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Accurate and up-to-date information on urban built-up areas is significant for managing urban growth and development. Earth Observation (EO) data are valuable sources for meeting this demand. However, the extraction of urban built-up areas from EO data is challenging due to the limitations of EO data sources. To overcome this challenge, this study follows an approach that assesses the performance of optical (Sentinel-2), radar (Sentinel-1) and fused (Sentinel-1 and Sentinel-2) data to extract urban built-up areas using machine learning algorithms including Random Forest (RF), K-Nearest Neighbors (KNN) and KDTree KNN. The results were statistically analyzed by considering the Overall Accuracy (OA) and kappa coefficient. In addition, 15 cm GSD (Ground Sample Distance) aerial photography of the study area was used to validate the results. According to the results, Sentinel-2 produced better representation and accuracy of urban built-up areas than Sentinel-1 and even the fused image. Regarding to machine learning algorithms classification performance, RF performed better in both OA and Kappa coefficient along all datasets. The research findings can have significant implications for various domains, such as urban planning, land use management and open avenues for further comparisons of different EO data sources and machine learning algorithms for built-up areas extraction. [ABSTRACT FROM AUTHOR]

Detecting the concrete crack depth has always been a technical problem. The traditional detection methods，which are based on stress wave theory, have limitations such as low accuracy, complex operation, time-consuming and laborious procedures. This paper proposes a new method based on the combination of tracer and hand-held radar to better complete this task. Its main procedures include firstly spreading tracer diffusion to the bottom of the crack propagation, and then mapping the location of the air-tracer interface by radar waves, thus visualising the crack depth information. This paper elaborates the detection principle of this new method, carried out the feasibility numerical analysis and simulation verification. Also, the concrete standard crack specimens were made, together with relevant experimental researches. As to the feasibility of the method, it is verified by using common distilled water and saturated sodium chloride solution as tracers. The detection effects of these two tracers are compared and analysed by comparing the test data with the actual depth of the specimen. This research innovatively applies tracers to actively improve the electrical ability of concrete cracks, and then uses radar to detect the electrical distribution of cracks, realising the visual detection of crack depth. [ABSTRACT FROM AUTHOR]

The Radar Cross Section (RCS) plays a crucial role in radar systems, serving to detect and characterize radar targets. Moreover, the need to penetrate foliage and capture images of man-made structures like roads and buildings has driven the development of very high frequency/ultra high frequency (VHF/UHF)-based radar systems. In this context, we propose a novel polyphase-coded spread spectrum (PCSS) radar system designed for RCS measurements and ISAR imaging over VHF/UHF radio channels in open environments. To provide a comprehensive understanding, we examined the open range, considering factors such as amplitude distribution function, root mean square (RMS) delay, and coherence bandwidth, followed by a link budget analysis. We also conducted a comparative study of RCS measurements to evaluate the performance of the proposed radar system, demonstrating its effectiveness through theoretical calculations. Furthermore, we assessed the radar imaging capabilities by applying the CLEAN algorithm to improve the quality of the generated images. [ABSTRACT FROM AUTHOR]

Radio Detection and Ranging (RADAR) equipment is a significant information and navigational system onboard vessels and a critical part of a ship's cyber space. It is an electronic system used not only for detecting surrounding objects, to indicate their positions, and tracking targets using radio waves, but also providing safe navigation by receiving and displaying data from other navigational devices. Therefore, it is concerning to see that marine RADAR systems have various cyber vulnerabilities, including data deletion and data relocation. These systems can be manipulated and penetrated via malicious software, unauthorised remote access, human error, or sabotage by internal and external attackers. This is critical to the cyber hygiene of the ship, which affects its reliability and safety. This study performs a cyber risk assessment using the CORAS framework for RADAR cyber security by developing case-based RADAR cyber scenarios in terms of both its specific information technology subsystems and the cyber security control measures. The output of this study includes a holistic and visual assessment of RADAR's cyber security for both its cyber vulnerabilities and cyber hygiene to better protect shipboard RADAR in the future. [ABSTRACT FROM AUTHOR]

High performance computing (HPC) electromagnetic (EM) emulators are used to simulate real-time EM wave interactions between numerous radar targets. Radar Cross Section (RCS) data stores reflection profiles of radar targets as a table; however, the needed storage size increases with frequency sampling density, aspect angle sampling density, and number of target types. The large quantity of data often exceeds storage capability and limits manipulation and representation feasibility. The spherical harmonic based monostatic anisotropic point scatterer model is proposed for HPC EM simulations where scattering responses can be computed with finite impulse response (FIR) filters. An efficient algorithm constructing this model with large scale RCS data is discussed. The scatterer position and the reflection profile of each scatterer are solved using least squares methods and particle swarm optimization (PSO). In addition, the function evaluations in PSO are accelerated by taking advantage of the matrix structure, making the algorithm 22 times faster [ABSTRACT FROM AUTHOR]

To investigate a thin layer, we conducted borehole radar measurements at the GPR facility of Tohoku University, Japan. Both zero-offset profiling and tomography measurements were used to obtain water content distributions. Analyzing first arrival times with amplitudes, we found that a borehole radar can detect velocity changes caused by a high-water content, which acts as an electromagnetic waveguide. We conducted a 3D FDTD simulation and confirmed that the EM wave propagates as a guided wave in a thin layer related to antenna frequency. We determined a thin geological layer compared to an antenna frequency that cannot be detected by reflection measurement due to the poor radar resolution. By comparing the simulated data with measurement data, we could determine the geological boundary between host layers and a thin layer with high water content. In contrast, both the amplitude and the travel time of direct waves are affected by antenna positions and electrical parameters, including permittivity and conductivity. A vertical velocity profile was obtained by combining complementary zero-offset profiling and tomography data, which can be used to investigate water content distribution. The low-velocity thin layer was characterised by amplitude attenuation and late arrival time. [ABSTRACT FROM AUTHOR]

