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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.

Coherent microwave radar, which is a rapidly emerging tool to sense waves, usually utilizes the orbit velocities extracted from either temporal or spatial radar echoes. The distribution of energy in the wavenumber-frequency spectrum is changed by some nonlinear features, and correspondingly, this kind of method always provides a significantly overestimated wave period. To address this problem, a novel inversion algorithm, which utilizes the spatial and temporal returns collected with a recently developed coherent S-band radar, is proposed for retrieving wave parameters. A 2-D Fourier transform is applied to the spatial-temporal matrix of velocities to estimate the wavenumber-frequency spectrum. Then the wavenumber-frequency spectrum is integrated over the wavenumber domain to obtain the 1-D velocity spectrum. And the wave height spectrum is estimated from the 1-D velocity spectrum by the direct transform relationship between the 1-D velocity spectrum and the wave height spectrum. Later, significant wave heights and mean wave periods can be derived by the zeroth and first moments of the wave height spectra, respectively. The algorithm is validated using simulation and real data. An approximately four-day dataset collected with a shore-based coherent S-band radar deployed at Beishuang island during a typhoon period is reanalyzed and used to retrieve significant wave heights and mean wave periods. Comparisons between the measurements of radar and wave buoy are conducted, and radar-derived and buoy-measured wave parameters are in a reasonable agreement with a coherent coefficient over 0.9. The results indicate that the proposed method is effective for wave measurements using coherent S-band radar.

High circular polarization ratio (CPR) characteristics were found in permanently shaded regions (PSRs) near the lunar poles. High CPR was regarded as a water ice index. The compact-polarimetric (CP) miniature radio frequency (Mini-RF) radar transmits left-circularly polarized signals and receives horizontally polarized (SHL ) and vertically-polarized (SVL ) echoes from the lunar surface. Statistics of the CPR data show its relations with the relative phase (δ ) between SHL and SVL and the degree of polarization (m) but few interpretations were provided. The average CPR data reach the maximum and minimum at δ =± 90°, respectively. As m becomes very small, the CPR approaches 1. It has been found that CPR is also affected by surface roughness and incidence angle of radar waves. The CPR is now expressed in CP mode to explain the Mini-RF observation. Full-polarimetric radar echoes and CP parameters of the lunar surface are numerically simulated using the bidirectional analytic ray-tracing method. Single-bounce and multiple-bounce scattering components are included in the simulation. Radar images of the lunar crater are simulated with the digital elevation model (DEM) data. The H-α decomposition derived from the full-polarimetric simulation is presented to analyze δ and m. Simulated radar images with different surface roughness are analyzed statistically to study the functional dependences of δ , m, and CPR on incidence angle and roughness. Relationships among δ , m, and CPR are used to analyze the effects of incidence angle, roughness, TiO₂ , and rock abundance on the scattering components. The CPR, m, and δ of PSR craters of different ages are compared with those of nonpolar craters. The results indicate that the CPR, m, and δ are unlikely to be unambiguous evidence of water ice.

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.

The synergy of active and passive microwave measurements has attracted considerable attention in recent years since they offer complementary information on the characteristics of the observed target (e.g., soil moisture), which motivates the launch of NASA's Soil Moisture Active Passive (SMAP) mission. An assumption of a near-linear relationship between active and passive measurements has been made in the SMAP active-passive baseline algorithm, which is essential to downscale coarse-resolution radiometer brightness temperature (TB) using high-resolution radar backscatter (σ⁰) but has not yet been fully tested under a wide range of ground conditions. Motivated by this, we first examined the validity of the linear assumption by using concurrent and coincident SMAP active and passive observations under diverse environmental factors (e.g., land cover, climate types, terrain and its complexity, soil texture, vegetation coverage, soil moisture, and its dynamics). We also adopted SMAP enhanced TB to evaluate the performance of the disaggregated TB at the same grid resolution of 9 km. The results reveal there is a generally good linear relationship between σ⁰ (no matter in dB or in linear unit) and TB at a global scale. There is no significant difference in the correlation among the four polarization combinations ( $σ ⁰_{hh}$ versus TB $_{h}$ , $σ⁰_{hh}$ versus TB $_{v}$ , $σ⁰_{vv}$ versus TB $_{h}$ , and $σ⁰_{vv}$ versus TB $_{v}$ ) with the $σ⁰_{vv}$ and TB $_{h}$ combination displaying an overall slightly higher correlation. The linear relationship between σ⁰ and TB is significantly affected by environmental factors. Particularly in bare soils and densely vegetated areas (e.g., large forest fraction and vegetation coverage), and arid and polar climate zones, the linear correlation between active and passive measurements worsens, whereas it is favorable in moderate vegetation and soil moisture as well as large soil moisture dynamic conditions. Interestingly, the linear correlation generally decreases as sand content increases while increases with the increase of clay content. The absolute linear correlation coefficient is higher with larger soil moisture dynamics. When compared to SMAP enhanced TB, it shows the linear assumption may have more influence on the correlation (i.e., temporal evolution) of downscaled TB than its absolute accuracy. These findings can enhance the understanding of the geophysical relationship between radar and radiometer signatures, and thus benefit active-passive joint algorithms for future satellite missions.

