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Journal articles on the topic 'Super Resolution Radar'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Tang, Junkui, Zheng Liu, Lei Ran, Rong Xie, and Jikai Qin. "Forward-Looking Super-Resolution Imaging of MIMO Radar Via Sparse and Double Low-Rank Constraints." Remote Sensing 15, no. 3 (January 19, 2023): 609. http://dx.doi.org/10.3390/rs15030609.

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Multiple-input multiple-output (MIMO) radar uses waveform diversity technology to form a virtual aperture to improve the azimuth resolution of forward-looking imaging. However, the super-resolution imaging capability of MIMO radar is limited, and the resolution can only be doubled compared with the real aperture. In the radar forward-looking image, compared with the whole imaging scene, the target only occupies a small part. This sparsity of the target distribution provides the feasibility of applying the compressed sensing (CS) method to MIMO radar to further improve the forward-looking imaging resolution. At the same time, the forward-looking imaging method for a MIMO radar based on CS has the ability to perform single snapshot imaging, which avoids the problem of a motion supplement. However, the strong noise in the radar echo poses a challenge to the imaging method based on CS. Inspired by the low-rank properties of the received radar echoes and the generated images, and considering the existing information about sparse target distribution, a forward-looking super-resolution imaging model of a MIMO radar that combines sparse and double low-rank constraints is established to overcome strong noise and achieve robust forward-looking super-resolution imaging. In order to solve the multiple optimization problem, a forward-looking image reconstruction method based on the augmented Lagrangian multiplier (ALM) is proposed within the framework of the alternating direction multiplier method (ADMM). Finally, the results of the simulation and the measurement data show that the proposed method is quite effective at improving the azimuth resolution and robustness of forward-looking radar imaging compared with other existing methods.

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12

Li, Yingchun, Qi Long, Zhongjie Wu, and Zhiquan Zhou. "Low-Complexity Joint 3D Super-Resolution Estimation of Range Velocity and Angle of Multi-Targets Based on FMCW Radar." Sensors 22, no. 17 (August 28, 2022): 6474. http://dx.doi.org/10.3390/s22176474.

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Multi-dimensional parameters joint estimation of multi-targets is introduced to implement super-resolution sensing in range, velocity, azimuth angle, and elevation angle for frequency-modulated continuous waveform (FMCW) radar systems. In this paper, a low complexity joint 3D super-resolution estimation of range, velocity, and angle of multi-targets is proposed for an FMCW radar with a uniform linear array. The proposed method firstly constructs the size-reduced 3D matrix in the frequency domain for the system model of an FMCW radar system. Secondly, the size-reduced 3D matrix is established, and low complexity three-level cascaded 1D spectrum estimation implemented by applying the Lagrange multiplier method is developed. Finally, the low complexity joint 3D super-resolution algorithms are validated by numerical experiments and with a 77 GHz FMCW radar built by Texas Instruments, with the proposed algorithm achieving significant estimation performance compared to conventional algorithms.

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13

Du, Jianhe, Meng Han, Libiao Jin, Yan Hua, and Shufeng Li. "Target Localization Methods Based on Iterative Super-Resolution for Bistatic MIMO Radar." Electronics 9, no. 2 (February 16, 2020): 341. http://dx.doi.org/10.3390/electronics9020341.

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The direction-of-departure (DOD) and the direction-of-arrival (DOA) are important localization parameters in bistatic MIMO radar. In this paper, we are interested in DOD/DOA estimation of both single-pulse and multiple-pulse multiple-input multiple-output (MIMO) radars. An iterative super-resolution target localization method is firstly proposed for single-pulse bistatic MIMO radar. During the iterative process, the estimated DOD and DOA can be moved from initial angles to their true values with high probability, and thus can achieve super-resolution estimation. It works well even if the number of targets is unknown. We then extend the proposed method to multiple-pulse configuration to estimate target numbers and localize targets. Compared with existing methods, both of our proposed algorithms have a higher localization accuracy and a more stable performance. Moreover, the proposed algorithms work well even with low sampling numbers and unknown target numbers. Simulation results demonstrate the effectiveness of the proposed methods.

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14

Zhang, Xing, Jianxin He, Qiangyu Zeng, and Zhao Shi. "Weather Radar Echo Super-Resolution Reconstruction Based on Nonlocal Self-Similarity Sparse Representation." Atmosphere 10, no. 5 (May 8, 2019): 254. http://dx.doi.org/10.3390/atmos10050254.

