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The ambiguity problem of targets in Doppler beam sharpening (DBS) with forward-looking radar is considered. While DBS is proposed earlier to improve the angular resolution of the radar while keeping the antenna aperture size limited, such a solution suffers from ambiguities in the case of targets positioned symmetrically with respect to the platform movement. To address this problem, an approach named unambiguous Doppler-based forward-looking multiple-input multiple-output (MIMO) radar beam sharpening scan (UDFMBSC) is proposed, based on the combination of MIMO processing and DBS. The performance of the proposed method is compared to existing approaches using simulated data with point-like and extended targets. The method is successfully verified using experimental data.

Linked to the increasing availability of datasets for radar-based human activity recognition (HAR), in this Student Highlights contribution, we report on a classification project that a group of 23 graduate students performed at TU Delft. The students were asked to work in groups of 2-3 members and to use the publicly available University of Glasgow dataset to develop the best classification pipeline as possible. This involved development and justification of both choices for the preprocessing techniques on the radar data (e.g., time-frequency distributions and cleaning of the signatures), and for the classification algorithms (e.g., the type of the algorithm, the hyperparameters' selection, the training-validation-testing split). While this student activity was performed at a small scale and with educational rather than research aims, we are happy to report it to the AESS readership, as we believe that such initiatives with open datasets sharing and classification algorithm benchmarking are beneficial for the wider radar research community. Furthermore, a list of publicly available datasets for radar-based HAR that can be used for similar initiatives is also reported in this article.

This paper presents a high-gain slotted ridge waveguide array antenna (SRWAA) with inductive diaphragms, which can realize a wide-sector beam with a low sidelobe simultaneously. The proposed antenna can cover a wide detection range and avoid interference from other directions. The expected excitation distribution for the antenna array is extracted through a beamforming method. To reduce the influence of the dispersion phenomenon on signal quality, inductive diaphragms are inserted into the sidewall of the ridge waveguide, which is fully analyzed from the point of the equivalent circuit. A cut-off-mode power divider is utilized, which can control the power ratio flexibly. A SRWAA working at 24.125 GHz, including a six-way feeding network, and a 6×24 slot array with the size of 330 mm × 66.8 mm is designed and fabricated. The measured sidelobe level (SLL) and half-power beamwidth (HPBW) in the elevation plane are -19.6 dB and 54.41°, with the counterparts in the azimuth plane -29.8 dB and 3.15°, respectively. The measured peak gain is 22.3 dBi at 24.125 GHz. The measured results are in good agreement with the simulated ones. This work has the potential to be applied in air detection, anti-unmanned aerial vehicles (UAVs), meteorological radar, and imaging radar.

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.

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.

The sparse-driven radar imaging can obtain the high-resolution images about target scene with the down-sampled data. However, the huge computational complexity of the classical sparse recovery method for the particular situation seriously affects the practicality of the sparse imaging technology. In this paper, this is the first time the quantum algorithms are applied to the image recovery for the radar sparse imaging. Firstly, the radar sparse imaging problem is analyzed and the calculation problem to be solved by quantum algorithms is determined. Then, the corresponding quantum circuit and its parameters are designed to ensure extremely low computational complexity, and the quantum-enhanced reconstruction algorithm for sparse imaging is proposed. Finally, the computational complexity of the proposed method is analyzed, and the simulation experiments with the raw radar data are illustrated to verify the validity of the proposed method., Comment: 5 pages, 3 figures

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.

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.

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.

Radar imaging with 1-bit data is attractive thanks to its low storage and transmission burden. Existing 1-bit radar imaging methods cannot satisfactorily suppress the artifacts in the imaging result induced by 1-bit quantization error and noise. In this article, we propose a new 1-bit compressive sensing (CS) based algorithm, i.e., the adversarial-sample-based binary iterative hard thresholding (AS-BIHT) algorithm, to improve the 1-bit radar imaging performance. First, we formulate a parametric model for 1-bit radar imaging with a new adjustable quantization level parameter. The parametric 1-bit radar imaging model updates the imaging scene and the quantization level parameter in an iterative fashion based on adversarial samples. Then, we design a mechanism to generate adversarial samples by attacking the 1-bit radar imaging model to resist the quantization consistency condition, such that forcing quantization consistent reconstruction on adversarial samples mitigates the quantization error and noise. The quantization level parameter is then tuned based on the adversarial samples. In this way, the ability of the model to adapt to echo data contaminated by noise and quantization error is enhanced, and the artifacts are well suppressed. Simulation and experimental results on real radar data demonstrate the effectiveness of the proposed AS-BIHT algorithm in 1-bit radar imaging.

Polysomnography (PSG) is the gold-standard for sleep apnea and hypopnea syndrome (SAHS) diagnosis. Because the PSG system is not suitable for long-term continuous use owing to the high cost and discomfort caused by attached multi-channel sensors, alternative methods using a non-contact sensor have been investigated. However, the existing methods have limitations in that the radar-person distance is fixed, and the detected apnea hypopnea (AH) event cannot be provided in real-time. In this paper, therefore, we propose a novel approach for real-time AH event detection with impulse-radio ultra-wideband (IR-UWB) radar using a deep learning model. 36 PSG recordings and simultaneously measured IR-UWB radar data were used in the experiments. After the clutter was removed, IR-UWB radar images were segmented by sliding a 20-s window at 1-s shift, and categorized into two classes: AH and N. A hybrid model combining the convolutional neural networks and long short-term memory networks was trained with the data, which consisted of class-balanced segments. Time sequenced classified outputs were then fed to an event detector to identify valid AH events. Therefore, the proposed method showed a Cohen’s kappa coefficient of 0.728, sensitivity of 0.781, specificity of 0.956, and an accuracy of 0.930. According to the apnea-hypopnea index (AHI) estimation analysis, the Pearson’s correlation coefficient between the estimated AHI and reference AHI was 0.97. In addition, the average accuracy and kappa of SAHS diagnosis was 0.98 and 0.96, respectively, for AHI cutoffs of ≥ 5, 15, and 30 events/h. The proposed method achieved the state-of-the-art performance for classifying SAHS severity without any hand-engineered feature regardless of the user’s location. Our approach can be utilized for a cost-effective and reliable SAHS monitoring system in a home environment.

Three upcoming Martian missions will deploy a ground-penetrating radar (GPR) to reveal the fine-resolution subsurface structure and dielectric properties of materials beneath the surface. Numerical forward simulations of radar echo using a model of the near-surface structure at the landing site can provide a valuable reference for processing and interpretation of future radar data collected on Mars. In this study, based on the geological information of the Jezero crater, a detailed stratigraphic model of the near-surface structure is derived, which includes several key features, for example, the randomness of the medium, terrain, and cracks. To identify correctly the reflections of subsurface interfaces and fractures from the radar image, a v(z) f-k migration is carried out, the performance of which is evaluated using the GPR data obtained near Antarctic Zhongshan Station since the electrical properties of Antarctic glaciers and Martian materials are to some extent comparable. The results in this work show that compared with common migration algorithm, the v(z) f-k method not only improves the clarity of radar image but also provides the permittivity profiles to infer the composition of the substrate, leading to a better understanding of Martian near-surface geology.

This study proposes a data fusion method for multiradar systems to enable measurement of the respiration of multiple people located at arbitrary positions. Using the proposed method, the individual respiration rates of multiple people can be measured, even when echoes from some of these people cannot be received by one of the radar systems because of shadowing. In addition, the proposed method does not require information about the positions and orientations of the radar systems used because the method can estimate the layout of these radar systems by identifying multiple human targets that can be measured from different angles using multiple radar systems. When a single target person can be measured using multiple radar systems simultaneously, the proposed method selects an accurate signal from among the multiple signals based on the spectral characteristics. To verify the effectiveness of the proposed method, we performed experiments based on two scenarios with different layouts that involved seven participants and two radar systems. Through these experiments, the proposed method was demonstrated to be capable of measuring the respiration of all seven people by overcoming the shadowing issue. In the two scenarios, the average errors of the proposed method in estimating the respiration rates were 0.33 and 1.24 respirations per minute (rpm), respectively, thus demonstrating accurate and simultaneous respiratory measurements of multiple people using the multiradar system., Comment: 8 pages, 11 figures, 5 tables. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Simultaneous multitarget vital signs measurement has become a hot issue for noncontact vital signs perception. However, there is still challenge in the multitarget heartbeat measurement due to the weakness of heartbeat signal and interference from complex environment. In this article, a new multiple-input–multiple-output (MIMO) continuous-wave (CW) radar system equipped with 2-D digital beamforming (DBF) is presented to measure the respiration and heartbeat of multiple human subjects at unknown positions simultaneously. Through 2-D beam scanning of the whole scene, a 2-D radar image is generated. From the image, the chest motion of multiple targets is accurately located. Then, the vital signs of targets are obtained through forming individual beams focusing on the chests of targets. Moreover, the low intermediate frequency (low-IF) architecture is adopted to minimize the impact of flicker noise in low-frequency amplifier stages. The experimental results demonstrate that the proposed MIMO 2-D imaging radar system can locate chest areas of multiple targets, suppress the clutters, and make vital signs measurement, heartbeat measurement in particular, more robust compared with single-input–multiple-output (SIMO) radar system in complex environment.

Radar imaging using the synthetic aperture radar (SAR) principle is a common method to obtain information about e.g., the surface of a target. However, most image formation algorithms for such systems assume (quasi-)static measurements. This may lead to errors in the processed images if the sensor is moving during the measurement process. This is especially the case for frequency-modulated continuous-wave (FMCW)-based sensors since the signal duration is longer than in a pulsed system and the achievable bandwidth is much larger and introduces additional challenges. Motion compensation in the context of radar imaging is usually related to the correction of deviations from an ideal trajectory. In contrast, this article presents a method to take the sensor movement during a single FMCW ramp into account and therefore addresses the effects caused by a continuous path during the transmit/receive process. Hence, faster movement can be achieved during the scanning of the synthetic aperture without being bound by stop-and-go approximations. In addition, it will be shown that the algorithm is suitable to reduce systematic errors due to aliasing caused by spatial sampling below the Nyquist rate. For this purpose, this article presents simulations and measurement results, obtained by an ultrawideband $D$ -band FMCW radar operating between 122 and 170 GHz.