Synthetic aperture radar raw data simulation (SAR-RDS) is beneficial to the SAR system design, signal processing method verification, and radar parameter optimization. Most SAR-RDS methods are based on the flat terrain assumption. However, the fluctuant terrain in real scene will induce severe SAR beam occlusion effect and produce radar shadow, leading to incorrect RDS results. Thus, a dynamic elevation angle interpolation (DEAI) algorithm is proposed for SAR shadow judgment by considering the actual SAR working process. The key of the proposed DEAI algorithm is the 1-D EAI and shadow visualization update, which avoids the problem that the existing methods cannot judge the shadow of partial areas due to the insufficiently refined mesh grid or the mismatch of the judgment model. Moreover, an evaluation framework named as joint image and signal criteria (JISC) is proposed from the perspectives of SAR imaging and signal processing results to objectively evaluate the SAR-RDS results and solve the problem that the existing evaluation methods cannot be compatible with fluctuant terrain. Finally, the numerical experiment verified our theoretical analyses.

Ionospheric Faraday rotation distorts satellite radar observations of the Earth's surface. While its impact on radiometric observables is well understood, the errors in repeat-pass interferometric synthetic aperture radar (InSAR) observations and hence in deformation analysis are largely unknown. Because Faraday rotation cannot rigorously be compensated for in nonquad-pol systems, it is imperative to determine the magnitude and nature of the deformation errors. Focusing on distributed targets at L-band, we assess the errors for a range of land covers using airborne observations with simulated Faraday rotation. We find that the deformation error may reach 2 mm in the copol channels over a solar cycle. It can exceed 5 mm for intense solar maxima. The cross-pol channel is more susceptible to severe errors. We identify the leakage of polarimetric phase contributions into the interferometric phase as a dominant error source. The polarimetric scattering characteristics induce a systematic dependence of the Faraday-induced deformation errors on land cover and topography. Also, their temporal characteristics, with pronounced seasonal and quasi-decadal variability, predispose these systematic errors to be misinterpreted as deformation. While the relatively small magnitude of 1-2 mm is of limited concern in many applications, the persistence on semiannual to multiannual time scales compels attention when long-term deformation is to be estimated with millimetric accuracy. Phase errors induced by uncompensated Faraday rotation constitute a nonnegligible source of bias in interferometric deformation measurements.

In this paper, an ultra-wideband (UWB) radar is firstly employed to probe through the opaque wall media to detect behind-the-wall human motions. By employing such a radar, a high-resolution time-range map with different body parts’ reflections highly discriminable in range direction can be obtained. Secondly, a high-pass filter is applied to remove the wall effects in the raw time-range map. Then, with the aim of exploiting the rich range information so as to enhance their corresponding micro-Doppler features, a novel range-max enhancement strategy is proposed to extract the most significant micro-Doppler feature of each time-frequency cell along range direction for a specific motion. Lastly, the effectiveness of the proposed motion feature enhancement strategy is investigated by means of onsite experiments. Comparative classifications using different convolutional neural network structures (CNNs) show that the proposed approach outperforms other state-of-art micro-Doppler feature extraction methods. The comparison with narrowband detection case also proves its superiority in feature enhancement in narrowband detection scene.

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.

The clutter encountered in ground-penetrating radar (GPR) systems seriously affects the performance of the subsurface target detection methods. A new clutter removal method based on convolutional autoencoders (CAEs) is introduced. The raw GPR image is encoded via successive convolution and pooling layers and then decoded to provide the clutter-free GPR image. The loss function is defined in terms of the reference clutter-free target image and the decoder output is optimized to learn the weight coefficients from the raw data. The method is compared to the conventional subspace methods, recently proposed nonnegative matrix factorization, as well as low-rank and sparse decomposition (LRSD) methods and dictionary separation-based morphological component analysis. CAE and its deeper version deep CAE (DCAE) are trained by several scenarios generated by the electromagnetic simulation tool gprMax. Simulation results demonstrate the effectiveness of the proposed method for challenging scenarios. While for real GPR image, the simulated data trained networks remain slightly behind the LRSD methods for the dry case, nonetheless, they outperform the aforementioned processing techniques for the more challenging wet case.

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.