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Weather radar echo plays an important role in early warning and timely forecasting of severe weather. However, the radar echo may not be accurate enough to predict or analyze small-scale weather phenomenon due to the degradation of the observed radar. In order to solve this problem, some radar echo super-resolution reconstruction algorithms have been proposed, but the algorithm may result in an excessively smooth edge and detail in a local region. To reconstruct radar echo with better edges and finer details, a novel nonlocal self-similarity sparse representation (NSSR) model is proposed. The NSSR model is based on the sparse representation of weather radar echoes to better reconstruct the echo edge and detail information. We exploit the radar echo nonlocal self-similarity to recover more realistic details based on the NSSR model. Experiment results demonstrate that the proposed NSSR outperforms current general-purpose radar echo super-resolution approaches on both visual effects and objective radar echo quality.

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15

Liu, Rui, Jindong Zhang, Xiaobo Deng, Daiyin Zhu, Huangrong Zhou, and Mingming Guo. "Angular Super-Resolution of Multi-Channel APAR in Interference Environments." Electronics 12, no. 2 (January 12, 2023): 392. http://dx.doi.org/10.3390/electronics12020392.

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Aiming to resolve azimuth-dense targets in interference environments, the radar needs to have the ability of single snap echo angular super-resolution with anti-interference. To solve the problem, the angular super-resolution algorithm based on single snap echo while anti-interference with blocking matrix method is studied for active phased array radar (APAR) in this paper. Since the super-resolution ability of the conventional MUSIC algorithm and iterative adaptive algorithm (IAA) algorithm are limited in single snap echo, the iterative re-weighted least squares (IRLS) algorithm under p-norm constraint is proposed. Further, the near main lobe interference suppression ability is enhanced by the adaptive diagonal loading method. The performance differences of IAA and IRLS algorithms for single-target detection and double-target angular super-resolution are analyzed in detail by numerical simulation on three scenes of no interference, side-lobe interference, and near-main-lobe interference. The simulation results show that the proposed algorithm can effectively solve the problem of target angle estimation and super-resolution based on single sample echo in an interference environment, including near main-lobe interference.

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16

Kumjian, Matthew R., Alexander V. Ryzhkov, Valery M. Melnikov, and Terry J. Schuur. "Rapid-Scan Super-Resolution Observations of a Cyclic Supercell with a Dual-Polarization WSR-88D." Monthly Weather Review 138, no. 10 (October 1, 2010): 3762–86. http://dx.doi.org/10.1175/2010mwr3322.1.

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Abstract In recent years, there has been widespread interest in collecting and analyzing rapid updates of radar data in severe convective storms. To this end, conventional single-polarization rapid-scan radars and phased array radar systems have been employed in numerous studies. However, rapid updates of dual-polarization radar data in storms are not widely available. For this study, a rapid scanning strategy is developed for the polarimetric prototype research Weather Surveillance Radar-1988 Doppler (WSR-88D) radar in Norman, Oklahoma (KOUN), which emulates the future capabilities of a polarimetric multifunction phased array radar (MPAR). With this strategy, data are collected over an 80° sector with 0.5° azimuthal spacing and 250-m radial resolution (“super resolution”), with 12 elevation angles. Thus, full volume scans over a limited area are collected every 71–73 s. The scanning strategy was employed on a cyclic nontornadic supercell storm in western Oklahoma on 1 June 2008. The evolution of the polarimetric signatures in the supercell is analyzed. The repetitive pattern of evolution of these polarimetric features is found to be directly tied to the cyclic occlusion process of the low-level mesocyclone. The cycle for each of the polarimetric signatures is presented and described in detail, complete with a microphysical interpretation. In doing so, for the first time the bulk microphysical properties of the storm on small time scales (inferred from polarimetric data) are analyzed. The documented evolution of the polarimetric signatures could be used operationally to aid in the detection and determination of various stages of the low-level mesocyclone occlusion.

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17

Zhang, Qiping, Yin Zhang, Yongchao Zhang, Yulin Huang, and Jianyu Yang. "A Sparse Denoising-Based Super-Resolution Method for Scanning Radar Imaging." Remote Sensing 13, no. 14 (July 14, 2021): 2768. http://dx.doi.org/10.3390/rs13142768.

