Abstract: Consideration is given to a class of suboptimum detectors for data transmitted asynchronously by K users employing direct-sequence spread-spectrum multiple access (DS/SSMA) on the additive white Gaussian noise (AWGN) channel. The general structure of these detectors consists of a bank of matched filters, a linear transformation that operates on the matched-filter outputs, and a set of threshold devices. The linear transformations are chosen to minimize either a mean-squared-error or a weighted-squared-error performance criterion. Each detector can be implemented using a tapped delay line. The number of computations performed per detected bit is linear in K in each case, and the resulting detectors are thus much simpler than the optimum detector. Under typical operating conditions, these detectors will perform much better than the conventional receiver and often nearly as well as the optimum detector. >

Signal Detection in Non-Gaussian Noise

Abstract: This book contains a unified treatment of a class of problems of signal detection theory. This is the detection of signals in addi tive noise which is not required to have Gaussian probability den sity functions in its statistical description. For the most part the material developed here can be classified as belonging to the gen eral body of results of parametric theory. Thus the probability density functions of the observations are assumed to be known, at least to within a finite number of unknown parameters in a known functional form. Of course the focus is on noise which is not Gaussian; results for Gaussian noise in the problems treated here become special cases. The contents also form a bridge between the classical results of signal detection in Gaussian noise and those of nonparametric and robust signal detection, which are not con sidered in this book. Three canonical problems of signal detection in additive noise are covered here. These allow between them formulation of a range of specific detection problems arising in applications such as radar and sonar, binary signaling, and pattern recognition and classification. The simplest to state and perhaps the most widely studied of all is the problem of detecting a completely known deterministic signal in noise. Also considered here is the detection random non-deterministic signal in noise. Both of these situa of a tions may arise for observation processes of the low-pass type and also for processes of the band-pass type."