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Publications about 'sparse signals'

Articles in journal or book chapters

  1. Alessandra Budillon, Angel Caroline Johnsy, and Gilda Schirinzi. Extension of a Fast GLRT Algorithm to 5D SAR Tomography of Urban Areas. Remote Sensing, 9(8), 2017. Keyword(s): SAR Processing, SAR Tomography, radar detection, GLRT, Fast GLRT, deformation, PSI, persistent scatterer interferometry, displacement mapping, radar receivers, radar resolution, synthetic aperture radar, tomography, GLRT detector, ROC curve, SAR tomography, Sup-GLRT, elevation direction, generalized likelihood ratio test detector, multiple scatterer detection, nominal system resolution, nominal system super-resolution, receiver operating characteristic curve, signal sparsity, signal support detection operation, signal support estimation operation, signal-to-noise ratio, subspace energy measurement, synthetic aperture radar, Earth, Estimation, Signal resolution, Spatial resolution, Synthetic aperture radar, Tomography, Generalized likelihood ratio test (GLRT), radar detection, sparse signals, synthetic aperture radar (SAR). [Abstract] [bibtex-entry]


  2. Alessandra Budillon and Gilda Schirinzi. GLRT Based on Support Estimation for Multiple Scatterers Detection in SAR Tomography. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(3):1086-1094, March 2016. Keyword(s): SAR Processing, SAR Tomography, radar detection, radar receivers, radar resolution, synthetic aperture radar, tomography, GLRT detector, ROC curve, SAR tomography, Sup-GLRT, elevation direction, generalized likelihood ratio test detector, multiple scatterer detection, nominal system resolution, nominal system super-resolution, receiver operating characteristic curve, signal sparsity, signal support detection operation, signal support estimation operation, signal-to-noise ratio, subspace energy measurement, synthetic aperture radar, Earth, Estimation, Signal resolution, Spatial resolution, Synthetic aperture radar, Tomography, Generalized likelihood ratio test (GLRT), radar detection, sparse signals, synthetic aperture radar (SAR). [Abstract] [bibtex-entry]


  3. E.J. Candes and Y. Plan. A Probabilistic and RIPless Theory of Compressed Sensing. IEEE Transactions on Information Theory, 57(11):7235-7254, November 2011. Keyword(s): Fourier coefficients, Gaussian model, RIPless theory, compressed sensing, frequency measurements, probabilistic theory, probability distribution, restricted isometry property, signal random model, sparse signals, Fourier analysis, data compression, random processes, signal reconstruction, statistical distributions;. [Abstract] [bibtex-entry]


  4. Joachim H.G. Ender. On compressive sensing applied to radar. Signal Processing, 90(5):1402 - 1414, 2010. Note: Special Section on Statistical Signal and Array Processing. Keyword(s): Compressive Sensing, Compressed Sensing, Radar, Sparse arrays, Pulse compression, Radar imaging, ISAR, Airspace surveillance, DOA estimation. [Abstract] [bibtex-entry]


  5. Emmanuel J. Candes, Justin K. Romberg, and Terence Tao. Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59(8):1207-1223, 2006. Keyword(s): Compressive Sensing, Compressed Sensing. [Abstract] [bibtex-entry]


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Please note that access to full text PDF versions of papers is restricted to the Chair of Earth Observation and Remote Sensing, Institute of Environmental Engineering, ETH Zurich.
Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.

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 16:41:15 2021
Author: Othmar Frey, Earth Observation and Remote Sensing, Institute of Environmental Engineering, Swiss Federal Institute of Technology - ETH Zurich .


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