Arc synthetic aperture radar (ArcSAR) is a ground-based remote imaging technology with the ability to cover wide fields of view. However, because its azimuth is formed by scanning angle rotation, the focusing of ArcSAR images is different from classical SAR focusing. Since the existing imaging methods cannot give a good balance between computational efficiency and accuracy, the wavenumber domain algorithm (WMA) could become an interesting alternative. Due to the fact that in ArcSAR imaging, the slant range measurement depends on a sine term of the scanning angle, no dedicated WMA-based approach for ArcSAR imaging has been formulated yet. The main challenge in this context is that the solution of the stationary phase point cannot be resolved explicitly via Fourier transform (FT). This article proposes a new wavenumber domain imaging method, which exploits the sine law in the process of solving the stationary phase point during FT along the direction of the received echo using the triangular relationship formed by the target, the radar, and the rotation center of radar and then obtains the exact phase error expression in range and angular wavenumber domain without any approximation of the slant range or scanning angle. Using this formulation, we develop the corresponding phase error compensation method and complete image focusing. Through point target simulation and experiments on real ArcSAR data, the effectiveness of this method is verified in terms of imaging accuracy and computational efficiency.

Weather radars play an important role in in situ rainfall monitoring owing to their ability to measure instantaneous rain rates and rainfall distributions. Currently, the Korea Meteorological Administration (KMA) provides instantaneous radar observation data and predictions based on the McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE) for up to 6 h, for short-term forecasting. This study presents a conditional generative adversarial network (CGAN)-based radar rainfall prediction method for very short-range weather forecasts from 10 min to 4 h. The CGAN-predicted model was trained and tested using KMA's constant altitude plan position indicator (CAPPI) observation data. The qualitative comparison between the radar observation and the CGAN-predicted rain rates displayed high statistical scores, such as the probability of detection (POD) = 0.8442, false alarm ratio (FAR) = 0.2913, and critical success index (CSI) = 0.6268, in the case of a 1-h prediction for rainfall on September 5, 2019, 15:20 KST. This study demonstrates the capability of the CGAN model for short-term rainfall forecasting. Consequently, the CGAN-generated radar-based rainfall prediction could complement the KMA MAPLE system and be useful in various forecasting applications.

Internal decays in trees can rapidly escalate into a full decomposition of the inner structural layer, i.e., the “heartwood” layer, due to the action of aggressive diseases and fungal infections. This process leads to the formation of big cavities and hollows, which remain surrounded by the sapwood layer only. Estimating the thickness of the sapwood layer with a high degree of accuracy is therefore crucial for a correct assessment of the structural integrity of hollow trees, as well as an extremely challenging task. In this context, ground-penetrating radar (GPR) has proven effective in providing details of the internal structure of trees. Nevertheless, the existing GPR processing methods still offer limited information on their internal configuration. This study investigates the effectiveness of GPR enhanced by a microwave tomography inversion approach in the assessment of hollow trees. To this aim, a living hollow tree was investigated by performing a set of pseudo-circular scans along the bark perimeter with a hand-held common-offset GPR system. The tree was then felled, and sections were cut for testing purposes. A dedicated data processing framework was developed and tested through numerical simulations of hollow tree sections. The internal structure of the real trunk was therefore reconstructed via a tomographic imaging approach and the outcomes were quantitatively analysed by way of comparison with the real sections’ main geometric features. The tomographic approach has proven very accurate in locating the sapwood-cavity interface as well as in the evaluation of the sapwood layer thickness, with a centimetre prediction accuracy.

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.

Buried craters within tens of meters of lunar regolith are rarely studied but are significant for understanding the evolution of surface processes on the Moon. Here, we first report the evidence of an intact buried crater within the layered strata at Chang'E-4 (CE-4) landing site revealed by the lunar penetrating radar (LPR). The time-frequency comparative analysis method based on the variational mode decomposition (VMD) and the rock quantitative analysis method based on the local unit correlation (LUC) are proposed and applied to the processing and analysis of LPR data within 15 lunar days. The results presented by the two methods provide evidence of a buried crater at the CE-4 landing site and simultaneously reveal the rock-concentrated structure within the buried crater. According to the results, it is considered that the filling materials within the buried crater have survived the impaction and gardening during the formation of the overlying fine-grained regolith. Recent works have proposed that the near-surface material at the CE-4 landing site is mainly the lunar mantle materials excavated from the nearby Finsen crater. Therefore, the buried crater probably preserves the initial lunar mantle materials.

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

With the popularity of unmanned aerial vehicles (UAVs), how to conduct automatic and effective detection to prevent unauthorized flying has become an important issue. The conventional constant false alarm rate (CFAR) detector based on radar signal has shown advantages in moving target detection. However, the CFAR-based detectors are strongly dependent on some manual experience, such as the ambient noise distribution estimation and the detection windows' size selection, and usually suffered poor performance on small UAV detection due to the weak signal. Inspired by the success of deep learning (DL) on natural scene object detection, this article tries to explore a DL-based method for UAV detection in pulse-Doppler radar. Concretely, we propose a convolutional neural network (CNN) with two heads: one for the classification of the input range-Doppler map patch into target present or target absent and the other for the regression of offset between the target and the patch center. Then, based on the output of the network, a nonmaximum suppression (NMS) mechanism composed of probability-based initial recognition, distribution density-based recognition, and voting-based regression is developed to reduce false alarms as well as control the false alarms. Finally, experiments on both simulated data and real data are carried out, and it is shown that the proposed method can locate the target more accurately and achieve a much lower false alarm rate at a comparable detection rate than CFAR.

Fil: Pascual, Juan Pablo. Instutito Balseiro. Universidad Nacional de Cuyo. Río Negro, Argentina. Fil: Pascual, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Argentina. Fil: Cogo, Jorge. Universidad Nacional de Río Negro. Centro Interdisciplinario de Telecomunicaciones, Electrónica, Computación y Ciencia Aplicada. Río Negro, Argentina. Fil: Cogo, Jorge. Instutito Balseiro. Universidad Nacional de Cuyo. Río Negro, Argentina. Fil: Collado Rosell, Arturo. Comisión Nacional de Energı́a Atómica (CNEA). Bariloche, Argentina. Fil: Collado Rosell, Arturo. Instutito Balseiro. Universidad Nacional de Cuyo. Río Negro, Argentina. Fil: Areta, Javier Alberto. Universidad Nacional de Río Negro. Centro Interdisciplinario de Telecomunicaciones, Electrónica, Computación y Ciencia Aplicada. Río Negro, Argentina. Fil: Areta, Javier Alberto. Instutito Balseiro. Universidad Nacional de Cuyo. Río Negro, Argentina. Fil: Areta, Javier Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Argentina. In this article, we present a novel algorithm termed multipulse processing (MPP) for improving mean Doppler velocity estimation in weather radar applications. It can be used for both staggered pulse repetition time (PRT) and uniform-PRT sequences. Essentially, MPP consists of finding a particular zero of a functional composed of data autocorrelation estimates at multiple lags. To select the proper zero, an initial Doppler velocity estimate is required. Therefore, MPP can be considered as an estimation refinement stage. Its advantage lies in the fact that it uses the complete information contained in the radar signal autocorrelation. After a theoretical analysis, we compare the performance of MPP against other well-established methods of similar complexity and the Cramér–Rao lower bound, by means of Monte Carlo simulations using synthetic data. We show that the proposed estimator offers the lowest root-mean-square error (RMSE) at low signal-to-noise ratio (SNR) situations for a wide range of spectral widths. Finally, we evaluate the MPP algorithm performance using real data measured by the RMA Argentinian weather radar. The results of tests performed are consistent with those of Monte Carlo simulations and validate the proposed method. En este artículo, presentamos un algoritmo novedoso denominado procesamiento multipulso (MPP) para mejorar la estimación de la velocidad Doppler media en aplicaciones de radares meteorológicos. Se puede utilizar tanto para secuencias de tiempo de repetición de pulso (PRT) escalonadas como de PRT uniforme. Esencialmente, MPP consiste en encontrar un cero particular de un funcional compuesto por estimaciones de la autocorrelación de los datos en múltiples retardos. Para seleccionar el cero adecuado, se requiere una estimación inicial de la velocidad Doppler. Por lo tanto, MPP puede considerarse como una etapa de refinamiento de la estimación. Su ventaja radica en que utiliza la información completa contenida en la autocorrelación de la señal del radar. Después de un análisis teórico, comparamos el rendimiento de MPP con otros métodos bien establecidos de complejidad similar y con la cota Cramér-Rao, mediante simulaciones de Monte Carlo utilizando datos sintéticos. Mostramos que el estimador propuesto ofrece el error cuadrático medio (RMSE) más bajo en situaciones de relación señal-ruido (SNR) baja para una amplia gama de anchos espectrales. Finalmente, evaluamos el rendimiento del algoritmo MPP utilizando datos reales medidos por el radar meteorológico argentino RMA. Los resultados de las pruebas realizadas son consistentes con los de las simulaciones de Monte Carlo y validan el método propuesto.

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.

