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This paper presents a radar cross-section (RCS)-based statistical recognition system for identifying/ classifying unmanned aerial vehicles (UAVs) at microwave frequencies. First, the paper presents the results of the vertical (VV) and horizontal (HH) polarization RCS measurement of six commercial UAVs at 15 GHz and 25 GHz in a compact range anechoic chamber. The measurement results show that the average RCS of the UAVs depends on shape, size, material composition of the target UAV as well as the azimuth angle, frequency, and polarization of the illuminating radar. Afterward, radar characterization of the target UAVs is achieved by fitting the RCS measurement data to 11 different statistical models. From the model selection analysis, we observe that the lognormal, generalized extreme value, and gamma distributions are most suitable for modeling the RCS of the commercial UAVs while the Gaussian distribution performed relatively poorly. The best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. The performance of the UAV statistical recognition system is evaluated at different signal noise ratio (SNR) with the aid of Monte Carlo analysis. At an SNR of 10 dB, the average classification accuracy of 97.43% or better is achievable., Comment: one column, 39 pages

Marine X-band radar has been suggested to be capable of monitoring significant wave heights in both offshore and open sea areas. In contrast to studies on offshore radar, significant wave height estimations from coastal radar images, which exhibit complicated radar backscattering features, have received little attention. This study proposes a method for retrieving the significant wave height from coastal areas that are often influenced by nononshore winds. The square root of the signal-to-noise ratio in radar images has been widely applied to estimate the significant wave height. However, nononshore wind cases show a poor correlation between the square root of the signal-to-noise ratio and the in situ significant wave height. In addition, the spectral shapes from radar images in nononshore wind cases are very different from those in onshore wind cases. To improve the significant wave height estimations from coastal radar images, we implement an artificial neural network algorithm. After training and testing the algorithm, we confirm that the estimated significant wave heights are more reliable for both onshore and nononshore wind cases if the square root of the signal-to-noise ratio, power from nearshore radar subimages, and in situ wind components are included in the input layer of the neural network.

Multiple-elevation-beam synthetic aperture radar (SAR) is a concept based on digital beamforming (DBF) in elevation and simultaneous recording of the echoes of multiple transmitted pulses. It enables high-resolution imaging of wide areas and is therefore ideal for the systematic observation of dynamic processes on the Earth's surface. Furthermore, if the pulse repetition interval (PRI) is continuously varied (staggered SAR), it is possible to map a wide continuous swath rather than multiple subswaths separated by ``blind'' ranges. Within the design of multiple-elevation-beam SAR, however, it is fundamental to consider how nadir echoes affect the mapping capabilities of systems with constant PRI and the image quality of staggered SAR systems, where nadir echoes are intrinsically smeared due to the PRI variation. This article addresses the characterization of nadir echoes in multiple-elevation-beam SAR with constant and variable PRI by presenting a parametric model for the nadir echo profile based on real radar measurements, a formulation of the nadir echo location and smearing in staggered SAR, and realistic simulations based on TerraSAR-X data, which show that nadir echo are likely to be barely visible in staggered SAR images. The results of this work are relevant to both the design of future SAR systems and the interpretation of the acquired data.

Snow is an important environmental variable and a primary water resource in many areas of the world. Monitoring seasonal snowpack properties is also crucial for properly managing snow-related hazards such as snow avalanches and snowmelt floods. Recently, an innovative radar architecture, based on the use of two receivers, has been proposed for snowpack monitoring for the case of dry snow, where the snowpack depth and bulk density can be calculated with one single radar measurement, without any kind of external aid. This article presents the extension of this innovative radar architecture for the case of wet snow. The approach to determine, not only the snowpack depth and bulk density but also the liquid water content, is outlined and discussed in detail, along with the experimental validation of the operating principle for two cases., This work was supported in part by the Italian Ministry of Education, University and Scientific Research (MIUR) under Project SIR2014 "SNOWAVE" RBSI148WE5. © 2022, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work.

One of the scientific payloads of Chang'E-5 (CE-5), i.e., the lunar penetrating array radar (LRPR), will carry out the in situ exploration of the regolith structure and guide the drilling sampling ...

The dual-frequency precipitation radar (DPR) onboard the global precipitation measurement (GPM) satellite provides valuable measurements of precipitation. In this study, the GPM DPR products (version 6) are validated against a ground-based S-band polarimetric radar in South China based on a volume-matching method. Good consistency is found for the reflectivity factor (Z) calibration of the two instruments. From the perspective of microphysics, the mass-weighted mean diameter ( $D_{m})$ estimates correspond well with those of the ground-based radar in the inner swath of the normal scan (NS); however, underestimation is found for the raindrop number concentration, indicated by the generalized intercept parameter ( $N_{w})$ , especially for the intense echoes. Thus, the GPM DPR product may fail to depict the microphysical characteristics of small-to-medium raindrops in high concentration for heavy rainfall in South China. This is attributed to the negative Z bias of the DPR caused probably by insufficient correction of attenuation, which also leads to clear underestimation in the liquid water content (W) and the rainfall rate (R) products for intense echoes. In the outer swath where only single-frequency retrieval is available, overestimation in $D_{m}$ exists regardless of echo intensity level, and more underestimation can be found in $N_{w}$ , W, and R especially for intense echoes. In the selected typhoon and squall line cases, better capability in revealing microphysical properties is also found for the inner swath of the NS. After adjusting the scan mode, the performance of the precipitation products in the outer swath can be improved by dual-frequency retrievals in the future.

