Publications about 'matrix algebra'

Articles in journal or book chapters

  1. T. M. Marston and J. L. Kennedy. Volumetric Acoustic Imaging via Circular Multipass Aperture Synthesis. IEEE_J_OE, 41(4):852-867, October 2016. Keyword(s): autonomous underwater vehicles, compressed sensing, matrix algebra, sonar, synthetic aperture radar, AUV, CSAS, analogous synthetic aperture radar tomography, autonomous underwater vehicle, compressive-sensing-based approach, data-driven technique, high-resolution volumetric images, joint sparsity assumption, multidimensional array, multipass circular synthetic aperture sonar, sensing matrices, standard joint sparse solving algorithm, volumetric acoustic imaging, Compressed sensing, Synthetic aperture radar, Synthetic aperture sonar, Tomography, Underwater vehicles, Compressive sensing, multipass sonar, synthetic aperture sonar (SAS), tomography, volumetric imaging. [bibtex-entry]

  2. B. Minchew, C.E. Jones, and B. Holt. Polarimetric Analysis of Backscatter From the Deepwater Horizon Oil Spill Using L-Band Synthetic Aperture Radar. Geoscience and Remote Sensing, IEEE Transactions on, 50(10):3812-3830, October 2012. Keyword(s): AD 2010 06 23, Bragg scattering mechanism, DWH slick, Gulf of Mexico, L-band synthetic aperture radar, backscatter polarimetric analysis, coherency matrix eigenvalue, deepwater horizon, deepwater horizon oil spill, dielectric constant, entropy parameters, fully-polarimetric uninhabited aerial vehicle, ocean wave spectral components, oil slick, oil volumetric concentration, radar backscatter, sea water, slick detection method, substantial variation parameter, surface scattering analysis, synthetic aperture radar data, backscatter, eigenvalues and eigenfunctions, entropy, marine pollution, matrix algebra, ocean chemistry, ocean waves, oceanographic regions, oceanographic techniques, permittivity, radar interferometry, remote sensing by radar, seawater, synthetic aperture radar;. [Abstract] [bibtex-entry]

  3. E.J. Candes and Y. Plan. Matrix Completion With Noise. Proceedings of the IEEE, 98(6):925-936, june 2010. Keyword(s): compressed sensing, convex optimization problem, data constraints, low rank matrices, matrix completion, nuclear norm minimization, data integrity, matrix algebra, minimisation, noise, signal sampling;. [Abstract] [bibtex-entry]

  4. S. Ozsoy and A.A. Ergin. Pencil Back-Projection Method for SAR Imaging. IEEE Transactions on Image Processing, 18(3):573-581, March 2009. Keyword(s): SAR Processing, SAR Tomography, Tomography, SAR imaging, forward-backward total least squares bandpass matrix pencil method, pencil back-projection method, projected target reflectivity density function, synthetic aperture radar, tomographic image reconstruction, image reconstruction, least squares approximations, matrix algebra, radar imaging, synthetic aperture radar, Algorithms, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Radar, Reproducibility of Results, Sensitivity and Specificity, Tomography;. [Abstract] [bibtex-entry]

  5. B.D. Carlson. Covariance matrix estimation errors and diagonal loading in adaptive arrays. IEEE Transactions on Aerospace and Electronic Systems, 24(4):397-401, July 1988. Keyword(s): SAR Processing, Beamforming, array processing, antenna phased arrays, antenna theory, digital simulation, eigenvalues and eigenfunctions, estimation theory, matrix algebraadapted antenna patterns, adaptive arrays, covariance matrix sample size, diagonal loading, distorted mainbeams, eigenvector decomposition, estimation errors, low-level interference, nulling, sample matrix inversion, sidelobes. [Abstract] [bibtex-entry]

Conference articles

  1. E. Candes, Xiaodong Li, Yi Ma, and J. Wright. Robust principal component analysis?: Recovering low-rank matrices from sparse errors. In Proc. IEEE Sensor Array and Multichannel Signal Processing Workshop, pages 201-204, October 2010. Keyword(s): bioinformatics, computer vision, low rank data matrix, nonvanishing fraction, nuclear norm, positive fraction, robust principal component analysis, simple convex program, weighted combination, matrix algebra, principal component analysis;. [Abstract] [bibtex-entry]

  2. Andreas Reigber, Maxim Neumann, Stephane Guillaso, Stefan Sauer, and Laurent Ferro-Famil. Evaluating PolInSAR parameter estimation using tomographic imaging results. In Proc. European Radar Conf., pages 189-192, 2005. Keyword(s): SAR Processing, SAR Tomography, Tomography, forestry, matrix algebra, radar imaging, radar polarimetry, radiowave interferometry, remote sensing by radar, synthetic aperture radar, tomography, vegetation mapping, PolInSAR parameter estimation, canopy, forest height, ground topography estimation, polarimetric SAR interferometry, tomographic imaging results. [Abstract] [bibtex-entry]



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This collection of SAR literature is far from being complete.
It is rather a collection of papers which I store in my literature data base. Hence, the list of publications under PUBLICATIONS OF AUTHOR'S NAME should NOT be mistaken for a complete bibliography of that author.

Last modified: Mon Feb 1 16:40:22 2021
Author: Othmar Frey, Earth Observation and Remote Sensing, Institute of Environmental Engineering, Swiss Federal Institute of Technology - ETH Zurich .

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