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This paper describes the first application of the fully adaptive radar (FAR) framework for cognition to the process of radar imaging. A cognitive radar adapts to its surroundings based on its perceptions of the environment, offering improved performance for a multitude of radar applications. We implemented an autoregressive backprojection (ARBP) imaging technique for the circular synthetic aperture radar (SAR) video within the structure of the FAR framework, allowing the system to adapt its down-range and cross-range resolutions to keep the detected targets visually distinct. This simple demonstration paves the way for more advanced adaptive imaging scenarios in the future. Application of the technique to the GOTCHA volumetric SAR data set demonstrated its capability in a realistic scenario in the presence of clutter and limited target persistence. When applied to the GOTCHA data set, the adaptive imaging system’s cumulative executive optimization cost (CEOC), which is used to quantify the overall performance, was 41.3% smaller than the constant, fine resolution case. This significant improvement in CEOC comes at the expense of occasionally failing to meet imaging performance goals as the system adjusts to changes in the environment.

This paper addresses the problem of radar-based human gait recognition based on the dual-channel deep convolutional neural network (DC-DCNN). To enrich the limited radar data set of human gaits and provide a benchmark for classifier training, evaluation, and comparison, it proposes an effective method for radar echo generation from the infrared, publicly accessible motion capture (MOCAP) data set. According to the different nonstationary characteristics of micro-Doppler (m-D) for the torso and limbs, it enhances their distinguishable joint time–frequency (JTF) features by applying the short-time Fourier transforms (SFTFs) with varying sliding window length and then designs the DC-DCNN structure to achieve refined human gait recognition by separate feature extraction and fusion. Experiments have shown that compared with the traditional single-channel deep convolutional neural network (SC-DCNN), the proposed method achieves higher recognition accuracy in refined human gait classification without incurring additional radar resources and could be readily extended to refined recognition of other human activities.

Source at https://doi.org/10.1109/TGRS.2019.2931375. Since the 1950s, specular meteor radars (SMRs) have been used to study the mesosphere and lower thermosphere (MLT) dynamics. Atmospheric parameters derived from SMRs are highly dependent on the number of detected meteors and the accuracy of the meteors' locations. Recently, incoherent and coherent multiple-input-multiple-output (MIMO) radar approaches combined with waveform diversity have been proposed to increase the number of detected meteors and to improve time, altitude, and horizontal resolution of the estimated wind fields. The incoherent MIMO approach refers to the addition of new transmit sites (widely separated), whereas the coherent MIMO refers to the addition of new transmit antennas in the same site (closely separated). In both the cases, a different pseudorandom sequence is transmitted from each antenna element. Unfortunately, the addition of new transmit antennas with different code sequences degrades the performance of conventional signal recovery algorithms. This is a consequence of the cross-interference between the transmitted waveforms, making it worse as the number of transmitters increases. In this article, we propose a signal recovery approach based on compressed sensing, taking advantage of the sparse nature of specular meteor echoes. The approach allows the exact recovery of weak echoes even in interference environments. Besides the advantage of the proposed approach to recover the meteor signal, we discuss the optimal selection of the transmitted waveforms and the minimum code length required for exact recovery. Additionally, we propose a modification of the orthogonal matching pursuit algorithm used in sparse problems to make it applicable in real-time analysis of large data. The success of the proposed approach is corroborated using Monte Carlo simulations and real data from a multi-static spread spectrum meteor radar network installed in northern Germany.

Single- and dual-polarimetric synthetic aperture radar (SAR) images provide very limited capabilities to interpret physical radar signatures. For generality and simplicity, we call single-polarimetric, dual-polarimetric, and fully polarimetric SAR (PolSAR) images flexible PolSAR images. In order to sufficiently extract physical scattering signatures from this kind of data and explore the potentials of different polarization modes on this task, this paper proposes a contrastive-regulated convolutional neural network (CNN) in the complex domain, attempting to learn a physically interpretable deep learning model directly from the original backscattered data. To achieve a better deep model containing physically interpretable parameters, the objective cost is compared to and selected from several commonly used loss functions in the complex form. The required ground-truth labels are generated automatically according to Cloude and Pottier’s H-alpha division plane, which significantly reduces intensive labor cost and transfers this method to an unsupervised learning mechanism. The boundaries between different scattering signatures, however, sometimes show an erroneous separation. With the aim of aggregating intra-class instances and alienating inter-class instances, meanwhile, a complex-valued contrastive regularization term is computed mathematically and is added to the objective cost by a tradeoff factor. Moreover, data augmentation is applied to relieve the side effects caused by data imbalance. Finally, we performed experiments on German Aerospace Center’s (DLR)’s L-band, high-resolution (HR), and airborne F-SAR data. Our results demonstrate the possibility of extracting physical scattering signatures from flexible PolSAR images. Physically interpretable potentials of SAR images with different polarization modes are analyzed, and we conclude with physical signature identification.

Spaceborne radar sounders are nadir-looking sensors operating in the high frequency (HF) or very high frequency (VHF) bands with subsurface sensing capabilities. Due to technological limitations, this type of sensors often deploys omnidirectional antennas. This results in undesired artifacts such as off-nadir clutter which could hinder data interpretation. Very recent technological advancements open up the possibility of synthesizing very large antenna apertures in HF/VHF band by using small satellites array deployed in suitable orbital formation flying. Accordingly, in this study, we propose a novel concept of distributed radar sounder system. The proposed concept is complemented with a mathematical model for performance prediction which takes into account the uncertainty on the position of the sensors. Moreover, we discuss possible orbital solutions for the problem of the deployment of the distributed radar sounder system. The results show that a distributed radar sounder operating in small satellites formation flying is particularly appealing as it can: 1) substantially reduce the impact of surface clutter; 2) increase the across-track resolution; 3) increase the signal-to-noise ratio (SNR) (or, alternatively, decrease the overall required transmitted power with respect to a traditional single configuration radar sounder design); and 4) provide large flexibility in the data processing of the signals acquired by the different sensors.

Although basic understanding of the synthetic aperture radar (SAR) imaging mechanism of ocean waves has been achieved, challenges still remain. In this paper, a large number of quad-polarized SAR images are analyzed to help assess how the standard SAR imaging transformation applies to all polarization channels. Foremost, the azimuth cutoff, a parameter essentially governed by the detected wave motions, is today solely related to radar configuration and the ocean wave spectrum but not to the polarization configuration. As obtained, the analyses based on quad-polarized Radarsat-2 and Gaofen-3 products document the distinct dependence of azimuth cutoff on polarization and incidence angle. Especially for cross-polarized VH measurements, azimuth cutoff estimates are generally larger than copolarized HH ones, the latter already being larger than values estimated under VV configuration. This trend increases with the incidence angle. The systematic comparisons between SAR measurements and simulations further demonstrate that the present SAR nonlinear transformation may not properly take into account the differing coherence time associated with the multi-polarized observation of ocean scenes. In particular, to reproduce the large azimuth cutoff parameters of cross-polarized images, a reduced coherence time shall be expected. This measurable sensitivity shall enhance the capabilities of polarized SAR systems to precisely derive more ocean surface properties, especially the influence of wave breakers, by combining both the copolarization and cross-polarization measurements.