In this article, an efficient radar fusion-before-imaging (RFBI) method for heterogeneous borehole radar systems is proposed. Different from conventional fusion-after-imaging processes, the sample sets collected from heterogeneous borehole radar systems (monostatic, bistatic, or multiple-input multiple-output) are first merged into one data set before the imaging process in RFBI, and a single imaging operation is demanded to obtain the target space image with high precision. Specifically, the diversity in heterogeneous borehole radar sample sets is taken into consideration, and the radar sample sets are inserted and fused into a high-dimensional sample set before imaging. The target space spectrum is generated according to the echo space-frequency constraint relationship, and the target space is extracted from one imaging process. The influence of clutters in RFBI results is reduced, and the imaging accuracy is satisfactory. Meanwhile, due to the fusion process ahead, the computational time of RFBI hardly increases with the number of radar sample sets. The synthetic and field experiment results show that RFBI demonstrates comparable accuracy as Kirchhoff migration and higher efficiency in processing large amounts of data sets at the cost of large memory requirement, which is suitable for the joint imaging of heterogeneous radar systems.

Undesired echo from a flying object (aerial clutter) significantly contaminates the received signal of a wind profiler radar (WPR) because it has high intensity and spreads over a wide Doppler velocity range. In this study, results of aerial clutter mitigation obtained by applying adaptive clutter suppression (ACS) to a 1.3-GHz WPR are shown. The 1.3-GHz WPR used in this study has a main antenna comprising 13 antenna subarrays (MSAs). five-element Yagi-Uda antennas were also used as antenna subarrays for detecting clutters from low elevation angles (CSAs). The CSAs were used only in reception and installed so that they covered most of the horizontal directions and the horizontal and vertical polarizations. The directionally constrained minimization of power (DCMP) method was used as the adaptive signal processing to mitigate clutter. By the DCMP method, the weighted sum of the signals collected by 13 MSAs and 11 CSAs was computed so that the power of output signals was minimized under the constraint of constant gain in the antenna beam direction. Results of a case study for an aerial clutter from a low elevation angle at 17:04:37 on October 1 2020 showed that an overlap of the aerial clutter over a desired echo (i.e., clear-air echo) was solved by decreasing the aerial clutter whose peak intensity was ~24 dB greater than that of the clear-air echo. In a case study at 09:30:27 on September 18 2020, effects of the DCMP method on the processed results were discussed.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2020-11-06, 資料番号: PA2110067000

For radar modeling and simulation of sea clutter, there are a number of key characteristics used to describe the observed features. These include the mean backscatter power, amplitude statistics, spatial and long-time temporal correlation, and the Doppler spectrum. Regarding the latter, a common modeling approach is to use the mean Doppler characteristics (width and center point) to relate to the sea conditions. However, this does not capture the time- and range-varying fluctuations of the Doppler spectra, which are important for assessing the behavior of coherent detection schemes. The evolving Doppler spectrum model offers a way to model these variations and can be described with a moderate number of parameters. Relating these to the observed geometry, environment and radar characteristics are important to make the models accessible. Previous work has characterized X-band radar sea clutter over a range of sea conditions and azimuth and grazing angles. In this article, new models are presented that capture variations with both the radar resolved area and integration time. This enables the evolved Doppler spectra model to be used over a much wider range of applications.

Multiple radar systems represent an attractive option for target tracking because they can significantly enlarge the area coverage and improve both the probability of trajectory detection and the localization accuracy. The presence of multiple extended targets or weak targets is a challenge for multiple radar systems. Moreover, their performance may be severely deteriorated by regions characterized by a high clutter density. In this article, an algorithm for detection and tracking of multiple targets, extended or weak, based on measurements provided by multiple radars in an environment with heavily cluttered regions, is proposed. The proposed method features three stages. In the first stage, past measurements are exploited to build a spatiotemporal clutter map in each radar; a weight is then assigned to each measurement to assess its significance. In the second stage, a track-before-detect algorithm, based on a weighted 3-D Hough transform, is applied to obtain target tracklets. In the third stage, a low-complexity tracklet association method, exploiting a lion reproduction model, is applied to associate tracklets of the same target. Three experiments are presented to illustrate the effectiveness of the proposed approach. The first experiment is based on synthetic data, the second one is based on actual data from a radar network with two homogeneous air surveillance radars, and the third one is based on actual data from a radar network with four different marine surveillance radars. The results reveal that the proposed method can outperform competing approaches.

Convective precipitation nowcasting remains challenging due to the fast change in convective weather. Radar images are the most important data source in nowcasting research area. This study proposes a radar data-based U-Net model for precipitation nowcasting. The nowcasting problem is first transformed into an image-to-image translation problem in deep learning under the U-Net architecture, which is based on convolutional neural networks (CNNs). The input of the model is five consecutive radar images; the output is the predicted radar reflectivity image. The model consists of three operations: upsampling, downsampling, and skip connection. Three methods, U-Net, TREC, and TrajGRU, are used for comparison in the experiments. The experimental results show that both deep learning methods outperform the TREC method, and the CNN-based U-Net can achieve almost the same performance as TrajGRU which is a recurrent neural network (RNN)-based model. With the advantages that U-Net is simple, efficient, easy to understand, and customize, this result shows the great potential of CNN-based models in addressing time-series applications.

