Search

Recently, radar-based human activity recognition (HAR) has attracted the attention of researchers as it has been proven that a deep learning (DL) model can be automatically determined by learning a radar dataset. However, unlike general optical image data, the process of collecting and labeling radar data for training the DL model requires considerable manpower and costs. Therefore, an approach that can learn the maximum number of features from a limited radar dataset is essential. Moreover, even if the DL models are trained using a dataset obtained from multiple geometries, performance can be degraded for geometries that are unknown to the trained model. Therefore, we propose a novel radar-based HAR combining a range–time–Doppler (RTD) map and a range-distributed-convolutional neural network (RD-CNN). Unlike the time–Doppler (TD) map, which is mainly used for radar-based HAR, the proposed RTD map provides several human activity-related features by extending the TD map to three dimensions according to the range. The proposed RD-CNN is a new DL model that performs HAR by using the RD layer to extract Doppler features, excluding the range information of RTD map. To verify the performance of the proposed model, experiments were conducted by using the University of Glasgow “radar signatures of human activities,” which is an open dataset for radar-based HAR research. The comparison results of the proposed model and CNNs with the same number of parameters demonstrated a higher recognition accuracy and a lower recognition error even for unknown geometries in the training dataset. [ABSTRACT FROM AUTHOR]

Fast factorized back-projection (FFBP) has significant advantages for bistatic forward-looking synthetic aperture radar (BFSAR) imaging with arbitrary geometry and complex configuration. Conventional FFBP is generally based on the polar coordinate system (PCS) for recursive processing; however, it involves huge interpolations and causes computational inefficiency. In this article, a novel FFBP is developed for BFSAR focusing based on the Cartesian coordinate system (CCS), which is referred to as Cartesian fast factorized back-projection (CFFBP). In the new algorithm, a two-step spectrum correction is designed to avoid spectrum aliasing, and the Nyquist sampling requirement (NSR) for the BFSAR image spectrum can be decreased significantly. With low NSR in CCS, subimage merging can be implemented with noninterpolation processing, so that the proposed algorithm can achieve high performance in both accuracy and efficiency. Moreover, the practical problem of motion error is particularly considered in algorithm development, and well-adapted data-driven motion compensation (DDMC) is integrated with CFFBP based on which a new Cartesian fast time-domain (CFTD) processing framework is developed for BFSAR application. Promising results from both simulation and raw data experiments are provided and analyzed to validate the high performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Although the through-the-wall radar imaging (TWRI) system working in the appropriate frequency band can penetrate the nonmetallic obstacles and sense the targets behind, its low imaging spatial resolution hinders the acquisition of more detailed information, such as human pose and shape. This article mainly discusses a deep learning-based human pose and shape recovery method from TWRI images. Inspired by cross-modal learning, the method follows a teacher–student learning pipeline that avoids the heavy cost of manual labeling. Specifically, a camera is attached to the self-develop radar system to simultaneously capture paired red-green-blue (RGB) images and TWRI images in a scenario without wall occlusion. A pose estimation framework (Hourglass) and a semantic segmentation framework (UNet) serve as the teacher network to convert the RGB images into the pose keypoints and the shape masks. By taking inspiration from the topological architecture of these frameworks, a student network radar pose shape network (RPSNet) is designed to extract the information from the corresponding radar images and predict the keypoints and masks that are close to the results above. Instead of learning two single-task objectives independently, multitasking learning is introduced to adaptatively learn common features. When applied to wall-occlusive scenarios, only the radar images are collected and fed into the student network for pose and shape recovery. The advantages of this method over computer vision-based methods for human recovery are demonstrated in scenarios both without and with wall occlusion. [ABSTRACT FROM AUTHOR]

For the target with 3-D rotation, the 3-D synthetic aperture radar (SAR) imaging is very important for the feature extraction and classification. To solve this issue, a novel 3-D imaging algorithm through the airborne SAR system with a single antenna is proposed in this article, which has great advantages for the simplification of system structure compared with the traditional interferometric system. The proposed 3-D airborne SAR imaging algorithm can be implemented with three steps: 1) the azimuth signal is modeled as multicomponent linear frequency modulation (LFM) signal due to the relative movement between the target and radar. 2) The scatterer height position can be obtained by estimating the frequency modulation rate (FMR) for the LFM signal. 3) The 3-D airborne SAR image is obtained via the range-Doppler (RD) algorithm. Furthermore, the reconstruction performance under different rotation patterns, including roll, pitch, and yaw, is analyzed. The availability of the presented novel technique is demonstrated by the results of simulated and real experimental data. [ABSTRACT FROM AUTHOR]

Arc-synthetic aperture radar (ArcSAR) can observe the surrounding ground objects at 360°, with the advantages of a wide imaging range and constant azimuth angular resolution. In this article, based on the stepped frequency continuous wave (SFCW), fourth-order Taylor expansion, and the series-inversion method, the accurate phase expression of the 2-D spectrum is derived, and then, the 2-D frequency-domain imaging algorithm (2-D-FDA) for ArcSAR is realized. In the simulation part, the imaging results of 2-D-FDA are compared with the backprojection algorithm (BPA) and Liao’s method, which is the reference for 2-D-FDA. The simulation results show that the 2-D-FDA is more applicable to process the data obtained from the antenna with the beamwidth larger than 70°. The SFCW-ArcSAR system consists of a vector network analyzer (VNA), a rotating platform, two antennas, and a control computer. Then, the system is used for the outdoor experiment. The experimental results of the corner reflector show that the point target analysis of 2-D-FDA is consistent with those of the BPA and theory. Under the system parameters in this article, the measured azimuth angular resolution is 0.0177 radian, the peak sidelobe ratio (PSLR) in the range direction is −12.46 dB, and the range resolution is 0.48 m on the ground-range plane. The imaging results of the real scenario processed by BPA and 2-D-FDA are consistent, and the ArcSAR image agrees with the optical image of the experimental scene. The effectiveness and correctness of the 2-D-FDA are verified by simulation and experiment. [ABSTRACT FROM AUTHOR]

