Relevant bibliographies by topics / Super Resolution Radar

Academic literature on the topic 'Super Resolution Radar'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Super Resolution Radar"

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Heckel, Reinhard, Veniamin I. Morgenshtern, and Mahdi Soltanolkotabi. "Super-resolution radar." Information and Inference 5, no. 1 (February 23, 2016): 22–75. http://dx.doi.org/10.1093/imaiai/iaw001.

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Seo, Bong-Chul, and Witold F. Krajewski. "Scale Dependence of Radar Rainfall Uncertainty: Initial Evaluation of NEXRAD’s New Super-Resolution Data for Hydrologic Applications." Journal of Hydrometeorology 11, no. 5 (October 1, 2010): 1191–98. http://dx.doi.org/10.1175/2010jhm1265.1.

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Abstract This study explores the scale effects of radar rainfall accumulation fields generated using the new super-resolution level II radar reflectivity data acquired by the Next Generation Weather Radar (NEXRAD) network of the Weather Surveillance Radar-1988 Doppler (WSR-88D) weather radars. Eleven months (May 2008–August 2009, exclusive of winter months) of high-density rain gauge network data are used to describe the uncertainty structure of radar rainfall and rain gauge representativeness with respect to five spatial scales (0.5, 1, 2, 4, and 8 km). While both uncertainties of gauge representativeness and radar rainfall show simple scaling behavior, the uncertainty of radar rainfall is characterized by an almost 3 times greater standard error at higher temporal and spatial resolutions (15 min and 0.5 km) than at lower resolutions (1 h and 8 km). These results may have implications for error propagation through distributed hydrologic models that require high-resolution rainfall input. Another interesting result of the study is that uncertainty obtained by averaging rainfall products produced from the super-resolution reflectivity data is slightly lower at smaller scales than the uncertainty of the corresponding resolution products produced using averaged (recombined) reflectivity data.
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Bialer, Oded, Amnon Jonas, and Tom Tirer. "Super Resolution Wide Aperture Automotive Radar." IEEE Sensors Journal 21, no. 16 (August 15, 2021): 17846–58. http://dx.doi.org/10.1109/jsen.2021.3085677.

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Almutiry, Muhannad. "Wideband Tomographic Super-Resolution Radar Image." IEEE Sensors Journal 20, no. 3 (February 1, 2020): 1208–16. http://dx.doi.org/10.1109/jsen.2019.2946491.

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Wang, Yunlai, Yanzhe Wang, and Zhongyi Guo. "OAM radar based fast super-resolution imaging." Measurement 189 (February 2022): 110600. http://dx.doi.org/10.1016/j.measurement.2021.110600.

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Geiss, Andrew, and Joseph C. Hardin. "Radar Super Resolution Using a Deep Convolutional Neural Network." Journal of Atmospheric and Oceanic Technology 37, no. 12 (December 2020): 2197–207. http://dx.doi.org/10.1175/jtech-d-20-0074.1.

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AbstractSuper resolution involves synthetically increasing the resolution of gridded data beyond their native resolution. Typically, this is done using interpolation schemes, which estimate sub-grid-scale values from neighboring data, and perform the same operation everywhere regardless of the large-scale context, or by requiring a network of radars with overlapping fields of view. Recently, significant progress has been made in single-image super resolution using convolutional neural networks. Conceptually, a neural network may be able to learn relations between large-scale precipitation features and the associated sub-pixel-scale variability and outperform interpolation schemes. Here, we use a deep convolutional neural network to artificially enhance the resolution of NEXRAD PPI scans. The model is trained on 6 months of reflectivity observations from the Langley Hill, Washington, radar (KLGX), and we find that it substantially outperforms common interpolation schemes for 4× and 8× resolution increases based on several objective error and perceptual quality metrics.
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Alpert, Jordan C., and V. Krishna Kumar. "Radial Wind Super-Obs from the WSR-88D Radars in the NCEP Operational Assimilation System." Monthly Weather Review 135, no. 3 (March 1, 2007): 1090–109. http://dx.doi.org/10.1175/mwr3324.1.