The accumulation rate is a key parameter in computing the mass balance of glaciers and ice sheets to estimate sea level rise. A shallow-layers-detection ice sounding radar (SLDISR) is developed to measure the accumulation rate and shape of near-surface internal layers with high resolution. With a transmitting frequency from 500 to 2000 MHz, this frequency-modulated continuous wave (FMCW) radar provides a range resolution of about 16 cm in free space by using a Hanning window and a penetrating depth about 150 m under polar ice. The spectral analysis and coherent integration techniques are used to obtain a high processing gain and to improve the signal-to-noise ratio of the system. A phase-locked loop with wideband yttrium iron garnet (YIG) oscillator is applied to generate a sweeping chirp signal as an input source for the transmitter. A stable, low-frequency reference chirp signal is generated with a direct digital synthesizer (DDS) integrated in field-programmable gate array (FPGA). To reduce the high-speed requirement to the analog-to-digital converter (ADC), dechirp technology is adopted at the RF section of the receiver. The implementation of the digital unit is based on an FPGA chip. The designed radar has been successfully deployed in Antarctica during the 31st Chinese Antarctic Research Expedition (CHINARE 31) and CHINARE 33, mainly over the East Antarctic Ice Sheet (EAIS). The echograms indicate the effectiveness of the radar system on detecting clear internal reflecting horizons (IRHs) over ice sheets.

The sea clutter, referred to as the time-varying radar backscatter from the ocean surface, plays a significant role in marine radar development. The ocean's complex hydrodynamics cause it to exhibit non-Gaussian and nonstationary characteristics, which brings challenges in the sea clutter modeling, especially for establishing its spatial-temporal correlated and coherent model. In this article, a data-driven method based on the Koopman mode decomposition (KMD) is proposed for modeling spatial-temporal correlated complex sea clutter. The method decomposes the coherent sea clutter dynamic behavior in terms of Koopman modes and corresponding temporal patterns. Then, these spatiotemporal patterns are used to construct the sea clutter state over time according to the approximate solution. Furthermore, this proposed state-of-the-art data-driven approach is benchmarked by the measured sea clutter data from intelligent PIXel processing radar (IPIX). It is demonstrated that the proposed approach accurately models the complex sea clutter with actual statistic characteristics, phase information, and spatial-temporal correlations. The mean absolute error (MAE) and root mean square error (RMSE) between the obtained and actual sea clutter are only 0.1817 and 0.2349, respectively. This work offers a practical approach for modeling sea clutter, especially when the spatial-temporal correlation and coherence information is needed.

Cross-polarized temporal coherence observations of a boreal forest, acquired using a tower-based radar, are presented in this article. Temporal coherence is analyzed with respect to frequency, temporal baseline, time of day of observation, season, meteorological variables, and biophysical variables. During the summer, P- and L-band temporal coherence exhibited diurnal cycles, which appeared to be due to high rates of transpiration and convective winds during the day. During the winter, freeze-thaw cycles and precipitation resulted in decorrelation. At temporal baselines of seconds to hours, a high temporal coherence was observed even at C-band. The best observation times of the day were midnight and dawn. Temporal coherence is the main limitation of accuracy in interferometric and tomographic forest applications. The observations from this experiment will allow for better spaceborne SAR mission designs for forest applications, better temporal decorrelation modeling, and more accurate forest parameter estimation algorithms using interferometric and tomographic SAR data.

Research based on Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) data detected unusual radar bright basal reflections located at about 1.5 km depth in a Mars region denoted as Ultimi Scopuli. These reflections were interpreted as a signature of subglacial liquid water even though this interpretation is still being debated in the literature. In this article, we propose a novel approach to the detection and imaging of candidate subglacial liquid water from radar sounding data. The approach combines the radar echo power traces with a suitable digital elevation model to provide a bidimensional representation of the surface. Even if the imaging method reconstructs a representation of the surface, we prove that it can be used to identify subsurface bright reflections in icy regions. Imaging is feasible even if the basal interface is not directly included in the processed data for image generation. To support this experimental evidence, we show that a relationship exists between the value of the reflected echo power originating from the englacial layers and the basal-to-surface-echo-power ratio. The observed relationship holds on both Ultimi Scopuli radar sounding data acquired on Mars and Lake Vostok data acquired on Earth. Our results show that the 2-D imaging provides an alternative way for locating candidate subglacial liquid water bodies on Mars over large areas also where the basal interface is not directly measured. The proposed approach complements previous research for further evaluation of the actual presence of liquid water on Mars.

This article studies changes in microwave signals of oceanic snowfall in response to the formation of snow-covered sea ice using active and passive coincident data from the radar and radiometer onboard the CloudSat and the global precipitation measurement satellites. Using reanalysis data of liquid and ice water path as well as satellite retrievals of sea ice snow-cover depth, spectral regions are determined over which the snowfall signatures are likely to be obscured or falsely detected. Relying on an a priori database populated with the active-passive coincidences, a Bayesian snowfall retrieval algorithm is presented that links a k-nearest neighbor matching with the inverse Gaussian estimator used in the Goddard profiling algorithm. Without relying on any ancillary data of air temperature, the results demonstrate that over open oceans (sea ice), we can passively retrieve the CloudSat active snowfalls with a true positive rate of 92 (85%) and the root mean squared error of 0.24 (0.15) mm h⁻¹.