Integrating compressed sensing (CS) with millimeter-wave (mmW) holography has shown great potential to achieve lightweight onboard hardware, low sampling ratio, and high-speed sensing. However, conventional CS-driven algorithms are always limited by nontrivial adjusting of parameters and excessive computational cost caused by plenty of iterations. To address this problem, we propose a lightweight model-based deep learning framework (LFIST-Net) for mmW 3-D holography, by combining the interpretability of fast iterative shrinkage-thresholding algorithm (FISTA) and tuning-free merit of data-driven deep neural network. First, the single-frequency (SF) holographic imaging technique is integrated into FISTA, which serves as the sensing kernels, to avoid large-scale matrix multiplications. Subsequently, the kernel-based FISTA (KFISTA) is mapped into layer-fixed and parameter-learnable LFIST-Net, whose weights are relaxed to be layer-varied. The updating of key parameters in LFIST-Net, including step sizes, thresholds, and momentum coefficients, are regularized by soft-plus function to ensure the non-negativity and monotonicity. As for 3-D holography implementation, the ``1-D + 2-D'' scheme is adopted, where the matched filtering (MF) and well-trained LFIST-Net are used for range focusing and reconstructions of azimuth slices. Without losing efficiency, the range-focused subechoes are processed parallelly in 3-D cube form. Experiments, including both simulated and measured tests based on a commercial mmW radar, prove that LFIST-Net is capable of reconstructing the imaging scene precisely. In particular, in near-field mmW 3-D holography tests, both numerical and visual results demonstrate LFIST-Net yields compelling reconstruction performance while maintaining high computational speed compared with MF-based, conventional CS-driven, and network-based methods.

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.

Recently, a novel design scheme of low-earth-orbit spaceborne mini-synthetic aperture radar (MiniSAR) system is proposed to exploit the integrated transceiver to collect the azimuth periodic block sampling data by using alternated transmitting and receiving operations. Because such collected data are downsampled, the images recovered by the typical matched filtering (MF)-based methods have the problems of obvious azimuth ambiguities, ghosts, and energy dispersion. To find a suitable method for such data, with the help of sparse signal processing technique, we first introduce sparse synthetic aperture radar (SAR) imaging with l₁-norm regularization-based approximated observation method to recover the large-scale considered scene. To further improve the imaging performance, a novel approximated observation unambiguous sparse SAR imaging method via l $_{2,1}$ -norm is proposed. Compared with l₁-norm-based method, the recovered image by the proposed one achieves better imaging quality with reduced azimuth ambiguities and ghosts. Experimental results on simulated and real data validate the proposed method.

Oils floating on the sea surface can be observed as ``dark'' patches on radar images since the backscattered signals from the contaminated area are reduced in two dominant ways. First, oil slicks could damp short gravity and capillary waves on the sea surface responsible for backscattering energy. Second, the oil-covered sea surface permittivity decreases significantly if the oil film is sufficiently thick or mixed with seawater, i.e., oil emulsion. In this article, the geometry of the oil-covered sea surface is accounted for by the damping of sea waves, which is described by the model of local balance (MLB) combined with the sea wave spectrum. The radar backscattering is predicted by the advanced integral equation method (AIEM) model. The reflection coefficients are calculated based on a layered-medium model to analyze the impact of oil thickness and emulsions on the radar scattering. Numerical simulations demonstrate that: 1) the sensitivity to oil thickness and water content of the oil spills increases when the radar frequency increases; 2) the backscattering signals exhibit a nonlinear behavior with respect to oil thickness; and 3) high wind speed can generally narrow the difference between the radar backscattering from the clean and oil-covered sea surface, while the incidence angle has little effect. Numerical simulations are then compared with the multifrequency synthetic aperture radar observations acquired during the Gulf of Mexico Deepwater Horizon (DWH) oil spill accident and the 2011 Norwegian Clean Seas Association for Operating Companies (NOFO) oil-on-water exercise. Comparison results show that it is possible to estimate the oil thickness at reasonably good accuracy.

Deconvolution technique can be utilized in the forward-looking radar (FLR). However, the forward-looking imaging performance degenerates greatly due to the effect of high-speed movement of the platform. In this article, an efficient Bayesian forward-looking superresolution imaging algorithm based on Doppler deconvolution in expanded beam space is proposed. First, the Doppler phase information caused by the high-speed platform is fully exploited and the Doppler matrix is integrated with the antenna pattern. The Doppler convolution model of the echo signal for forward-looking is derived in this article. Then, the Doppler phase information is adopted to perform the Doppler deconvolution. Moreover, an expanded beam space is constructed to enhance the sparsity of the imaging scene. The complex Gaussian distribution and the Laplace distribution have been used to model the distribution characteristics of noise and targets in the imaging scene, respectively. Finally, based on the Bayesian framework, the forward-looking imaging problem is converted into the convex optimization problem. The performance assessment based on simulated and experimental data, also in comparison to the conventional real beam, truncated singular value decomposition (TSVD), iterative adaptive approach (IAA) methods, has demonstrated the effectiveness of our proposed algorithm under high-speed platform scenarios.