In the actual scene of interferometric inverse synthetic aperture radar (InISAR) imaging, the noncooperative targets may make a nonuniform 3-D rotational motion (3-D-RM), which contributes not only to the time-variant Doppler modulation but also to the spatial-variant wave path difference (SVWPD). This, in turn, seriously degrades the 3-D geometry reconstruction accuracy of the targets. Furthermore, it is an enormous challenge to realize InISAR imaging from sparse frequency band and sparse aperture (SFB-SA) signals. This article seeks to address the problems of fine image registration and 2-D joint sparse reconstruction (2-D-JSR) for InISAR imaging with SFB-SA signals. With regard to the maneuvering targets with 3-D-RM, a novel SVWPD signal model is established. Moreover, a new algorithm, named joint wave path difference compensation (JWPDC) algorithm, is developed to perform fine image registration. It can not only combine multiple channels to achieve image registration but also jointly compensate for the non-SVWPD (NSVWPD) and SVWPD. A joint multichannel 2-D-JSR (JMC-2-D-JSR) ISAR imaging algorithm is also proposed according to the SFB-SA signal model to produce high-resolution ISAR images. Underpinned by the Bayesian compressive sensing (BCS) theory, the JMC-2-D-JSR ISAR imaging can be realized by solving a sparsity-driven optimization problem via a modified quasi-Newton solver. Through iterative processing of JMC-2-D-JSR and JWPDC, the high-quality 3-D InISAR images of maneuvering targets with 3-D-RM can be obtained. Extensive experimental results based on both simulated and real data corroborate the effectiveness of the proposed algorithm that outperforms other available InISAR imaging frameworks in 2-D imaging, 3-D imaging, and motion compensation.

We report on the realization of a multichannel imaging radar that achieves uniform 2-D cross-range resolution by means of a linear array of a special form of leaky-wave antennas. The presented aperture concept enables a tradeoff between the available range resolution and a reduction in the number of channels required for a given angular resolution. The antenna front end is integrated within a multichannel radar based on stepped-carrier orthogonal frequency-division modulation, and the advantages and challenges specific to this combination are analyzed with respect to signal processing and a newly developed calibration routine. The system concept is fully implemented and verified in the form of a mobile demonstrator capable of soft real-time 3-D processing. By combining radio frequency (RF) components operating in the $W$ -band (85–105 GHz) with the presented aperture, a 3-D resolution of less than ${1.5}^\circ \times {1.5}^\circ \times $ 15 cm is demonstrated using only eight transmitters and eight receivers.

As the electromagnetic environment in battlefields is more and more complex, automatic modulation recognition for radar signals is becoming vital and challenging. Traditional methods are more likely to cause lower recognition accuracy with higher computational complexity in low signal-to-noise ratio (SNR). Feature redundancy especially for handcrafted features is one of the shortcomings of deep-learning-based methods. In this article, a novel end-to-end sequence-based network that consists of a shallow convolutional neural network, a bidirectional long short-term memory (Bi-LSTM) network strengthening with a self-attention mechanism, and a dense neural network is constructed to recognize eight kinds of intrapulse modulations of radar signals. The autocorrelation functions of received radar signals are first calculated as autocorrelation features. Then, these features are employed as inputs of the proposed network which owns significant sequence processing advantages and adaptive selection ability of features. Finally, the proposed network outputs prediction modulations directly. The simulation results verify the robustness and effectiveness of autocorrelation features. And the proposed network achieves about 61.25% accuracy at −20 dB and more than 95% accuracy at −10 dB. Compared with four state-of-the-art networks, the proposed network has better recognition performance especially at low SNRs with much lower computational complexity. Results on measured signals also demonstrate that the proposed network outperforms these four networks.

Linear frequency-modulated (LFM) continuous-wave (CW) radar is usually the first choice in synthetic aperture radar (SAR) imaging missions due to its relatively low cost and hardware simplicity. However, the use of continuous-wave unnecessarily introduces the problem of intrapulse motion. Furthermore, a large amount of data generated by the CW imaging process may overburden the onboard communication system with its high streaming data rate. On the other hand, a large amount of data may, indeed, not be necessary for high-resolution SAR imaging. In this article, we took full consideration of the intrapulse motion in LFMCW radar systems and modeled the phase preserving extended frequency scaling algorithm (EFSA) reconstruction process with far fewer data samples as compressive sensing problem and used subgradient descent algorithm with optimal step size to realize compressive sensing reconstruction. Our compressive sensing problem is different from the traditional direct inversion-based problem in that our reconstructed results contain both mainlobe and sidelobes. Comparisons were made with mainstream iterative soft thresholding-based reconstruction algorithm to demonstrate its capability to reconstruct large imaging scenes with high resolution. Both simulations and experiments with measured data have verified our proposed algorithm.

Forward-looking multichannel synthetic aperture radar (FLMC-SAR) is an important tool for modern remote sensing applications, which has the capability to reconstruct the high-resolution image of the front area. However, due to the azimuth-variant characteristics of the motion errors over a long aperture, FLMC-SAR data processing is usually a challenging task, especially when involving the motion compensation (MOCO) coupled with Doppler ambiguity resolving. To accomplish an accurate MOCO for FLMC-SAR, a novel parametric azimuth-variant MOCO approach is proposed in this article. Aiming at the coupling problem of MOCO and Doppler ambiguity resolving over the full aperture, we can decouple them through the subaperture division. As a full synthetic aperture is decomposed into several subapertures, the high-order motion errors of the full aperture can be decomposed into the first-order motion errors of the subaperture. On this basis, the mismatch of the space–time spectrum caused by the motion errors can be solved by spectral estimation, yielding Doppler ambiguity resolving for each subaperture. Meanwhile, the azimuth-variant characteristic of motion errors in FLMC-SAR system is characterized by a parametric angle-dependent quadratic phase error (QPE) model. The motion parameters are estimated by a joint multichannel angle estimation-based signal quadratic decomposition method. Immediately, the MOCO for ambiguous targets with different motion errors can be processed separately to improve the imaging performance. Experimental results based on both simulated and real data demonstrate that the proposed method is suitable for FLMC-SAR system.