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Scanning radar enables wide-range imaging through antenna scanning and is widely used for radar warning. The Rayleigh criterion indicates that narrow beams of radar are required to improve the azimuth resolution. However, a narrower beam means a larger antenna aperture. In practical applications, due to platform limitations, the antenna aperture is limited, resulting in a low azimuth resolution. The conventional sparse super-resolution method (SSM) has been proposed for improving the azimuth resolution of scanning radar imaging and achieving superior performance. This method uses the L1 norm to represent the sparse prior of the target and solves the L1 regularization problem to achieve super-resolution imaging under the regularization framework. The resolution of strong-point targets is improved efficiently. However, for some targets with typical shapes, the strong sparsity of the L1 norm treats them as strong-point targets, resulting in the loss of shape characteristics. Thus, we can only see the strong points in its processing results. However, in some applications that need to identify targets in detail, SSM can lead to false judgments. In this paper, a sparse denoising-based super-resolution method (SDBSM) is proposed to compensate for the deficiency of traditional SSM. The proposed SDBSM uses a sparse minimization scheme for denoising, which helps to reduce the influence of noise. Then, the super-resolution imaging is achieved by alternating iterative denoising and deconvolution. As the proposed SDBSM uses the L1 norm for denoising rather than deconvolution, the strong sparsity constraint of the L1 norm is reduced. Therefore, it can effectively preserve the shape of the target while improving the azimuth resolution. The performance of the proposed SDBSM was demonstrated via simulation and real data processing results.

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18

Bu, Lijing, Shuang Zhao, Guo Zhang, and Ruichao Song. "Simulations of Spotlight Synthetic Aperture Radar Super-resolution Algorithm." Journal of the Indian Society of Remote Sensing 50, no. 3 (January 14, 2022): 493–505. http://dx.doi.org/10.1007/s12524-021-01469-5.

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19

Dickey, F. M., L. A. Romero, J. M. DeLaurentis, and A. W. Doerry. "Super-resolution, degrees of freedom and synthetic aperture radar." IEE Proceedings - Radar, Sonar and Navigation 150, no. 6 (2003): 419. http://dx.doi.org/10.1049/ip-rsn:20030701.

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20

Zhang, Yongchao, Andreas Jakobsson, and Jianyu Yang. "Range-Recursive IAA for Scanning Radar Angular Super-Resolution." IEEE Geoscience and Remote Sensing Letters 14, no. 10 (October 2017): 1675–79. http://dx.doi.org/10.1109/lgrs.2017.2728038.

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21

Yang, Lei, Jianxiong Zhou, and Huaitie Xiao. "Super‐resolution radar imaging using fast continuous compressed sensing." Electronics Letters 51, no. 24 (November 2015): 2043–45. http://dx.doi.org/10.1049/el.2015.2525.

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22

Lin, Mingtuan, Yue Gao, Peiguo Liu, and Jibin Liu. "Super‐resolution orbital angular momentum based radar targets detection." Electronics Letters 52, no. 13 (June 2016): 1168–70. http://dx.doi.org/10.1049/el.2016.0237.

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23

Liu, S., and J. Xiang. "Novel method for super-resolution in radar range domain." IEE Proceedings - Radar, Sonar and Navigation 146, no. 1 (1999): 40. http://dx.doi.org/10.1049/ip-rsn:19990267.

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24

Wu, R., and J. Li. "Autofocus and super-resolution synthetic aperture radar image formation." IEE Proceedings - Radar, Sonar and Navigation 147, no. 5 (2000): 217. http://dx.doi.org/10.1049/ip-rsn:20000616.

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25

Liu, Kang, Yongqiang Cheng, Yue Gao, Xiang Li, Yuliang Qin, and Hongqiang Wang. "Super-resolution radar imaging based on experimental OAM beams." Applied Physics Letters 110, no. 16 (April 17, 2017): 164102. http://dx.doi.org/10.1063/1.4981253.

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26

Zheng, Le, and Xiaodong Wang. "Super-Resolution Delay-Doppler Estimation for OFDM Passive Radar." IEEE Transactions on Signal Processing 65, no. 9 (May 1, 2017): 2197–210. http://dx.doi.org/10.1109/tsp.2017.2659650.

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27

Li, Xuehua, Jianxin He, Zishu He, and Qiangyu Zeng. "Geostationary weather radar super-resolution modelling and reconstruction process." International Journal of Simulation and Process Modelling 7, no. 1/2 (2012): 81. http://dx.doi.org/10.1504/ijspm.2012.047867.