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Abstract The spatial and temporal densities of Weather Surveillance Radar-1988 Doppler (WSR-88D) raw radar radial wind represent a rich source of high-resolution observations for initializing numerical weather prediction models. A characteristic of these observations is the presence of a significant degree of redundant information imposing a burden on an operational assimilation system. Potential improvement in data assimilation efficiency can be achieved by constructing averages, called super-obs. In the past, transmission of the radar radial wind from each radar site to a central site was confined to data feeds that filter the resolution and degrade the precision. At the central site, super-obs were constructed from this data feed and called level-3 super-obs. However, the precision and information content of the radial wind can be improved if data at each radar site are directly utilized at the highest resolution and precision found at the WSR-88D radar and then transmitted to a central site for processing in assimilation systems. In addition, with data compression from using super-obs, the volume of data is reduced, allowing quality control information to be included in the data transmission. The super-ob product from each WSR-88D radar site is called level-2.5 super-obs. Parallel, operational runs and case studies of the impact of the level-2.5 radar radial wind super-ob on the NCEP operational 12-km Eta Data Assimilation System (EDAS) and forecast system are compared with Next-Generation Weather Radar level-3 radial wind super-obs, which are spatially filtered and delivered at reduced precision. From the cases studied, it is shown that the level-3 super-obs make little or no impact on the Eta data analysis and subsequent forecasts. The assimilation of the level-2.5 super-ob product in the EDAS and forecast system shows improved precipitation threat scores as well as reduction in RMS and bias height errors, particularly in the upper troposphere. In the few cases studied, the predicted mesoscale precipitation patterns benefit from the level-2.5 super-obs, and more so when greater weight is given to these high-resolution/precision observations. Direct transmission of raw (designated as level 2) radar data to a central site and its use are now imminent, but this study shows that the level-2.5 super-ob product can be used as an operational benchmark to compare with new quality control and assimilation schemes.
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Guo, Bowen, Yunsong Huang, Anders Røstad, and Gerard Schuster. "Far-field super-resolution imaging of resonant multiples." Science Advances 2, no. 5 (May 2016): e1501439. http://dx.doi.org/10.1126/sciadv.1501439.

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We demonstrate for the first time that seismic resonant multiples, usually considered as noise, can be used for super-resolution imaging in the far-field region of sources and receivers. Tests with both synthetic data and field data show that resonant multiples can image reflector boundaries with resolutions more than twice the classical resolution limit. Resolution increases with the order of the resonant multiples. This procedure has important applications in earthquake and exploration seismology, radar, sonar, LIDAR (light detection and ranging), and ultrasound imaging, where the multiples can be used to make high-resolution images.
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Sharif, Hatim O., and Fred L. Ogden. "Mass-Conserving Remapping of Radar Data onto Two-Dimensional Cartesian Coordinates for Hydrologic Applications." Journal of Hydrometeorology 15, no. 6 (December 1, 2014): 2190–202. http://dx.doi.org/10.1175/jhm-d-14-0058.1.

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Abstract Recent upgrades to operational radar-rainfall products in terms of quality and resolution call for reexamination of the factors that contribute to the uncertainty of radar-rainfall estimation. Remapping or regridding of radar observations onto Cartesian coordinates is implemented by practitioners when radar estimates are compared against rain gauge observations, in hydrologic applications, or for merging data from different radars. However, assuming perfect radar observations, many of the widely used remapping methodologies do not conserve mass for the rainfall rate field. The most popular remapping approaches used are those based on extracting information from radar bins whose centers fall within a certain distance from the center of the Cartesian grid. This paper develops a mass-conserving method for remapping, which is called “precise remapping,” which is compared against two other commonly used remapping methods. Results show that the choice of the remapping method can make a substantial difference in grid-averaged rainfall accumulations (up to more than 100%). Differences were quantified using observations from two radars, collected during a field experiment. The interpolation grid resolution was also found to affect interpolated rainfall estimates. Approximate remapping methods tend to be much more sensitive to the interpolation grid resolution than precise remapping. High-resolution radar data such as those from radars with short gate spacing or narrow beams, or the super-resolution Weather Surveillance Radar-1988 Doppler (WSR-88D) sampling format, are significantly more sensitive (by up to 100%) to the remapping method and the interpolation grid resolution than the legacy WSR-88D rainfall data resolution of 1° × 1 km.
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MLDES, Maria del Carmen, and Minoru INAMURA. "Super-Resolution of Thermal, Radar and Ultrasonic Images." Journal of Agricultural Meteorology 60, no. 6 (2005): 1197–200. http://dx.doi.org/10.2480/agrmet.1197.