Multiple-input–multiple-output synthetic aperture radar (MIMO-SAR) has extensive application prospects, mainly including the acquisition of multidimensional scattering information, high-resolution and wide-width (HRWS) imaging, and moving target indication (MTI). Its echo separation is the most technical challenge, and so far, the confirmation for orthogonal waveform encoding MIMO-SAR by airborne experiments has not been reported in any literature. Here, an echo separation experiment based on the segmented phase code (SPC) waveforms and an airborne digital beamforming SAR (DBF-SAR) system is demonstrated for the first time. In the experiment, the SPC waveforms are cyclically transmitted within the adjacent pulse repetition intervals (PRIs) to simulate multiple transmitters, and the scattered echoes are received by the 16-channel antennas in elevation at the same time. In the postprocessing, the echo signals of continuous PRIs are added to obtain the mixed echoes, and a detailed echo separation method is adopted. In the method, the mixed echo signals from close arrival angles and far arrival angles are separated by the time shift and weighting, and by the bandpass filtering and DBF technique, respectively. Through the presented method, the mixed echoes of dual-transmit and 16-receive (2T16R) SAR imaging mode are separated and imaged successfully. The experimental results not only validate the echo separation scheme but also indicate that it is very promising in future MIMO-SAR missions. [ABSTRACT FROM AUTHOR]

It is challenging to estimate satellite targets’ absolute attitude and size with limited observational data. This article proposes an innovative way to jointly estimate satellite targets’ absolute attitude and size in the 3-D stable coordinates based on inverse synthetic aperture radar (ISAR) image interpretation with only one image. By taking advantage of the rectangular solar panels commonly equipped on satellites, this article extracts solar panel’s principal components, line features, and phase features of single ISAR imagery with principal component analysis (PCA), radon transform (RT), and minimum-entropy (ME)-based autofocus method, respectively. The projection relationship between these features and the absolute attitude and size of the satellite are established separately. Through multi-features fusion, a joint parameter estimation optimization function is established. This optimization is solved iteratively by the quasi-Newton method. The attitude and size parameters can be estimated simultaneously and rapidly, realizing the satellite state estimation under limited observation data. The excellent performance of the proposed algorithm is verified through different experiments. [ABSTRACT FROM AUTHOR]

A novel noncoherent microwave imaging principle with periodical or random radio frequency (RF) illumination is proposed in this article. Implemented with circular array synthetic aperture radar (SAR) frontend and low-complexity signal processing algorithms, the imaging device, called RF camera, achieves some desired properties similar to an optical camera, such as the capability to operate with multiple uncoordinated illuminators. Different from conventional multistatic imaging, the RF camera does not require any knowledge about an illuminator’s location or signal waveform. A static illumination sensor (IS) can be used to provide a reference signal for image reconstruction. With periodical illumination, the RF camera can even operate without IS, but the imaging performance can be improved with IS. With random illumination, the IS is necessary for the RF camera operation, and the imaging distortion can be described by a point blur function. Theoretical analyses on the imaging signal-to-noise ratios are performed under different RF camera operation modes. Simulation and experimental tests are conducted using 77-GHz millimeter wave frequency to verify the noncoherent imaging principle and its performance. [ABSTRACT FROM AUTHOR]

Drone-borne ground-penetrating radar (GPR) has proven to be efficient and promising for high-resolution topsoil moisture mapping at the field scale. However, in practice, the radar incident angle may change as a function of the terrain slope and unstable flying conditions. In this respect, we analyzed the effect of radar incident angle on full-wave inversion for soil permittivity characterization. Specifically, using the frequency-domain radar equation, antenna characteristic functions with different incident angles were determined for a horn antenna as an example. Then, numerical analyses and field measurements were performed to quantify the errors on the retrieved permittivity resulting from incident angle drifts. In agreement with the radar equation concept, we observed that the angle- and frequency-dependent global transmission functions of the antenna correspond well to its radiation pattern. Numerical and field results show that errors in the permittivity estimation can be very significant as a function of the antenna radiation pattern and the incident angle. Nevertheless, if the incident angle is known and the characteristic antenna functions are determined as a function of the incident angle, full-wave modeling and inversion remain accurate, provided that the signal-to-noise ratio remains sufficient. [ABSTRACT FROM AUTHOR]

For weather radar, although a phased array system is useful for rapid scanning, its development cost is several times higher than that of radar with a dish-type antenna, and it requires relatively more antenna elements, analog-to-digital converters, and phase shifters. To reduce antenna elements with the devices of phased array systems, array geometry in a random or triangular pattern is often used. However, relatively fewer antenna elements result in observation accuracy degradation due to the increased sidelobe level or the wider beamwidth. We employ compressive sensing (CS) processing [i.e., L1 minimization (L1) and basis pursuit denoising (BPDN)] to reduce the number of antenna elements. We conducted numerical simulations for point and distributed like targets and discussed the observation accuracy using L1 and BPDN. Compared to the estimations made using a full array antenna, where BPDN can be estimated with extremely high accuracy given a 25% reduction in antenna elements, we also applied CS to real measurement data obtained using the PAWR. The BPDN was also very effective for measurement data. The novelty of the study is highlighted in its discussion of the feasibility of applying CS in observation data with the PAWR. The study findings provide a reference for development cost reduction and the mass production of PAWR. [ABSTRACT FROM AUTHOR]