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28

Liu, Zhixing, Yinghui Quan, Yaojun Wu, and Mengdao Xing. "Super-Resolution Range and Velocity Estimations for SFA-OFDM Radar." Remote Sensing 14, no. 2 (January 7, 2022): 278. http://dx.doi.org/10.3390/rs14020278.

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Sparse frequency agile orthogonal frequency division multiplexing (SFA-OFDM) signal brings excellent performance to electronic counter-countermeasures (ECCM) and reduces the complexity of the radar system. However, frequency agility makes coherent processing a much more challenging task for the radar, which leads to the discontinuity of the echo phase in a coherent processing interval (CPI), so the fast Fourier transform (FFT)-based method is no longer a valid way to complete the coherent integration. To overcome this problem, we proposed a novel scheme to estimate both super-resolution range and velocity. The subcarriers of each pulse are firstly synthesized in time domain. Then, the range and velocity estimations for the SFA-OFDM radar are regarded as the parameter estimations of a linear array. Finally, both the super-resolution range and velocity are obtained by exploiting the multiple signal classification (MUSIC) algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed method.

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29

Nagornykh, I. L., and N. D. Bazhenov. "On range super - resolution in a radar with many carriers. Simulation and experiment." Journal of «Almaz – Antey» Air and Space Defence Corporation, no. 1 (March 30, 2019): 24–29. http://dx.doi.org/10.38013/2542-0542-2019-1-24-29.

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The paper focuses on radar operation and the results of its simulation. The probing signal of the radar is a set of 16 orthogonal carriers. To determine the range in such radar, the MUSIC algorithm was applied, which relates to super - resolution methods. Findings of research show that the MUSIC algorithm makes it possible to increase the radar range resolution in the signal - to-noise 0-20 dB ratio by 4-8 times as compared with the traditional method based on the Fourier transform. The developed models were experimentally verified

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Kim, Sangdong, Bongseok Kim, Youngseok Jin, and Jonghun Lee. "Super-Resolution-Based DOA Estimation with Wide Array Distance and Extrapolation for Vital FMCW Radar." Journal of Electromagnetic Engineering and Science 21, no. 1 (January 31, 2021): 23–34. http://dx.doi.org/10.26866/jees.2021.21.1.23.

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This paper proposes a super-resolution-based direction-of-arrivals (DOA) estimation with wide array distance and extrapolation for vital frequency-modulated continuous-wave (FMCW) radar. Most super-resolution algorithms employ the distance between adjacent arrays of half a wavelength, i.e., λ/2. Meanwhile, in the case of narrow field of view of FMCW radar, the resolution of the angle is maintained by increasing the spacing between the arrays even if the number of arrays decreases. In order to employ these characteristics of array spacing and resolution, the proposed algorithm confirms whether or not to use the case where the distance between the adjacent arrays is greater than λ/2. In the case of an array distance >λ/2, a super-resolution algorithm is performed to obtain the enhanced DOA resolution. Moreover, the proposed algorithm virtually generates data between antennae by using extrapolation in order to further improve the performance of the resolution. The simulation results show that the proposed algorithm achieves the results of root-mean-square error similar to conventional super-resolution algorithms while maintaining low complexity. In order to further verify the performance of the proposed estimation algorithm, we demonstrate its employment in practice: experiments in a chamber room and an indoor room were conducted.

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31

Chen, Zhang, Liu, and Zeng. "Generative Adversarial Networks Capabilities for Super-Resolution Reconstruction of Weather Radar Echo Images." Atmosphere 10, no. 9 (September 16, 2019): 555. http://dx.doi.org/10.3390/atmos10090555.

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Improving the resolution of degraded radar echo images of weather radar systems can aid severe weather forecasting and disaster prevention. Previous approaches to this problem include classical super-resolution (SR) algorithms such as iterative back-projection (IBP) and a recent nonlocal self-similarity sparse representation (NSSR) that exploits the data redundancy of radar echo data, etc. However, since radar echoes tend to have rich edge information and contour textures, the textural detail in the reconstructed echoes of traditional approaches is typically absent. Inspired by the recent advances of faster and deeper neural networks, especially the generative adversarial networks (GAN), which are capable of pushing SR solutions to the natural image manifold, we propose using GAN to tackle the problem of weather radar echo super-resolution to achieve better reconstruction performance (measured in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)). Using authentic weather radar echo data, we present the experimental results and compare its reconstruction performance with the above-mentioned methods. The experimental results showed that the GAN-based method is capable of generating perceptually superior solutions while achieving higher PSNR/SSIM results.

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32

Kim, Bong-seok, Youngseok Jin, Jonghun Lee, and Sangdong Kim. "High-Efficiency Super-Resolution FMCW Radar Algorithm Based on FFT Estimation." Sensors 21, no. 12 (June 10, 2021): 4018. http://dx.doi.org/10.3390/s21124018.

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This paper proposes a high-efficiency super-resolution frequency-modulated continuous-wave (FMCW) radar algorithm based on estimation by fast Fourier transform (FFT). In FMCW radar systems, the maximum number of samples is generally determined by the maximum detectable distance. However, targets are often closer than the maximum detectable distance. In this case, even if the number of samples is reduced, the ranges of targets can be estimated without degrading the performance. Based on this property, the proposed algorithm adaptively selects the number of samples used as input to the super-resolution algorithm depends on the coarsely estimated ranges of targets using the FFT. The proposed algorithm employs the reduced samples by the estimated distance by FFT as input to the super resolution algorithm instead of the maximum number of samples set by the maximum detectable distance. By doing so, the proposed algorithm achieves the similar performance of the conventional multiple signal classification algorithm (MUSIC), which is a representative of the super resolution algorithms while the performance does not degrade. Simulation results demonstrate the feasibility and performance improvement provided by the proposed algorithm; that is, the proposed algorithm achieves average complexity reduction of 88% compared to the conventional MUSIC algorithm while achieving its similar performance. Moreover, the improvement provided by the proposed algorithm was verified in practical conditions, as evidenced by our experimental results.

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33

Le, Yi, Ji Tang, Qiang Huang, Linhai Guan, Jianyang Zhou, and Xiaoxuan Dong. "A Super-Resolution Altitude Measurement Technology for VHF Radar Based on Synthetic Steering Vector and Model Matching." Journal of Physics: Conference Series 2366, no. 1 (November 1, 2022): 012029. http://dx.doi.org/10.1088/1742-6596/2366/1/012029.

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Abstract As radar is a kind of measuring device, the altitude measuring accuracy is a quite important core tactical indicator. In engineering practice, the altitude measuring accuracy deteriorates significantly in low elevation range, especially for low frequency radar such as VHF radar. This urgent problem to be solved is what we focus on in this article. After building a multipath reflection model, a method of altitude super-resolution measurement based on synthetic steering vector and model matching is proposed. Then some simulation analysis is finished, and some validation experiments are carried out on a practical VHF radar prototype. The results of theoretical simulation and prototype experiments show obviously improved altitude measuring performance compared with traditional measuring methods, verifying the correctness and engineering feasibility of the super-resolution altitude measurement technology proposed in this article. This study should have certain practical guiding significance for the theoretical and application research of related fields.

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Huo, Weibo, Qiping Zhang, Yin Zhang, Yongchao Zhang, Yulin Huang, and Jianyu Yang. "A Superfast Super-Resolution Method for Radar Forward-Looking Imaging." Sensors 21, no. 3 (January 26, 2021): 817. http://dx.doi.org/10.3390/s21030817.

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The super-resolution method has been widely used for improving azimuth resolution for radar forward-looking imaging. Typically, it can be achieved by solving an undifferentiable L1 regularization problem. The split Bregman algorithm (SBA) is a great tool for solving this undifferentiable problem. However, its real-time imaging ability is limited to matrix inversion and iterations. Although previous studies have used the special structure of the coefficient matrix to reduce the computational complexity of each iteration, the real-time performance is still limited due to the need for hundreds of iterations. In this paper, a superfast SBA (SFSBA) is proposed to overcome this shortcoming. Firstly, the super-resolution problem is transmitted into an L1 regularization problem in the framework of regularization. Then, the proposed SFSBA is used to solve the nondifferentiable L1 regularization problem. Different from the traditional SBA, the proposed SFSBA utilizes the low displacement rank features of Toplitz matrix, along with the Gohberg-Semencul (GS) representation to realize fast inversion of the coefficient matrix, reducing the computational complexity of each iteration from O(N3) to O(N2). It uses a two-order vector extrapolation strategy to reduce the number of iterations. The convergence speed is increased by about 8 times. Finally, the simulation and real data processing results demonstrate that the proposed SFSBA can effectively improve the azimuth resolution of radar forward-looking imaging, and its performance is only slightly lower compared to traditional SBA. The hardware test shows that the computational efficiency of the proposed SFSBA is much higher than that of other traditional super-resolution methods, which would meet the real-time requirements in practice.