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Dissertations / Theses on the topic "Super Resolution Radar"

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Lane, R. O. "Bayesian super-resolution with application to radar target recognition." Thesis, University College London (University of London), 2008. http://discovery.ucl.ac.uk/10593/.

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This thesis is concerned with methods to facilitate automatic target recognition using images generated from a group of associated radar systems. Target recognition algorithms require access to a database of previously recorded or synthesized radar images for the targets of interest, or a database of features based on those images. However, the resolution of a new image acquired under non-ideal conditions may not be as good as that of the images used to generate the database. Therefore it is proposed to use super-resolution techniques to match the resolution of new images with the resolution of database images. A comprehensive review of the literature is given for super-resolution when used either on its own, or in conjunction with target recognition. A new superresolution algorithm is developed that is based on numerical Markov chain Monte Carlo Bayesian statistics. This algorithm allows uncertainty in the superresolved image to be taken into account in the target recognition process. It is shown that the Bayesian approach improves the probability of correct target classification over standard super-resolution techniques. The new super-resolution algorithm is demonstrated using a simple synthetically generated data set and is compared to other similar algorithms. A variety of effects that degrade super-resolution performance, such as defocus, are analyzed and techniques to compensate for these are presented. Performance of the super-resolution algorithm is then tested as part of a Bayesian target recognition framework using measured radar data.
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Blacknell, David. "Synthetic aperture radar motion compensation using autofocus with implications for super-resolution." Thesis, University of Sheffield, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295700.

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Borokhovych, Yevgen [Verfasser], and Rolf [Akademischer Betreuer] Kraemer. "High-speed data capturing components for Super Resolution Maximum Length Binary Sequence UWB Radar / Yevgen Borokhovych. Betreuer: Rolf Kraemer." Cottbus : Universitätsbibliothek der BTU Cottbus, 2012. http://d-nb.info/1023040662/34.

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Holl, Jr David J. "State-Space Approaches to Ultra-Wideband Doppler Processing." Digital WPI, 2007. https://digitalcommons.wpi.edu/etd-dissertations/251.

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National security needs dictate the development of new radar systems capable of identifying and tracking exoatmospheric threats to aid our defense. These new radar systems feature reduced noise floors, electronic beam steering, and ultra-wide bandwidths, all of which facilitate threat discrimination. However, in order to identify missile attributes such as RF reflectivity, distance, and velocity, many existing processing algorithms rely upon narrow bandwidth assumptions that break down with increased signal bandwidth. We present a fresh investigation into these algorithms for removing bandwidth limitations and propose novel state-space and direct-data factoring formulations such as * the multidimensional extension to the Eigensystem Realization Algorithm, * employing state-space models in place of interpolation to obtain a form which admits a separation and isolation of solution components, * and side-stepping the joint diagonalization of state transition matrices, which commonly plagues methods like multidimensional ESPRIT. We then benchmark our approaches and relate the outcomes to the Cramer-Rao bound for the case of one and two adjacent reflectors to validate their conceptual design and identify those techniques that compare favorably to or improve upon existing practices.
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Chang, Paul Chinling. "Physics-Based Inverse Processing and Multi-path Exploitation for Through-Wall Radar Imaging." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306646674.

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Salman, Rahmi [Verfasser], Ingolf Akademischer Betreuer] Willms, and Thomas [Akademischer Betreuer] [Kaiser. "Short-Range Super-Resolution Feature Extraction of Complex Edged Contours for Object Recognition by Ultra-Wideband Radar / Rahmi Salman. Gutachter: Thomas Kaiser. Betreuer: Ingolf Willms." Duisburg, 2014. http://d-nb.info/1057837229/34.

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Boussidi, Brahim. "Textural-based methods for image superresolution : Application to Satellite-derived Sea Surface Temperature imagery." Thesis, Télécom Bretagne, 2016. http://www.theses.fr/2016TELB0404/document.