Spatial-variant phase errors (PEs) are important factors which defocus the synthetic aperture radar (SAR) image. In time-domain SAR imaging, the exact calculation of instantaneous range is carried out to realize imaging. Accurate trajectory is the key to compensate spatial-variant PEs and ensure image focus. Thus, an improved time-domain autofocus method based on three-dimensional motion errors (3-D MEs) estimation is proposed in this article. First, an improved maximizing-maximum-pixel-value method is used to estimate nonspatial-variant PEs. Meanwhile, a theoretical explanation combined with $N$ -dimensional Euclidean space is described. Then, residual PEs and wrapped PEs are discussed successively. A part-overlapped partitioning scheme for sub-block images (SBIs) and a wrapped-PE model are proposed for 3-D MEs estimation. Then, the estimation problem is turned into a mixed integer programming problem, which can be solved by the combination of genetic algorithm (GA) and Tikhonov regularization. Finally, the well-focused image is obtained through updated trajectory. The effectiveness of the proposed method is proven by results of simulation and real SAR data processing. [ABSTRACT FROM AUTHOR]

This article proposes a low-cost three-dimensional (3-D) millimeter-wave (MMW) holographic imaging system using helical scanning with multiple receivers to achieve a fast continuous scanning over a large two-dimensional (2-D) cylindrical surface. First, the system geometry and its imaging process based on the back-projection algorithm (BPA) are presented. The corresponding imaging point spread function (PSF) and resolutions are analyzed accordingly. To reduce the computational cost significantly, a novel 3-D helical imaging algorithm is then proposed based on the piecewise constant Doppler (PCD) principle. The slant range difference resulting from the helical scanning can be compensated jointly along angular and vertical directions. The proposed imaging is prototyped using the AWR1843 radar sensor from Texas Instruments (TIs) and a moving platform composed of step motors and a micro-controller unit (MCU). The digital imaging process and the number of the required complex multiplications are also discussed in detail. Finally, simulation and experimental results are provided to validate the accuracy and efficiency of the proposed imaging system. [ABSTRACT FROM AUTHOR]

In previous work, a methodology was proposed to obtain a sea surface object detection model based on the FasterR-CNN architecture using Sperry Marine commercial navigation radar images. Unfortunately, the percentage of recall using the validation dataset was 75.76% with a minimum score for true positives of 7% due to a network overfitting problem. In this research, the overfitting problem is solved by comparing three experiments. Each experiment consists of the combinations of different hyperparameters within the Faster RCNN architecture. The main hyperparameters modified to improve the performance of the model were weights initialization and the optimizer. The results finally achieved, show a significant improvement in relation to the previous work. The improved persistent object detection model shows a recall of 93.94% with a minimum score for true positives of 98%. [ABSTRACT FROM AUTHOR]

IEEE Standard 1502 recommends standardized practices for radar cross-section (RCS) measurements. This document—geared toward test-range operators and managers as well as users of the acquired data—outlines suggested measurement processes, measurement techniques, imaging concepts, and documentation practices. Consequently, there is a need to revisit and update the standard periodically to remain current with the measurement technology and typical test objects. This article outlines the updates that have been made to the standard in its current release. [ABSTRACT FROM AUTHOR]

The actual image resolution achieved by synthetic aperture systems often differs from the theoretical performance, and it is advantageous to automatically measure the realized resolution of these image products to monitor their quality. This article describes a novel image processing algorithm for measuring resolution based on the detection of prominent point scatterers. The algorithm proceeds in the following three main steps. 1) Identify candidate point scatterers via connected-component labeling and dual thresholding. 2) Prune the candidate scatterers and measure the size of the accepted detections by fitting an ellipse to the half-power contour traced in an oversampled image snippet. 3) Calculate the mode of the ellipse semiaxes lengths from a collection of scatterers to represent the local resolution estimate. The fitting of an ellipse to the half-power contour is a new contribution to resolution estimation that facilitates both detection and measurement, and it provides estimates of resolution in any direction, instead of only the range and along-track directions, which is important for images with excessive sensor motion. The accuracy and robustness of the algorithm are tested by applying it to simulated imagery with a series of parameter sweeps. Finally, the algorithm is applied to collected synthetic aperture sonar data and shown to be insensitive to seafloor textures. [ABSTRACT FROM AUTHOR]

In the view of national security, radar micro-Doppler (m-D) signatures-based recognition of suspicious human activities becomes significant. In connection to this, early detection and warning of terrorist activities at the country borders, protected/secured/guarded places and civilian violent protests is mandatory. Designing an automated human suspicious activities: army crawling, army jogging, jumping with holding a gun, army marching, boxing, and stone-pelting/grenades-throwing, recognition system using a suitable deep convolutional neural network (DCNN) model is rapidly growing due to its inherent in-depth features extraction capability. As a value addition to this research, an X-band continuous wave (CW) 10 GHz radar has been developed at our radar systems laboratory and used to acquire the m-D signatures, to prepare a dataset (DIAT- $\mu $ RadHAR) corresponding to above mentioned suspicious activities. In order to prepare a realistic dataset, human targets of different heights, weights, and gender are directed to perform the suspicious activities in front of the radar at different ranges between 10 m - 0.5 km and at different target aspect angles (0°, ±15°, ±30° and ±45°). A lightweight DCNN architecture ($\mu $ RadNet) is also designed and trained with the prepared DIAT- $\mu $ RadHAR dataset comprising 3780 samples. The performance and recognition accuracy of $\mu $ RadNet is statistically computed, and the results are compared to the state-of-the-art (SOTA) CNN models. The $\mu $ RadNet DCNN model outperforms the SOTA CNN models, giving 99.22% of overall classification accuracy, 0.09M parameters, and 0.40G floating point operations (FLOPs) with minimal false negative/positive rates. The time-complexity of the designed lightweight $\mu $ RadNet DCNN model is 0.12 s, which evidences the suitability of our DCNN model for the on-device implementation. [ABSTRACT FROM AUTHOR]

