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The problem of accurately measuring the motion parameters of low radar cross-section (RCS) maneuvering targets has long been a hurdle in the radar technology landscape. Small targets, due to their elusive characteristics, are particularly difficult to detect with conventional radar systems. In this paper, we investigate the capabilities of dechirp-receiving stepped-frequency radar, a modern system using a linear frequency modulation signal for downconversion. This permits the radar to function at reduced sampling rates while maintaining the transmission of large-bandwidth signals and achieving synthetic imaging. Our primary contribution is introducing the stepped dechirp generalized Radon–Fourier transform (stepped DGRFT) algorithm. This novel approach allows the radar system to perform coherent accumulation, enhancing the accuracy of motion parameter estimates for low-RCS maneuvering targets. Results from our simulations and measured data analysis validate the effectiveness of our proposed algorithm, demonstrating its superiority over other methods. [ABSTRACT FROM AUTHOR]

Hand gesture recognition based on micro‐Doppler (MD) radar has garnered considerable attention from researchers as a potential method for human–computer interaction (HCI). However, two significant challenges, that is, obvious differences in MD map size and lots of redundant information contained in the MD maps, are encountered. Here, a multi‐scale graph‐based hand gesture recognition framework is proposed. First, a MD graph representation method is developed, which is adapted to arbitrary‐sized frames and enables to map the key features in a sparse manner. Then, the multi‐scale information from MD graph is fully extracted for hand gesture recognition. Experimental results show that the proposed framework achieves a state‐of‐the‐art accuracy of 96.73% on the four‐class radar hand gesture dataset, while reducing up to 99% of the redundant information in the MD maps. This framework requires only a little amount of memory storage with good hand gesture recognition capability, demonstrating its high potential in the HCI field. [ABSTRACT FROM AUTHOR]

The goal of this paper is to propose a method to achieve a higher classification rate in Polarimetric Synthetic Aperture Radar (PolSAR) image classification. In our work, PolSAR features are extracted from Convolutional Neural Networks (CNNs) and also Graph Convolutional Networks (GCNs). Due to the construction of the adjacency matrix on all the data, traditional GCNs usually suffer from a substantial computational cost, particularly in large‐scale remote sensing (RS) problems. To this end, first of all, we propose a new mini‐batch GCN (called Mini‐GCN), which allows to train large‐scale GCNs in a mini‐batch fashion. More significantly, our designed mini‐GCN is capable of inferring out‐of‐sample data without re‐training networks and improving classification performance. Furthermore, as CNNs and GCNs can extract different types of PolSAR features, an intuitive solution to break the performance bottleneck of a single model is to fuse them. Since mini‐GCNs can perform batch‐wise network training (enabling the combination of CNNs and GCNs), we can use the feature fusion strategy. Therefore, in this paper, a local graph‐based fusion method is proposed to couple dimension reduction and feature fusion of the information extracted from the designed CNN and Mini‐GCN. Experimental results on real PolSAR data are very encouraging. Compared to the methods using only single feature and stacking all the features together, the proposed feature fusion method improves the overall classification accuracy on the real PolSAR data sets for more than 5%. Moreover, the experiments conducted on PolSAR datasets, demonstrate the advantages of the used mini‐GCNs over the traditional GCNs and the superiority of the proposed feature fusion method with regards to the single CNN or GCN models. [ABSTRACT FROM AUTHOR]

Radio waves propagating in the atmosphere are affected by the prevailing atmospheric state. The state of the atmosphere can cause radio waves to refract more or less towards the ground. When the refractive index of the atmosphere differs from standard atmospheric conditions, the propagation is considered to be anomalous. Radars which are affected by anomalous propagation can observe ground clutter far beyond the radar horizon. In this work, 4.5 years' worth of data from five operational Swedish C-band dual-polarization weather radars are presented. Analyses of the data reveal a strong seasonal cycle and a weaker diurnal cycle in ground clutter from coastal regions across nearby waters. A comparison was drawn between the impacts of anomalous propagation on ground clutter measured with horizontal polarization and vertical polarization, respectively; however, no clear difference was found. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is an imaging modality that can estimate a visualization of the conductivity distribution inside the human body. However, the spatial resolution of EIT is limited because measurements are sensitive to noise. We investigate a technique to incorporate a priori information into the EIT reconstructions of the D-Bar algorithm. Our paper aims to help engineers understand the behavior of the D-Bar algorithm and its implementation. The a priori information is provided by a radar setup and a one-dimensional reconstruction of the radar data. The EIT reconstruction is carried out with a D-Bar algorithm. An intermediate step in the D-Bar algorithm is the scattering transform. The a priori information is added in this exact step to increase the spatial resolution of the reconstruction. As the D-Bar algorithm is widely used in the mathematical community and thus far has limited usage in the engineering domain, we also aim to explain the implementation of the algorithm and give an intuitive understanding where possible. Different parameters of the reconstruction algorithm are analyzed systematically with the help of the GREIT figures of merit. Even a limited one-dimensional a priori information can increase the reconstruction quality considerably. Artifacts from noisy EIT measurements are reduced. However, the selection of the amount of a priori information and the estimation of its value can worsen the reconstruction results again. [ABSTRACT FROM AUTHOR]