Similar in strength to hurricanes, Extratropical Cyclones (ECs) are responsible for innavigable sea states, coastal inundation and erosion, and subsequent destruction to coastal infrastructure. Across modern operational wave models, there exists a known systematic underestimation of wave heights during these extreme events. Using a global database of EC storm tracks and 36 years of satellite altimeter data, we examine EC structure and assess model performance through storm centered composite analyses of significant wave height (${H_s}$ H s ) and U10 wind speed ($|U10|$ | U 10 | ). Through the collocation of satellite altimeter observations with a state-of-the-art reanalysis product (ERA5), we investigate model performance with respect to $|U10|$ | U 10 | and ${H_s}$ H s within ECs of varying intensities. By rotating our data reference frames, we align all storm directions to account for asymmetry in EC wind fields and subsequent increased wave growth This rotation results in the organization of the strongest wind speed and ${H_s}$ H s , as well as trends in the ERA5 performance. A characteristic EC radii is calculated and used to normalize data coordinates in the storm centered reference frame, which highlights the organization of EC structures. Performance metrics are then compared within different EC quadrants to explore the relationship between wind forcing accuracy and underestimation of ${H_s}$ H s . [ABSTRACT FROM AUTHOR]

The applicability of high-frequency (HF) radar systems for wave measurement in an estuary was explored by extracting the significant wave height (${H_{sr}}$ H sr ) using a traditional Barrick equation from the Doppler spectra observed by three radar systems installed in Ise Bay, Japan. The minimum value of ${H_{sr}}$ H sr estimated around each grid point was relatively consistent with the wave height observed with a wave gauge/buoy, except for a deterioration of wave measurement accuracy caused by a decrease in seawater conductivity from the freshwater inflow after flooding. Furthermore, the relationship between the accuracy and the signal-to-noise ratios for the first- and second-order peaks (SNR1 and SNR2, respectively) highlighted the difficulty in determining the threshold values of SNRs in the bay. Thus, we suggest the use of ${H_{sr}}$ H sr as a criterion for quality control when applying a nonlinear inversion method for estimating ocean wave spectra based on the Bayesian possibility theorem (BIM). Our suggestion is to select the appropriate Doppler spectra and increase the acquisition rates of wave data with low relative error compared to BIMs using SNR1 and SNR2. These results can promote the applicability of the nonlinear inversion in estuary regions. [ABSTRACT FROM AUTHOR]

X-band radar observations with a transmitting frequency of 9.4 GHz were conducted for over 10 h at the mouth of Tenryu River, Japan, to estimate surface velocity distributions during floods in 2010 and 2020 and analyse changes in the flow field caused by dredging in 2014. X-band radar is an imaging radar that can collect backscatter of floating objects flowing downstream with the flood flow every 2 s. The observation area extends approximately 1000 m × 1000 m. Particle image velocimetry is applied to the image sequences to guess the surface flow pattern. Flow direction in the main river channel changed smoothly, while surface flow speeds ranged from 1 to 3 m s–1 depending on the discharge. Water levels were computed using the estimated surface flow velocities, river cross-section from survey data, and observed discharge for indirect validation: it agreed within an error of 0.3 m with the observed water level. This paper highlights radar's potential for estimating surface velocity distribution during floods and harsh weather conditions. [ABSTRACT FROM AUTHOR]

A Phase- and amplitude-adjustable metasurface radome (PAMR) is proposed to achieve a wideband high gain and low radar cross-section (RCS) antenna with arbitrary sidelobe levels (AS-PGMA) at a 9.8 GHz operating frequency. The proposed PAMR is a special multilayer phase gradient metasurface lens whose amplitude distribution is modified based on the Dolph-Tschebyscheff technique. The AS-PGMA simulation results indicate that a high gain, low RCS, and low sidelobe level (SLL) antenna can be achieved over a broad impedance frequency bandwidth of 9.25–10.1 GHz with in-band average values of 17 dB, −7.5dBsm, −20 dB/−27 dB for gain, RCS, and E-/H-plane SLL, which are measured at 9.8 GHz as 17.4 dB, −9dBsm, and −23.02 dB/−27.91 dB, respectively. Additionally, a novel analytical method for calculating the RCS of the proposed PAMR and AS-PGMA is described and verified by measurement and simulation findings. Measurement results show that the E-/H-plane SLL of the AS-PGMA is improved by 12.2 dB/5.1 dB at the operating frequency, respectively, while its gain and RCS reduction are reduced by 1.6 and 2.1 dB, respectively. Moreover, RCS of the AS-PGMA is reduced over an ultrawide bandwidth of 1–20 GHz for orthogonal polarizations. The analysis, simulations, and measurements are all in good agreement. [ABSTRACT FROM AUTHOR]

Spaceborne precipitation radars provide an unprecedented opportunity to study three-dimensional structure of precipitation, particularly over the open ocean where in-situ observations are rather meagre. In this study, instantaneous surface precipitation characteristics and their vertical structures during the tropical cyclones (TCs) over the North Indian Ocean (NIO) between 2014 and 2022 have been analysed using the dual-frequency precipitation radar onboard the Global Precipitation Measurement Core Observatory. Although stratiform precipitation accounts for more than 70% of total TC surface precipitation area, convective precipitation contributes about half of the total TC surface precipitation amount over the NIO. About 90% of stratiform TC precipitation area yields surface precipitation of less than 10 mm/hour. The vertical structures of stratiform and convective TC precipitation vary with surface precipitation intensity and have nearly similar characteristics over both basins of the NIO. This preliminary quantitative TC precipitation analysis would be useful for better understanding of precipitation processes during TCs over the NIO, and for further advancement in numerical models through improved parameterization schemes for TC precipitation forecasting. [ABSTRACT FROM AUTHOR]

The aims of the present study were to: 1) investigate the within and between-session reliability of the Trackman 4 launch monitor system, and 2) determine the inter-relationships of some of these commonly used metrics. Golfers attended two test sessions at an indoor golf academy and performed 10 shots using their own driver. Results showed excellent within and between-session reliability for CHS (ICC = 0.99; SEM = 1.64–1.67 mph), ball speed (ICC = 0.97–0.99; SEM = 2.46–4.42 mph) and carry distance (ICC = 0.91–0.97; SEM = 7.80–14.21 mph). In contrast, spin rate showed the worst reliability (ICC = 0.02–0.60; SEM = 240.93–454.62 º/s) and also exhibited significant differences between test sessions (g = –0.41; p < 0.05), as did smash factor (g = 0.47; p < 0.05) and dynamic loft (g = –0.21; p < 0.05). Near perfect associations were evident in both test sessions between CHS and ball speed (r = 0.98–0.99), CHS and carry distance (r = 0.94–0.95), ball speed and carry distance (r = 0.97–0.98), and launch angle and dynamic loft (r = 0.98–0.99). Collectively, CHS, ball speed and carry distance serve as the most consistently reliable metrics making them excellent choices for practitioners working with golfers. [ABSTRACT FROM AUTHOR]