Radio frequency interference (RFI) has become a growing concern for weather radar, distorting radar variable estimation. By simultaneously or alternately transmitting the horizontal and vertical polarized waves, polarimetric weather radar can be referred to as SHV radar or AHV radar. The SHV radar can mimic the AHV radar by discarding either H- or V-channel measurements, which leads to an alternating scheme. In this research, the real RFI measurements from an operational C-band SHV radar are used to characterize the RFI temporal, spectral, and polarimetric features. Then, the RFI is simulated to quantify the performance of the object-orientated spectral polarimetric (OBSPol) filter in RFI mitigation. The OBSPol filter has been previously proposed by the authors to mitigate the narrowband clutter (both stationary and moving) and noise. This work extends the application of the filter to remove the RFI for SHV radar. Specifically, by taking advantage of the low copolar correlation of the RFI signal measured in AHV radar, the RFI mitigation method is designed, and its effectiveness is proven by qualitative and quantitative analyses. In particular, in the case of RFI overlapped to weather echoes in the time domain, the RFI can be mitigated, also when the duty cycle of the RFI is high. However, this work does not provide a full evaluation of the RFI mitigation performance on all radar data outputs but a proof of concept to show the effectiveness of the proposed filter for RFI mitigation.

A satellite mission onboard a radar sounder for the observation of the earth's polar regions can greatly support the monitoring of the cryosphere and climate change analyses. Several studies are in progress proposing the design and demonstrating the performance of such an earth-orbiting radar sounder (EORS). However, one critical aspect of the cryospheric targets that are often ignored and simplified in these studies is the complex geoelectrical nature of the polar ice. In this article, we present a performance assessment of the polar ice target detectability by focusing on their realistic representation. This is obtained by simulating the orbital radargrams corresponding to different regions of the polar cryosphere by leveraging the data available from airborne campaigns in Antarctica and Greenland. We propose novel performance metrics to analyze the detectability of the internal reflecting horizons (IRHs), the basal interface, and to analyze the nature of the basal interface. This performance assessment strategy can be applied to guide the design of the signal-to-noise ratio (SNR) budget at the surface, which can further support the selection of the main orbital instrument parameters, such as the transmitted power, the two-way antenna gain, and the processing gains.

Synthetic aperture radar (SAR) is a valuable tool for lake ice monitoring. The recently proposed SAR configuration for Earth observation called compact polarimetric (CP) SAR could be a good compromised choice between conventional (single or dual) and fully polarimetric (FP) SAR for operational ice applications, including lake ice. Given its enhanced radar target information compared with conventional SAR systems over wider swath coverage compared with FP SAR, CP systems could play important role in the new generation of Earth observation systems. Herein, we study the evolution of CP SAR parameters from simulated CP SAR data in relation to early ice growth. Focus of the study is on four lakes located in Cornwallis Island, Canadian Central Arctic. We adopt parameters extracted from dual circular polarimetric and right circular transmit, linear (horizontal and vertical) receive configurations. In this study, we consider the ice thickness calculated from an established empirical model. Meteorological and ice climatological data were used to support the analysis. Results demonstrated a potential connection between a number of CP parameters and lake ice growth. Furthermore, we were able to highlight the relationship between the density of air bubbles in ice layer and the intensity of volume scattering mechanism, leading to the identification of lakes with increased gas production activities. Thus, differences between lakes in terms of density of air bubbles were detected and statistically evaluated.

The NOAA Wide Swath Radar Altimeter (WSRA) and its processing are described. The WSRA provides real-time measurements of sea surface significant wave height and directional wave spectra during flights in hurricanes and other environments. The characteristics of near nadir scattering from the sea surface and the resulting distortion of the wave topography measured by the WSRA are discussed, as well as the simulation which generated a matrix to correct the directional wave spectra produced from the WSRA wave topography.

Recent work has demonstrated a passive radio sounding approach that uses the Sun as a source for echo detection and ranging. As the Sun is a moving source with a position that is known a priori , we evaluate this technique’s capabilities to measure the echo’s phase history, map topography, and perform synthetic aperture radar (SAR) focusing. Here, we present our approach to implementing passive SAR using a compact, temporally incoherent radio-astronomical source as a signal of opportunity. We first evaluate the passive system’s capabilities to obtain an echo from a rough surface by determining the critical signal-to-noise ratio (SNR) for reliably observing the Sun’s echo reflection with our passive instrument. We then demonstrate that our technique can detect the necessary changes in range, phase, and reflectivity of an echo from the Sun. We next present the experimental results of our passive radar testing using the Sun at Dante’s View, Death Valley, to highlight this technique’s ability to perform 2-D imaging. Finally, with synthetic data, we demonstrate that we can use time-domain backprojection to focus a planar white noise signal, perform passive SAR imaging, and improve the measurement’s SNR and azimuth resolution. The results of passive SAR focusing on white noise highlight the potential for the Sun and Jupiter’s radio emissions to perform surface and subsurface imaging for planetary and terrestrial observations.

