D-Index & Metrics Best Publications

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Engineering and Technology D-index 51 Citations 18,349 274 World Ranking 1873 National Ranking 732

Research.com Recognitions

Awards & Achievements

2016 - Jack S. Kilby Signal Processing Medal For pioneering and sustained contributions to statistical signal processing and its practice.

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Algorithm
  • Algebra

The scientist’s investigation covers issues in Algorithm, Covariance, Subspace topology, Signal processing and Mathematical optimization. He works in the field of Algorithm, focusing on Wiener filter in particular. His Covariance research incorporates elements of Covariance matrix, Constant false alarm rate, Canonical correlation and Estimator.

His Subspace topology research incorporates themes from Invertible matrix, Stochastic process, Baseband, Matched filter and Electronic engineering. His studies in Signal processing integrate themes in fields like Fourier analysis, Harmonic analysis, Control theory and Random variable. His biological study spans a wide range of topics, including Singular value decomposition, Radar imaging, Applied mathematics, Sonar and Compressed sensing.

His most cited work include:

  • Statistical signal processing : detection, estimation, and time series analysis (1424 citations)
  • Matched subspace detectors (801 citations)
  • A multistage representation of the Wiener filter based on orthogonal projections (735 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Algorithm, Subspace topology, Mathematical optimization, Covariance matrix and Covariance. His Algorithm research includes elements of Detector, Detection theory, Speech recognition, Estimator and Signal processing. The study incorporates disciplines such as Signal subspace, Linear subspace, Matched filter and Pattern recognition in addition to Subspace topology.

His research in Mathematical optimization intersects with topics in Adaptive filter, Maximum likelihood sequence estimation and Autoregressive model. Louis L. Scharf has researched Covariance matrix in several fields, including Control theory, Likelihood-ratio test, Noise and Combinatorics. He combines subjects such as Eigenvalues and eigenvectors and Rank with his study of Covariance.

He most often published in these fields:

  • Algorithm (49.47%)
  • Subspace topology (21.20%)
  • Mathematical optimization (17.31%)

What were the highlights of his more recent work (between 2011-2020)?

  • Algorithm (49.47%)
  • Mathematical optimization (17.31%)
  • Subspace topology (21.20%)

In recent papers he was focusing on the following fields of study:

Louis L. Scharf spends much of his time researching Algorithm, Mathematical optimization, Subspace topology, Covariance matrix and Covariance. The Algorithm study combines topics in areas such as Detector, Signal, Noise, Radar and Multivariate normal distribution. Louis L. Scharf works mostly in the field of Mathematical optimization, limiting it down to topics relating to Decision rule and, in certain cases, Sensor fusion, as a part of the same area of interest.

His Subspace topology research includes themes of Dimension, Projection, Linear subspace, Linear combination and Signal subspace. His study on Covariance matrix also encompasses disciplines like

  • Likelihood-ratio test that intertwine with fields like Applied mathematics, Series and Multivariate random variable,
  • Statistical hypothesis testing most often made with reference to Cyclostationary process. The concepts of his Covariance study are interwoven with issues in Discrete mathematics and Probability distribution.

Between 2011 and 2020, his most popular works were:

  • Locally Most Powerful Invariant Tests for Correlation and Sphericity of Gaussian Vectors (45 citations)
  • Analysis of fisher information and the Cramer-Rao bound for nonlinear parameter estimation after compressed sensing (29 citations)
  • Detection of Multivariate Cyclostationarity (28 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Algebra
  • Algorithm

Louis L. Scharf spends much of his time researching Algorithm, Mathematical optimization, Likelihood-ratio test, Covariance matrix and Applied mathematics. His study in Algorithm is interdisciplinary in nature, drawing from both Subspace topology, Control theory, Signal processing, Electro-optical sensor and Data set. His Mathematical optimization research is multidisciplinary, incorporating elements of Eigenvalues and eigenvectors, Direction of arrival, Noise and Compressed sensing.

His Likelihood-ratio test study combines topics in areas such as Detection theory and Estimation of covariance matrices. His Covariance matrix study combines topics from a wide range of disciplines, such as Series and Cyclostationary process. His work deals with themes such as Random matrix, Cramér–Rao bound, Covariance, Fisher information and Multivariate normal distribution, which intersect with Applied mathematics.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Statistical signal processing : detection, estimation, and time series analysis

Louis L. Scharf;Cédric Demeure.
Published in <b>1991</b> in Reading Mass by Addison-Wesley Pub Co (1991)

4150 Citations

A multistage representation of the Wiener filter based on orthogonal projections

J.S. Goldstein;I.S. Reed;L.L. Scharf.
IEEE Transactions on Information Theory (1998)

1143 Citations

Initial results in Prony analysis of power system response signals

J.F. Hauer;C.J. Demeure;L.L. Scharf.
IEEE Transactions on Power Systems (1990)

1120 Citations

Matched subspace detectors

L.L. Scharf;B. Friedlander.
IEEE Transactions on Signal Processing (1994)

1093 Citations

Sensitivity to Basis Mismatch in Compressed Sensing

Yuejie Chi;Louis L Scharf;Ali Pezeshki;A Robert Calderbank.
IEEE Transactions on Signal Processing (2011)

896 Citations

Statistical Signal Processing of Complex-Valued Data: The Theory of Improper and Noncircular Signals

Peter J. Schreier;Louis L. Scharf.
(2010)

799 Citations

Adaptive subspace detectors

S. Kraut;L.L. Scharf;L.T. McWhorter.
IEEE Transactions on Signal Processing (2001)

758 Citations

The CFAR adaptive subspace detector is a scale-invariant GLRT

S. Kraut;L.L. Scharf.
IEEE Transactions on Signal Processing (1999)

607 Citations

Signal processing applications of oblique projection operators

R.T. Behrens;L.L. Scharf.
IEEE Transactions on Signal Processing (1994)

581 Citations

Second-order analysis of improper complex random vectors and processes

P.J. Schreier;L.L. Scharf.
IEEE Transactions on Signal Processing (2003)

423 Citations

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