Radio waves propagating in the atmosphere are affected by the prevailing atmospheric state. The state of the atmosphere can cause radio waves to refract more or less towards the ground. When the refractive index of the atmosphere differs from standard atmospheric conditions the propagation is called anomalous. Radars which are affected by anomalous propagation can receive ground clutter far beyond the radar horizon. In this work 4.5 years of data from five operational Swedish C-band dual polarization weather radars are presented. Analyses of the data reveal a strong seasonal cycle and weaker diurnal cycle in ground clutter from coastal regions across nearby waters. The impact of anomalous propagation on ground clutter, measured with horizontal and vertical polarization, was compared but no clear difference was found. [ABSTRACT FROM AUTHOR]

In this study, hourly data assimilation (DA) cycling is performed during a 24-h time period for Hurricane Matthew (2016), assimilating ground-based (GBR) and tail-Doppler radar (TDR) observations together, as well as separately using HWRF and its Hybrid 3DEnVar DA system. The objective is to examine the impacts of assimilating such data on the analysis and prediction of the weakening and re-intensification stages of the eyewall replacement cycle (ERC) of Matthew. Experiments assimilating GBR observations make quicker corrections to the initially inconsistent storm structure than does the TDR experiment, resulting in the primary and secondary eyewalls being realistically represented during the DA cycling period. The TDR experiment analyses show less-realistic concentric eyewall structure before, during, and after TDR observations become available. The forecasts from experiments assimilating GBR observations show more-realistic structural and point intensity changes for the ERC consistently throughout the cycling period when compared with the experiments assimilating TDR observations. Combined assimilation of GBR and TDR observations show similar ERC forecasts, on average, to the GBR experiment. The superior performance of the GBR experiments is shown to be tied to its earlier and longer availability despite its limited low-level coverage especially at the early stage of the cycling. The inferior performance of the TDR experiments even during the availability of TDR is hypothesized to be a result of rapidly changing 3D observational coverage during the high-frequency cycling. Brief mechanism diagnostics additionally suggest the need of properly initializing the TC concentric eyewalls to capture the ERC during the forecasts. [ABSTRACT FROM AUTHOR]

The radar on the Global Precipitation Measurement (GPM) mission observes precipitation at 13.6 GHz (Ku-band) and 35.6 GHz (Ka-band) and also receives echoes from the earth's surface. Statistics of surface measurements for non-raining conditions are saved in a database for later use in estimating the precipitation path-integrated attenuation. Previous work by Meneghini and Jones (2011) showed that while averaging over larger latitude/longitude bins increase the number of samples, it can also increase sample variance due to spatial inhomogeneity in the data. As a result, Meneghini and Kim (2017) proposed a new, adaptive method of database construction, in which the number of measurements averaged depends on the spatial homogeneity. The purpose of this work is to re-visit previous, single-frequency results using dual-frequency data and optimal interpolation (kriging). Results include that (1) temporal inhomogeneity can create similar results as spatial, (2) Ka-band behavior is similar to Ku-band, (3) the Ku-/Ka-band difference has less spatial inhomogeneity than either band by itself, and (4) kriging and the adaptive method can reduce the sample variance. The author concludes that finer spatial and temporal resolution is necessary in constructing the database for single frequencies but less so for the Ku-/Ka-band difference. The adaptive approach reduces sample standard deviation with a relatively modest computational increase. [ABSTRACT FROM AUTHOR]