In this article, we propose a multichannel sea clutter model in a spaceborne early warning radar system and analyze the influence of the sea clutter motion characteristics on the space-time adaptive processing (STAP) performance. To establish a multichannel sea clutter model, the 3-D Gerstner wave model is applied to construct the sea surface. Then the Pierson–Moskowitz wave spectrum and the stereo wave observation project (SWOP) directional spectrum are combined to describe the amplitude distribution of waves in different frequencies and directions. At the same time, the two-scale model is applied to obtain the specific backscattering coefficients of sea clutter at different time and positions. In addition, breaking waves are added in sea clutter returns with the form of false targets. Finally, the space-time distribution characteristics of sea clutter in a spaceborne multichannel array system and the influences of sea clutter under different wind speeds and directions on STAP performance are analyzed based on the simulation processing results. Processing results of some real-measured radar data are also exhibited to verify the theoretical analyses.

Multistatic ground-penetrating radar (GPR) signals can be imaged tomographically to produce three-dimensional distributions of image intensities. In the absence of objects of interest, these intensities can be considered to be estimates of clutter. These clutter intensities spatially vary over several orders of magnitude, and vary across different arrays, which makes direct comparison of these raw intensities difficult. However, by gathering statistics on these intensities and their spatial variation, a variety of metrics can be determined. In this study, the clutter distribution is found to fit better to a two-parameter Weibull distribution than Gaussian or lognormal distributions. Based upon the spatial variation of the two Weibull parameters, scale and shape, more information may be gleaned from these data. How well the GPR array is illuminating various parts of the ground, in depth and cross-track, may be determined from the spatial variation of the Weibull scale parameter, which may in turn be used to estimate an effective attenuation coefficient in the soil. The transition in depth from clutter-limited to noise-limited conditions (which is one possible definition of GPR penetration depth) can be estimated from the spatial variation of the Weibull shape parameter. Finally, the underlying clutter distributions also provide an opportunity to standardize image intensities to determine when a statistically significant deviation from background (clutter) has occurred, which is convenient for buried threat detection algorithm development which needs to be robust across multiple different arrays., 12 pages, 11 figures

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2021-08-10, 資料番号: PA2220049000

This work presents the design of a 24-GHz digitally controlled oscillator (DCO) in an advanced 28-nm bulk CMOS technology for short-range frequency-modulated continuous-wave radar system-on-chip for mobile and Internet-of-Things devices. The power minimization is therefore the primary focus. The oscillator consumes a record low power of 1.2 mW at a 0.65-V supply voltage. It achieves a very large frequency tuning range (TR) of 5.8 GHz (27%) and a 150 kHz resolution without significantly degrading the phase noise (PN). The proposed design methodology results in a state-of-the-art -193 dBc/Hz FoM(T).

Radar external calibration is an important measurement in determining the radar cross sections of unknown targets and the reflectance coefficients of the sea surface. This measurement is often made in an anechoic chamber by standard programs. Sometimes, this measurement must be made in outdoor conditions, and the ambient environment severely affects the calibration precision. Thus, the scheme of the measurement must be specially designed. In this article, a calibration conducted at the sea near Lingshan Island is reported, and a full program of an external calibration on the sea surface and relevant analysis are presented for a P-band island-based sea clutter measurement radar. Data analysis shows that the multipath effect from the sea surface is a major factor to degrade the precision of the calibration. Measurement data over 4 days and the multipath reflectance model of the sea surface are combined to estimate the power increment from the multipath effect. Radar system constant is estimated based on the estimated power increments at different sites, where the viewing geometries of the radar are different. The results show that the proposed external calibration method on the sea surface attains a satisfactory precision.

The ground-based radar quantitative precipitation estimation (QPE) faces various challenges including the overestimation caused by the bright band (BB) in the stratiform region and the underestimation in mountainous areas when the terrain-enhanced precipitation occurs at the levels below ground-based radar measurements. The vertical precipitation structure provided by spaceborne radars, i.e., the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the Global Precipitation Measurement mission (GPM) Dual-frequency PR (DPR), is valuable for mitigating the above problems. Since the spaceborne radars and ground-based radars usually operate in different frequencies, e.g., the TRMM PR and the KuPR of GPM DPR work in Ku-band (13.8 and 13.6 GHz, respectively) and the ground-based radars in western China work in C-band (5.4 GHz), the reflectivity conversion from Ku-band to C-band is necessary before the vertical profile of reflectivity (VPR) measured by spaceborne radars can be utilized to improve the ground-based radar QPE in western China. This study presents a conversion method using GPM DPR measurements, i.e., the drop size distribution (DSD) for different precipitation types (the stratiform with/without BB and the convective cases) and particle phases (the solid, melting, and liquid). Using the ${T}$ -matrix method, the reflectivity difference between Ku-band and C-band is found and the Ku-band to C-band conversion relations are derived with the linear regression. These conversion relations have been validated by matching and comparing the converted C-band reflectivity with the C-band ground-based radar measurements. The results demonstrate the effectiveness and reliability of the conversion. This method can be extended for the reflectivity conversion in other frequencies and can facilitate the incorporation of reflectivity measurements from various instruments.

Pulse compression has enabled a new generation of low-cost and compact spaceborne weather radar systems. To successfully utilize pulse compression techniques for cloud and precipitation applications, the effects of Doppler-range migration must be considered during the design and operation of the radar. Pulse compression for spaceborne weather applications introduces additional interdependence between the radar system and the operations when compared with traditional pulsed radar systems, primarily as a result of the large platform velocities. Pulse compression signals for weather radar can be simulated with high fidelity to predict and optimize the radar’s performance. In this article, we evaluate the pulse compression performance of RainCube, a Ka-band precipitation radar in a CubeSat, through analysis and comparison of observations and radar simulations. Through these comparisons, design and operational considerations for pulse compression weather radar are discussed. This work shows that the optimal pointing angle for RainCube to achieve the finest vertical resolution is not at nadir, but when pointing forward approximately 2.25°, in the direction of the spacecraft’s orbit.

