2016 - Jack S. Kilby Signal Processing Medal For pioneering and sustained contributions to statistical signal processing and its practice.
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 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.
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
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.
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)
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)
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)
Matched subspace detectors
L.L. Scharf;B. Friedlander.
IEEE Transactions on Signal Processing (1994)
Sensitivity to Basis Mismatch in Compressed Sensing
Yuejie Chi;Louis L Scharf;Ali Pezeshki;A Robert Calderbank.
IEEE Transactions on Signal Processing (2011)
Statistical Signal Processing of Complex-Valued Data: The Theory of Improper and Noncircular Signals
Peter J. Schreier;Louis L. Scharf.
Adaptive subspace detectors
S. Kraut;L.L. Scharf;L.T. McWhorter.
IEEE Transactions on Signal Processing (2001)
The CFAR adaptive subspace detector is a scale-invariant GLRT
S. Kraut;L.L. Scharf.
IEEE Transactions on Signal Processing (1999)
Signal processing applications of oblique projection operators
R.T. Behrens;L.L. Scharf.
IEEE Transactions on Signal Processing (1994)
Second-order analysis of improper complex random vectors and processes
P.J. Schreier;L.L. Scharf.
IEEE Transactions on Signal Processing (2003)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: