Spectral density estimation

About: Spectral density estimation is a research topic. Over the lifetime, 5391 publications have been published within this topic receiving 123105 citations.

Fast Equalization System

Abstract: A discrete frequency domain equalization system is disclosed for utilization in a high-speed synchronous data transmission system where, in a preferred embodiment, samples of an input signal in the time domain are transformed by a discrete fast Fourier transform device into samples in the frequency domain. Reciprocal values of these frequency domain samples are derived from a reciprocal circuit and then transformed by an inverse discrete fast Fourier transform device into time domain samples which are the desired tap gains that are applied to a transversal equalizer in order to minimize the errors in a received signal caused by intersymbol interference and noise.

Using Periodic Autoregressions for Multiple Spectral Estimation

Abstract: A new method of estimating the spectral density of a multiple time series based on the concept of periodically stationary autoregressive processes is described and illustrated. It is shown that the method can often overcome some difficulties inherent in the traditional smoothed periodogram and autoregressive spectral-estimation methods and that additional insights into the structure of a multiple time series can be obtained by using periodic autoregressions.

Spectral detector for interference time blanking using quantized correlator

Abstract: Given the large flow of data to be processed, quantized correlators are widely used in radio astronomy. Unfortunately, the occurrence of non-Gaussian interference combined with a coarse quantization can strongly alter the shape of the estimated spectra. The final spectral estimation can be preserved by blanking the correlator in real-time. A new interference detection criterion is proposed within this framework. It uses the real-time capabilities of correlators and compares contaminated and non-contaminated correlation functions. No a priori information on the interfering signals is required. Simulations, using synthetic and actual data, are presented. This new technique of real time detection can significantly improve the quality of spectral line observations.

Uncertainty Bounds for Spectral Estimation

Abstract: The purpose of this paper is to study metrics suitable for assessing uncertainty of power spectra when these are based on finite second-order statistics. The family of power spectra which is consistent with a given range of values for the estimated statistics represents the uncertainty set about the “true” power spectrum. Our aim is to quantify the size of this uncertainty set using suitable notions of distance, and in particular, to compute the diameter of the set since this represents an upper bound on the distance between any choice of a nominal element in the set and the “true” power spectrum. Since the uncertainty set may contain power spectra with lines and discontinuities, it is natural to quantify distances in the weak topology-the topology defined by continuity of moments. We provide examples of such weakly continuous metrics and focus on particular metrics for which we can explicitly quantify spectral uncertainty. We then consider certain high resolution techniques which utilize filter-banks for preprocessing, and compute worst case a priori uncertainty bounds solely on the basis of the filter dynamics. This allows the a priori tuning of the filter-banks for improved resolution over selected frequency bands.