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Publications about 'signal reconstruction'

Articles in journal or book chapters

  1. Nida Sakar, Marc Rodriguez-Cassola, Pau Prats-Iraola, Andreas Reigber, and Alberto Moreira. Analysis of Geometrical Approximations in Signal Reconstruction Methods for Multistatic SAR Constellations With Large Along-Track Baseline. IEEE Geoscience and Remote Sensing Letters, 15(6):892-896, June 2018. Keyword(s): Doppler effect, Geometry, History, Image reconstruction, Receivers, Spaceborne radar, Synthetic aperture radar, Digital beamforming, high-resolution wide-swath (HRWS) radar, multistatic radar signal processing, synthetic aperture radar (SAR). [Abstract] [bibtex-entry]


  2. Esteban Aguilera, Matteo Nannini, and Andreas Reigber. Multisignal Compressed Sensing for Polarimetric SAR Tomography. IEEE Geosci. Remote Sens. Lett., 5(9):871-875, September 2012. Keyword(s): SAR Processing, SAR Tomography, Tomography, Compressed sensing, Remote sensing, Sensors, Tomography, Vectors, compressed sensing, compressive sensing, CS, geometry, image reconstruction, image sensors, radar imaging, radar polarimetry, synthetic aperture radar, tomography, 3D imaging, DCS, E-SAR sensor, German Aerospace Center, azimuth-range pixel, data collection processing, distributed compressed sensing, multisignal compressed sensing, parallel track, polarimetric L-band data, polarimetric SAR sensor, polarimetric SAR tomography, polarimetric channel, polarimetric synthetic aperture radar sensor, repeat-pass acquisition geometry, signal reconstruction, temporal baseline, tomographic signal, vertical reflectivity function, Compressed sensing (CS), distributed compressed sensing (DCS), polarimetry, synthetic aperture radar (SAR) tomography, L-band, E-SAR, F-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. G. Fornaro, A. Pauciullo, and D. Reale. A Null-Space Method for the Phase Unwrapping of Multitemporal SAR Interferometric Stacks. IEEE Transactions on Geoscience and Remote Sensing, 49(6):2323-2334, June 2011. Keyword(s): SAR Processing, DInSAR, SAR Interferometry, Phase unwrapping, 2-D azimuth-range domain, Earth surface displacement, MCF algorithm, data analysis, full 3-D unwrapping method, interferogram generation scheme, interferogram spatial structure, multitemporal SAR interferometric stack analysis, multitemporal differential interferometric synthetic aperture radar analysis, null-space method, optimization technique, phase unwrapping method, spatial baseline domain, time series, unrestricted phase signal reconstruction, data analysis, geophysical techniques, radar interferometry, synthetic aperture radar, time series, topography (Earth). [Abstract] [bibtex-entry]


  5. E.J. Candes, J. Romberg, and T. Tao. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2):489-509, feb. 2006. Keyword(s): Fourier coefficient, convex optimization, discrete-time signal, image reconstruction, incomplete frequency information, linear programming, minimization problem, nonlinear sampling theorem, piecewise constant object, probability value, robust uncertainty principle, signal reconstruction, sparse random matrix, trigonometric expansion, Fourier analysis, convex programming, image reconstruction, image sampling, indeterminancy, linear programming, minimisation, piecewise constant techniques, probability, signal reconstruction, signal sampling, sparse matrices;. [Abstract] [bibtex-entry]


Conference articles

  1. E. Candes, N. Braun, and M. Wakin. SPARSE SIGNAL AND IMAGE RECOVERY FROM COMPRESSIVE SAMPLES. In Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. 4th IEEE International Symposium on, pages 976-979, april 2007. Keyword(s): compressive sampling, data acquisition, image recovery, magnetic resonance imaging, medical imaging, model-based framework, random measurements, random noise-like basis, signal reconstruction, sparse signal recovery, biomedical MRI, biomedical measurement, data acquisition, image coding, image reconstruction, image sampling, medical image processing, random noise, sparse matrices;. [Abstract] [bibtex-entry]


  2. E. Candes and J. Romberg. Robust Signal Recovery from Incomplete Observations. In Proc. IEEE Int. Conf. Image Processing, pages 1281-1284, October 2006. Keyword(s): convex optimization program, linear measurement, sparse signal reconstruction, convex programming, signal reconstruction;. [Abstract] [bibtex-entry]


  3. Gerhard Krieger, Nicolas Gebert, and Alberto Moreira. SAR Signal Reconstruction from Non-Uniform Displaced Phase Centre Sampling. In IGARSS '04, International Geoscience and Remote Sensing Symposium, September 2004. Keyword(s): SAR Processing, Bistatic SAR, Bistatic Processing, Dual Receive Antenna. [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 16:41:09 2021
Author: Othmar Frey, Earth Observation and Remote Sensing, Institute of Environmental Engineering, Swiss Federal Institute of Technology - ETH Zurich .


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