The Sissingué Gold Project is in Northern Côte d'Ivoire and part of the southern extension, a large peneplain of the landscape in South Mali. The area has experienced extensive weathering and erosion over a long time, resulting in a widespread and complex regolith cover. This diverse and vast regolith cover poses considerable difficulties for exploration. To overcome this problem, we adopted mapping of regolith units using machine learning (ML) with support vector machine (SVM) and artificial neural network (ANN) algorithms. The main objectives were to (1) make the predictive regolith map of the study region using these algorithms with data obtained from Landsat-8 operational land imager, Advanced Land Observing Satellite Phased Array Type L-Band Synthetic Aperture Radar digital elevation model and Sentinel-1A; (2) show the method of pre-processing and processing the required data and (3) develop the regolith landform unit (RLU) map from the predictive regolith map and collected data based on the Relict – Erosional – Depositional model. Tests using the SVM and ANN algorithms showed that both ML tools could accurately map regolith landforms. An innovative method of optimizing data using parameters is presented. The results showed that ANN outperformed SVM with an overall accuracy of 87.01% and a kappa coefficient of 0.84, whereas the corresponding values for SVM were 86.69% and 0.84, respectively. However, the validation data obtained from SVM-based prediction exhibited a better score than that of validation data obtained from ANN-based prediction. The Sentinel-1A radar band combined with Landsat-8 data reduced the vegetation-masking effect and improved the classification results. The RLUs of this area are composed primarily of relicts at 24.91%, including lateritic residuum and soil, depositional material at 36.39% with exotic sediments and ferrierite and the remaining 38.7% of erosional material made of saprolite, colluvial fragments, and mottled zones on flanks. [ABSTRACT FROM AUTHOR]

Terrestrial radar interferometry (TRI) provides accurate observations of displacements in the line-of-sight (LOS) direction and is therefore used in various monitoring applications. However, relating these displacements directly to the 3d world is challenging due to the particular imaging process. To address this, the radar results are projected onto a 3d model of the monitored area, requiring georeferencing of the 3d model and radar observation. However, georeferencing relies on manual alignment and resource-intensive on-site measurements. Challenges arise from the significant disparity in spatial resolution between radar images and 3d models, the absence of identifiable common natural features and the relationship between image and spatial coordinates depending on the topography and instrument pose. Herein, we propose a method for data-driven, automatic and precise georeferencing of TRI images without the need for manual interaction or in situ installations. Our approach (i) uses the radar amplitudes from the TRI images and the angle of incidence based on the 3d point cloud to identify matching features in the datasets, (ii) estimates the best-fitting transformation parameters using Kernel Density Correlation (KDC) and (iii) requires only rough initial approximations of the radar instrument's pose. Additionally, we present the correct relation between cross-range and azimuth for ground-based radar instruments. We demonstrate the application on a geomonitoring case using TRI data and a point cloud of a large rock cliff. The results show that the positions of the radar image can be localized in the monitored 3d space with a precision of a few metres at distances of over 1 k m. This is an improvement of almost one order of magnitude compared to what had been achieved using standard approaches and direct observation of the radar instrument's pose. The proposed method thus contributes to the automation of TRI data processing and improved localization of small-scale deformation areas detected in radar images. [ABSTRACT FROM AUTHOR]

Due to the high temperature oxidation resistance, corrosion resistance, and good radar waves absorption performance, the radar absorbing coatings (RACs) composed of ceramics and its composites have attracted extensive attention. The RACs is composed of dielectric absorbent and matrix. According to the absorbing mechanism, RACs can be divided into magnetic loss RACs and dielectric loss RACs. High temperature RACs is usually dielectric loss RACs. This article describes the basic principle of radar waves absorption by dielectric loss RACs, and the influence mechanism of temperature, dielectric absorbent content, coating thickness, coating structure design, and other parameters on its radar waves absorption performance. Subsequently, this article introduces the test method of radar waves absorbing performance of RACs and the process of spraying method to prepare RACs, especially the atmospheric plasma spraying method. Finally, this article summarizes the latest research progress of high temperature RACs, mainly focusing on the RACs of doped oxide ceramics (especially alumina), carbide ceramics, and MAX phase ceramics. This review suggests the possibilities of improving RACs absorbing performance through the composition optimization and structural design. [ABSTRACT FROM AUTHOR]

We evaluate the correlation function of a radar signal scattered by inhomogeneities of turbulent medium and received by a radar station at the output of the matched filter. For the turbulent medium, we employ the model of wandering inhomogeneities, which is a generalization of the 'frozen' turbulence. The concepts of strong and weak wanderings are introduced. The case of bistatic radar station is considered. For a monostatic radar station, simple asymptotic formulas are obtained. It is shown that, in the short-wavelength limit, wanderings are almost always strong, and in the long-wavelength limit, the weak wandering limit takes place mainly in the long-wavelength approximation. [ABSTRACT FROM AUTHOR]

The Interferometric Imaging Radar Altimeter (InIRA) is alow-orbit single-frequency altimeter whose signals are subject to an ionospheric delay, which introduces measurement errors. Additionally, some difficulties, such as selecting the interpolation algorithm and intercepting the height scale, arise when applying the ionospheric correction model. Based on Sentinel-3A dual-frequency measurements, this paper suggests that linear variogram-based kriging is suitable for spatial interpolation, and the total electron content height scale coefficient calculated by the hierarchical IRI forecast model can be applied to the interpolated GIM intercept. When the improved GIM value is applied to four scenes of InIRA data, the obtained Ku-band ionospheric correction varies from 1.49cm to 1.57cm. [ABSTRACT FROM AUTHOR]

In the present work, synthesized U-type barium hexagonal (Ba4Co1.5Zn0.5Fe36O60 or BaU) ferrite powder is further transformed into BaU/glass fiber-epoxy structural composites by hand lay-up process and compression moulding techniques using different volume percentages (11.4, 17, and 26.4) within reinforced polymer matrix. Phase formation, surface morphology, and magnetic properties by XRD, FESEM, and VSM techniques, respectively. Subsequently, the electromagnetic and absorbing properties of the designed structures were measured for typical incident EM waves in the frequency range of the C-band. The radar absorbing performance of the designed RAS composites was observed as −16.8 dB in the whole band, with maximum absorption (RL ≤ −10 dB) of adequate thickness at 2.2 mm. Additionally, the effects of the BaU particles on the mechanical properties of the designed polymeric composites were measured through ASTM standard data. The proposed RAS composites are the most demanding absorbers from a military and commercial point of view. [ABSTRACT FROM AUTHOR]