Microwave imaging with large rotating angle and sparse sampling is an attractive approach to obtain the high-resolution target image with reduced radar resource. However, the popular imaging methods, e.g., Range-Doppler (RD), back projection (BP), and sparse recovery (SR), are difficult to deal with large rotating angle and sparse sampling simultaneously. In recent years, deep learning (DL) has been widely studied and been successfully used to handle the problems in computer vision. However, since most existing DL networks are put forward for the real visual image and a large amount of data is essential for network training, DL cannot be directly used to process the complex and sparse target echo for microwave imaging. In this article, a new learning imaging framework is proposed and a model-driven learning imaging network (MDLI-Net) is built for high-resolution microwave imaging with large rotating angle and sparse sampling. In the proposed framework, the electromagnetic scattering model is used to generate the training data efficiently, and the sparse microwave imaging theory is applied to guide the design of the deep imaging network. By inputting the 2-D sparse complex-valued target echo, the trained MDLI-Net can output the high-resolution and focused target image efficiently. The effectiveness of the proposed learning imaging method is validated by experiment results with both simulated and real data.

We present a generalized power-law roughness spectrum to account for the spatial anisotropy effects on the radar scattering of a rough surface, where both the correlation anisotropy and the scaling anisotropy are accounted for. The spatial anisotropy is essential to correctly interpret the radar scattering from an agriculture field where both plow and sow are practiced. We investigate the dependence of the backscattering coefficient on the correlation anisotropy and the scaling anisotropy through a model simulation. A drastic change in backscattering strength is observed due to the anisotropy. The correlation anisotropy and the scaling anisotropy generate similar backscattering angular behavior, implying that in the context of spatial anisotropy, merely using correlation length in scattering modeling is insufficient. Equivalently, the correlation length retrieved from the backscattering coefficients perhaps is not unique. Fair use of the generalized anisotropic power-law roughness spectrum in conjunction with the scattering model is illustrated by comparing the backscattering coefficients with experimental measurements. However, the anisotropy complicates the roughness description in terms of surface parameters retrieval because we can generate similar backscattering angular patterns by combining different correlation anisotropy and scaling anisotropy. When the soil moisture is of primary interest, a more suitable radar observation geometry to minimize the spatial anisotropy influence is desirable.

Recent deep learning (DL) detectors adopted by radar or sonar (RS) are normally trained with transfer learning, where the typical workflow is to pretrain a convolutional neural network (CNN) on external large-scale classification datasets (e.g., ImageNet) as the backbone and then finetune the entire detector on detection datasets. Though transfer learning could effectively avoid overfitting, transferred models are usually redundant and might not generalize well on RS datasets. To achieve high generalization and to eliminate the dependence on transfer learning, a self-trained target detection method is established by including Automatic Deep Learning (AutoDL) to design optimal detectors. This self-trained target detection consists of three stages. First, a derived classification dataset (DCD) consisting of image blocks of targets and backgrounds is derived from detection datasets. Then, a memory-efficient Differentiable Architecture Search algorithm with flexible search space and large inputs (FL-DARTS), which is characterized by its predefined multistride convolutions, poolings, and unique super-structure, is proposed to automatically design and self-train optimal CNNs on DCDs. Finally, self-trained AutoDL detectors are implemented with the automatic backbone designed by FL-DARTS. We evaluated three self-trained AutoDL detectors on the public SAR ship detection dataset (SSDD) and the self-made sonar common target detection dataset (SCTD). The experiments show that while the number of parameters of automatic backbones designed for SSDD and SCTD are only 11.8% and 15.2% of that of ResNet50, self-trained AutoDL detectors implemented with automatic backbones significantly outperform their transfer learning detectors and achieve state-of-the-art detection precisions and high detection speeds. Data, codes are publicly available.

This article addresses the detection problem of radar targets embedded in nonhomogeneous and non-Gaussian sea clutter. Nonhomogeneity leads to insufficiency of secondary data for estimating the clutter speckle covariance matrix, and non-Gaussianity causes sea clutter to become spiky. In this article, the persymmetry of the clutter covariance matrix is adopted to alleviate the requirement of secondary data, and the prior distribution of clutter texture is exploited to tackle the clutter non-Gaussianity. Based on such clutter knowledge, three adaptive detectors are proposed according to the principles of the generalized likelihood ratio test, the Wald test, and the Rao test. It is proven that three detectors ensure constant false alarm rate (CFAR) properties with respect to both the clutter speckle covariance matrix and the clutter power mean. Simulation experiments show that three detectors outperform their competitors.

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.

This article presents similarities and differences in clutter detection between electronic scans and mechanical scans with a cylindrical polarimetric-phased array radar (CPPAR). Theoretical explanations of clutter features in electronic scans that are different from those in mechanical scans are explored and verified by observations with the CPPAR. Clutter detection results with the CPPAR, based on the copolar correlation coefficient, dual-scan cross-correlation coefficient, power ratio, and their combinations in the electronic scan and mechanical scan modes are presented and compared.