It is always an important problem to recover sparse signals from observations corrupted by Gaussian noise and has been extensively investigated. In high-resolution maritime surveillance radars working at scan mode, ship classification, and recognition need to recover high-resolution range profiles (HRRPs) of ships from radar returns of several pulses with severe range sidelobe effect. Multipulse synthetic data by the aid of ship Doppler information are complex sparse signals corrupted by non-Gaussian correlated interference. In this article, a sparse recovery via iterative minimization (SRIM) method is proposed to estimate complex HRRPs of ships from multipulse synthetic data. The SRIM method adapts the non-Gaussianity nature of the interference in the multipulse synthetic data and ship complex HRRPs are modeled by the random sequences of the biparametric generalized Gaussian distributions (GGDs) (0 p ≤ 1). In the SRIM method, the parameters of the GGD model are iteratively searched by the minimal criterion of the Kolmogorov–Smirnov distance of the residue and the interference model. The SRIM method is compared with the recent linear-programming-based method and the classic sparse learning via iterative minimization (SLIM) method by using simulated and measured radar data and the results show that the SRIM method obtains the better performance.

Forward-looking correlated imaging plays an increasingly important role in modern radar imaging systems. It overcomes disadvantages of traditional side or squint synthetic aperture radar (SAR) which is dependent on specific relative motion between the radar and target scene. A new microwave forward-looking correlated 3-D imaging method based on random radiation field combined with sparse reconstruction is proposed in this article. Firstly, phased array radar (PAR) is adopted to form different and random antenna patterns. Then, combined with the compressed sensing (CS) theory, the target image can be recovered with very few samples which can break through Rayleigh resolution limitation. Furthermore, the proposed method can achieve resolution at least 5.5 times higher than real aperture imaging. To raise computation efficiency of sparse reconstruction, an improved quasi-Newton iteration method based on graphics processing unit (GPU) platform is developed. Meanwhile, a GPU-based (NVIDIA Tesla K40c) accelerated computing method can significantly reduce the processing time compared with the time given by a personal computer (PC). Both simulation and field experiment verify the validity of the proposed method.

Limited by the total length, the total number of the antenna units as well as their topology, the radar images always suffered from the sidelobe/grating lobe which severely impacts the quality of the radar images. In this article, a convolutional neural network (CNN)-based radar image-enhancing method is proposed. Using the original radar images as the input samples and using their corresponding ideal radar images with no sidelobe/grating lobe as the label to train the CNN. A well-trained CNN can suppress the sidelobe/grating lobe in the radar images. The structure of the specific CNN, the generation methods of the samples and the labels, the training procedure of the CNN, as well as some other detailed implementation strategies are specifically illustrated in this article. The proposed method is utilized to suppress the sidelobe/grating lobe in both the simulated and real recorded radar images. Compared to other existing methods, the proposed method is with better sidelobe/grating lobe suppressing performance and better robustness.

The Chinese Gaofen-3 satellite currently provides us with an open way to access fully polarimetric data in the C-band frequency. The noise equivalent sigma zero (NESZ) is a crucial factor when calibrating the additive noise in radar imagery. For most radar sensors, NESZ coefficients are stored in header files, but these are not provided for the Gaofen-3 products. The minimum eigenvalue estimator (MEE) and maximum likelihood estimator (MLE) are the two most common techniques used to derive the NESZ from polarimetric imagery. Nevertheless, the bias has been found to be higher than 5 dB compared with the noise measurement circuit (NMC) of the hardware. In this article, we propose a minimum noise envelope estimator (MNEE) for the robust estimation of the Gaofen-3 NESZ. In this article, we carried out an in-depth investigation to analyze the error sources of the MEE and MLE techniques. Based on our analysis, the MNEE framework requires the use of the ocean surface as a reference, and MNEE is combined with the minimum operation to suppress overestimation. In the experimental section, we describe how we validated the proposed algorithm with Radarsat-2 images, and the MNEE is treated as a tool to estimate the NESZ of Gaofen-3 polarimetric products. We found that the Gaofen-3 NESZ is generally less than −20 dB, which satisfies the design specification. The range-dependent NESZ coefficients are provided here to allow convenient noise correction for Gaofen-3 data users.

This article introduces a probabilistic Bayesian model for addressing the problem of compressive multipolarization through-wall radar imaging (TWRI). The proposed approach formulates the task of wall-clutter mitigation and multipolarization image reconstruction as a Bayesian inference problem for a joint distribution between observed radar measurements and latent wall-clutter matrix and indoor target images. The joint probability distribution incorporates three prior beliefs: low-dimensional structure of the wall reflections, group sparsity structure of the target images, and joint sparsity among the polarization images. These signal attributes are modeled through hierarchical priors, whose parameters and hyperparameters are treated with a full Bayesian formulation. Furthermore, this article presents a variational Bayesian inference algorithm that estimates wall-clutter and multipolarization images as posterior distributions and optimizes the model parameters and hyperparameters simultaneously. Experimental results on simulated and real radar data show that the proposed model is very effective at removing wall clutter and enhancing target localization even when the radar measurements are significantly reduced.