In this paper, the shadow statistical method, which has the merit of without calibration, is further investigated for retrieving significant wave height (SWH) based on shadowing from X-band marine radar images. For the shadow statistical method, the Smith fitting function (SFF) is utilized to fit the illumination probability obtained from the radar image and calculate the SWH. By deeply investigating the estimation of wave steepness from the shadow image, it is found that the analytic solution of wave steepness is not obtained, due to the complementary error function and exponential term in the SFF. Meanwhile, several screening processes of gradually discarding illumination probability are required to obtain accurate root mean square (RMS) wave steepness, which makes the calculation time-consuming. To solve these problems, both the complementary error function and exponential term in the SFF are approximated under the working conditions of marine radar in practice, and the analytic solution of the wave steepness is described with the illumination probability. The shore-based X-band radar images at Pingtan station from November 9 to 17, 2014 are collected to validate the validity of the proposed method. By using 254 sets of radar data with the SWH range of 0.5 ~ 3.5 m, the experimental results demonstrate that the correlation coefficient (CC) of the estimated SWH between the proposed method and the traditional method is 0.996, and the root mean square error (RMSE) is 0.034. The average running time is significantly reduced from 2.76 s to 0.44 s. [ABSTRACT FROM AUTHOR]

Remote sensing plays an increasingly important role in climate research, and as remote sensing observation files span longer periods of time, we are receiving more and more data on climate parameters such as clouds. Four decades ago, it was proposed that the area of planar shapes change with their perimeter following the power law. The exponent of this relation for the areas of clouds (typical and noctilucent) and rain determined by satellites and radar is 1.35 close to 4/3 of the classical turbulence theory of 1941. In this paper, we propose a new relationship between the area and the perimeter of rain and clouds using remote sensing observations. This relationship leads to the development of a new model for the detection of extreme natural phenomena related to rain and clouds. [ABSTRACT FROM AUTHOR]

An 8-bit chipless radio-frequency identification (RFID) tag with a compact structure is proposed in this paper. Designing a RFID tag with a compact size is highly demanded in RFID technology. The original RFID tag is composed of 8 octagonal rings. In order to minimize its dimensions, the tag is divided in two equal parts. One part is completely omitted with its substrate. A grounded wall is placed in front of the remaining part to compensate the effects of the removed part and generate an image of it. Comparing a radar cross-section (RCS) of the compact and normal structure implies that by minimizing the tag, the RCS response of the structure is approximately similar with that of the normal tag. An equivalent circuit model for the RFID tag is presented, which shows a reasonable agreement with its full-wave result. The presented tag exhibits an RCS of σ = −26 dBsm from 2 to 7 GHz with an overall size of 15 × 30 mm2. Through using the aforementioned method, a 50% reduction in the overall area of the structure is observed. [ABSTRACT FROM AUTHOR]

This letter addresses high-resolution sea clutter modelling. Radar clutter modelling is essential to develop constant false alarm rate (CFAR) detection schemes. In this paper, we propose a compound model by the use of the inverted exponentiated Rayleigh (IER) distribution as texture component. The probability density function (PDF) of the compound inverted exponentiated Rayleigh (CIER) distribution is a function of three parameters including the thermal noise power. To assess the modelling performance of the CIER distribution, several tests have been performed using the Intelligent PIXel Processing Radar (IPIX) database where the fitted PDFs and the complimentary cumulative density functions (CCDFs) of the CIER, the well-known compound K and the compound inverse Gaussian (CIG) distributions are compared with the real PDF and CCDF. The results obtained validate the ability of the CIER distribution to model high-resolution sea clutter. [ABSTRACT FROM AUTHOR]

Both empirical and assimilative global ocean tidal models are significantly more accurate in the deep ocean than in shelf and coastal waters. In this study, we answered whether this is due to the quality of the models used to reduce tide and surge or the general approach to treat tide and surge as two separate components of the water level obtained from stand-alone models, which ignores the nonlinear tide–surge interaction. In doing so, we used tide gauge observations as partially synthetic altimeter time series, tide–surge water-level time series obtained with the 2D Dutch Continental Shelf Model – Flexible Mesh (DCSM), and tide and surge water-level time series obtained using the DCSM, FES2014 (FES) and the Dynamic Atmospheric Correction (DAC) product. Expressed in the root-sum-square (RSS) of the eight main tidal constituents, we obtained a reduction > 50 % when removing the DCSM tide–surge water levels compared to when we removed the sum of the DCSM tide and DCSM surge water levels. The RSS obtained in the latter case was only 3.3% lower than with FES and DAC. We conclude that the lower tidal estimates accuracy in shelf-coastal waters derives from the missing nonlinear tide–surge interactions. [ABSTRACT FROM AUTHOR]

A fully integrated 2-transmitter–4-receiver (2TX–4RX) multiple-input multiple-output (MIMO) radar front-end in the 57–64-GHz band for short-range applications is presented in this paper. The proposed architecture with 8 elements within a one-dimensional virtual array enables theoretical angular resolution down to 14.32 ∘ . The TX chains operate in time-division multiplexing (TDM) mode, and each delivers 12 dBm of saturated output power with output-referred 1-dB compression point (OP1dB) larger than 5 dBm over 7-GHz bandwidth. The millimeter-wave (mm-wave) part of the RX chains has a noise figure lower than 13 dB, and a conversion gain of 17 dB in the 60-GHz unlicensed band. The receiver chain shows a maximum conversion gain of 77 dB, while achieving an input-referred 1-dB compression point (IP1dB) greater than – 12.5 dBm, including baseband circuitry. The programmable baseband processing chain with 60-dB gain allows beat frequency selection in the frequency range from 100 kHz up to 1.3 MHz. Linear chirps are synthesized using a fractional-N phase-locked loop (PLL) with a fundamental voltage-controlled oscillator (VCO). The integrated system includes a programmable multiplying delay-locked loop (MDLL) which scales-up external high purity 40-MHz frequency reference to a referent 240-MHz clock used in the frequency-modulated continuous-wave (FMCW) synthesizer. The front-end is implemented in a 130-nm SiGe:C BiCMOS process. The chip occupies an area of 12 mm2 including pads and consumes 930 mW, combined from 1.2-V and 3.3-V supplies. [ABSTRACT FROM AUTHOR]