Changes in firn properties affect the shape of pulse-limited radar altimeter echoes acquired over the polar ice sheets. We apply a waveform deconvolution model to CryoSat-2 low-resolution mode echoes to determine the depth distribution of radar backscattering across the Greenland Ice Sheet. The deconvolution allows us to calculate the relative contributions of surface and volume scattering and the effective penetration depth of the radar echoes into the snowpack. The most prominent signal is associated with the extreme surface melting of summer 2012, which resulted in a shift of the dominant radar scattering horizon toward the snow surface in the accumulation zone. At locations above 2000 m, the average penetration depth in July 2012 (prior to the melt event) was 3.79 ± 1.12 m. Following the melt event, there was an abrupt reduction in the average penetration depth across the same region to 1.45 ± 0.94 m. The average penetration depth then gradually increased to 3.28 ± 1.13 m by the end of 2017, as fresh snow accumulated on the ice sheet surface. Although the variation in penetration is evident in surface height estimates derived from the CryoSat-2 echoes, the magnitude of the effect is reduced by waveform retracking. Using airborne laser altimeter data recorded over the same time period, we show that the penetration variation can be compensated effectively by incorporating the deconvolution penetration depth into the surface height retrieval.

Through-the-wall radar imaging (TWRI) is a sensing technology that can be used for detecting, locating, and identifying targets inside enclosed building structures. Many of the existing target classification approaches focus on single-target scene. For multi-target classification, the radar signal has to be segregated into different target components. However, target separation in TWRI is a challenging problem since the radar signals consist of both strong wall reflections and weak target echoes. Furthermore, the target signals are attenuated and distorted when propagating through the wall. In this paper, a variational model with low-rank constraint is proposed for decomposing the radar signal into target components and removing the wall returns. Experimental results show that the proposed method can effectively separate the radar signal into different target components.

Full-waveform inversion (FWI) in the time domain of ground-penetrating radar (GPR) data involves a vast number of calculations; thus, it requires a large amount of memory and is difficult to calculate on a personal computer (PC). In this paper, GPR data are analyzed with multiscale FWI using two parameters (permittivity and conductivity) based on total variation (TV) regularization, which is implemented on a PC using a graphics processing unit (GPU) parallel acceleration strategy. The inverse problem is considered to be a nonlinear optimization problem and is solved with limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) process, which is a quasi-Newton method. The gradient of the objective function is calculated using the adjoint-state method, and the finite-difference method is required to solve the forward problem many times. A multiscale serial inversion strategy is applied to optimize the inversion algorithm and to decompose the inversion problem into 2–3 frequency bands to search in the direction of the global minimum point instead of local minimums. Taking the complex model as an example, experiments are carried out to assess the parameter adjustment factor and regularization parameter. The appropriate parameter adjustment factor and regularization parameter can effectively guarantee the convergence speed and stability of dual-parameter inversion method and improve the accuracy of GPR data inversion. Finally, FWI of the noise-free and 25-dB signal-to-noise ratio (SNR) noise data of the overthrust model is performed. The results show that the multiscale and dual-parameter inversion method proposed in this paper can provide reliable constraints, has better adaptability to noisy data, and can reliably and accurately reconstruct the dielectric properties distribution of the subsurface.

The main challenging problems in ocean current retrieval from along-track interferometric (ATI)-synthetic aperture radar (SAR) are phase calibration and wave bias removal. In this paper, a method based on differential InSAR (DInSAR) technique for correcting the phase offset and its variation is proposed. The wave bias removal is assessed using two different Doppler models and two different wind sources. In addition to the wind provided by an atmospheric model, the wind speed used for wave correction in this work is extracted from the calibrated SAR backscatter. This demonstrates that current retrieval from ATI-SAR can be completed independently of atmospheric models. The retrieved currents, from four TanDEM-X (TDX) acquisitions over the Oresund channel in the Baltic Sea, are compared to a regional ocean circulation model. It is shown that by applying the proposed phase correction and wave bias removal, a good agreement in spatial variation and current direction is achieved. The residual bias , between the ocean model and the current retrievals, varies between 0.013 and 0.3 m/s depending on the Doppler model and wind source used for wave correction. This paper shows that using SAR as a source of wind speed reduces the bias and root-mean-squared-error ( RMSE ) of the retrieved currents by 20% and 15%, respectively. Finally, the sensitivity of the sea current retrieval to Doppler model and wind errors are discussed.

International audience; The resolution achieved by current Synthetic Aperture Radar (SAR) sensors provides detailed visualization of urban areas. Spaceborne sensors such as TerraSAR-X can be used to analyze large areas at a very high resolution. In addition, repeated passes of the satellite give access to temporal and interferometric information on the scene. Because of the complex 3-D structure of urban surfaces, scatterers located at different heights (ground, building façade, roof) produce radar echoes that often get mixed within the same radar cells. These echoes must be numerically unmixed in order to get a fine understanding of the radar images. This unmixing is at the core of SAR tomography. SAR tomography reconstruction is generally performed in two steps: (i) reconstruction of the so-called tomogram by vertical focusing, at each radar resolution cell, to extract the complex amplitudes (a 1-D processing); (ii) transformation from radar geometry to ground geometry and extraction of significant scat-terers. We propose to perform the tomographic inversion directly in ground geometry in order to enforce spatial regularity in 3-D space. This inversion requires solving a large-scale non-convex optimization problem. We describe an iterative method based on variable splitting and the augmented Lagrangian technique. Spatial regularizations can easily be included in this generic scheme. We illustrate on simulated data and a TerraSAR-X tomographic dataset the potential of this approach to produce 3-D reconstructions of urban surfaces.; La résolution atteignable par les Radar à Synthèse d'Ouverture (RSO ou SAR) actuelle permet la visualisation détaillée de milieu urbains. Les capteurs embarqués sur satellites comme TerraSAR-X peuvent être utilisés pour analyser avec une très haute résolution de larges zones à la surface terrestre. Les structures complexes présentes dans les milieu urbains possèdent généralement plusieurs réflecteurs à des hauteurs différentes (sol, façades de bâtiment, toits, etc). Ceux-ci génèrent des échos se projetant dans les même cases radar qui doivent être séparés pour permettre une compréhension fine des images radar. Ce travail de séparation de sources constitue le coeur de la tomographie SAR. La tomographie SAR est généralement effectuée en deux étapes : (i) construction des dits tomogrammes par séparation verticale en chaque case radar (traitement 1-D), (ii) transformation pour passer d'une géométrie radar à une géométrie sol et extraction des réflecteurs significatifs. Nous proposons d'effectuer l'inversion tomographique directement en géométrie sol pour favoriser une régularité dans l'espace 3-D. Cette inversion requière de résoudre un problème d'optimisation non convexe de large échelle. Pour ce faire, nous utilisons une méthode itérative basée sur une séparation de variables et l'utilisation du Lagrangien augmenté. Nous validons notre algorithme et présentons des reconstructions obtenues à partir de données simulées ainsi que d'un jeu d'image TerraSAR-X.