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La caractérisation des dynamiques de sous-mésoéchelle (<10km) à la surface de l'océan et leurs impacts sur les processus océaniques globaux sont des enjeux scientifiques majeurs. L'imagerie satellitaire est un outil essentiel dans ce contexte, qui présente toutefois des limitations liées aux instruments de télédétection. Dans le cas des images de température de surface des océans (SST), les mesures satellitaires des structures océaniques sont limitées par la résolution grossière des capteurs micro-ondes (~50km) d'une part, et par la sensibilité aux conditions climatiques (e.g., couverture nuageuse) des instruments de mesure infrarouge haute-résolution. Dans cette thèse, nous nous intéressons à l'analyse, la modélisation et la reconstruction des structures turbulentes haute-résolution capturées par imagerie satellitaire de SST, et proposons quatre contributions principales. Dans un premier temps, nous développons une méthode de filtrage conjointe Fourier-ondelettes pour le prétraitement d'artefacts géométriques dans les observations satellitaires infrarouges. Dans un deuxième temps, nous nous focalisons sur la caractérisation de la variabilité géométrique de champs de température de surface (SST) en utilisant des modèles de marches aléatoires appliqués aux lignes de niveaux. En particulier, nous considérons des processus aléatoires de type schramm Loewner (SLE). Nous nous intéressons ensuite à la modélisation stochastique des variabilités inter-échelles de champs de SST. Des modèles stochastiques de textures multivariées sont introduits. Ces modèles permettent de reproduire des propriétés statistiques et spectrales similaires à celles des données ayant servi à les calibrer. Nous développons ensuite des méthodes de super-résolution de champs de SST conditionnellement à une observation basse-résolution. Nous utilisons des modèles multivariés de textures formulés dans le domaine des ondelettes, en exploitant l'apprentissage d'à priori statistiques (i.e., covariances et covariances croisées) des différentes sous-bandes à partir d'images haute-résolution. Des contraintes supplémentaires imposées sur la phase de Fourier des différentes sous-bandes simulées permettent la reconstruction de structures géométriques marquées tels que les fronts. Nous démontrons la pertinence de la méthode proposée sur des images satellitaires de SST obtenues à partir du capteur Modis/Aqua
The characterization of sub-mesoscale dynamics (<10 km) in the ocean surface and their impact on global ocean processes are major scientific issues. Satellite imagery is an essential tool within this framework. However, the use of remote sensing techniques still raise challenging. For instance, regarding Sea Surface Temperature (SST) images, satellite measurements of oceanic structures are limited by the coarse resolution of microwave sensors (~50km) on one hand, and by sensitivity to climatic conditions (eg., Cloud cover) of high-resolution infrared instruments on the other hand. In this thesis, we are interested in analysis, modeling and reconstruction of high-resolution turbulent structures captured by satellite SST imagery. In this context, we propose four main contributions. First, we develop a joint Fourier-Wavelet filtering method for the pre-processing of geometrical noises in satellite-based infrared observations, namely the striping noises. Secondly, we focus on the characterization of the geometric variability of sea surface temperature (SST) fields using random walk models applied to SST isolines. In particular, we consider the class of Schramm Loewner evolution curves (SLE). We then focus on the stochastic modeling of the cross-scale variabilities of SST fields. Stochastic multivariate texture-based models are introduced. These models are designed to reproduce several statistics and spectral properties that are observed on the data that are used to calibrate the model. We then develop our framework for stochastic super-resolution of SST fields conditionally to low-resolution observations. We use multivariate texture-based models formulated in the wavelet domain. These models exploit the formulation of statistical and spectral priors (i.e., covariances and cross-covariances) on wavelet subbands. These priors are directly learned from exemplar high-resolution images. Additional constraints imposed on the Fourier-phase of the different simulated subbands allow the reconstruction of coherent geometric structures such as the edge information. Our method is tested and validated using infrared high-resolution satellite SST images provided by Aqua Modis sensor
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"Synthetic Aperture Radar Image Formation Via Sparse Decomposition." Master's thesis, 2011. http://hdl.handle.net/2286/R.I.9211.

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abstract: Spotlight mode synthetic aperture radar (SAR) imaging involves a tomo- graphic reconstruction from projections, necessitating acquisition of large amounts of data in order to form a moderately sized image. Since typical SAR sensors are hosted on mobile platforms, it is common to have limitations on SAR data acquisi- tion, storage and communication that can lead to data corruption and a resulting degradation of image quality. It is convenient to consider corrupted samples as missing, creating a sparsely sampled aperture. A sparse aperture would also result from compressive sensing, which is a very attractive concept for data intensive sen- sors such as SAR. Recent developments in sparse decomposition algorithms can be applied to the problem of SAR image formation from a sparsely sampled aperture. Two modified sparse decomposition algorithms are developed, based on well known existing algorithms, modified to be practical in application on modest computa- tional resources. The two algorithms are demonstrated on real-world SAR images. Algorithm performance with respect to super-resolution, noise, coherent speckle and target/clutter decomposition is explored. These algorithms yield more accu- rate image reconstruction from sparsely sampled apertures than classical spectral estimators. At the current state of development, sparse image reconstruction using these two algorithms require about two orders of magnitude greater processing time than classical SAR image formation.
Dissertation/Thesis
M.S. Electrical Engineering 2011
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Book chapters on the topic "Super Resolution Radar"