The presence of rain can negatively affect the performance of many sensors such as X-band radar. In this paper, an effective approach is proposed to mitigate the effect of rain on significant wave height (${H}_{s}$) estimation from X-band radar sensor data along with a machine-learning (ML)-based method. First of all, the haze removal algorithm is applied to rain-contaminated radar images as pre-processing. Then, three different features are extracted from the processed radar images. Different combinations of these three features are utilized to estimate ${H}_{s}$ under the rain condition by using support vector regression (SVR)-based and temporal convolutional network (TCN)-based regression methods. It is found that the root-mean-square-errors (RMSEs) of ${H}_{s}$ estimation results using two typical methods (signal-to-noise ratio (SNR)-based and ensemble empirical mode decomposition (EEMD)-based linear fitting methods) are decreased by 0.14 m and 0.48 m after introducing the haze removal algorithm, respectively. Also, a relatively high accuracy can be achieved using the SVR-based regression method with the combination of SNR and gray level co-occurrence matrix (GLCM) features. Compared to the SNR-based and EEMD-based linear regression methods, the proposed SVR-based method further improves the estimation accuracy, with reductions of RMSE by 0.19 m and 0.82 m, respectively. [ABSTRACT FROM AUTHOR]

In view of the fact that the traditional radar system follows the Nyquist sampling theory and have the constraint of high sampling rate, we analyze a low‐speed sampling radar imaging based on compressed sensing. In compressed sensing, it uses the low‐rank characteristics of the signal to make it non‐adaptively sample the signal at a rate of far lower than the Nyquist sampling rate, so that it can reduce the requirements of hardware. Therefore, it not only can help to solve the problems of power, size, weight, reliability, and cost, but also can be seen as an upgrade and supplement of the traditional radar. [ABSTRACT FROM AUTHOR]

Computational microwave imaging with frequency-diverse metasurface (FDM) apertures is an emerging technology. In this article, we establish an experimental FDM microwave imaging system and address the waveform design problem based on the information theory, aiming to enhance the advantages of the FDM imaging. Two waveform design methods based on the different criteria are proposed for the FDM imaging system. The first waveform is designed by maximizing the mutual information between the object and the measured data with the constant transmitted energy, and the second one is designed by minimizing the transmitted energy while the mutual information is not less than a threshold. The performance of the proposed waveform design methods is evaluated by the data gathered by the self-established experimental FDM imaging system. The results show that the proposed waveform design methods are capable of improving the imaging quality or the imaging efficiency of the FDM imaging system. [ABSTRACT FROM AUTHOR]

Due to the small size of foreign object debris (FOD) and varied complex weather conditions, the detection of FOD on airport runways is a great challenge. Radar is an important method for detecting FOD targets. However, almost all the existing systems are based on real apertures, which have disadvantages such as low azimuth resolution and susceptibility to rain interference. Here, an innovative FOD detection radar system based on arc-scanning synthetic aperture radar (AS-SAR) technology, the AS-SAR based FOD detection system (AS-FODR), achieves omnidirectional coverage with a very high azimuth resolution and the suppression of flicker clutter, such as rain drops in severe weather. According to the radar imaging simulation of a scene under rainy conditions and the information processing analysis of field experiments, a prototype system was built, and an efficient data process flow was proposed. In short, a one centimeter FOD target was detected on the runway more than 250 m away, proving that the use of AS-FODR is feasible and effective. [ABSTRACT FROM AUTHOR]

The concurrent imaging technique enables parallel acquisitions with different beams or modes, e.g., a wide area Stripmap mode with a High-Resolution Spotlight mode. Such a concurrent Stripmap/Spotlight imaging technique is investigated for TerraSAR-X. This technique employs a pulse-to-pulse interleaving scheme to acquire two acquisitions-even of disjunctive areas-at the same time, offering products with different resolution and coverage portfolios. This capability is especially interesting for customers interested in an overview of a larger area but at the same time observing an area of interest with higher resolution, e.g., for infrastructure monitoring or reconnaissance applications. The basic concepts, as well as the driving system parameters, are discussed in detail, together with a coverage analysis revealing the high availability rate of the mode combinations on a global scale. A processing approach re-using a substantial part of the existing infrastructure is described and exemplary acquisitions are shown, together with a detailed performance analysis with respect to resolution and ambiguities. [ABSTRACT FROM AUTHOR]

The attitude and size of satellite targets are essential information for their activity analysis. This article proposes a novel approach to estimate the absolute attitude and size of satellite targets in the 3-D stable coordinates based on inverse synthetic aperture radar (ISAR) image interpretation. In an ISAR image of a satellite, the satellite’s body is chosen as an individual structure segmented from each ISAR image by employing pix2pix generative adversarial network (Pix2pixGAN). By exploring the shape feature of the satellite body with principal component analysis (PCA), the satellite attitude and size are estimated jointly through solving an optimization based on the gradient iteration method. The optimization is established by bridging range-Doppler (RD) images and the target feature parameters (attitude and size) with the accommodation of target trajectory information and the ISAR geometric projection model. In the experiments, the simulation data are generated from real satellite orbital parameters and the computer-aided-design (CAD) models of two satellite targets: TianGong (TG) and KeyHole (KH). Compared with the factorization-based reconstruction method, the proposed method can estimate the attitude and size of the satellite simultaneously and has a higher size estimation accuracy. [ABSTRACT FROM AUTHOR]