The impact of assimilating China's operational X‐band Phased‐Array radar's (X‐PAR) data on the analysis and warning forecast of the vortex structure and intensity of the June 8, 2018 Foshan, Guangdong province, tornadic storm was investigated for the first time using an Ensemble Kalman Filter (EnKF) data assimilation system. Both radar radial velocity (Vr) and reflectivity (Z) from two S‐band operational radars and one X‐PAR were assimilated. Deterministic forecasts were launched every 6 min from 05:42 UTC (20 min before the tornado touched down) to 06:00 UTC from the EnKF mean analysis field. Five experiments were conducted to examine the added capability of Z assimilation of the EnKF system, and to investigate the impact of assimilating X‐PAR data on the analysis and prediction of the tornadic storm. Compared to the experiment without Z assimilation, the assimilation of Z reduced the analysis error and greatly reduced the forecast error of Z. The assimilation of X‐PAR data greatly improved the vortex structure of the tornadic storm at low levels, and improved the intensity of the rear inflow of the tornadic storm, especially with a higher assimilation frequency. Compared to the experiments without X‐PAR data assimilation, assimilating X‐PAR data improved the predictability of tornadic storm. Plain Language Summary: Tornadoes are difficult to predict worldwide. However, the scan speed of current operational S‐band (10 cm wavelength) radar is too slow to capture the evolution of tornadoes. The newly built X‐band (3 cm wavelength) Phase‐Array radar (X‐PAR) network in Guangdong Province, China, are hopeful to fill in the gap with their higher scan frequency and higher spatial resolution, which can capture the fine‐scale structure and rapid evolution of small scale systems such as tornadic storms. This study focuses on a case where a tornado embedded within the typhoon Ewiniar was well captured by the X‐PAR network. It demonstrated for the first time the use of X‐PAR data on the prediction of a tornadic storm in China. We used ensemble‐based data assimilation techniques named Ensemble Kalman Filter to utilize the information provided by the X‐PAR data. With the improved information, the analyzed tornadic storm vortex structure at low levels was greatly improved, especially with a higher assimilation frequency. Compared to the experiments without X‐PAR data, the experiment using X‐PAR data successfully predicted the tornadic vortex a few minutes in advance. Key Points: The impact of assimilating China's X‐band Phased‐Array radar (X‐PAR) radar data on the analysis and forecast of a tornadic storm was first investigated using an Ensemble Kalman Filter systemThe assimilation of X‐PAR data helps to produce a more realistic‐looking tornadic storm structure, especially at low levelsIncreasing assimilation frequency of X‐PAR data reduces the analysis error and subsequently improves the 8‐min tornadic storm forecast [ABSTRACT FROM AUTHOR]

Based on the WRF-3DVar system, different momentum control variables in radar data assimilation are investigated for their effects on the analysis and forecast of strong convective weather systems in the study. The single observation assimilation tests are performed besides three cycling data assimilation experiments based on U-wind/ V -wind (UV) and stream-function and velocity potential (ψχ) that are scalar representations of flow fields for their irrotational and divergence components. The wind field increments for both control variables in single observation tests show that when UV are the control variables, the wind field increments have a smaller range of influence that it better reflects the characteristics of the observed information itself at the convective scale than ψχ. When ψχ are used as control variables, negative increments are generated in the analyzed wind field because ψχ have the property of maintaining the integrated value of the wind field. In addition, radar data from 5 Doppler radars in northeast China are assimilated. The cycling assimilation experiments show that CV_UV produces enhanced forecast than CV_ ψχ in terms of radar reflectivity and precipitation. Another case also proves this conclusion. In particular, CV_UV improves the dynamics field and water vapor convergence, which lead to better intensity and structure prediction of the strong convection process. In contrast, CV_ ψχ yields relative discontinuous wind field structure, which tends to reduce the organization of the forecast strong echoes. Another set of experiments are further designed with only radar reflectivity assimilated with CV_UV. The results show that assimilating radial velocity is helpful to improve the forecasting skill. • Different control variables are studied for their effects on the analysis and forecast of strong convective systems. • CV_UV is relatively more localized in terms of analysis increments and is applicable to the convective scale. • CV_UV improves the dynamics field and water vapor convergence for the strong convection process. • CV_UV produces enhanced forecast than CV_ψχ in terms of radar reflectivity and precipitation. [ABSTRACT FROM AUTHOR]