In this paper, an advanced signal processing algorithm based on low-rank sparse representation is proposed to enhance the vital signal in life detection radar applications. The pre-processed echo signal can be decomposed into low-rank, sparse, and noisy parts, where the sparse part is the vital signal. A non-convex regularization method is adopted to obtain the required components, and the approximate solution method is used to accelerate the calculation to meet real-time requirements. Simulated and experimental results show that the proposed algorithm outperforms existing methods in terms of output signal-to-noise ratio (SNR). [ABSTRACT FROM AUTHOR]

The sparse array imaging radar could largely reduce the system complexity, but it also suffers from poor azimuthal resolution due to the limited aperture length and incomplete observation caused by non-uniform array configuration. In this letter, a radar forward-looking imaging method with improved imaging quality and reduced system complexity is proposed. Firstly, the matrix completion method is utilized to recover the missing signals of sparse array. Then, based on the plane-wave assumption, a fast 2-dimensional atomic norm minimization (ANM) is adopted to solve the positions and amplitudes of scatterers. As a continues compressed sensing method, the result of ANM-based method earns sharper main-lobes and much lower-level side-lobes compared to the fast Fourier transform -based method. Although the resolution of proposed method is still limited by Rayleigh resolution, the imaging quality can be greatly improved by emphasizing main-lobes and avoiding the perturbation of side-lobes. Simulations and experiments are carried out to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Traffic volume data collected by radar detector contain inherent types of errors that are hard to observe. Therefore, the identification of missing data and outliers based on reliable verification volume data is essential, and the development of a methodology for correcting detector traffic data while considering various specificities at the detector installation sites is required. This study proposed a deep-learning-based long short-term memory–multiple linear regression (LSTM–MLR) hybrid model. First, the corrected detector traffic volume was calculated via the MLR model after the preprocessing of the missing and outlier detector traffic volumes. Second, the corrected radar detector traffic volume was learned via the LSTM model to predict the detector traffic volume for the target time periods. The results confirmed a correction effect in terms of radar detector traffic volume data at most of the 30 target sites. The results of this study provide three contributions. First, the missing and outlier traffic volume correction algorithm is easy to apply and can be applied to traffic volume data collected from various detectors. Second, the MLR model developed in this study derived a causal relationship between the traffic volume of the detector and the complex factors that could not reveal the obvious cause, such as the undetected small car driving next to a large vehicle and the radio transmission/reception problem. Third, complex neural networks and dropout techniques to avoid overfitting do not necessarily improve prediction accuracy. [ABSTRACT FROM AUTHOR]

Location Fixing (LF) of Radar Emitters is carried out in an Electronic Warfare scenario by Sensors with Direction-of-Arrival and signal parameter measurement capabilities, geographically distributed over few tens of kilometres. The present paper brings out the Scenario Simulation and Synchronous generation of Radar Signal at Geographically distributed locations for comprehensive testing of the Emitter Location Systems (ELS). This test facility permits exhaustive testing of the ELS against various parameters since the performance of the ELS depends on factors such as Sensors' geometry with respect to the location of the Emitter of interest, signal parameter measurement accuracies, LF algorithms used, time synchronization accuracy achievable among the Sensors, etc. The Radar signals are synthesized synchronously, locally, at each of the distributed Sensors by replicating the Emitter Signal characteristics as received at the Sensors. The Simulator System uses a High Accuracy GPS Receiver as the Global Clock against which the signals are timed and a Field Programmable Gate Array for generating controls triggered on the absolute time of the Global Clock. [ABSTRACT FROM AUTHOR]

Crop type mapping visualizes the spatial distribution pattern and proportion of planting areas of different crop types, which is the basis for subsequent agricultural applications. Although optical remote sensing has been widely used to monitor crop dynamics, data are not always available due to cloud and other atmospheric effects on optical sensors. Satellite microwave systems such as Synthetic Aperture Radar (SAR) have all-time and all-weather advantages in monitoring ground and crop conditions, combining optical imagery and SAR imagery for crop type classification is of great significance. Our study mainly proposes seven feature combination schemes based on the combination of multi-temporal spectral features and texture features of Sentinel-2 (S2), and radar backscattering features of Sentinel-1 (S1) evaluate the influence of different data sources and different features on classification accuracy, obtains the optimal classification strategy and analyses the contribution of different features to classification result, in the aim of providing a new technical approach for the fine identification of crops from multi-source remote-sensing data. Results show that the crop classification accuracy of combined multi-time series spectral, texture, and radar features is higher than that of combining two types of features. The features subset selected by multi-period spectral, texture, and radar features have the best classification result, the overall accuracy (OA) and kappa coefficients reach 96.40% and 0.93, respectively. The study provides a method reference for future research on larger-scale remote-sensing crop precise extraction. [ABSTRACT FROM AUTHOR]

The Cognitive Radar is almost a new research topic, in which most efforts have been done for its conceptual description and the adaptive waveform design feature of these radars. While other aspect of adaptive action for optimum performance of cognitive radars has been ignored. In this paper, a framework for adaptive time resource management in Cognitive Radars is proposed. The main purpose of this paper is proposing an algorithm for time resource management, with incorporation of adaptive waveform design capability of cognitive radars, to enhance the radar performance for an efficient time resource usage. After developing the equations for radar time resource management using adaptive waveform design for the first time, an implementable algorithm is proposed and its performance is simulated and analysed. The results show that using the proposed algorithm resulted in a more efficient time resource management compared to the existing ones. [ABSTRACT FROM AUTHOR]

In this study, a new method combining wave-number energy spectrum (WES) and Genetic algorithm-back propagation neural network (GA-BPNN) is proposed to retrieve the rainfall intensity level from rain-contaminated X-band marine radar image. Since the intensity of spatial rainfall can be reflected by the distribution of energy in the wavenumber frequency domain, the obtained WES is divided into three wavenumber segments (low, medium and high wavenumber segments), and the ratio of the wavenumber in each wavenumber segment to the total wavenumber is calculated separately as the characteristic parameters. Based on the excellent network convergence speed and data prediction accuracy of the GA-BPNN, these calculated parameters are input into the constructed GA-BPNN for training to complete the task of rainfall intensity level retrieval. The proposed method is tested using data collected at the ocean observation station of Haitan Island in Pingtan County. Referring to the actual rainfall intensity synchronously recorded by the rain gauge, the retrieval accuracy of the proposed method is 97.4%, which is 4.3% higher than that of back propagation neural network (BPNN) not optimized by Genetic algorithm (GA). In addition, compared with the retrieval performance of the ratio of zero intensity to echo (RZE) method based on the occlusion area of radar image, the retrieval accuracy of the proposed method is improved by about 12.9%. [ABSTRACT FROM AUTHOR]