It is a difficult task for high-resolution maritime radars operating at a fast scan mode to find sea-surface floating and low-velocity small targets due to ubiquitous sea clutter, sporadic sea spikes like target returns, and shortage of shared database. In this paper, a simulation method is presented to generate high-resolution radar returns of a local sea surface with a structural trend in texture and sea spikes by integrating the existing results on large-scale sea surface generation, sea surface reflectivity, Doppler characteristics of sea clutter, and properties of sea spikes. Generally, sea-surface small target detection at a fast scan mode is composed of intrascan integration to suppress sea clutter and interscan integration to exclude false alarms and sea spikes. Based on the Doppler difference between targets and sea clutter at the two time scales of a coherent processing interval (CPI) of tens of milliseconds and a scan period of several seconds, a Doppler-guided retrospective filter (DGRF) detector is proposed, which uses the optimum coherent detection in intrascan integration and a DGRF in interscan binary and test statistic integrations. The two integrations and Doppler consistency of integrated plots are fused for the final decision. Owing to the Doppler guidance, the proposed detector effectively impedes the integration of false alarms of the intrascan processing and provides significant detection performance improvement, which is verified by the simulated data and real radar data with test small targets.

Boundary layer radar (L-band) wind profilers frequently encounter a significant problem arising from the contamination of intermittent clutter, produced by seasonal and nocturnal migrating birds, which often yields erroneous wind velocity and boundary layer information. Classical harmonic wavelet transforms (HWTs) are inadequate in removing the transient clutter contamination under certain conditions, particularly when the clutter is significant. We implemented an adaptive complex harmonic discrete wavelet transform with an advanced statistical method to overcome the shortcomings of the classical wavelet method. This algorithm effectively eliminates the bird contamination even where the classical method fails. Finally, a multiple peak-picking (MPP) algorithm was added to select true atmospheric signals and estimate accurate moments. The obtained wind velocity measurements were compared with those derived using the conventional method and validated with global positioning system radiosonde winds. The comparison shows that the proposed method is more effective than the conventional one.

Shore-based coherent microwave radar and high-frequency (HF) surface wave radar are two components of a rapidly emerging set of technologies suitable for ocean wave remote sensing. To investigate their differences, this paper describes and analyzes the relationship between the water particle velocity and the wave height spectrum based on linear wave theory which underpins the algorithms developed for the analysis of data collected by coherent microwave radar. The backscatter mechanism which addresses the interaction of the HF radio waves with the ocean surface waves, as well as the empirical method adopted in our HF radar is also presented. The wave characteristics observed by the shore-based coherent S-band radar [Microwave Ocean Remote SEnsor (MORSE)] are analyzed. A multifrequency HF (MHF) radar based on a circular receiving array, which is capable of sensing waves up to 100-km offshore, is also introduced. An intercomparison of the wave height measurements obtained from the MORSE, MHF radar, and wave buoy is made. The comparison indicates that the wave heights measured by the MORSE and the MHF radar are consistent with the buoy-derived wave heights, with the root-mean-square differences (RMSDs) of 0.27 and 0.37 m, respectively.

The vertical structure of wind is a key factor in modulating air quality, from which the determination of boundary layer height (BLH) remains a major challenge. In this paper, we developed an improved threshold method to determine the BLH from radar wind profiler (RWP) measurements. The normalized signal-to-noise ratio (SNR) profiles were used instead of the original SNR profiles to avoid instrumental inconsistencies. Additionally, a peak filter was designed to indicate the BLH based on the maximum SNR by taking into account the multiple peaks in the SNR profile. This algorithm was then applied to the RWP measurements taken in the summer (June–July–August) of 2018 in Beijing to obtain the BLHs. Validation analyses suggested that the BLH retrievals from RWP exhibited high consistency with those from radiosondes, with an average correlation coefficient of 0.69 (0.66) and a root mean squared error of 0.39 (0.41) in the daytime (nighttime). Additionally, the major features of summertime BLHs in Beijing were examined. In particular, a distinct diurnal variation in BLH was observed with a peak (1630 ± 510 m) occurring at 0600 universal time coordinated (UTC) and a minimum (587 ± 343 m) at 2300 UTC. Therefore, the algorithm presented here has great potential to be applied to other regions to obtain reliable BLHs. The findings obtained here highlight the importance of vertical wind structure in air quality studies.

Low-frequency synthetic aperture radar (SAR) sensors are prone to ionospheric irregularity structures that affect the amplitude and/or phase of the radar signal. Azimuthal striping caused by amplitude scintillation in SAR data is an initial observation of such effects. In the absence of any physical model and/or technique to mitigate scintillation stripes, we have applied a 2-D fast Fourier transform (FFT) approach for improved interpretation and target identification in fully polarimetric SAR (POLSAR) model-based decomposition scattering powers. Few scenes of fully polarimetric Advanced Land Observation Satellite-Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) and ALOS-2/PALSAR-2 under different ionospheric conditions are acquired and used in this study. As a reference, data sets over the same areas are also acquired on different dates with negligible ionospheric activity (ionospheric quiet day). All the data sets are corrected for the Faraday rotation angle ( $\Omega$ ), which is estimated using the Bickel–Bates approach. Three correction strategies of scintillation-affected model-based scattering power decomposition images are presented. The strategies (zero masking, thresholding, and averaging-post-thresholding) are implemented on 2-D FFT of scattering power images. An intercomparison of corrected decomposition results from the correction strategies demonstrates the capability of 2-D-FFT-based correction strategies to improve the dominant scattering component by 3%–5% for homogeneous terrains. The correction method based on averaging-post-thresholding gives the best results that are further tested by performing a supervised classification. The overall accuracy and kappa coefficient ( ${OA}, \hat {k}$ ) of the averaging-post-thresholding technique (72.87%, 0.59) is comparable to those of the reference data (78.74%, 0.69).

Studies on the potential of using synthetic aperture radar (SAR) imaging data for remote sensing in oil palm growth needs to be done in depth. In this paper, we utilized a scattering model for oil palm to study and understand how L-band microwave interacts with the structure of the oil palm. The model is developed based on the radiative transfer theory and solved iteratively up to the second order. The knowledge obtained can likely be used to interpret the backscattering behavior of oil palm with respect to growth. The oil palm is modeled as a multilayer dense medium with leaflets as elliptical disk while the trunk and fronds as cylinders. The model takes into consideration of the close spacing effects of scattering in a dense medium with the use of phase correction theory. Furthermore, near-field interactions between closely spaced scatterers are also considered via the Fresnel phase and amplitude corrections. Ground truth data were measured from the oil palm fields and are used as input parameters in the model. Five different age groups of oil palm were chosen and their backscatter coefficient simulated at L-band frequency. L-band SAR data acquired were compared with the simulated results, and it was found that there was a good agreement between the simulated results and measured data. For further understanding, the model also simulated the breakdown of backscatter contribution by various components of oil palm which allows a better understanding of the interaction between microwave backscatter with specific components.