Superresolution methods can be applied to real aperture radar (RAR) to improve its angular resolution by solving an inverse problem. However, traditional superresolution methods are achieved after batch data collection, which requires extensive operational complexity and storage space. To solve this problem for RAR, an online detect-before-reconstruct (DBR) framework is proposed in this article based on the sparse property of targets. First, along the range direction, each sample of the echo data is detected to reduce the computational complexity by reducing the dimension of the effective data. Second, along the azimuth direction, a data-adaptive online processing structure is proposed to reduce the storage requirement for the angular superresolution problem. Finally, within the online processing structure, a target data-adaptive updating strategy is proposed to reduce the number of iterations for each target grid. The online DBR-based framework can effectively reduce the operational complexity caused by the noise values of the echo data. Based on the proposed online processing structure, the storage requirement and the operational complexity of the angular superresolution for an RAR system can be greatly reduced without significant reconstruction performance loss. The results of simulations and experimental data verify the proposed framework. [ABSTRACT FROM AUTHOR]

Coherent microwave radar can provide reasonable wave heights, while wave periods are always overestimated. The “group-line” in the wavenumber–frequency spectrum is a possible mechanism to interpret the overestimation, but the previous model cannot fully explain it. To address the issue, a new model integrating with the breaking and its evolution is proposed for microwave composite sea surface scattering. The upwind-grazing Doppler characteristics for coherent microwave radar under VV polarization are analyzed. The model is validated by comparing the results between the simulation and the radar observation. It explains the creation of “group-lines” in the wavenumber–frequency spectra and indicates that the short waves generated after breaking and their evolution should be the dominant reason for the overestimation of wave period using coherent microwave radar. The result can provide a theoretical basis for improving the performance of wave measurements with coherent microwave radar and for microwave ocean remote sensing. [ABSTRACT FROM AUTHOR]

In this article, a new microwave-radar-based technique for short-range detection and classification of multiple human and vehicle targets crossing a monitored area is proposed. This approach, which can find applications in both security and infrastructure surveillance, relies upon the processing of the scattered-field data acquired by low-cost off-the-shelf components, i.e., a 24 GHz frequency-modulated continuous wave (FMCW) radar module and a Raspberry Pi mini-PC. The developed method is based on an ad hoc processing chain to accomplish the automatic target recognition (ATR) task, which consists of blocks performing clutter and leakage removal with an infinite impulse response (IIR) filter, clustering with a density-based spatial clustering of applications with noise (DBSCAN) approach, tracking using a Benedict-Bordner $\alpha $ - $\beta $ filter, features extraction, and finally classification of targets by means of a $k$ -nearest neighbor ($k$ -NN) algorithm. The approach is validated in real experimental scenarios, showing its capabilities in correctly detecting multiple targets belonging to different classes (i.e., pedestrians, cars, motorcycles, and trucks). [ABSTRACT FROM AUTHOR]

Translational motion compensation plays an important role in the inverse synthetic aperture radar (ISAR) imagery. In this study, a new translational motion compensation algorithm for ISAR imaging under low signal-to-noise ratio (SNR) conditions is proposed. This method is formed based on the optimization of dynamic image sharpness metric, by which the translational parameters are accurately estimated from the returned signals. The important properties of the locally and globally optimal points of dynamic image sharpness function are proved and discussed by first using the dominant point-targets model. These properties are employed in the scheme to search for the globally optimal point and prevent the optimization being trapped at a locally optimal point. Based on the properties of optimal points and Gauss–Newton method, the algorithm to estimate the translational parameters by dynamic image sharpness metric optimization (DISMO) is devised. The DISMO can find the accurate translational parameters corresponding to the globally optimal point without being affected by local optima under low SNR conditions with high efficiency. Further, the translational compensation is completed based on the estimates. The proposed method is applied to simulated and real data. The processing results confirm the effectiveness of this new algorithm. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low resolution and poor noise suppression for maneuvering targets with traditional inverse synthetic aperture radar (ISAR) imaging algorithms, we propose a motion compensation-driven high-resolution imaging algorithm based on Fourier ptychographic microscopy (FPM) technology. FPM is a phase retrieval technology that uses the concept of synthetic aperture to obtain the high-resolution image of the target. The process of FPM imaging is composed of the enhancement of the known target information in the spatial domain and some fixed restricted alternating iteration in the frequency domain. This article analyzes the mathematical model of maneuvering target echo and then studies the rationality of applying the FPM phase retrieval algorithm to ISAR maneuvering target imaging. The proposed algorithm mainly includes two steps. First, a series of low-resolution ISAR images are generated by sampling echo data through an aperture function. Second, the FPM phase retrieval technology is applied to splice and reconstruct the spectrum of the low-resolution images to achieve the focused ISAR image. The effectiveness of the proposed algorithm is verified by using multiple groups of simulation data and real radar data. [ABSTRACT FROM AUTHOR]

The ice–bedrock interfaces at the bottoms of ice sheets and the internal ice layers record the historical evolution of the ice sheets and are important indicators for inferring glacier dynamics and explaining subglacial topographies. Radar-detected bedrock and internal ice layers can be used to observe conditions in the interiors and bottoms of ice sheets. The low contrast of the internal layers’ feature in radar images, as well as the effects of ice flow, makes it challenging to automatically extract and trace the layers and interfaces of ice sheets. Manual or semiautomatic methods are extensively applied in bedrock and internal ice layers’ extractions. However, the conventional methods require large amounts of time. Especially when processing a large number of radar observation data, the accuracy and efficiency of conventional methods are untoward. Therefore, we propose EisNet, a fusion system consisting of deep neural networks, to extract the multiple types of internal layers. Because of the rareness of high-precision extraction of ice layers, it is virtually impossible to train EisNet in observational data with the corresponded label of ice layers. To train EisNet, we design a novel synthetic method for radar images based on artifact noise and characteristic textures that are similar to those in the observation data. The validation in synthetic data proves EisNet’s capacity for layer extraction and noise removal. The field data application shows the generalization and adaptation abilities of EisNet and indicates that EisNet can significantly improve the efficiency of the automatic processing of ice sheet radiostratigraphy. [ABSTRACT FROM AUTHOR]