An intelligent single radar image de-raining method based on unsupervised self-attention generative adversarial networks is proposed to improve the accuracy of wave height parameter inversion results. The method builds a trainable end-to-end de-raining model with an unsupervised cycle-consistent adversarial network as an AI framework, which does not require pairs of rain-contaminated and corresponding ground-truth rain-free images for training. The model is trained by feeding rain-contaminated and clean radar images in an unpaired manner, and the atmospheric scattering model parameters are not required as a prior condition. Additionally, a self-attention mechanism is introduced into the model, allowing it to focus on rain clutter when processing radar images. This combines global and local rain clutter context information to output more accurate and clear de-raining radar images. The proposed method is validated by applying it to actual field test data, which shows that compared with the wave height derived from the original rain-contaminated data, the root-mean-square error is reduced by 0.11 m and the correlation coefficient of the wave height is increased by 14% using the de-raining method. These results demonstrate that the method effectively reduces the impact of rain on the accuracy of wave height parameter estimation from marine X-band radar images. [ABSTRACT FROM AUTHOR]

Lineament mapping is a very important step in geological studies and mineral exploration, with the evolution of remote sensing, processing methods as well as the availability of several optical and radar satellite data, lineaments can be detected without using traditional methods. The objective of this work is to recommend the most suitable data for lineaments to be used in the field of geological science, by comparing the performance of three different RADAR data, namely ALOS PALSAR (The Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar), Radarsat-1 and Sentinel-1 for automatic lineament extraction using a combination of edge detection and line linking algorithms. The methodology consists to link between the length, number, orientation and density of lineaments with surface characteristics such as the slope, faults, lithology, discontinuities and mineral veins. The results obtained show that the extracted lineaments from Sentinel-1 VH polarization have a better correlation with geological units, tectonic system direction, as well as shading and slope maps, which is due to the effectiveness of VH polarization, which is independent of soil properties compared to the other polarizations that have shown an overestimation of the lineaments. [ABSTRACT FROM AUTHOR]

A novel algorithm is developed to estimate the shadowing ratio for the significant wave height (SWH) inversion of the ocean wave fields imaged by horizontal polarized X-band nautical radar intelligently and conveniently. To solve the problem that the accuracy of the calculated ratio of shadowing in local image areas is not ideal, and the high resolution radar images will lead to time-consuming in estimation of root mean square slope and angle-blurred for sea surface image edge detection, a shadow estimation model from marine X-band radar images based on Convolutional Neural Network (CNN) is established. The model applies the improved CycleGAN to SWH estimation using the geometric shadow effect, which is visible on the marine X-band radar sea surface images due to the presence of the modulation effect of the rough surface. The neural network model can be successfully trained from simulation-based data and then applied to real measured data, and the algorithm does not require any reference measurements. Compared with the traditional shadow-based method, the SWH derived by using this proposed method matches well with that measured by an in-situ buoy nearby, which indicates the goodness of our proposal. [ABSTRACT FROM AUTHOR]

Potentiality of Synthetic Aperture Radar (SAR) based Radar Forest Degradation Index (RFDI) combined with field observations for monitoring spatio-temporal dynamics of intertidal mudflat vegetation was assessed in this study. Five vegetation zones were delineated in the Junput mudflat of eastern India with very high classification accuracy (Kappa coefficient ≥ 0.79). Fragmentation and coalescence patterns of different vegetation zones under two plantation initiatives were also analysed by different spatial metrics. Results reveal gradual degradation of tree dominated vegetation zones and growth of shrub dominated and herbaceous ones from 2007 to 2019. Plantation of exotic species like Eucalyptus globulus and Casuarina equisetifolia along the shoreline had been found to be less effective against storms and sea surges. Conversely, native mangrove plantations and associated herbs had demonstrated remarkable growth in the intertidal areas. Based on the findings, the study pointed out that a zone-wise cum site-specific plantation strategy is needed towards developing effective bio-shields. [ABSTRACT FROM AUTHOR]

This paper developed a simple and quantitative evaluation system for a single-leg standing test. The proposed method represents the stand-up motion as a state transition model by quantitative measurement using a camera with a depth sensor (Azure Kinect) and a force plate. It objectively evaluates the movement of each state. In the experiment using the prototype, the evaluation results by the system and the skilled athletic trainer were the same for all subjects, and the shape of the radar chart was different among the subjects whose evaluation scores were the same. The results show that the proposed system can be quantitative evaluation even at worksites without skilled evaluators. [ABSTRACT FROM AUTHOR]

Synthesized carbon nanotubes (CNTs)/glass fabrics (GFs) composites using an interface design strategy is an effective method to fabricate radar absorbing structures. In this work, CNTs/GFs composites were prepared by the electrophoretic deposition (EPD) method, and the influence of deposition voltage, deposition time, and the concentration of CNTs solution on the microstructure, electromagnetic parameters, and absorbing properties of CNTs/GFs composites was studied. The results indicated the deposition content of CNTs can be controlled by the EPD parameters and thereby realizing the regulation of absorbing performance of the CNTs/GFs composites. The minimum reflection loss (RL) of the synthesized composites can reach – 69.3 dB with the thickness of 2.3 mm when the concentration of CNTs was 1 g/L, the deposition voltage was 16 V and deposition time was 4 min. Such good absorbing performance demonstrate that the CNTs/GFs composites can be applied as a promising candidate for radar absorbing structures. [ABSTRACT FROM AUTHOR]

Graph HT ht (3) Hence considering PCR results of Figure 6 as well as other results discussed earlier, it is conclusively shown that the proposed design is as cross-polarizer rather than as a perfect absorber. Comment on "An ultrathin and broadband radar absorber using metamaterials" In a recent publication, Zhou et al. [[1]] has proposed "an ultrathin and wide-band radar metamaterial absorber" based on double different sized L-shaped structures. [Extracted from the article]