Frequency-modulation continuous-wave (FMCW) synthetic-aperture radar (SAR) can minimize the peak transmission power of sensors and reduce the size and weight of the systems. Wavenumber domain algorithm (WDA) is an accurate focusing method for SAR imaging. By using the exact signal form to compensate the phase error, WDA can achieve exact scene recovery from high-squint and long aperture data as long as the platform velocity is stable. In this paper, we introduce WDA to FMCW SAR and discuss the WDA-based FMCW SAR sparse imaging method. There are two main contributions of this paper: 1) a motion compensation-based WDA imaging method is introduced to correct the motion error that is often associated in practical airborne FMCW SAR data and 2) a novel WDA-based FMCW SAR sparse imaging method is developed to further improve the performance of recovered image. Compared with the typical WDA algorithm, the sparse imaging method can suppress the noise and sidelobes and perform the sparse scene recovery from the downsampled data. Experimental results via simulated and real airborne data verify the presented WDA-based FMCW SAR imaging methods.

We present an advanced differential synthetic aperture radar (SAR) interferometry (DInSAR) processing chain, based on the Parallel Small BAseline Subset (P-SBAS) technique, for the efficient generation of deformation time series from Sentinel-1 (S-1) interferometric wide (IW) swath SAR data sets. We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015-April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h.The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale.

This paper focuses on the retrieval of terrain topography below dense tropical forests by means of synthetic aperture radar (SAR) systems. Low-frequency signals are needed to penetrate such a thick vegetation layer; however, this expedient alone does not guarantee proper retrieval. It is, here, demonstrated that the phase center of P-band backscatter may lie several meters above the ground, depending on the slope and incidence angle. SAR tomography is shown to overcome this problem and retrieves the actual topography even in the presence of dense trees up to 50 m tall. Digital terrain models returned by SAR tomography are, here, put in comparison with light detection and ranging (LiDAR) terrain models: the accuracy of radar-derived maps is found to be at least comparable with the one offered by LiDAR systems. Moreover, the discrepancy between tomography and LiDAR is larger if large-footprint LiDAR is considered thus suggesting that, in this case, tomographic maps should be considered the reference height. Analyses are carried out by processing three data sets gathered over different tropical forests in western Africa. The robustness of the radar estimates is assessed with respect to both ground slope and treetop height.

Interferogram filtering for noise reduction is a key to many radar interferometric applications. Repeat pass radar interferometry often uses data with less than ideal correlation levels resulting from either long spatial or temporal baselines or changes between observations leading to high levels of temporal correlation. To maximize the utility of such pairs filtering the interferogram to get maximal noise reduction is often needed. One technique that has proved quite useful in the geophysical community is power spectral or Goldstein–Werner filtering of the interferogram whereby a power-weighted version of the Fourier transform is used to enhance fringe visibility. Although this paper defining the filter briefly touched upon the spatial resolution and noise reduction induced by the filter, it did not provide a useful formula for predicting the phase noise after filtering. This paper derives a formula for the phase noise obtained from power spectral filtering albeit under the restriction of several simplifying assumptions to make the problem analytically tractable. In particular, it is assumed that the interferometric phase is locally well approximated by a linear phase ramp with nonlinear phase perturbations small in a spectral energy sense compared to the linear term.

A novel procedure based on filtering and interpolation approaches is proposed to estimate the sea state parameters, including significant wave height, peak wave direction, peak period, peak wavenumber, and peak wavelength in shallow waters using the X-band marine radars. The method compensates the distortions introduced by the radar acquisition process and the power decay of the radar signal along the distance applying image-enhancement techniques instead of empirical and semiempirical calibration methods that use signal-to-noise ratio and in situ measurements as external references. To determine the threshold value for the interpolation approach, the influence of the antenna height on shadowing modulation effects is examined through performing an analysis of variance (ANOVA) that uses data from two X-band radars deployed at 10 and 20 m above MSL. ANOVA results reveal that it is possible to explain the increment of intensities affected by shadowing throughout the distance using an adaptive threshold retrieved from a third-order polynomial function of the mean radar cross section (RCS). Finally, an X-band radar is installed at 13 m above MSL to test the proposed technique. During measurements, the wind and wave conditions varied, and the antenna-look direction remained constant. Errors for H, θ, and T calculated as the difference between estimated and true data show a mean bias and a relative value of 0.05 m (2.72%), 1.52° (5.94%), and 0.15 s (1.67%), respectively. The directional and wave energy spectra derived from radar estimates, acoustic wave and current, ADVs record, as well as JONSWAP formulation are presented to illustrate the improvement resulting from the proposed method over the frequency domain.

Studies pertaining to small scale structures producing scintillations using synthetic aperture radars (SARs) have predominantly been conducted at low-latitude regions. The high-latitude region (auroral belt and polar caps) is highly dynamic and varies in response to stimuli from solar winds and the magnetosphere in complex ways. In this paper, the authors have shown the capability of SAR for scintillation observation in the auroral region. An attempt has been made to fit an irregularity anisotropy model to SAR measurements for characterizing the ionospheric irregularities in the auroral regions. The dependency of anisotropy irregularity model on parameters, such as irregularity structure (axial ratio), their orientation with respect to magnetic field lines, and the ionospheric plasma drift, is closely studied using Advanced Land Observing Satellite (ALOS)-2 datasets acquired over Alaska. Geomagnetic indices and total electron content data are consistent with the occurrence of the scintillation event under study. Drift velocity measurements from high-frequency radars in the super dual array radar network (SuperDARN) showed that the anisotropy is independent of the magnitude and the azimuth angle of the plasma drift. The typical range of orientation angle suitable for the high latitude regions probed by ALOS-2 is demonstrated to be between 120°–135°. This paper explores the idea of inferring irregularity anisotropy by comparing the amplitude scintillation (S4) index measured in SAR data pairs using two well-established techniques. The image contrast technique heavily relies on the accurate modeling of anisotropy, whereas the radar cross-sectional enhancement method is independent of it. This feature has been exploited in the $S_{4}$ comparison to finally fit the choice of irregularity axial ratio and conclude that the sheet-like structures best describe the ionospheric irregularity structure in the region under observation.

