Corpus ID: 235458220

Maximum Entropy Spectral Analysis: a case study

  title={Maximum Entropy Spectral Analysis: a case study},
  author={A. Martini and S. Schmidt and W. D. Pozzo},
The Maximum Entropy Spectral Analysis (MESA) method, developed by Burg, provides a powerful tool to perform spectral estimation of a time-series. The method relies on a Jaynes’ maximum entropy principle and provides the means of inferring the spectrum of a stochastic process in terms of the coefficients of some autoregressive process AR(p) of order p. A closed form recursive solution provides an estimate of the autoregressive coefficients as well as of the order p of the process. We provide a… Expand


The maximum entropy spectrum and the Burg technique. Technical report no. 1: Advanced signal processing
Abstract : This tutorial paper describes the maximum entropy spectrum and the Burg technique for computing the prediction error power and prediction error filter coefficients in the associatedExpand
Maximum Entropy Spectral Analysis
A review of the maximum entropy spectral analysis (MESA) method for time series is presented. Then, empirical evidence based on maximum entropy spectra of real seismic data is shown to suggest that MExpand
Gravitational-wave astronomy with an uncertain noise power spectral density
In order to extract information about the properties of compact binaries, we must estimate the noise power spectral density of gravitational-wave data, which depends on the properties of theExpand
Toeplitz and Circulant Matrices: A Review
  • R. Gray
  • Mathematics, Computer Science
  • Found. Trends Commun. Inf. Theory
  • 2005
The fundamental theorems on the asymptotic behavior of eigenvalues, inverses, and products of banded Toeplitz matrices and Toepler matrices with absolutely summable elements are derived in a tutorial manner in the hope of making these results available to engineers lacking either the background or endurance to attack the mathematical literature on the subject. Expand
Temporal Convolutional Networks for Action Segmentation and Detection
A class of temporal models that use a hierarchy of temporal convolutions to perform fine-grained action segmentation or detection, which are capable of capturing action compositions, segment durations, and long-range dependencies, and are over a magnitude faster to train than competing LSTM-based Recurrent Neural Networks. Expand
Probability and Information Theory with Applications to Radar
Recognizing the mannerism ways to get this books Probability And Information Theory With Applications To Radar International Series Of Monographs On Electronics And Instrumentation Volume 3 isExpand
Classical and Quantum
Molecular Thermodynamics By J. H. Knox. Pp. xiii+264. (Wiley Inter-science: London and New York, July 1971.) £4.25.
Unlike the standard approach, we do not enforce the constraints in Eq
  • (11) with the standard Lagrange Multipliers approach. We write instead the PSD S ( f ) as the Fourier Transform of the sample au-
  • 1975
Maximum Entropy Spectral Analysis: characterization and applications to on-source parameter estimation of time series
  • 2020
Noise curves used for Simulations in the update of the Observing Scenarios Paper
  • 2020