In low-moisture regimes, strongly-reflecting bedrock underlying soil could provide a dominant return. This offers a novel opportunity to retrieve both the volumetric moisture fraction (mv) and depth (d) of a soil layer using a differential phase. A radar wave traversing the overlying soil slows in response to moisture state; moisture dynamics are thus recorded as variations in travel time—captured back at a radar platform as changes in phase. The Phase Scaled Dielectric (PSD) model introduced here converts phase changes to those in soil dielectric as an intermediate step to estimating mv. Simulations utilizing a real soil moisture timeseries from a site in Sudan were used to demonstrate the linked behaviors of the soil and radar variables, and detail the PSD principle. A laboratory validation used soil with a wet top layer variable in depth 1–2 cm and drying from mv ∼ 0.2 m3m−3, overlying a gravel layer at a depth of 11 cm. The scheme retrieved d = 1.49 ± 0.33 cm and a change Δmv = 0.191–0.021 ± 0.009 m3m−3. The PSD scheme outlined here promises a new avenue for the diagnostic measurement of soil parameters which is not currently available to radar remote sensing. [ABSTRACT FROM AUTHOR]

This paper establishes a novel method to generate an ultra-wideband (UWB) bandpass filter (BPF) with four notches. Initially, this method employs a stub-loaded rectangular ring resonator (SLRRR) based compact structure to develop a UWB bandpass filter. Upgradation of the SLRRR structure into a stub-loaded asymmetric rectangular ring resonator (SLARRR) based structure in the next move facilitates the prospective filter with four notches at 4.34 GHz (S-band radar), 5.48 GHz (WLAN), 6 GHz (Wi-Fi 6E), and 9.3 GHz (X band radar) respectively possessing high rejection ability. This compact-sized (1.14λg × 0.29λg) proposed filter is characterized by a wide passband extending from 3.3 to 10.84 GHz with low insertion loss of <−0.69 dB, good selectivity of 0.8, and a 4.6 GHz wide upper stopband with −20 dB high rejection level. Structures are fabricated, measured, and compared with the obtained simulation outcomes. The resemblance between the compared results corroborates the applicability of such compact structure. [ABSTRACT FROM AUTHOR]

The dual polarimetric study including degree of polarization ( m L ) and energy span (λ1+ λ2) for vegetation targets infer the accuracy of vegetation algorithms. The Sentinel − 1 A and ALOS − 2 satellite data were utilized for dual polarimetric radar vegetation index (DPRVI), polarimetric radar vegetation index (PRVI) and radar vegetation index (RVI) computation and simulation of backscattering coefficient (σ0 (dB)) at VV polarization. The DPRVI, PRVI and RVI were computed at VV + VH and HH + HV polarizations, respectively. The simulation of σ0 (dB) at HH + HV can be show better results than VV + VH polarization using derived radar vegetation descriptors (V). The DPRVI, PRVI and RVI showed higher R2 and lower RMSE values at L – band than C - band. The DPRVI at L – band (HH + HV polarization) was indicated highest R2 = 0.91 and lower RMSE = 0.31 (dB) among all proposed V in the algorithm. [ABSTRACT FROM AUTHOR]

The objective of this study is to analyze the possibility of using vehicle sensors to estimate traffic density in future traffic environments. For the analysis, the simulation experiments used in this study consist of three steps to estimate traffic density based on vehicle sensor data. The first step is to develop software module that can generate error of distance measurement in a similar way to the real road driving. In the second step, this study conducted traffic simulation experiments by combining the true distance value measured in the simulation with the error value calculated by the error generating module of the first step. Finally, we conducted evaluation procedure by comparing the estimation results of traffic density to the true density value. According to the experiment results, the use of front and rear radar showed best performance in that this case maintained low MAPE (Mean Absolute Percentage Error) results even at low probe vehicle ratio as compared to other cases. In case of camera sensor, it can be important alternative at the standpoint of potential future applications, especially under limited traffic environments of high ratio of sensor-equipped vehicle and congested traffic conditions such as LOS (Level of Service) D or E. [ABSTRACT FROM AUTHOR]

Dual-pol Shannon Entropy captures the dynamic crop growth parameters. It was used to evaluate biophysical parameters of important crops using Sentinel-1 data. Improved phenology information is vital input to crop-growth models. Mustard experienced increase as it advanced in phenology but for wheat, decreased due to absorption. A significant relationship at initial crop stages (height < 150 cm and biomass < 5 kg m−2) R2 =0.65 between Shannon Entropy (SE) and crop parameters, though less strong (R2=0.33) for entire mustard cycle and wheat (plant height < 80 cm and biomass < 6 kg m−2) was observed. The SE appeared sensitive to low-medium biomass and useful in monitoring crop phenology in low biomass and initial phenophases. The dynamic crop profile is manifested conjunctively as Radar Vegetation Index (RVI). Random Forest Regression (RFR) and Support Vector Regression (SVR) were evaluated to predict the phenophases. Phenology responses till peak/beyond were found to perform close to ground observations. [ABSTRACT FROM AUTHOR]

In this paper, an anti-jamming scheme based on transmitted multiple pulses through random initial phases and an adaptive iterative filtering algorithm is considered for counteracting range-velocity deceptive jamming in Pulse-Doppler (PD) radar system. This anti-jamming scheme relies on the fact that the digital radio frequency memory (DRFM) repeated jammer needs more than one pulse repetition interval PRI (at least one PRI) to identify the radar transmitted pulses, and therefore uses the captured pulses of the lag PRIs to reproduce multiple false targets. As a consequence, an adaptive-iterative algorithm to estimate the range-Doppler plane of the real targets in range dimension processing and Doppler dimension processing can appropriately suppress the deceptive false targets, which employed the lagged PRIs for the radar deception. Firstly, the proposed method achieves the optimal estimation of the true targets and false targets in the range dimension. Then, with the estimation information in the range dimension, the presented method estimates the range-Doppler plane of the true targets and false targets. Finally, we evaluate the performance of the proposed method as well as achieve a good trade-off between anti-jamming performance and computational complexity via numerical simulations. [ABSTRACT FROM AUTHOR]

