About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
SampTA 2015
Conference paper
Parameter estimation from samples of stationary complex Gaussian processes
Abstract
Sampling stationary, circularly-symmetric complex Gaussian stochastic process models from multiple sensors arise in array signal processing, including applications in direction of arrival estimation and radio astronomy. The goal is to take narrow-band filtered samples so as to estimate process parameters as accurately as possible. We derive analytical results on the estimation variance of the parameters as a function of the number of samples, the sampling rate, and the filter, under two different statistical estimators. The first is a standard sample variance estimator. The second, a generalization, is a maximum-likelihood estimator, useful when samples are correlated. The explicit relationships between estimation performance and filter autocorrelation can be used to improve process parameter estimation when sampling at higher than Nyquist. Additionally, they have potential application in filter optimization.