Oil spills in the Arctic are becoming more likely as shipping traffic increases in response to climate-related sea ice loss. To improve oil spill detection capability, we used a controlled mesocosm to analyze the multi-polarized C-band backscatter response of oil in newly-formed sea ice. Artificial sea ice was grown in two cylindrical tubs at the University of Manitoba’s Sea-ice Environmental Research Facility. The sea ice physical characteristics, including surface roughness, thickness, temperature, and salinity, were measured before and after oil injection below the ice sheet. Time-series C-band radar backscatter measurements detected the differences in the sea ice evolution and oil migration to the sea ice surface in the oil-contaminated tub, which was compared to uncontaminated ice in a control tub. Immediately prior to the presence of oil on the ice surface, the co-polarized backscatter increased by 13 dB local maximum, while the cross-polarized backscatter decreased by 9 dB. Ice physical properties suggest that the local backscatter maximum and minimum, which occurred immediately before oil migrated onto the surface, were related to a combination of brine and oil upward migration. The findings of this work provide a baseline data interpretation for oil detection in the Arctic Ocean using current and future C-band multi-polarization radar satellites.

Nonline-of-sight (NLOS) radar imaging is a novel technique that can inverse the scattering characteristics of targets in the NLOS area, which has been one of the hot pots of radar imaging field. However, the existing NLOS radar mainly focuses on 1-D or 2-D imaging, which inevitably suffers from the geometric loss of real 3-D scenes, and its applications are restricted in the urban environment. In this article, we propose an NLOS radar 3-D imaging model and method for looking around corner (LAC) situation by multi-input-multioutput (MIMO) millimeter-wave (mmW) array antennas. In this scheme, first, the model of NLOS radar 3-D imaging with mmW MIMO antennas is established and the multipath scattering of targets with this model is analyzed. Then, the theoretical resolution of LAC 3-D imaging is derived and discussed. Second, exploiting the three bounces of LAC and extraction of linear structure, an effective imaging algorithm with mirror projection theory and Radon transform, dubbed as mirror symmetry backprojection (MSBP), is proposed for 3-D image focusing. Moreover, to suppress the uncertainties of phase caused by both LAC and system error, the minimum entropy principle is introduced to MSBP. Finally, an NLOS 3-D imaging system with 77-GHz mmW MIMO radio frequency module and 2-D rails is developed. Different types of targets, such as metal balls and ornaments, are tested in LAC. The results demonstrate that our NLOS technique can not only provide a high-quality 3-D focusing of the hidden targets but also extract positions of targets without prior knowledge of the NLOS area.

Traditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or ``zero-bandwidth,'' where the decorrelation within the ST snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid due to the deteriorated decorrelation of the received signals within the ST snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the 2-D frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP filters is widened, resulting in a relative poor ability to detect slow-moving targets. In this article, we focus on the clutter suppression for wideband radar STAP. A generalized signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the characteristics of the ground clutter and quantitatively describe the 2-D spreading of the ground clutter on the Doppler-spatial frequency plane. Moreover, the model of clutter covariance matrix for wideband STAP (W-STAP) is established. Finally, a 2-D keystone transform (KT) algorithm, referred to as ST KT (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2-D frequency domain caused by increasing bandwidths. Simulation results are employed to validate the theoretical analysis and verify the overperformance of the ST-KT based W-STAP method in terms of the output signal-to-clutter-plus-noise ratio (SCNR) of moving targets.

State-of-the-art weather forecasting systems depend on a variety of data collected by airborne, orbiting, and ground sensors. Regional CubeSat constellations have the potential to improve hurricane forecasting by collecting sensor data over data-starved oceanic regions. Even in regions where strong terrestrial sensor networks exist, constellation sensor data can help reduce forecasting model errors. To this end, the paper considers the problem of designing a low-Earth orbit CubeSat constellation that meets given resolution requirements over a region of interest. We propose a novel optimization framework that utilizes the concept of satellite coverage maps to determine the number of satellites and constellation pattern. Numerical simulations are presented for asymmetric constellation design that can provide sensor data over important geographical regions within a specified repeated time-window.

Spaceborne synthetic aperture radar (SAR) real-time imaging is especially important for disaster emergencies and real-time monitoring applications with highly desired real-time requirements. Therefore, the continuous improvement of real-time imaging efficiency is an important development trend. At present, traditional real-time imaging algorithms based on constant pulse repetition frequency (PRF) have low accuracy when processing spaceborne SAR data with nonlinear trajectory. For this problem, the existing methods usually introduce some complex signal processing steps, such as scaling or interpolation processing, to improve the accuracy of the real-time imaging, but this will reduce its efficiency. Therefore, this article proposes a new real-time imaging algorithm based on variable PRF for nonlinear trajectory spaceborne SAR. By introducing the variable PRF, the proposed algorithm is equivalent to complete the complex signal processing steps in the radar signal transmission stage, which can greatly improve the efficiency of real-time imaging. Simulation experiments verify the effectiveness of the algorithm.