Special attention has been devoted to joint wireless communication and radar sensing systems in recent years. However, since communication and radar have conflict requirements in terms of waveforms, transceiver developments, and signal processing algorithms, realization of this system concept is still a great challenge. In this paper, we introduce an airborne multi-input multi-output (MIMO) radar, along with the modified orthogonal frequency-division multiplexing (OFDM) and space–time coding (STC) waveform schemes, for the implementation of the joint wireless communication and synthetic aperture radar (SAR) imaging. The proposed system, which simultaneously transmits multidimensional waveforms with reconfigurable channels, can acquire adequate degrees of freedom. Thereby, it becomes a feasible method to perform both data transmission and high-resolution SAR imaging at the same time without intramodal interference. Theoretical analysis is validated by laboratory and flight experiments. Through our analysis, we aim to open up a new perspective of using MIMO radar to realize joint wireless communication and SAR imaging.

Wind wave spectrum describes the quasi-periodic nature of the ocean surface oscillations and plays an indispensable role in the study of microwave electromagnetic scattering from sea surface. A reliable spectrum model suitable for radar cross section (RCS) predictions at different radar frequencies is desired. This paper evaluated the performances of five common spectrum models (i.e., Fung spectrum, Durden–Vesecky spectrum, Apel spectrum, Elfouhaily spectrum, and the newest version of Hwang spectrum, H18) on the normalized radar backscattering cross section (NRBCS) simulations based on advanced integral equation model (AIEM) at L-, C-, X-, and Ku-bands versus incidence angle, wind direction, and wind speed by comparing with the model and measured data for validation. These results indicate no single wave spectrum of them is satisfying for all the four radar frequencies, e.g., Apel and H18 spectra are better for L- and C-bands, Apel spectrum for X-band, and Elfouhaily and H18 spectra for Ku-band. Given this, three average composite spectrum models are constructed using different spectral models (i.e., all five spectra, Apel + Elfouhaily + H18, and Apel + H18) to simulate NRBCSs, similar to that of the individual spectrum model. It is concluded that the combination of Apel and H18 spectra overall performs best among the individual one and other composited spectra in like-polarized NRBCSs versus incidence angles, wind directions, and wind speeds, for wind speed greater than 30 m/s where the combination of the five spectra work well at Ku-band.

Radar-based human motion recognition is crucial for many applications, such as surveillance, search and rescue operations, smart homes, and assisted living. Continuous human motion recognition in real-living environment is necessary for practical deployment, i.e., classification of a sequence of activities transitioning one into another, rather than individual activities. In this paper, a novel dynamic range-Doppler trajectory (DRDT) method based on the frequency-modulated continuous-wave (FMCW) radar system is proposed to recognize continuous human motions with various conditions emulating real-living environment. This method can separate continuous motions and process them as single events. First, range-Doppler frames consisting of a series of range-Doppler maps are obtained from the backscattered signals. Next, the DRDT is extracted from these frames to monitor human motions in time, range, and Doppler domains in real time. Then, a peak search method is applied to locate and separate each human motion from the DRDT map. Finally, range, Doppler, radar cross section (RCS), and dispersion features are extracted and combined in a multidomain fusion approach as inputs to a machine learning classifier. This achieves accurate and robust recognition even in various conditions of distance, view angle, direction, and individual diversity. Extensive experiments have been conducted to show its feasibility and superiority by obtaining an average accuracy of 91.9% on continuous classification.

The Canadian Department of National Defence (DND) is developing an experimental over-the-horizon radar (OTHR) with the potential for surveillance of Canada. Because of dynamically changing ionospheric conditions in the Earth’s high-latitude and polar regions, the operating OTHR transmission frequency and elevation angle need to be adjusted regularly to maintain constant illumination of downrange targets. In this paper, the feasible operating frequency and elevation angle radar parameters are determined for short- and long-range OTHR operation using 3-D ionosphere ray-tracing simulations. Together, the collection of all feasible radar configurations forms a characteristic profile which shifts and deforms as factors such as the time of day, season, and solar activity are varied. The range of operating frequencies and elevation angles obtained from this paper will aid developing the transmitter and receiver antenna layouts for experimental OTHR configurations in the poorly understood high-latitude and polar regions. These methods will also help to form the basis of the frequency monitoring systems (FMS) that will control the configuration of these polar OTHR systems in real time.

Micro-Doppler (m-D) effect, induced by the rotation of rotor blades, provides an important signature to discriminate between small unmanned aerial vehicles (UAVs) and other aircrafts or birds in remote surveillance. Compared with the Doppler signal induced by the translation, m-D signal, however, is rather weak and consists of multiple frequency components. In this paper, empirical mode decomposition (EMD) algorithm is applied to addressing the mode-mixing problem in the returned signal. Theoretically, Doppler features are consequently allocated in the first few intrinsic mode functions (IMFs). Rather, the partial components of the subsequent IMFs hold a similar property with the rotation signal. Those components are selected as the input data for the sparse recovery. With the sinusoidal frequency-modulated basis (SFMB), the essence of the recovery problem is converted into 1-D parameter optimization. Then, phase orthogonal matching pursuit (POMP) method is developed for the sparse solution. The proposed method is contrasted with the prevailing approach to solving the mode-mixing problem. Simulation results confirm the theoretical analysis, showing the feasibility in the estimation of m-D frequency. The preliminary findings from the measured data suggest that the proposed method has a potential application in the identification of small UAVs.

Existing ship inverse synthetic aperture radar (ISAR) imaging algorithms are not applicable, when the signal-to-noise ratio (SNR) is low, for the translational motion that cannot be well compensated by existing algorithms. To achieve ship ISAR imaging with low SNR, a ship ISAR imaging algorithm based on the generalized radon-Fourier transform (GRFT) is proposed in this paper. Considering not only the rotational motion but also the translational motion between the radar and the ship, the proposed algorithm uses the GRFT to simultaneously compensate the time-variant range envelopes and the Doppler phase. Thus, the signal coherence is fully utilized, and the coherent integration of the ship’s multicomponent echo signal is realized. Subsequently, to overcome the problem of the heavy computational load and improve the efficiency of the proposed algorithm, the scheme of cascaded GRFTs that consists of the coarse GRFT and the subsequent fine GRFT is adopted. The coarse GRFT with large search ranges and intervals is aimed at obtaining the real ranges of ship scatter points’ motion parameters. Based on the coarse GRFT result, the fine GRFT with small search ranges and intervals is performed to efficiently obtain the coherent integration result. Then, based on the coherent integration result, the constant false alarm rate (CFAR) detection is performed to obtain the desired scatter points and their amplitudes and motion parameters, and the multicomponent signal is reconstructed. Finally, based on the reconstructed multicomponent signal, the high-quality instantaneous ship ISAR image can be obtained. Computer simulations and experiment results validate the effectiveness of the proposed algorithm.

