Maximum Entropy Spectral Analysis

By James A. Cadzow. 2006

795 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b52bc97a225d2203a67c006a8bf7a2fbd4f32684

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In Artificial Intelligence

Citations in the corpus

Developing a fuzzy TOPSIS approach based on subjective weights and objective weights (2009, methodology): “Later research has applied his measure to a wide range of applications including spectral analysis (Burg, 1967), language modeling (Rosenfeld, 1994) and economics (Golan, Judge, & Miller, 1996).”
Positive extensions, Fejér-Riesz factorization and autoregressive filters in two variables (2004, background): “The spectral density function f of p has in fact a so-called maximum entropy property (see [9]), which states that among all positive functions on the unit circle with the prescribed Fourier coefficients ck, k = −n, .”
Universal Estimation of Information Measures for Analog Sources (2009, methodology): “The principle of maximum differential entropy has been applied to density estimation [39, 203, 235, 236] and spectral estimation [40, 48].”
Shadow analysis: A method for measuring soil surface roughness (2008, methodology): “, 1990) and the Richardson number (Gallart and Pardini, 1996; Pardini and Gallart, 1998), and variational methods, including the semi-variogram (Armstrong, 1986; Vivas Miranda, 2000; Vivas Miranda and Paz Gonzalez, 2002), spectral analysis (Burg, 1967) and root mean square (RMS) or roughness length ”
Spectral Analysis of Signals (2009, methodology): “This extension, called the Maximum Entropy extension [Burg 1975], thus gives an ACS sequence that corresponds to an AR process of order less than or equal to (m− 1).”
Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications (2009, methodology): “The Burg estimation method [43]– [45] is used in this study to adaptively determine the parameters of the stochastic AR model for each inertial sensor.”