One of the fundamental technical challenges of any new spaceborne vegetation remote sensing mission is the determination of what sensor(s) to place onboard and what, if any, overlapping modes of operation they will employ as each onboard sensor adds significant cost to the overall mission. In this article, the remote sensing of forest parameters using multimodal remote sensing is presented. In particular, polarimetric radar, Light Detection And Ranging (LiDAR), and near-IR passive optical sensing platforms are employed in conjunction with physics-based models. These models are used to accurately estimate forest aboveground biomass as well as canopy height in homogeneous areas. It is shown that this proposed method is capable of achieving high accuracy estimates while using minimal ancillary data in the estimation process. We present a method to combine measured data sets with our geometric and electromagnetic sensor models to develop a forest parameter estimation algorithm that fuses multimodal remote sensing technologies with a minimal amount of ground information and yields an accurate estimate of forest structure including dry biomass and canopy height with rms errors of 1.6 kg/m2 and 1.68 m respectively.

Radar sounder surface imaging is a rather unexplored approach to the analysis of planetary bodies. While a radar sounder is an instrument specifically designed for subsurface investigations, a particular set of power measurements (denoted as off-nadir surface echoes) can be exploited together with an external digital elevation model to produce images of the investigated surface at meters wavelength. The use of the off-nadir data may also reveal the presence of previously undetected subsurface features. In this article, we present a method for producing surface roughness images by high-frequency (HF) radar sounder data. The study of surface roughness in the HF band is particularly useful for both geologic studies and landing-zone reconnaissance as it is evaluated at meters to hundreds of meters horizontal scale. The proposed method combines off-nadir data of the Shallow Radar Sounder (SHARAD) with the Mars Orbiter Laser Altimeter (MOLA) digital elevation model. The produced roughness images at 20 MHz (15-m wavelength) of the Martian surface provide higher coverage and resolution of the surface roughness characterization at a 10–100-m horizontal scale than previous SHARAD work. By comparing the experimental roughness image with the one obtained by radar backscattering simulations, it is possible to identify subsurface features. In our experiments, we were able to produce a bidimensional image of a previously undetected large buried crater (10 km $\times12$ km) located in the Nili Fossae. This finding opens up new possibilities in exploiting radar sounding data for better detecting shallow subsurface features.

In this article, an unmanned aerial vehicles detection system is proposed for an X -band ground-based surveillance frequency modulated continuous wave Doppler radar. Two novel features are directly extracted from the Doppler processing results without a time-frequency analysis. Experimental results on measured radar echo signals show that our system consistently outperforms the state of the art in terms of detection accuracy and computational efficiency.

We propose a lightweight radar that autonomously measures snow depth over sea ice from an unmanned aerial vehicle (UAV). Development of this snow radar and its integration with an octocopter UAV is presented. Field trials of the UAV-mounted snow radar, conducted in Antarctica during the summer season of 2017/2018, are also described. The radar allows measurements of snow depths on sea ice between 10 and 100 cm. Additional reflections due to internal layers within the snow are evident at a few measurement points. The snow radar is evaluated for various flight parameters: stationary; flying at speeds between 1 and 3 m/s, and at heights from 5 to 15 m. Evaluation of snow-depth results indicates that a depth accuracy of ±3.2 cm is achieved with stationary measurements, and of ±9.1 cm with measurements at the various flight speeds.

Based on the sea surface scattering mechanism of radio wave, compact high-frequency surface wave radar that employs the direction finding technology also shows promising potential for remotely mapping of ocean wave parameters. However, due to the low signal-to-noise ratio (SNR) of scattered echoes, the diverse external interference and clutter signals, other unresolved measurement uncertainties, the quality (such as accuracy, temporal, and spatial coverage rates) of wave maps are often limited. In this article, a novel, real-time, data quality control method is proposed to alleviate these issues. A comprehensive three-stage processing scheme is established, including the range-Doppler spectral processing, the spatial-grid processing, and the temporal-scale Kalman filtering. The first two stages aim to improve the echo signal quality and reduce the spatial gaps, respectively. The third stage is designed to mitigate the estimation error using an autoregression prediction model and to relate the observation error variance to the SNR of second-order Doppler spectral peak. A detailed verification and performance analysis between the field radar data and in situ ground truth data over one-month period is carried out, indicating that the proposed method can improve the reliability of wave maps with respect to the conventional Doppler spectral smoothing or averaging method, particularly in low sea state (i.e., low SNR) scenarios.

Determining snow water equivalent (SWE) in a fast and nondestructive way is a key request for many hydrologists and snow scientists. To this aim, microwave ground-based radars represent a viable solution, but often the simultaneous measurement of both the snowpack depth and density (the key ingredients for the SWE) is very complex, inaccurate, or requires difficult procedures and equipment. This letter presents a novel radar technique for self-standing calculation of the SWE that can be applied to bi-static radars. This technique, based on the multipath propagation of the radar signal into the snowpack, only requires a radar with two fixed antennas, without any other device, movement of the antennas, or a priori empirical assumptions. This makes such a technique particularly suitable for light and portable radars for rapidly probing large areas, providing, for example, an innovative validation means for satellite-based microwave remote sensing methods. The proposed technique was demonstrated using a stepped frequency modulated continuous wave (FMCW) radar in field conditions for dry snow, delivering results for snow depth and SWE, benchmarked by manual analyses of the snowpack, with a mean absolute error better than 5 cm.