This article presents a comprehensive technique for calibrating wideband frequency-modulated continuous-wave (FMCW) fully polarimetric radars employing linear FM (LFM) signal chirps. A detailed system distortion model is developed that accounts for both polarimetric distortions and distortions caused by nonlinearities in the transmitted and received radar chirps over multiple polarization channels. Using the radar responses of two point targets at known distances from the radar, the calibration technique is able to estimate the range-dependent nonlinearities in phase and correct them for all targets at all ranges. This allows the radar to achieve its ideal range resolution. In addition, the calibration technique corrects for channel imbalances in the polarimetric radar using a metallic sphere of known diameter and any depolarizing target. Both numeric simulations and actual polarimetric radar measurements using a wideband FMCW radar operating over 76.5–82 GHz were used to validate the calibration technique. [ABSTRACT FROM AUTHOR]

Conventional synthetic aperture radar (SAR) processing can be adapted to obtain 2-D high-resolution images of moving targets. The resulting images, however, suffer from layover effects, therefore making their interpretation more challenging. This problem can be dealt with by combining radar imaging with interferometry, which allows for 3-D target reconstruction. This article presents a 3-D imaging technique built upon the well-known global backprojection (GBP) algorithm, which has been adapted to focus moving targets rather than the stationary scene. The proposed algorithm is tested on real datasets acquired with a circular acquisition geometry. Using a nonlinear flight-path allows to monitor the target from multiple viewing angles, therefore overcoming the limitations that are common to stripmap SAR (e.g., shadowing effects, geometrically adverse cases, and so on). The obtained results, which were compared with independent ground measurements, demonstrate the accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

An intrinsic challenge in the geophysical interpretation of low-frequency synthetic aperture radar (SAR) imagery of semitransparent media, such as ice sheets, is the position ambiguity of the scattering structures within the glacial volume. Commonly tackled by applying interferometric and tomographic techniques, their spaceborne implementation exhibits by orders higher complexity compared to missions relying on single SAR images, making them cost expensive or, in the context of planetary missions, even impossible due to limited navigation capability. Besides, even these sophisticated techniques are commonly biased due to inaccurate permittivity estimates, leading to geometric distortions up to several meters. We present a novel inversion procedure to estimate volume parameters of ice sheets, namely, the depth of the scattering layer within the glacial volume and the dielectric permittivity of the ice, based on single-image single-polarization SAR acquisitions. The information is inherent in the processed SAR data as phase errors on the azimuth signals resulting from uncompensated nonlinear propagation of the radar echoes through ice. We suggest a local map-drift autofocus approach to quantify and spatially resolve the phase errors and an inversion model to relate them to the penetration depth and permittivity. Testing the proposed technique using P-band SAR data acquired using DLR’s airborne sensor F-SAR during the ARCTIC15 campaign in Greenland shows promising results and good agreement with tomographic products of the analyzed test site. [ABSTRACT FROM AUTHOR]

Low-frequency radar sounders have the potential to generate altimetric profiles, but the feasibility of utilizing planetary radar sounding data as an alternative to laser altimetry has not been assessed using existing data to date. Therefore, we have developed, implemented, and evaluated an algorithm to process SHAllow RADar sounder (SHARAD) data on Mars (Experiment Data Records as available on the planetary data system) first into altimetry profiles and ultimately into digital terrain models (DTMs). The minimally processed data are pulse compressed, corrected for ionospheric distortion, zero-Doppler filtered, and incoherently summed. We then apply pulse re-tracking techniques adapted from terrestrial ocean altimetry to identify the surface return. From the surface return we compute the time-of-flight and hence the range from the spacecraft to the surface of the planet. The altimetry groundtracks are then co-registrated with Mars Orbiter Laser Altimeter (MOLA) to remove any biases resulting from residual ionospheric effects or timing issues. The altimetric profiles are finally used to create DTMs based on SHARAD data. While the SHARAD altimetry data have coarser inherent resolution than laser altimeters or imaging radars, we demonstrate that radar sounding data is still a viable source for satellite-based altimetry measurements. This is particularly important for future planetary missions not carrying laser altimeters but radar sounders, such as the upcoming Europa Clipper mission. [ABSTRACT FROM AUTHOR]

Radio spectrum bands allocated to spaceborne synthetic aperture radar (SAR) imagery are shared by multiple missions. In practical radio spectrum environments, these bands are also used by some ground radars, e.g., C-band weather radar. Due to this fact, radio frequency interference (RFI) may occur for a spaceborne SAR when its received signals contain the transmitted waveforms from another SAR or radar operating at the same frequency band. This particular class of RFI is usually linear-frequency-modulation (LFM) signals, which can cause bright radiometric artifacts in focused SAR images. Most existing signal processing approaches designed for addressing this problem belong to the class of preprocessing methods, which removes RFI in level-0 raw radar data before SAR focusing. In this article, we propose a postprocessing kernel—2-D SPECtral ANalysis (2-D SPECAN) filter, for removing the class of LFM RFI in level-1 SLC images. The filtering consists of three main steps: Step 1: focus LFM RFI artifacts in SLC images as point-like responses in the spectral domain via 2-D SPECAN; Step 2: perform 2-D notch filtering in the spectral domain to remove the most contribution of the RFI responses; and Step 3: transform the filtered spectrum back into the SLC image domain using the inverse operation of the 2-D SPECAN. For computation efficiency, we design a simplified processing flow and adopt a blockwise processing strategy. Experiments with several Sentinel-1 SLC images demonstrate that severe RFI artifacts in SLC images can be removed significantly by the proposed method. [ABSTRACT FROM AUTHOR]