In this article, we present a novel method that incorporates the range points migration (RPM) method, $k$ -space decomposition-based accurate, and noise-robust range extraction filter for microwave or millimeter-wave (MMW) short-range radar using a considerably lower fractional bandwidth signal. The advantage for higher angular resolution in higher frequency systems, such as MMW radar, has been implemented to the incoherent-based RPM method, using the simple 1-D or 2-D Fourier transform-based processing to maintain the imaging accuracy in RPM processing for both the range and the angular directions. As an additional advantage of our method, it also offers data clustering in $k$ -space, which can enhance the imaging accuracy of the RPM method. The numerical and experimental tests demonstrated that the proposed method offers numerous advantages over the Capon-based super-resolution algorithm or coherent-based imaging approaches.

In this article, we focus on ground-penetrating radar (GPR) for subsurface utility pipe detection. Due to the dense and high-speed 3-D monitoring, GPR is a promising tool. However, because of enormous amount of radar data and difficulty of interpretation, inspection time and cost are the bottlenecks. In this article, we propose a novel detection algorithm by the combination of 3-D convolutional neural network (3-D-CNN) and Kirchhoff migration. A 3-D-CNN architecture was trained utilizing transverse and longitudinal pipes’ measurement data. The classification accuracy of the developed model was about 91%, accurately estimating the pipes’ existences and directions. The 3-D-CNN improved the classification accuracy by about 6% compared to 2-D-CNN in the case of transverse pipes by considering the 3-D geometries of the pipes. After box-by-box search by 3-D-CNN, Kirchhoff migration was applied to cross section images and peaks were extracted. From the result of experimental field data, the algorithm provides the clear understandings of pipes’ 3-D positions and arrangement with reasonable calculation time.

Ship motions during long coherent processing interval (CPI) have six degrees of freedom, and the oscillatory motions are roughly periodical. The traditional ship imaging methods usually use a short time interval to form an image, while the image quality may suffer from low resolution, poor signal-to-noise ratio (SNR), and scatter scintillation. Using a longer CPI to generate an image may improve the quality but, however, largely increase the focusing difficulty. In this article, we investigate the focusing challenges of oscillatory ships with long CPI. Through analyzing the relative motion between the radar and the ship, the properties of wavenumber domain support (WDS) and point spreading function (PSF) of oscillatory ship imaging are studied. It is illustrated that the WDS is a 3-D sparse curved surface generated by the complex relative motion, with a time-variant energy density, nonparallel spectrum boundaries, and a complex structure. The PSF of an oscillatory ship may have a 3-D resolution but also multiple high-level sidelobes. The relationship between the WDS and the nonideal PSF is illustrated with the projection slice theorem (PST). Moreover, it is discussed that the scatterers distributed on a 3-D ship cannot be focused uniformly on a 2-D imaging plane (IP) due to the variation of the slant-range plane (SRP). The projection relationships of the resolutions and focusing positions between the SRP and the IP are also derived. Simulation results are presented to validate the analyses throughout this article.

Millimeter-wave radars are being increasingly integrated into commercial vehicles to support advanced driver-assistance system features. A key shortcoming for present-day vehicular radar imaging is poor azimuth resolution (for side-looking operation) due to the form factor limits on antenna size and placement. In this paper, we propose a solution via a new multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) imaging technique, that applies coherent SAR principles to vehicular MIMO radar to improve the side-view (angular) resolution. The proposed 2-stage hierarchical MIMO-SAR processing workflow drastically reduces the computation load while preserving image resolution. To enable coherent processing over the synthetic aperture, we integrate a radar odometry algorithm that estimates the trajectory of ego-radar. The MIMO-SAR algorithm is validated by both simulations and real experiment data collected by a vehicle-mounted radar platform., 13 pages

A backprojection-based synthetic aperture radar (SAR) processing approach for fast and accurate imaging of moving objects and stationary background is proposed. This divide-and-conquer backprojection approach allows for efficient detection of moving objects, estimation of their positions and velocities, and overlaying their focused images over the stationary background. Two algorithms are presented to exploit the tradeoff between the complexity and performance. The full 4-D phase space imaging algorithm provides the optimal performance under low signal-to-clutter conditions, while the adaptive detection and imaging leads to reduced complexity in more favorable scenarios. We show that in a single-channel SAR the conventional straight-line data collection path does not resolve the target’s velocity, therefore a curved path is assumed in the formulation and numerical examples.

Conventional security screening systems cannot operate in real time and often suffer from false alarms due to the profile-only imaging without further analysis on the object material properties. Reflectarrays were recently proposed as devices capable of performing real-time security screening using a single-frequency radar signal. This article presents first a physical optics (PO)-based simulation method to facilitate the design of a multireflectarray system. Then, an object material characterization method using geometrical optics (GO) is derived for such a system. The characterization process not only retrieves the complex relative permittivity of the object but also improves its profile reconstruction in terms of a more accurate thickness. Both simulations and experiments are carried out to verify the effectiveness and efficiency of the proposed methods. Primary results show great potentials for security screening, especially in scenarios where the inspection of human bodies for threat materials, such as narcotics and explosives, is required.

Recently, mutually orthogonal complementary sequence sets (MOCSSs) have been found many important applications in communication systems, radar, etc. Most of the known constructions of MOCSSs are based on generalized Boolean functions (GBFs) and hence mostly have lengths of power-of-two. A few constructions of MOCSSs are also based on paraunitary (PU) matrices and hence mostly have power-of-an-integer lengths. The objective of this paper is to develop a general framework to construct more MOCSSs consisting of sequences with non-power-of-two lengths. The proposed framework is based on complete complementary codes and even-shift complementary sequence sets.