Wind velocity is of great importance for weather monitoring, aviation hazard alerting, wind energy exploring, etc. Doppler radar is widely used to measure wind under rainy condition by sensing the raindrops entrained by the background wind. However, the Doppler velocity, which is a reflection of the raindrops’ velocity in radial direction, is not coincident with the background wind because of the strong inertia of raindrops. Efforts should be made to distinguish the difference between the raindrops’ velocity and the background wind velocity. In this article, we try to establish a relationship between the background wind velocity and the raindrops’ velocity by introducing a definition of the raindrops’ characteristic size, which is related to the velocity characterized by the strongest Doppler spectral component. It is found that the fusion of differential reflectivity and the depolarization ratio can serve as a good proxy for the estimation of the characteristic size. Simulation results for $S$/$C$/$X$-band radars and radar measurements verify the good performance of the proposed retrieval model for the characteristic size, which lays a solid foundation for the retrieval of the background wind velocity.

International audience; This paper describes first results obtained from the SWIM (Surface Waves Investigation and Monitoring) instrument carried by CFOSAT (China France Oceanography Satellite), which was launched on October 29th, 2018. SWIM is a Ku-Band radar with a near-nadir scanning beam geometry. It was designed to measure the spectral properties of surface ocean waves. First, the good behavior of the instrument is illustrated. It is then shown that the nadir products (significant wave height, normalized radar cross-section and wind speed) exhibit an accuracy similar to standard altimeter missions, thanks to a new retracking algorithm, which compensates a lower sampling rate compared to standard altimetry missions. The off-nadir beam observations are analyzed in details. The normalized radar cross-section varies with incidence and wind speed as expected from previous studies presented in the literature. We illustrate that, in order to retrieve the wave spectra from the radar backscattering fluctuations, it is crucial to apply a speckle correction derived from the observations. Directional spectra of ocean waves and their mean parameters are then compared to wave model data at the global scale and to in situ data from a selection of case studies. The good efficiency of SWIM to provide the spectral properties of ocean waves in the wavelength range [70m-500m] is illustrated. The main limitations are discussed, and the perspectives to improve data quality are presented.

Ground-penetrating radar (GPR) technology for underground exploration consists of the transmission of an electromagnetic signal in the ground for sensing the presence of buried objects. While monostatic or bistatic configurations are usually adopted, a limited number of multistatic GPR systems have been proposed in the scientific literature. In this article, we investigate the recovery performance of a specific and unconventional contactless multistatic GPR system, designed at the Georgia Institute of Technology for the subsurface imaging of antitank and antipersonnel plastic mines. In particular, for the first time, tomographic approaches are tested against this experimental multistatic GPR system, while most GPR processing in the scientific literature processes multimonostatic experimental data sets. First, by mimicking the system at hand, an accurate theoretical as well as numerical analysis is performed in order to estimate the data information content and the performance achievable. Two different tomographic linear approaches are adopted, i.e., the linear sampling method and the Born approximation (BA) method, this latter enhanced by means of the compressive sensing (CS) theoretical framework. Then, the experimental data provided by the Georgia Institute of Technology are processed by means of a multifrequency CS- and BA-based method, thus generating very accurate 3D maps of the investigated underground scenario.

The TanDEM-X satellite mission has revolutionized DEM generation from spaceborne synthetic aperture radar. In addition to the primary objective of generating a consistent digital elevation model with global coverage and unprecedented accuracy, the mission has acquired time series of topographic data over several volcanoes, providing an excellent opportunity to test the use of this innovative dataset for volcano monitoring and research. In this article, we review the utilization of the single-pass TanDEM-X data for studying various kinds of volcanic activity, such as basaltic lava flows, the formation and destruction of lava domes and related pyroclastic density currents, and subsurface magma withdrawal and intrusion. We then consider the uses of these data for hazard assessment and forecasting. Our goal is to highlight the importance of timely and repeated topographic information in volcanology, and to suggest the development of future spaceborne bistatic synthetic aperture radar satellite missions, such as ESA's Earth Explorer 10 candidate mission, “Harmony.”

Snow avalanches cause a sudden change of snow properties making them detectable with synthetic aperture radar (SAR). However, steep alpine terrain combined with the slant view geometry of SAR sensors complicates detection: the avalanche brightness depends on the incidence angle and the observed area is limited by radar layover and shadow. Likewise, the spatial resolution varies strongly with the local incidence angle relative to the terrain. To increase the avalanche brightness and to improve the imaging coverage and resolution we apply local resolution weighting (LRW) on Sentinel-1 (S1) backscatter images from ascending and descending orbits. LRW merges acquisitions by averaging them, weighted with the local ground-range resolution. To analyze the relative avalanche brightness with respect to the local incidence angle and polarization, and to quantify the benefit of LRW, we created a dataset of 914 manually drawn avalanche outlines based on S1 imagery of an extreme avalanche event on January 4, 2018 in the Swiss Alps. We show that avalanches appear brightest at slopes facing away from the radar at local incidence angles of 55±20∘ ; such slopes are weighted considerably stronger through LRW. With a processing pipeline for avalanche segmentation using a fixed threshold on the backscatter difference we obtain a higher F1 score (0.75) with LRW compared to an unweighted orbit average (F1 = 0.68) or single orbit acquisitions (F1 = 0.5). For automatic segmentation, we used the manually drawn dataset for training and testing of a deep neural U-Net and achieved a F1 score of 0.81 on LRW backscatter differences., IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, ISSN:1939-1404, ISSN:2151-1535