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Gu, Chenghua, Xuegang Wang, Wang Hong, and Xuelian Yu. "Improvement of Azimuth Super Resolution of Radar via Generalized Inverse Filtering." In Lecture Notes in Electrical Engineering, 519–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34528-9_53.

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Yang, Tianyuan, Tao Su, and Jibin Zheng. "A Novel Range Super-Resolution Algorithm for UAV Swarm Target Based on LFMCW Radar." In Lecture Notes in Electrical Engineering, 1088–95. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9409-6_127.

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"Array processing for super-resolution in angle." In Radar Techniques Using Array Antennas, 295–346. Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/pbra026e_ch12.

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"Super-Resolution Radar Imaging via Convex Optimization." In Compressed Sensing in Radar Signal Processing, 193–224. Cambridge University Press, 2019. http://dx.doi.org/10.1017/9781108552653.008.

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Salman, Rahmi, and Ingolf Willms. "Super-Resolution Object Recognition Approach for Complex Edged Objects by UWB Radar." In Object Recognition. InTech, 2011. http://dx.doi.org/10.5772/15574.

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Conference papers on the topic "Super Resolution Radar"

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Heckel, Reinhard. "Super-resolution MIMO radar." In 2016 IEEE International Symposium on Information Theory (ISIT). IEEE, 2016. http://dx.doi.org/10.1109/isit.2016.7541532.

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Zeng, Zhiyuan, Xiangwei Dang, Yanlei Li, Xiangxi Bu, and Xingdong Liang. "Angular Super-Resolution Radar SLAM." In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2021. http://dx.doi.org/10.1109/iros51168.2021.9636438.

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Blunt, Shannon D., Karl Gerlach, and Thomas Higgins. "Aspects of Radar Range Super-Resolution." In 2007 IEEE Radar Conference. IEEE, 2007. http://dx.doi.org/10.1109/radar.2007.374301.

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Cataldo, Davide, and Marco Martorella. "Super-resolution for bistatic distortion mitigation." In 2016 IEEE Radar Conference (RadarConf16). IEEE, 2016. http://dx.doi.org/10.1109/radar.2016.7485141.

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Shun-jun Wu, Lei Zhang, and Meng-dao Xing. "Super-resolution ISAR imaging via statistical compressive sensing." In 2011 IEEE CIE International Conference on Radar (Radar). IEEE, 2011. http://dx.doi.org/10.1109/cie-radar.2011.6159599.

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Kasilingam, Dayalan, Dean Schmidlin, and Paulo Pacheco. "Super-resolution processing technique for vector sensors." In 2009 IEEE Radar Conference. IEEE, 2009. http://dx.doi.org/10.1109/radar.2009.4977017.

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Goyal, Vivek K., Dongeek Shin, and Jeffrey H. Shapiro. "Photon-efficient super-resolution laser radar." In Wavelets and Sparsity XVII, edited by Yue M. Lu, Manos Papadakis, and Dimitri Van De Ville. SPIE, 2017. http://dx.doi.org/10.1117/12.2273208.

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Kai-Bor Yu and M. F. Fernández. "Analog beamspace super-resolution radar processing." In 2010 IEEE International Symposium on Phased Array Systems and Technology (ARRAY 2010). IEEE, 2010. http://dx.doi.org/10.1109/array.2010.5613385.

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Fischer, C., H. L. Bloecher, W. Menzel, J. Dickmann, and F. Ruf. "Evaluation of different super-resolution techniques for automotive applications." In IET International Conference on Radar Systems (Radar 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.1641.

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Fernandez, Manuel, Earl Turner, and Kai-Bor Yu. "Main-beam multi-target monopulse super-resolution." In 2011 IEEE Radar Conference (RadarCon). IEEE, 2011. http://dx.doi.org/10.1109/radar.2011.5960578.

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