Emerging 5G millimeter-wave (mm-wave) networks use electronic beamforming and beamsteering and support signal bandwidths on the order of hundreds of MHz. Given these characteristics, opportunities exist to develop 3-D sensing applications that leverage 5G mm-wave communications infrastructure. In this context, this work introduces a signal processing pipeline that can: 1) accurately extract the Time of Flight (ToF) of reflected orthogonal frequency division multiplexing (OFDM) communications signals and 2) enhance range resolution by coherently aggregating the reflection information from separate frequency bands. In combination with precise beamsteering, the proposed signal processing techniques enable high-resolution 3-D radar imaging without affecting communications protocols . An experimental system demonstrating this concept has been implemented and is described. This system consists of two software-defined phased array radios (SDPARs), one configured as a prototype 5G base station TX, and one as an auxiliary prototype 5G RX. Each SDPAR primarily consists of a Si-based 28-GHz, 64-element, phased array transceiver module and software-defined radio. Simulation and benchmark results show that our coherent bandwidth stitching enables accurate OFDM-based ranging with 15-cm resolution. Measurement results show 3-D radar images of indoor scenes with 2° angular and 15-cm ranging resolution, created by processing reflected 5G-like communication waveforms at 28 GHz. The produced 3-D radar images effectively depict the location of objects in the scene, and these locations are in close agreement with the ground truth.

Presents a review of "Stimson’s Introduction to Airborne Radar, Third Edition" (Stimson, G.W., et al; 2014). There are four authors and nine expert contributors to this third edition of a classic introductory text. The original and key author, George Stimson, started the original manuscript when he was with Hughes Aircraft Company in the early 1960s to 1970s. As a private publication, the book was initially given away or sold at a nominal charge to Hughes’s customers and Stimson’s friends. He is a fine writer and has managed the production of the book for years. From the beginning, the book has had clear graphics and meticulous formatting of pages. This is an impressive book with a total of 52 chapters in 10 parts. It covers almost everything concerning airborne radar. It could serve as an airborne radar encyclopedia. Each chapter briefly introduces its subject, providing neither too much nor too little material. There are some mathematical equations, but all are at the algebra level and easy to grasp. The text includes many color graphs, drawings, and nice pictures, which makes it very easy to read. I would recommend this book to every radar designer, technician, operator, administrator, and program manager. The book covers the Doppler frequency of an aircraft, ground return, and return by a semiactive missile.

Sensitivity and performance of homodyne radar are notably improved if the phase noise terms of transmit and receive signals are correlated and canceled out by the receiver. However, in a bistatic setup with separate oscillators, this correlation is inherently not given. To achieve a good carrier phase and phase noise coherency, it is required to transmit a radio frequency (RF) reference, e.g., by using high-quality RF cables, optical fibers, or wireless point-to-point links. All these options are complex, costly, and prone to distortions and signal delay variations. Sharing a clock signal is also barely helpful since even small distortions of this reference are converted to notable uncorrelated phase noise in the radar signal synthesizers. In this article, a novel full-duplex system concept and signal processing scheme is presented that solves the above-mentioned problems completely. This concept allows for coherent measurements within a bistatic radar setup, even if the two radar stations are only loosely coupled employing a simple clock-rate adjustment. By theory and experimental results with bistatic frequency-modulated continuous-wave radar, it is shown that our concept allows for bistatic measurements with the same sensitivity and performance that is usually only known from homodyne radar systems. To the authors’ best knowledge, this is the first time that phase noise coherent measurements in a bistatic multiple-input multiple-output radar setup are shown. This concept paves the way for advanced netted radar setups, e.g., in automotive radar and applications with large apertures or baselines or multiperspective coherent tomographic measurements.

This article presents a robust hand-gesture recognition method via multidimensional feature representation and learning specifically designed for commercial frequency-modulated continuous wave (FMCW) multi-input multi-output (MIMO) millimeter-wave radar. First, the optimal configuration of the radar system parameters for the hand-gesture recognition scenario is investigated and a standard procedure to determine the system configuration is given. Then a moving scattering center model is proposed to represent the 3-D point cloud in the range–Doppler (RD)-angular multidimensional feature space. A scattering point detection and tracking algorithm is presented based on a set of motion constraints in terms of position, velocity, and acceleration. It is derived from the space-time continuity of a nonrigid target. Finally, a lightweight multichannel convolutional neural network (CNN) is designed to learn and classify multidimensional gesture features including radial RD and tangential azimuth–elevation. Extensive experiments are carried out with the developed system and a large data set is obtained to train and test the classifier. The results show that the proposed gesture recognition method can effectively distinguish gestures that are easily confused in the RD domain and achieve robust performances under various conditions.

Fast Fourier transform (FFT) is one of the fundamental signal processing algorithms widely used in radar applications. The Radon–Fourier transform (RFT) can be seen as an FFT generalization that can overcome some of its limitations. This work derives three spectral RFT (SRFT) based approaches to address major challenges of the multiple-input multiple-output automotive radars. First, two SRFT-based approaches are derived to increase maximal target detection range by mitigation of target migration in range and direction of arrival, jointly, and by multidwell integration processing, which increases the radar coherent integration time without compromising its detection update rate. Next, SRFT-based approach is proposed to address the cluster-to-track association problem that arises in multiple distributed target tracking scenarios that characterize automotive radar operation in dense urban environments.