Directional borehole radar (BR) 3-D imaging is a challenging problem due to its limited observation space within a borehole. We present a linear prediction (LP)-based direction-of-arrival (DOA) estimation and a migration-based 3-D imaging algorithm for the directional BR, which is composed of a transmitting antenna and a uniform circular array including four small receiving antennas. Only the azimuth is estimated, while the elevation and the distance are included in the imaging process. LP is a useful tool for DOA estimation in the radar domain, but a single linear array (LA) has azimuth ambiguity in DOA estimation. The four receiving antennas are treated as two orthogonal uniform LAs. The combination of two arrays removes the ambiguity in DOA estimation. In addition, the moving window and the threshold-based estimation increase the calculation efficiency. According to the estimated azimuth angles, the measured signal at each depth is decomposed to the corresponding directions. Therefore, there is a time-series signal in each direction at each depth. After processing the measured signals at all depths, we obtain a 3-D data set that can be used for horizontal- and radial vertical-section displays. Then, the radial vertical-section data are processed by migration which transforms the time domain signal into a wavefield-based 3-D image. Simulated data from a fracture model are used to validate the algorithm, and the reconstructed image can reveal the real model well. It is concluded that the novel method we propose is an effective one with real-time processing capability.

We propose a method to estimate the active layer properties, including soil dielectric profiles and active layer thickness (ALT), in permafrost regions using time-series P-band polarimetric synthetic aperture radar (SAR) observations. The active layer and underlying permafrost are modeled as a three-layer dielectric structure with the layer dielectric constants representing the soil moisture and freeze-thaw state of the layer. To resolve the ambiguity of the retrieved layer thicknesses, an approach of finding the largest possible depths (LPDs) is combined with time-series observations, where the ALT is assumed time-invariant between the maximum thaw and before the upward freezing front rises significantly from the permafrost table. The LPD-assisted time-series retrieval algorithm is applied to the radar data acquired by the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band SAR in August and October 2014 and 2015 over the Alaska North Slope. The results show that the retrieved ALT values are generally underestimated for the sites where the in situ ALT is larger than the P-band sensing depth, with a retrieval bias ranging from −0.05 to −0.24 m as validated against the in situ ALT collected at Circumpolar Active Layer Monitoring (CALM) sites. For the sites where the in situ ALT is smaller than 0.55 m, the retrieval errors are generally less than 0.1 m. The retrieval results also show that the active layer properties are strongly influenced by the land cover types at the regional scale, and not as much by the North–South temperature gradient across a 180-km-long transect along the Deadhorse flight line.

Wave–current interaction is a common and important phenomenon in the ocean. As an ocean remote sensing tool, high-frequency (HF) radar can be used to measure currents and wave parameters. In this paper, the possibility of studying wave–current interaction using HF radar is investigated. The first-order spectral power (FSP) of HF radar is used to explore the effect of current on the Bragg wave. By analyzing the FSP change with current (FSP-current distribution), we find that, in deep water, the wave–current interactions mainly belong to 2-D refraction case, while, over a relatively shallow shelf, the interactions are stronger and more complicated. Based on local topography and current field data at Taiwan Strait, the simulation results obtained using the SWAN model confirm the 2-D refraction of the Bragg wave. When the wave–current interaction is stable, we compensate the FSP with radar-measured currents according to the radar extracted FSP-current distribution and achieve a more accurate wind estimation. Comparisons between the original and refined wind fields show the effectiveness and necessity of the current-based compensation.

With the development of high-resolution radar satellite for global comprehensive environmental monitoring, day-and-night and all-weather surveillance has become an active and growing research field. However, in all cases, these applications require radar to have a high-efficiency radar module (e.g., T/R module), and high system transmitting power. These requirements may put an important limitation on the performance of a radar satellite with a high-power configuration. In this paper, we report a novel waveform optimization framework. Through this framework, an advanced nonlinear frequency modulation (NLFM) waveform with lower sidelobes and a smaller main lobe, which can significantly relieve the restriction of very limited satellite power, is constructed. In addition, we apply it in a real synthetic aperture radar (SAR) system with a bandwidth of 100 MHz at 9.6-GHz carrier frequency and the whole process of the NLFM waveform for radar imaging is discussed in detail, including the system architecture and configuration, a system error compensation method, and a modified chirp scaling algorithm (CSA). The imaging results demonstrate the excellent performance of the advanced NLFM waveform. Moreover, we observe that the SAR system with the advanced waveform has a higher signal-to-noise ratio (SNR) of 1.29 dB compared with the conventional linear frequency modulation (LFM) waveform. The improvement of 1.29-dB SNR means that the real radar system can reduce transmitting power with a ratio of 25%. This effect is likely to be a potential feature of NLFM waveform, which can reduce the transmitting power requirement, especially for radar satellite.

This paper describes an automated data processing algorithm to identify the turbulence echo in the multipeaked very high-frequency radar spectra, generally observed during the precipitation. Although a few multipeak-picking algorithms exist, they identify the turbulence echo from the clutter and noise, but they do not distinguish turbulence and precipitation echoes. The present algorithm identifies the turbulence echo from multiple peaks based on several criteria, such as the significance of the echoes, separation between the echoes within a sliding window, and a reference turbulence echo. A new method [symmetric method (SM)] is introduced to estimate the moments after proper identification of the turbulence echo, in fact, half portion of the echo. Comparison of the moments estimated by the SM and dual-frequency method (another popular method for the extraction of moments from multipeaked spectra) with those obtained by the routine single-peak detection algorithm reveals large discrepancies, particularly in convective cores and in and above the radar bright band region. In these regions, the error in moments by the traditional single-peak-picking algorithm is found to be substantial. Performance evaluation of these two methods has also been carried out against simulated spectra. The analysis reveals that the error in obtaining moments by the SM and dual-frequency method is less than 15%, except for a few cases. Also, the error in moments by the SM is, in general, smaller than by the dual-frequency method.

Microwave backscattering models play key roles in surface scattering modeling and soil moisture inversion in active microwave remote sensing. However, numerous evaluations indicate that significant discrepancies between the model simulations and radar observations remain, and these discrepancies are regarded to be attributed to inaccuracies in the models. What do such discrepancies originate from is unclear and has not been comprehensively analyzed. To this end, this paper presents an uncertainty analysis to explore the intrinsic reason for the discrepancies between the backscattering model simulations and radar observations. The probability distribution function and the corresponding statistical characteristics are introduced to describe the uncertainty in the model outputs. We find that the scale dependence of the key model inputs leads to significant uncertainties in the model inputs, and the uncertainties are transferred into the model outputs. Thus, the discrepancies between the model simulations and radar observations are intrinsically caused by the spatial scaling and related uncertainties of key model inputs. In short, the scale mismatch between the model inputs and remote sensing pixels is an intrinsic factor that causes the discrepancies between the model simulations and radar observations. This finding suggests that the scaling effect of model inputs should be carefully considered when using the backscattering models at the pixel scale, and equivalent inputs matched at the corresponding scales should be developed for remote sensing applications. Thus, this analysis insights into the scale dependence of inputs for backscattering models and suggests to provide scale-matched inputs where the models are applied at different scales.