Digital surface models (DSMs) are sets of elevation data of the Earth's surface, useful for applications such as urban studies and height estimation of buildings. They can be derived from a set of synthetic aperture radar (SAR) images acquired in an interferometric or tomographic configuration. Each image acquisition is usually focused in radar geometry. In this work, we present steps required to derive DSMs from SAR single-look complex (SLC) products focused in map geometry (geocoded). We modified existing tomographic reconstruction techniques to be able to operate with geocoded SLCs and extended methods to operate with 3-D geocoded SLCs. The performance analysis showed that methods using 3-D geocoded SLC products yielded DSMs with fewer outliers and retained more information of the illuminated area, with a cost of higher computational complexity. Compressive sensing methods using 2-D geocoded SLCs can be a good alternative due to their comparatively moderate computational complexity.

As radar sensors are being miniaturized, there is a growing interest for using them in indoor sensing applications such as indoor drone obstacle avoidance. In those novel scenarios, radars must perform well in dense scenes with a large number of neighboring scatterers. Central to radar performance is the detection algorithm used to separate targets from the background noise and clutter. Traditionally, most radar systems use conventional constant false alarm rate (CFAR) detectors but their performance degrades in indoor scenarios with many reflectors. Inspired by the advances in nonlinear target detection, In this article, we propose a novel high performance, yet low-complexity target detector and we experimentally validate our algorithm on a dataset acquired using a radar mounted on a drone. We experimentally show that our proposed algorithm drastically outperforms ordered statistics CFAR (OS-CFAR) (standard detector used in automotive systems) for our specific task of indoor drone navigation with more than 19% higher probability of detection for a given probability of false alarm. We also benchmark our proposed detector against a number of recently proposed multitarget CFAR detectors and show an improvement of 16% in probability of detection compared to censored harmonic averaging CFAR, with even larger improvements compared to both outlier-robust CFAR and truncated statistics log-normal CFAR in our particular indoor scenario. To the best of authors’ knowledge, this article improves the state-of-the-art for high-performance yet low-complexity radar detection in critical indoor sensing applications.

Millimeter-wave (mmWave) radar, as an emerging technique, is increasing in popularity for human activity recognition. In contrast to traditional sensors and radars, mmWave radars give detailed information on objects from the range domain to the Doppler domain. The short wavelength allows mmWave radars to achieve a high resolution and a small antenna size, but also makes them prone to noise. In this article, we present a system framework for human detection and tracking using mmWave radars. We show that mmWave radars have good performance in indoor environments, with over 90% sensitivity. We show that using a single radar can raise a large number of false alarms due to unstable data and noise. However, with two radars the precision of the system can be improved significantly.

Distinguishing humans from animals using ultra-wideband (UWB) radar is necessary in post-disaster emergency rescues to prioritize and thereby optimize the distribution of labor and material resources. However, current studies are few and have only been implemented in simple laboratory environments, such that the effectiveness of these approaches cannot be guaranteed in rescue situations. This study describes experiments under actual post-disaster emergency rescue scenarios, for which the signal-to-noise ratio of UWB radar is seriously degraded owing to multipath effects and a complicated ruin environment. Four distinguishing features are extracted from aspects of wavelet entropy, correlation coefficient, and energy to classify humans from animals. Analysis of feature effectiveness showed that each feature could identify humans from animals individually. The largest difference between humans and animals was found in a feature which combines advantages of the correlation coefficient and energy simultaneously. There was no overlap between the human and animal values for this feature among the 20 sets of radar data collected. This is the first attempt to distinguish humans from animals in an actual post-disaster trapped condition, and it yielded four features of strong classification ability. We envision this study to advance real-world applicability of UWB radar in post-disaster emergency rescue.

Soil moisture ( Mv ) estimation and monitoring over agricultural areas using Synthetic Aperture Radar (SAR) are often affected by vegetation cover during the growing season. Volume scattering and vegetation attenuation can complicate the received SAR backscatter signal when microwave interacts with the vegetation canopy. To address the existing problems, this article employed the model-based polarimetric decomposition method considering the two-way attenuation to remove the volume scattering and vegetation attenuation. A deorientation process of SAR data was applied to remove the influence of randomly distributed target orientation angles before the polarimetric decomposition. To parameterize the two-way attenuation, Radar Vegetation Index derived from the SAR intensity images was adopted. The Dubois model was used to describe backscattering from the underlying bare soil. Since the soil roughness parameters are difficult to measure under vegetation cover, the optimum surface roughness method was used to parameterize the Dubois model. This soil moisture retrieval algorithm was applied to the polarimetric multitemporal RADARSAT-2 SAR data over soybean fields. The validation indicates the root-mean-square error of 9.2 vol.% and 8.2 vol.% at HH and VV polarization, respectively, over the entire soybean growing period, suggesting that the proposed method is capable of reducing the effect of vegetation cover for soil moisture monitoring over the soybean field.