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

Randomized stepped-frequency (RSF) radar, which transmits random-frequency pulses in a coherent processing interval (CPI), has an excellent electronic counter-countermeasures (ECCM) performance and the ability to synthesize wide-frequency band with a narrow bandwidth receiver. However, due to the use of different frequency pulses, RSF waveform is incompatible with the conventional moving-target-detection (MTD) technique, which makes clutter suppression a difficult problem for RSF radar and limits its application. In this article, the effect of clutter on the coherent processing for RSF radar is first analyzed. The result shows that coherent processing can improve the signal-to-clutter ratio on target Doppler channel by $N$ (number of pulses in one CPI) times, which suppresses weak clutter scatterers to a large extent whilst strong clutter scatterers would still be left influential. Focusing on dominant clutter scatterers, the adaptive range-Doppler clutter suppression algorithm, which is based on designing clutter suppression filters with two-dimensional property to obtain the clear high-range-resolution profile of moving target in clutter environment, is then proposed for RSF radar. The proposed algorithm, which takes into account clutter Doppler extension, can be used for RSF radar coherent processing in clutter environment.

This paper deals with the multiple targets localization problem via multi-channel ultra-wideband (UWB) imaging radar non-line-of-sight (NLOS) signal processing. A novel matching-based radar imaging algorithm is proposed to obtain the positions of multiple targets in the L-shaped corner scenario with complex multipath ghost signals. Firstly, a multipath propagation model for the multiple targets scenario is established. Then the positions of the actual multipath ghosts are extracted from the radar image, and the candidate targets corresponding to these multipath ghosts are derived. Secondly, the ellipse-cross-localization method is proposed to obtain the positions of the candidate multipath ghosts, followed by two defined matching factors to measure the similarity between actual and candidate multipath ghosts. According to the similarity, decision rules are designed to determine the actual targets. Compared with the localization algorithm based on one-dimensional range profile, the proposed algorithm can effectively cope with the cases of multiple targets, even in the cases of rough walls and noise. Finally, simulations and experimental data are used to validate the effectiveness of the proposed algorithm.

Image segmentation and classification of surfaces and obstacles in automotive radar imagery are the key technologies to provide valuable information for path planning in autonomous driving. As opposed to traditional radar processing, where clutter is considered as an unwanted return and should be effectively removed, autonomous driving requires full scene characterization. Hence, clutter carries necessary information for situational awareness of the autonomous platform and needs to be fully assessed to find the passable areas. In this paper, we proposed a method of automatic segmentation of automotive radar images based on two main steps: unsupervised image pre-segmentation using marker-based watershed transformation, followed by the supervised segmentation and classification of regions containing objects and surfaces based on the use of statistical distribution parameters. Several distributions were considered to characterize returns from specific region types of interest within the scene (denoted as classes) in calibrated radar imagery—the extracted distribution parameters were assessed for their ability to distinguish each class. These parameters were then used as features in a multivariate Gaussian distribution model classifier. Both the performances of the proposed supervised classification algorithm and the automatically segmented results were investigated using F1-score and Jaccard similarity coefficients, respectively.

The microwave photonic (MWP) radar technique is capable of providing ultrawide frequency bandwidth waveforms to generate ultrahigh-resolution (UHR) inverse synthetic aperture radar (ISAR) imagery. Nevertheless, conventional ISAR imaging algorithms have limitations in focusing UHR MWP-ISAR imagery, where high-precision high-order range cell migration (RCM) and phase correction are crucially necessary. In this article, a UHR MWP-ISAR imaging algorithm integrating rotation estimation and high-order motion terms compensation is proposed. By establishing the relationship between parametric ISAR rotation model and high-order motion terms, an average range profile sharpness maximization (ARPSM) is developed to obtain rotation velocity by using nonuniform fast Fourier transform (NUFFT). Second-order range-dependent RCM is corrected with parametric compensation model by using the rotation velocity estimation. Furthermore, the spatial-variant high-order phase error is extracted to compensation by the entire image sharpness maximization (EISM). A new imaging framework is established with two one-dimensional (1-D) parameter estimations: ARPSM and EISM. Extensive experiments demonstrate that the proposed algorithm outperforms traditional ISAR imaging strategies in high-order RCM correction and azimuth focusing performance.

To unlock the full potential of composite materials, reliable measurement methods during and after their manufacturing are required. Established measuring methods are commonly based on ultrasound or thermographic imaging techniques and offer only a limited usability. A promising alternative to the aforementioned methods are millimeter-wave-based systems. It has already been demonstrated that such systems can provide a tomographic representation of composite materials, enabling the detection, localization, and classification of critical defects within the component. The tomographic millimeter-wave imaging system presented here is operating in the W band and is packaged for the operation in a manufacturing environment. For this purpose, it is enclosed by a dustproof housing and can be mounted on an industrial robot, which enables the development of an automated measurement procedure during and after the manufacturing process of composite materials. The direct integration of the measurement system into the manufacturing process allows for early-stage fault detection and classification, which is essential for the production of high-quality, high-performance, and highly reliable composite materials.

With the advances in radar technology, through-the-wall radar imaging (TWRI) has become a viable sensing modality that can allow fire-and-rescue personnel, police, and military forces to detect, localize, and identify targets behind opaque obstacles. Many of the existing TWRI approaches detect either stationary or moving targets but not both of them simultaneously. In this article, a method is proposed to detect both stationary and moving targets from a sequence of radar signals. The proposed method decomposes the 3-D radar data, i.e., frequency, space, and time data into a low-rank tensor and two sets of sparse images. One set of images comprises the stationary targets, and the other set of images contains the moving targets. Wall clutter removal and target detection are formulated into an optimization problem regularized by tensor low-rank, joint sparsity, and total variation constraints. Then, an alternating direction technique is developed to reconstruct the sets of stationary and moving target images. Experiments using simulated and real radar signals are conducted. The experimental results illustrate the effectiveness of the proposed method to detect and separate the stationary and moving targets into a pair of sparse images.