The polarimetric two-scale model (PTSM) was introduced a few years ago as an electromagnetic scattering model to be used within algorithms for soil moisture retrieval from polarimetric synthetic aperture radar (SAR) data. PTSM inherits the ability to account for depolarization effects from the original two-scale model (TSM), and, with respect to the latter, it has the advantage to provide closed-form expressions of the elements of the covariance matrix that hold for moderate large-scale surface slopes. This allows a very fast evaluation of scattering, since numerical integration needed by the original TSM is avoided. The TSM, also called composite model (CM), has been extensively used to study scattering from the sea surface, so that it is natural to explore the use of PTSM for the same purpose. However, in its current formulation, PTSM assumes that large-scale surface slope distribution and small-scale roughness spectrum are isotropic. This is not realistic for the sea surface, for which anisotropy is dictated by the wind direction. Accordingly, we here extend PTSM to account for surface roughness anisotropy, so obtaining the anisotropic PTSM (A-PTSM). In addition, as a second contribution, we provide A-PTSM expressions also in the circular polarization basis, which may be useful for some SAR sensor polarimetric configurations. Finally, we compare A-PTSM results with sea surface scattering measurements available in the literature and with results of the second-order small-slope approximation (SSA2). In particular, as a third original contribution of this paper, an analytical closed-form expression of the ratio of crosspolarized normalized radar cross sections (NRCSs) obtained by SSA2 and A-PTSM is provided.

A linear frequency modulation continuous-wave mini-synthetic aperture radar (SAR) system mounted on small aircrafts promises a high-flexibility and cost-effective microwave remote sensing technology. However, the mini-SAR system suffers from considerable trajectory deviations due to aircraft’s lightweight, low flight height, and limited capacity for a high-accuracy inertial measurement unit (IMU). With the rapid increasing requirements for resolution, the residual range cell migration (RCM) exceeds a single range cell. Under such circumstances, traditional autofocus algorithms fail to guarantee well-focused mini-SAR images and further accurate interferometric SAR (InSAR) applications. To solve these problems, this paper proposed a novel residual RCM correction scheme for a mini-SAR system mounted on small aircrafts without high-accuracy IMU. The core idea is to estimate the misalignments of adjacent range profiles based on fast-time split-band signal interferometry at each azimuth time and integrate them with time to obtain an estimation of residual RCM. The proposed method promises a high-accuracy misalignment estimation result without resampling operation in the traditional cross-correlation methods. Simulation and experimental results show the improvement of mini-SAR image focusing quality and refinement of coherence map between the master and slave images for InSAR applications, which demonstrated the effectiveness and reliability of our proposed residual RCM correction scheme for the mini-SAR system.

International audience; In this paper, we report the accurate estimation of vine grape yield from a three-dimensional radar imagery technique. Three ground-based frequency-modulated continuous-wave radars operating respectively at 24GHz, 77GHz and 122GHz are used for the contact-less estimation of grape mass in vineyards. 3D radar images are built from the beam-scanning of the vine plants and allow estimating the mass of grapes from the computation of appropriate statistical estimators. These estimators are derived from the measured polarization and magnitude of radar echoes. It is shown that the estimation of grape mass from the proposed ground-based radar imagery technique at millimeter-wave frequency range may be accurate within 1 per cent.

We address the problem of image series denoising for high-resolution radar in a nonparametric Bayesian framework. By exploiting the characteristics of amplitude variation at different pixels in the image series, we impose the Gaussian process (GP) model to the corresponding time series of each pixel and achieve effective image series denoising by GP regression. Particularly, the model parameters are solved conveniently by the maximum likelihood estimation. Compared with available denoising techniques in the data domain, spatial domain, and image frequency domain, the proposed method has exhibited more flexibility in data description and better performance in structure preserving and denoising, especially in low signal-to-noise ratio scenarios.

Cryosat-2 has provided measurements of pan-Arctic sea ice thickness since 2010 with unprecedented spatial coverage and frequency. However, it remains uncertain how the Ku-band radar interacts with the vast range of scatterers that can be present within the satellite footprint, including sea ice with varying physical properties and multiscale roughness, snow cover, and leads. Here, we present a numerical model designed to simulate delay-Doppler synthetic aperture radar (SAR) altimeter echoes from snow-covered sea ice, such as those detected by Cryosat-2. Backscattered echoes are simulated directly from triangular facet-based models of actual sea ice topography generated from Operation IceBridge Airborne Topographic Mapper data, as well as virtual statistical models simulated artificially. We use these waveform simulations to investigate the sensitivity of SAR altimeter echoes to variations in satellite parameters (height, pitch, and roll) and sea ice properties (physical properties, roughness, and presence of water). We show that the conventional Gaussian assumption for sea ice surface roughness may be introducing significant error into the Cryosat-2 waveform retracking process. Compared to a more representative lognormal surface, an echo simulated from a Gaussian surface with rms roughness height of 0.2 m underestimates the ice freeboard by 5 cm--potentially underestimating sea ice thickness by around 50 cm. We present a set of ``ideal'' waveform shape parameters simulated for sea ice and leads to inform existing waveform classification techniques. This model will ultimately be used to improve retrievals of key sea ice properties, including freeboard, surface roughness, and snow depth, from SAR altimeter observations.

For sparse aperture (SA) radar echoes, the coherence between the undersampled pulses is destroyed, which challenges the effectiveness of the traditional autofocusing and scaling in inverse synthetic aperture radar (ISAR) imaging. A novel Bayesian ISAR autofocusing and scaling algorithm for sparse aperture is proposed, which utilizes Laplacian scale mixture, as the sparse prior of ISAR image, and variational Bayesian inference based on the Laplacian approximation to derive its posterior. In addition, it learns the phase error, rotational velocity, and center of target from radar echo automatically during the reconstruction of ISAR image, so as to achieve ISAR autofocusing and scaling for SA. Because the parameters learning is not easy to converge with the undersampled data, a modified Newton method based on joint constraint of entropy and sparsity is proposed to guarantee fast convergence in a right direction. Experimental results based on both simulated and measured data validate the robustness of the proposed ISAR imaging algorithm against SA and noise.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2018-11-20, 資料番号: PA1910036000

Radio frequency interference (RFI) causes serious problems to ultrawideband (UWB) radar operations due to severely degrading radar imaging capability and target detection performance. This paper formulates proper data models and proposes novel methods for effective RFI mitigation. We first apply the single-snapshot Sparse Iterative Covariance-based Estimation (SPICE) algorithm to data from each pulse repetition interval for RFI mitigation and discuss the connection of SPICE to the $l_{1}$ -penalized least absolute deviation ( $l_{1}$ -PLAD) approach. Then, we devise a modified group SPICE algorithm and we prove that it is equivalent to a special case of the $l_{1,2}$ -PLAD method. The modified group SPICE algorithm can be applied to data from a coherent processing interval for effective RFI mitigation. Both the single-snapshot SPICE and the modified group SPICE methods simultaneously exploit the sparsity properties of both RFI spectrum and UWB radar target echoes. Unlike the existing sparsity-based RFI suppression methods, such as the robust principal component analysis algorithm, the proposed methods are hyperparameter-free and therefore easier to use in practical applications. Furthermore, the fast implementation of the SPICE methods is considered by exploiting the special structures of both single-snapshot and multiple-snapshot covariance matrices. Finally, the results obtained from applying the SPICE methods to simulated data as well as measured data collected by the U.S. Army Research Laboratory synthetic aperture radar system are presented to demonstrate the effectiveness of the proposed methods.