In inverse synthetic aperture radar (ISAR) imaging for a uniformly moving rigid-body target, a finely focused ISAR image can be obtained by using the conventional range-Doppler algorithm. However, the ISAR image quality may significantly deteriorate when the time-vary Doppler phases in virtue of target maneuvering motions are present, such as an airplane with nonuniformly rotation and a ship with fluctuation. This has become a challenging task, especially under nonhigh signal-to-noise ratio (SNR) environment. In this article, a novel ISAR imaging algorithm for a maneuvering target with moderate reflection intensity is proposed. After motion compensation, the radar echo signal in a range cell is modeled as a multicomponent cubic phase signal (CPS), in which the chirp rate and the quadratic chirp rate are two important physical quantities that may determine the target ISAR focusing quality. Based on a symmetrical instantaneous autocorrelation function, the received CPSs are transformed into the time and lag-time plane, and then a 2-D coherent integration can be realized after the generalized time-scaled transform and 1-D maximization. This forms a high-quality ISAR image. The effectiveness and superiority of the proposed algorithm are validated by the ISAR imaging results of simulated and real measured data.

Sea-Surface small target detection is challenging for maritime radar. Unfortunately, conventional detection methods are often limited to complex marine environment and low signal-to-clutter ratio (SCR). This article presents a multi-frame detection approach for sea-surface small target by using deep convolutional neural network. The moving targets can be reconstructed and detected from the sequential Range-Doppler (RD) spectra. A two-step detection framework is proposed, where the intra-frame and the inter-frame detection is achieved by using the differences of features and inter-frame correlations between moving target and sea clutter, respectively. The proposed approach has been verified on both the simulated and real sea-surface small targets, which shows better detection performance than the conventional multi-frame detection algorithms. Additionally, this approach exhibits acceptable generalization ability.

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.

Vertical-looking radar (VLR) is a significant milestone in the development of insect radars with the capability of detecting the behavior of migratory insects and their biological parameters. In current VLRs, high-speed continuous sampling and long-time integration can barely be performed simultaneously, leading to a low detection probability for tiny insects (weight < 10 mg). Based on the large amount of data acquired by our developed high-range resolution insect radar, the insect echo signals and vertical motion characteristics are initially analyzed and demonstrate that the linear-motion mode is dominant in insect migration; also, the echo signal power of most insects follows the gamma distribution. Based on these characteristics, a long-time integration and detection method for detecting migratory insects, especially tiny targets from echo signals that often dip below the noise level, is proposed. The radial target velocity is also measured as one of the output parameters. The theoretical derivation and optimal choice of detection thresholds are also presented. Simulation and experimental results demonstrate that the proposed method exhibits better insect detection performance and effectively increases the detection range compared with conventional methods. In addition, the measured target velocity can be directly applied to current continuous-sampling VLRs for the ascent and descent rate analysis. Many typical insect migration phenomena have been detected effectively utilizing our developed VLR, and the measured ascent and descent rates of insects agree well with typical take-off, cruising, and landing behaviors. This is the first reported successful VLR application on take-off and landing behaviors of migratory tiny and dense insects.

Signal fluctuations at the receiving antenna have been studied from decades by the radar community, especially to understand the decorrelation of the scattering in radar interferometry. This was done assuming uncorrelated point-like scatterers, leading to a simple model for the geometric decorrelation. In this case, the scattering is certainly incoherent. The quasi-specular reflections gathered under the illumination of signals of opportunity can exhibit significant temporal fluctuations. They are related to the statistical features of the surface roughness and can be observed even in almost flat regions, where a predominant coherent reflection could be expected. The presence of gentle undulations, however, i.e., those showed by surfaces having variations of the profiles comparable with the wavelength over the vertical scale, but much longer over the horizontal one, can determine transition regions where the scattering is neither coherent nor completely incoherent. In these conditions, the nature of the fluctuations of the scattering is not well understood and it requires additional studies. A discussion about the dominance of coherent or incoherent reflection in the Global Navigation Satellite System Reflectometry (GNSS-R) community is presently ongoing. To describe the nature of the scattering, and to understand the decorrelation of the near-specular components in GNSS-R, we propose a numerical study of the field collected by a moving airborne receiver based on the Kirchhoff approximation. Our study demonstrates that the near-specular scattering collected over representative natural landscapes by a GNSS-R receiver is partially coherent and essentially incoherent in most cases. Its correlation time is a function of the roughness parameters, namely standard deviation and correlation length, as well as of the system parameters (i.e., spatial resolution and height). The analysis can provide useful information for the interpretation of GNSS data, which present intrinsic variability that can significantly affect the retrieval of the relevant bio-geophysical parameters.