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Kazantsev, Alexandr A., Denis A. Perov, Alexey A. Samorodov, and Boris A. Samorodov. "Super resolution algorithm for satellites inverse synthetic aperture radar imaging." Ural Radio Engineering Journal 2, no. 2 (2018): 67–86. http://dx.doi.org/10.15826/urej.2018.2.2.005.

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Daoran Gong, 龚道然, 李思宁 Sining Li, 姜鹏 Peng Jiang, 刘迪 Di Liu, and 孙剑峰 Jianfeng Sun. "Research on super resolution reconstruction of laser radar range profile." Infrared and Laser Engineering 49, no. 8 (2020): 20190511. http://dx.doi.org/10.3788/irla20190511.

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Xuehua LI, Jianxin HE, Zishu HE, and Qiangyu ZENG. "Weather Radar Super-resolution in CINRAD Based on Azimuth Weighting." International Journal of Advancements in Computing Technology 4, no. 21 (November 30, 2012): 592–98. http://dx.doi.org/10.4156/ijact.vol4.issue21.70.

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Daoran Gong, 龚道然, 李思宁 Sining Li, 姜鹏 Peng Jiang, 刘迪 Di Liu, and 孙剑峰 Jianfeng Sun. "Research on super resolution reconstruction of laser radar range profile." Infrared and Laser Engineering 49, no. 8 (2020): 20190511. http://dx.doi.org/10.3788/irla.6_2019-0511.

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Shao, Zelong, Xiangkun Zhang, and Yingsong Li. "ANALYSIS AND VALIDATION OF SUPER-RESOLUTION MICRO-DEFORMATION MONITORING RADAR." Progress In Electromagnetics Research M 62 (2017): 41–50. http://dx.doi.org/10.2528/pierm17072612.

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Zhang, Xin, Xiaoming Liu, Chang Liu, and Zhenyu Na. "An iterative shrinkage threshold method for radar angular super-resolution." International Journal of Embedded Systems 11, no. 3 (2019): 285. http://dx.doi.org/10.1504/ijes.2019.099416.

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Liu, Chang, Zhenyu Na, Xiaoming Liu, and Xin Zhang. "An iterative shrinkage threshold method for radar angular super-resolution." International Journal of Embedded Systems 11, no. 3 (2019): 285. http://dx.doi.org/10.1504/ijes.2019.10020756.

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Bajwa, Waheed U., Kfir Gedalyahu, and Yonina C. Eldar. "Identification of Parametric Underspread Linear Systems and Super-Resolution Radar." IEEE Transactions on Signal Processing 59, no. 6 (June 2011): 2548–61. http://dx.doi.org/10.1109/tsp.2011.2114657.

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Rudresh, Sunil, and Chandra Sekhar Seelamantula. "Finite-Rate-of-Innovation-Sampling-Based Super-Resolution Radar Imaging." IEEE Transactions on Signal Processing 65, no. 19 (October 1, 2017): 5021–33. http://dx.doi.org/10.1109/tsp.2017.2721917.

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Zhang, Qiping, Yin Zhang, Yulin Huang, Yongchao Zhang, Jifang Pei, Qingying Yi, Wenchao Li, and Jianyu Yang. "TV-Sparse Super-Resolution Method for Radar Forward-Looking Imaging." IEEE Transactions on Geoscience and Remote Sensing 58, no. 9 (September 2020): 6534–49. http://dx.doi.org/10.1109/tgrs.2020.2977719.

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Ebihara, S., M. Sato, and H. Niitsuma. "Super-resolution of coherent targets by a directional borehole radar." IEEE Transactions on Geoscience and Remote Sensing 38, no. 4 (July 2000): 1725–32. http://dx.doi.org/10.1109/36.851971.

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Liu, Song, Lan Tang, Yechao Bai, and Xinggan Zhang. "A Super-Resolution DOA Estimation Method for Fast-Moving Targets in MIMO Radar." Mathematical Problems in Engineering 2020 (March 16, 2020): 1–11. http://dx.doi.org/10.1155/2020/4049785.