General theory of holographic inversion with linear frequency modulation (GHI-LFM) has been proposed recently, providing the framework for further advances in the holographic methods for image reconstruction with millimeter-wave linear frequency modulation (LFM) radar. Here, accelerated holographic imaging (AHI)-LFM, an AHI method for LFM radar that exploits range stacking, is proposed. The method is developed and validated for the purpose of whole-body security screening. The image quality and the computational efficiency are compared with the original GHI-LFM algorithm. It is demonstrated that the AHI-LFM algorithm improves the reconstruction speed by at least an order of magnitude while preserving the same image quality for the image sizes arising in practical whole-body security screening. [ABSTRACT FROM AUTHOR]

A millimeter-wave (mmW) range enhancement imaging technique for multistatic planar arrays is presented. The echo in an active mmW imaging system that contains both amplitude and phase information, and the latter can be potentially exploited for improving imaging performance in the range direction. With the help of a two-dimensional (2-D) phase unwrapping scheme, and the multi-input–multi-output range migration algorithm (MIMO-RMA) for a multistatic planar array, along with the requisite pretreatment processes such as target extraction and phase error correction for the reconstructed image region, a reconstructed image having a finer range profile could be achieved. The underlying principle of the phase information exploitation to achieve finer range distance information is discussed, the range enhancement imaging implementation steps are outlined, and the necessary pretreatment processes in simulation and experiment are introduced. The efficiency of the proposed approach is analyzed theoretically and demonstrated experimentally. Numerical simulation and experimental results show that the reconstructed image using the new range enhancement imaging technique contains the promised finer range distance information. [ABSTRACT FROM AUTHOR]

A high-resolution vector-sum phase shifter fabricated using a 28 nm CMOS was developed for a 79 GHz imaging radar. The phase shifter incorporates a process and temperature (P/T) stable in-phase (I)/quadrature-phase (Q) signal generator with a robust matching network and highly linear variable gain amplifiers (VGAs) with a crossing metal-line layout. These techniques contribute to 0.5° phase-setting resolution and a 0.9° root-mean-square (rms) phase error with no calibration process. The developed phase shifters were applied to a 79 GHz imaging radar prototype that consists of eight-channel transmitters and eight-channel receivers. The measurement results show that our prototype can recognize human posture, recognize multiple people, and monitor vital signs. [ABSTRACT FROM AUTHOR]

High-performance millimeter-wave (mm-wave) multiple-in-multiple-out (MIMO) radars have been using efficient multiplexing with increasingly complicated radar waveforms, where conventional hardware architectures suffer from inevitably high design complexity and cost. This article presents a new software-defined mm-wave multistatic imaging system which provides great efficiency and flexibility to generate and process complicated radar waveforms. A general system block diagram and its signal model are proposed to use space-time-coded orthogonal frequency-division multiplexing (OFDM) for simultaneous MIMO transmission at the same time and frequency. The space-time coding (STC) is designed based on the Hadamard matrix which allows simple decoupling and decoding for imaging processing. A radar prototype is designed to validate the proposed system architecture, which operates at 83.5 GHz with a frequency bandwidth of 4.8 GHz. The mm-wave front-end consists of a metal-printed 8-by-8 waveguide array with a small element-separation of 9 mm to form 64 virtual channels for three-dimensional imaging. Experimental results show good imaging performance, giving great potential to design future cost-effective high-performance mm-wave imaging with efficient multiplexing. [ABSTRACT FROM AUTHOR]

Aiming at realizing on-chip real-time human activity recognition based on the miniaturized and integrated radar with limited computational resources, hyperdimensional computing (HDC) is a promising method due to its unique advantages including high energy efficiency, excellent robustness, lightweight model, and superfast learning process. This article reports on the first use of HDC in the radar system to recognize human activities. First, we design an image mask method to minimize the impact of interference in original radar micro-Doppler spectrograms and apply a fast envelope extraction method to fully reflect features of different human activities. Subsequently, we select the most applicable representation methods for HDC to encode and recognize these features. To better accelerate the convergence speed, enable subtle adjustments of the HDC model, and improve the recognition accuracy, we propose a new adaptive retraining method, Mask-Retraining. Finally, the accuracy of recognizing six human activities using HDC with Mask-Retraining reaches 92.93%. Experimental results and comparisons between HDC and ten recognition algorithms demonstrate the validity of our methods. In most cases, HDC outperforms other algorithms being compared both in recognition accuracy and computational cost. Especially, HDC becomes the prime and irreplaceable candidate when only small datasets or finite data points are available. [ABSTRACT FROM AUTHOR]

Automotive radars, along with optical sensors such as cameras or lidars, offer a reliable way of obtaining the 3-D information about the environment. Of particular interest in autonomous driving (AD) is the reliable detection of particularly vulnerable road users (VRUs). Modern radar sensors are able to detect, distinguish, and track targets with high resolution. Relying on that, a backscattering model of complex traffic targets can be generated from the reflected signals of their scattering points (SPs). These models can be employed in the radar channel simulations for verification methods of advanced driver assistance systems. Therefore, in this work, different persons as the most vital VRUs are measured with high radial and high angular resolution. The necessary signal processing steps are explained in detail for the determination of the relevant SPs. Thus, the corresponding radar cross section (RCS) values can be assigned to certain body regions. In addition to real persons, further measurements are compared with a dummy of the corresponding size. Based on the measurement results, not only accurate models of road users can be derived, but also the measurement results can be employed for calculating wave propagation in traffic scenarios. From the measured SPs, the classification of the persons by size and stature is derived. [ABSTRACT FROM AUTHOR]

Radar-based near-field 3-D imaging technology has recently attracted increasing attention in the field of security systems. This article presents a novel multiple-input–multiple-output (MIMO) synthetic aperture radar imaging algorithm for security checkpoints attached to a moving walkway. The proposed algorithm is characterized by transmitting-signal multiplexing on the Doppler frequency domain, which allows for a reduced data acquisition time compared with that of conventional time-division multiplexed MIMO systems and therefore achieves the image reconstruction of dangerous objects with smaller motion errors. This algorithm can be applied to 1-D antenna array systems, and image reconstruction is based on a back-projection algorithm with some modifications. The mathematical derivation of the algorithm and its advantages and disadvantages in comparison with those of a conventional system are detailed in this article. Details of numerical simulation of a point target as well as the experimental results of image reconstruction of a pinball and toy gun, using cascaded millimeter-wave sensors, are also presented for verifying the performance of the algorithm. [ABSTRACT FROM AUTHOR]