Although numerous methods based on sequence image classification have improved the accuracy of synthetic-aperture radar (SAR) automatic target recognition, most of them only concentrate on the fusion of spatial features of multiple images and fail to fully utilize the temporal-varying features. In order to exploit the spatial and temporal features contained in the SAR image sequence simultaneously, this article proposes a sequence SAR target classification method based on the spatial–temporal ensemble convolutional network (STEC-Net). In the STEC-Net, the dilated 3-D convolution is first applied to extract the spatial–temporal features. Then, the features are gradually integrated hierarchically from local to global and represented as the united tensors. Finally, a compact connection is applied to obtain a lightweight classification network. Compared with the available methods, the STEC-Net achieves a higher accuracy (99.93%) in the moving and stationary target acquisition and recognition (MSTAR) data set and exhibits robustness to depression angle, configuration, and version variants.

A frequency-modulated continuous-wave (FMCW) radar, operated at the central frequency of 27.75 MHz and the bandwidth of 300 kHz has been established on the seashore near the Taichung harbor ( $24^{\circ } 18.591^\prime $ N, $120^{\circ } 31.389^\prime $ E), Taiwan. Sixteen vertical dipole antennas were located linearly and attached with 16 receiving channels. One purpose of the radar is to monitor the ships that navigate toward, away, and around the harbor. In this article, we applied the radar beamforming methods that transform the temporal radar signals as brightness on the 2-D range-azimuthal domain, making the ship echoes visible directly on the spatial domain. Three beamformers, linear Fourier, directionally constrained minimum power (DCMP), and norm-constrained DCMP (NC-DCMP) algorithms, were employed to produce range–angle (RA) brightness distribution that is different from the conventionally used range–Doppler (RD) spectra in ship detection. Both DCMP and NC-DCMP are adaptive beamforming methods. With the auxiliary of a band-stop filter to suppress the sea echoes, the NC-DCMP beamformer was demonstrated to surpass the other two beamformers and could provide more visible ship echoes in the RA brightness distribution. Automatic Identification System (AIS) information was also used to validate the radar-determined ship locations from the RA brightness distribution. Although some ships having the AIS information were not observed clearly by the radar, the radar detected some targets without AIS information.

To perform 3-D inverse synthetic aperture radar (ISAR) imaging through interferometric processing, all ISAR images should be correctly registered such that the same scatterer appears at the same position along both the range and Doppler directions. However, this condition generally does not hold in most situations because different radar view angles introduce additional angular motion errors that must be compensated. Hence, we herein propose a new ISAR registration method based on joint-channel phase difference (JC-PD) operations. From the result of the JC-PD function, JC-PD profiles are obtained, in which the locations and phases of the maximum peak contain constant and time-varying angular motions, respectively. Using our proposed method, correct ISAR registrations can be achieved in both the range and Doppler directions. In particular, compared to conventional Doppler registration methods, time-varying angular motions are more accurately compensated without the problem of cross-term phase interference. Through simulations and experiments, we verified that the proposed method yielded accurate ISAR registration results; hence, an excellent 3-D target reconstruction through interferometric ISAR imaging was demonstrated.

Through-the-wall radar imaging is a sensing technology that can be used by first responders to see through obscure barriers during search-and-rescue missions or deployed by law enforcement and military personnel to maintain situational awareness during tactical operations. However, the strong reflections from the front wall and other obstacles render the detection of stationary targets very difficult. In this article, a learning-based approach is proposed to mitigate the effect of the wall and background clutter. A sparse autoencoder with a low-rank projection is developed to mitigate the wall clutter and recover the target signal. The weights of the proposed autoencoder are determined by solving an augmented Lagrange multiplier optimization problem, and the regularization parameters are estimated using the Bayesian optimization technique. Experiments using real data from a stepped-frequency radar were conducted to illustrate its effectiveness for wall clutter removal. The results show that the proposed method achieves superior performance compared with the existing approaches.

Next-generation radar sensors require imaging capabilities with high angular resolution. As for a single sensor, the aperture, and thus the achievable resolution, is limited due to the constraints of the front end, radar networks consisting of multiple sensors are a possible solution. However, their incoherency usually makes joint angle estimation impossible. This article presents a network concept consisting of an orthogonal frequency-division multiplexing (OFDM) radar and repeater elements, which receive the reflections from targets and retransmit them back to the radar. Thereby, any frequency conversion from radio frequency to baseband and vice versa is omitted such that the signal remains coherent to the initial transmit signal. To distinguish the bistatic signal transmitted by the repeater from the monostatic one of the OFDM radar, the orthogonal subcarrier structure of OFDM waveforms is exploited by combining a sparse radar transmit signal with a low-frequency modulation in the repeater. This allows to evaluate the bistatic signals at the radar with standard multiple-input-multiple-output (MIMO)-OFDM signal processing, leading to separate range-Doppler images for each virtual channel. Finally, it is shown that this method offers a coherent angular estimation based on the extended aperture of the network. For this purpose, a method to establish phase coherency by a reconstruction of the modulation phase is presented. The network concept is proved with measurements at 77 GHz., publishedVersion