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Direction of arrival (DOA) estimation is an essential problem in the radar systems. In this paper, the problem of DOA estimation is addressed in the multiple-input and multiple-output (MIMO) radar system for the fast-moving targets. A virtual aperture is provided by orthogonal waveforms in the MIMO radar to improve the DOA estimation performance. Different from the existing methods, we consider the DOA estimation method with only one snapshot for the fast-moving targets and achieve the super-resolution estimation from the snapshot. Based on a least absolute shrinkage and selection operator (LASSO), a denoise method is formulated to obtain a sparse approximation to the received signals, where the sparsity is measured by a new type of atomic norm for the MIMO radar system. However, the denoise problem cannot be solved efficiently. Then, by deriving the dual norm of the new atomic norm, a semidefinite matrix is constructed from the denoise problem to formulate a semidefinite problem with the dual optimization problem. Finally, the DOA is estimated by peak-searching the spatial spectrum. Simulation results show that the proposed method achieves better performance of the DOA estimation in the MIMO radar system with only one snapshot.

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Jiao, Zekun, Chibiao Ding, Longyong Chen, and Fubo Zhang. "Three-Dimensional Imaging Method for Array ISAR Based on Sparse Bayesian Inference." Sensors 18, no. 10 (October 20, 2018): 3563. http://dx.doi.org/10.3390/s18103563.

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The problem of synthesis scatterers in inverse synthetic aperture radar (ISAR) make it difficult to realize high-resolution three-dimensional (3D) imaging. Radar array provides an available solution to this problem, but the resolution is restricted by limited aperture size and number of antennas, leading to deterioration of the 3D imaging performance. To solve these problems, we propose a novel 3D imaging method with an array ISAR system based on sparse Bayesian inference. First, the 3D imaging model using a sparse linear array is introduced. Then the elastic net estimation and Bayesian information criterion are introduced to fulfill model order selection automatically. Finally, the sparse Bayesian inference is adopted to realize super-resolution imaging and to get the 3D image of target of interest. The proposed method is used to process real radar data of a Ku band array ISAR system. The results show that the proposed method can effectively solve the problem of synthesis scatterers and realize super-resolution 3D imaging, which verify the practicality of our proposed method.

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Huan, Sha, Man Zhang, Gane Dai, and Huaguo Gan. "Low Elevation Angle Estimation with Range Super-Resolution in Wideband Radar." Sensors 20, no. 11 (May 31, 2020): 3104. http://dx.doi.org/10.3390/s20113104.

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Height detection of a low elevation angle target is crucial in radar applications. Due to the presence of the multiple path reflections, elevation angle estimation is difficult with conventional narrowband radar waveforms. The reflection ground area parameters are especially hard to obtain for modeling. In this paper, we proposed a wideband, low elevation angle estimator based on range super-resolution, achieving a high robustness to variations in reflection coefficients. A relaxation (RELAX) algorithm was applied as the range super-resolution algorithm to separate the direct target echo and the reflected signal thanks to the relatively abundant frequency diversity. The grazing angle was obtained by synthesizing the steering vector of the direct signal and the range structure relationship between the two signal components. Theoretical analysis and simulation results confirmed the improved behavior of the proposed robust estimator in contrast to other conventional algorithms.

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Cong, Jingyu, Xianpeng Wang, Xiang Lan, Mengxing Huang, and Liangtian Wan. "Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network." Remote Sensing 13, no. 10 (May 17, 2021): 1956. http://dx.doi.org/10.3390/rs13101956.

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The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

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Zhang, Xin, Xiaoming Liu, and Zhenyu Na. "A Method for Angular Super-Resolution via Big Data Radar System." International Journal of Mobile Computing and Multimedia Communications 8, no. 3 (July 2017): 1–20. http://dx.doi.org/10.4018/ijmcmc.2017070101.

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This paper proposes a novel method for enhancing angular resolution in multimedia big data navigation radar system. A new radar scanning model is designed on the basis of quadratic programming theory, by which the proposed Gradient Projection (GP) algorithm is used for solving the optimal solution of this model, and then the target information can be restored successfully at low signal to noise ratio (SNR). Simulations further confirm our theoretical discussion, and manifest that the efficiency and applicability of the proposed method is favorable that the resolution ratio reaches 4~11 times under our proposed scanning model framework if SNR is above 10dB. Moreover, the designed model is suitable for some other angular super-resolution methods, the restoration ratio of which can be improved while SNR is be equal or greater than 10dB. In this case, a higher signal to reconstructed error ratio (SRER) is provided by our method.

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