During the past decades, radar sounder (RS) instruments have been effectively used to detect subglacial lakes (SLs). SLs appear as flat, smooth, and bright reflectors in RS radargrams. The visual interpretation has been the main approach to SL detection in radargrams. However, this approach is subjective and inappropriate for processing large amounts of radargrams. While the analysis of RS data for understanding the subglacial hydrology has recently received increased attention, the literature on the development of automatic methods specifically designed for SL detection is still limited. In order to fill this gap, in this paper, we propose a novel automatic technique for SL detection. The technique is made up of two steps: 1) feature extraction and 2) automatic detection. In the first step, we define and extract three families of features for discriminating between the lake and nonlake radar reflections. The features model locally the basal topography, the shape of the basal reflected waveforms, and the statistical properties of the basal signal. In the second step, we provide the features as input to a support vector machine classifier to perform the automatic SL detection. The proposed technique has been applied to radargrams acquired over two large regions in East Antarctica and Siple Coast. The obtained results, which are validated both quantitatively and qualitatively, confirm the robustness of the features and their capabilities to effectively characterize SLs. Moreover, they prove the potentiality of the method to process large amounts of radargrams and update the current SL inventory.

The Japan Meteorological Agency (JMA) successfully launched the Himawari-8 (H-8) new-generation geostationary meteorological satellite with the Advanced Himawari Imager (AHI) sensor on October 7, 2014. The H-8/AHI level-2 (L2) operational cloud property products were released by the Japan Aerospace Exploration Agency during September 2016. The Voronoi light scattering model, which is a fractal ice particle habit, was utilized to develop the retrieval algorithm called “Comprehensive Analysis Program for Cloud Optical Measurement” (CAPCOM-INV)-ice for the AHI ice cloud product. In this paper, we describe the CAPCOM-INV-ice algorithm for ice cloud products from AHI data. To investigate its retrieval performance, retrieval results were compared with 2000 samples of the ice cloud optical thickness and effective particle radius values. Furthermore, AHI ice cloud products are evaluated by comparing them with the MODIS collection-6 (C6) products. As an experiment, cloud property retrievals from AHI measurements, with an observation interval time of 2.5 min and ground-based rainfall observation radar data (the latter of which is supplied by the JMA, with a 1-km grid mesh), are used to investigate the generation processes of deep convective (DC) cloud in the vicinity of the Kyushu island, Japan. It revealed that AHI measurements have the capability of monitoring the growth processes, including variation of the cloud properties and the precipitation in the DC cloud.

This paper proposes a novel approach to interpreting the satellite attitude based on inverse synthetic aperture radar (ISAR) images. In the conventional viewpoint, quadratic and higher order phase terms of ISAR imagery are regarded as negative factors causing the defocusing phenomenon. In this paper, we introduce how to apply quadratic phase coefficients to estimate target attitude from the ISAR imagery. A geometric projection model of ISAR imaging is built according to radar line of sight, and an explicit expression is also derived to connect target attitude parameters and the image defocusing property. With the accommodation of Broyden–Fletcher–Goldfarb–Shanno algorithm, spatial-variant quadratic phase coefficients together with attitude parameters are determined by an image contrast maximization. We also extend the proposed algorithm to multistatic ISAR applications, where the quadratic phase information lying in simultaneous multistatic ISAR images can be mined to enhance the performance of target attitude estimation. Experimental results illustrate the feasibility of the proposed algorithm.

Turbulence has been one of the major concerns for aviation safety. This paper applies evolutionary machine learning (ML) technology to turbulence level classification for civil aviation. An artificial neural network ML approach based on radar observation is developed for classifying the cubed root of the Eddy Dissipation Rate (EDR)1/3, an accepted measure of turbulence intensity. The approach is validated using Typhoon weather data collected by Hong Kong Observatory’s Terminal Doppler Weather Radar (TDWR) located near Hong Kong International Airport and comparing TDWR EDR1/3 detections and predictions with in situ EDR1/3 measured by commercial aircraft. The testing results verified that the ML approach performs reasonably well for both detecting and predicting tasks.

The cross correlation power spectrum of multiple signal sequences in the singularity domain is studied in this paper. With theoretical derivation and quantitative analysis, the cross correlation singularity power spectrum (CSPS) distribution theory is proposed. Developed from correlation function (CF), spectrum CF (SCF), singularity power spectrum (SPS), and multifractal cross correlation analysis, the CSPS can be applied for the correlation analysis of multiple fractal time series. In this paper, the CSPS is rigorously derived based on SPS and SCF, and is verified with classical multifractal time series. Furthermore, a target detection method based on the proposed CSPS method is also proposed. The proposed methodology is tested on sea clutters, both with and without target, from the Ice Multiparameter Imaging X-Band radar data set. The simulation results indicate that the target detection based on CSPS performs better than conventional multifractal spectrum methods, and can achieve almost 100% detection probability of detecting low-observable targets within sea clutters.

We present a new algorithm for suppressing azimuth ambiguities when measuring surface currents with an along-track interferometric (ATI) synthetic aperture radar in heterogeneous scenes, such as coastal waters or rivers. The key of the proposed algorithm involves a careful analysis of a parameter called the eigenvalue spectrum entropy (EVSE), which is defined as the entropy of the eigenvalue spectrum of the ATI covariance matrix computed in the Doppler domain. The physical meaning of EVSE is that it serves as a descriptor for the degree to which an unambiguous signal component and an ambiguous one are mixed. With the help of EVSE, azimuth ambiguities can be suppressed. Simulation results demonstrate that as compared with the conventional ATI method without azimuth ambiguity suppression, the proposed algorithm allows for a pronounced improvement in the current measuring accuracy. Other advantages of the proposed algorithm lie in the fact that it is not only adaptive, due to its ability to automatically capture the useful Doppler band whose width and position may both vary for different radar and scene parameters, but it also needs a minimal number of user inputs, making it a quite attractive algorithm for routine implementation.

The absence of systematic estimates of vertical transport of water by convective storms is one of the main causes of uncertainties in weather forecasting in the tropics, in severe weather forecasting, and in the climate-scale analysis and prediction of global circulation. The difficulty of providing such estimates lies in that the only instruments capable of providing such estimates at present, radiometers onboard satellites, operate at short wavelengths, and that makes difficult the retrievals. This paper describes a method capable of providing robust estimates of the maximum height of precipitation and the 3-D structure of condensed water in convective systems using a constellation of radiometers. Using hurricane Winston and the Global Precipitation Measurement (GPM) mission Core Observatory satellite dual precipitation radar as a reference, it is shown that the method is suitable to improve the parameterization of convection in numerical models.