Gaussian Process Regression (GPR) emerged as a powerful algorithm since last decade in many applications. However, it is not fully explored in remote sensing applications, especially for predicting plant biophysical variables in the heterogeneous environment of India. This kernel-based machine learning technique has effectively replaced conventional approaches for estimating vegetation characteristics from remotely sensed data. In this work, an attempt has been made to test the ability of GPR to estimate the leaf area index (LAI) of wheat crops using the Sentinel-1 (S1) derived Dual-Polarized Radar Vegetation Index (DpRVI) and Sentinel-2 (S2) Top of Atmosphere (TOA) products. Further, the ability of the atmospheric correction procedure was tested by S2 Bottom of Atmosphere (BOA) images. To accomplish this, the field measurements of LAI were carried out from January to March 2020. Further comparisons of the GPR's performance were made with the Artificial Neural Network (ANN) coupled with the PROSAIL (PROSPECT+SAIL) radiative transfer model, available through the Sentinel Application Platform (SNAP) Biophysical processor. The accuracy of the estimated LAI was evaluated using the statistical indicators, for example, coefficient of determination (R2), Root Mean Square Error (RMSE), and Nash Sutcliffe Efficiency (NSE). The results showed that the synergy of the GPR and DpRVI provided the most accurate result (R2 = 0.822, RMSE = 0.503 m2 m−2, NSE = 0.831) as compared to the GPR and TOA (R2 = 0.816, RMSE = 0.596 m2 m−2, NSE = 0.803) and SNAP biophysical processor (based on ANN) (R2 = 0.696, RMSE = 0.760 m2 m−2, NSE = 0.578). Therefore, the study demonstrated the importance of S1 SAR images and GPR as an alternative tool among the other well-established machine learning algorithms to estimate the crop biophysical parameters. [ABSTRACT FROM AUTHOR]

The mutual interference between multiple-input multiple-output (MIMO) millimeter wave (mmWave) radars reduces the signal-to-noise ratio (SNR) of the target, which in turn affects the angle estimation of the target. The singular spectrum analysis (SSA) can be used to suppress the interference by signal reconstruction. However, when there is interference in the intermediate frequency (IF) signal, it is hard to select the principal components (PCs) to reconstruct the IF signal. Therefore, an adaptive segmentation and template matching based on SSA (ASTM-SSA) is proposed. In the adaptive segmentation, the IF signal is divided into interference and interference-free segments based on the location of the interference. In the template matching, the desired PCs of the interference-free segment are considered as the templates to match with PCs of the interference segment, and the matched PCs are used to reconstruct the IF signal. After all the interference are suppressed, the classical multiple signal classification (MUSIC) is performed to estimate the angle of the target. The simulation and field experiments are conducted to verify the effectiveness of the ASTM-SSA, and the results indicate that the ASTM-SSA can effectively suppress interference and accurately estimate the angle of the target. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the accuracy of a method proposed for passive localization of radar emitters around irregular terrains with a single receiver in Electronic Support Measures systems. Previously, the authors targeted only the theoretical development of the localization method. In fact, this could be a serious concern in practice since there is no evidence regarding its accuracy under the real data gathered from realistic scenarios. Therefore, an accurate ray-tracing algorithm is adapted to enable the implementation of the method in practice. Then, realistic scenarios are determined based on the geographic information system map generated to collect high-resolution digital terrain elevation data, as well as realistic localization problems for radar emitters. Next, simulations are performed to test the localization method. Thus, the performance of the method is verified for practical implementation in the electronic warfare context for the first time. Lastly, the performance bounds of the method are discussed. [ABSTRACT FROM AUTHOR]

In previous research, the identification of the Bragg region, which is often achieved using image recognition, was easily affected by ionospheric interference and different manual parameter settings. Strong disturbances from the ionosphere and other environmental noise may interfere with high-frequency radar (HFR) systems when determining first-order Bragg regions and thus may directly influence the surface current mapping performance. To avoid human intervention in first-order Bragg-region recognition, deep learning methods were used to extract multiple levels of feature abstractions of the first-order Bragg region. In this study, to assist in developing sufficient training data, a procedure integrating a morphological approach and Otsu's method was proposed to provide manual labelled data for a U-Net deep learning model. Additionally, several sets of activation functions and optimizers were used to achieve optimal deep learning model performance. The best combination of model parameters results in an accuracy of over 90%, an F1 score of over 80%, and an intersection over union (IoU) reaching 60%. The identification time of one image is approximately 70 ms. These results demonstrate that this deep learning model can predict the position of first-order Bragg regions under ionospheric interference and avoid being affected by strong noise that could cause prediction errors. Hence, deep learning and image fusion processing can effectively recognize the first-order Bragg regions under strong interference and noise and can thereby improve the surface current mapping accuracy. [ABSTRACT FROM AUTHOR]

Very-high frequency (VHF) radar echoes are known to be aspect sensitive in general, which could be either the manifestation of a thin stable layer providing sharp refractive index gradient or shear-driven steep layer structures. The present study for the first time shows the volume imaging of aspect sensitivity in the recently upgraded Advanced Indian Mesosphere Stratosphere Troposphere (MST) radar (AIR). The present configuration of radar can provide a better understanding of three-dimensional structures of turbulence and instabilities, which was not possible when the radar was operated in two orthogonal planes. Both isotropic and anisotropic layers/turbulence are observed at various height levels over the radar volume. Enhanced aspect sensitivity is observed between 6 and 9 km, which is attributed to the presence of dynamic instability arising due to strong wind shear and atmospheric stability. All the scatterers tend to become isotropic when both the square of wind shear and stability parameters are above 0.25 × 10−3 s−2. Due to the high aspect sensitivity, there may be an underestimate of 5–10% in wind velocity measurements, and it is higher for the low off-zenith beam angle. [ABSTRACT FROM AUTHOR]

In high-frequency surface wave radar (HFSWR) systems, the main-lobe of the receiving angle spectrum suffers from severe broadening due to a small-array aperture effect. This broadening causes more clutter components to be mixed into the main-lobe, resulting in target vessels more prone to be submerged in the clutter. This problem severely affects the target detection performance. A reduction in the number of array elements decreases the degrees of freedom (DOF), making clutter suppression more difficult. Space-time adaptive processing (STAP) has recently become a crucial tool for clutter suppression in advanced HFSWR systems, using two-dimensional data to increase DOF. Focusing on low DOF and typical nonhomogeneous clutter with high energy, this study proposes a STAP clutter suppression method by estimating clutter components accurately in the space-time domain based on sparse representation in few snapshots. The experimental results verify that the proposed algorithm provides high suppression performance for measured clutter. [ABSTRACT FROM AUTHOR]