Wideband two‐dimensional multiple input multiple output (MIMO) radar can be used for target three‐dimensional (3‐D) imaging. As the echo of wideband MIMO radar is derived from both time and spatial sampling, the time‐frequency coupling is contained, resulting in imaging defocussing. To solve this problem, a time‐frequency decoupling algorithm based on matrix enhancement and matrix pencil (MEMP) is proposed to achieve high‐resolution 3‐D imaging. By combining the enhanced matrix constructed via MEMP with the modern spectral estimation theory, the time‐frequency coupling term is regarded as a space rotation factor and is eliminated in the process of target position estimation. Simulation results show that the proposed algorithm can reduce the negative influence of time‐frequency coupling and improve the imaging accuracy of wideband two‐dimensional MIMO radar. [ABSTRACT FROM AUTHOR]

Due to the rapid increase in 76 GHz automotive spectrum use in recent years, wireless interference is becoming a legitimate area of concern. However, the recent rise in interest of automated vehicles (AVs) has also spurred new growth and adoption of low frequency vehicle-to-everything (V2X) communications in and around the 5.8 GHz unlicensed bands, opening the possibility for communications spectrum reuse in the form of joint radar-communications (JRC). In this work, we present a low frequency 5.9 GHz side-looking polarimetric synthetic aperture radar (SAR) for automotive use, utilizing a ranging waveform in a common low frequency V2X communications band. A synthetic aperture technique is employed to address the angular resolution concerns commonly associated with radars at lower frequencies. Three side-looking fully polarimetric SAR images in various urban scenes are presented and discussed to highlight the unique opportunities for landmark inference afforded through measurement of co- and cross-polarized scattering. [ABSTRACT FROM AUTHOR]

A submillimeter-wave quasi-optical high-resolution imaging system, based on a frequency-modulated continuous wave radar sensor operating between 343GHz and 423GHz utilizing a bandwidth of 80GHz, is presented. The system setup in-tandem with the essential building blocks is described and the generation of high-resolution power- and elevation-profile images of various samples under test is presented. Experimental results show a lateral resolution limit in the order of wavelength. [ABSTRACT FROM AUTHOR]

Radar sensing has become very popular over the last two decades, and research has focused on high-bandwidth and high-resolution systems. Due to the steadily increasing center frequency of front-end circuits, on-chip antennas are the preferred choice over PCB antennas and horn antennas when frequencies get close to THz. However, conventional on-chip antennas are severely limited in bandwidth, leading to increased use of wideband and multiresonant on-chip antennas. Besides a more complex design process of multiresonant antennas, they have the disadvantage of a nonconstant dispersive group delay (GD). This reduces the resolution of sensing systems, such as the range resolution and angular resolution of a radar system. In this work, we show how GD affects the imaging properties of a radar system. The measured S-parameter data from a 240-GHz multiresonant antenna are used to generate synthetic intermediate frequency (IF) signals of a rectangular array. Subsequently, simulated 3-D radar images are generated using the backprojection algorithm. These images are compared with those of a nondispersive imaging system. Finally, two compensation methods using a phase correction method and an all-pass filter are explained, and their performance is compared. [ABSTRACT FROM AUTHOR]

For autonomous driving vehicles, highly accurate representations of the environment are essential for both trajectory planning and self-localization. Different possibilities allow to generate detailed maps of the environment based on chirp-sequence radar sensors for advanced driver assistance systems (ADASs). For the first time, this letter shows a qualitative comparison between synthetic aperture radar (SAR)- and occupancy grid map (OGM)-based environment representation using identical measurement data. The differences of existing signal processing chains as well as a visual measurement-based comparison of the resulting environmental maps is presented. [ABSTRACT FROM AUTHOR]

This letter presents a novel approach to build a compact lightweight unmanned aerial vehicle (UAV) radar for remote sensing applications. The proposed radar exploits the recent advancement of an automotive radar chip for broadband chirp generation and rapid data processing. To compensate for the path losses and improve penetration at the millimeter-wave (mm-wave) frequency range, up- and down-converters are developed to generate an ultra-wideband (UWB) (3.25–5.15 GHz) chirp signal. The total payload of the radar is 2.5 kg. The proposed radar is installed on an UAV and tested in the field at a 100-m altitude above the ground surface. The results show that the compact low-power UWB radar can be used to map vegetation and soil moisture with fine resolution. [ABSTRACT FROM AUTHOR]

Recent advancements demonstrate the application of machine learning techniques to radar signal processing, particularly the use of neural networks in one-dimensional (1-D) radar data to assist constant false alarm rate (CFAR) based target detection. However, the efficacy of using neural networks alone on 2-D data for target extraction remains to be explored. This research aims to develop a novel approach of designing a convolution neural network (CNN) based target detection system for real radar data. We propose a dual neural network detection scheme in which the first neural net filters out noisy images. Thereafter, we employ a second CNN-based target detector that extracts information from both radar data axes (fast-time and slow-time), thus utilizing additional information for better detection of targets even with low SNR. Moreover, the same CNN, in conjunction with a region-based convolutional network, exploits micro-Doppler features of rotary-wing aircraft in radar returns and uses them to further classify detected targets as fixed-wing or rotary-wing aircraft. To the best of our knowledge, no generalized methodology exists to address these two issues together. The proposed solution replaces the critical CFAR and peak detection algorithms from the radar signal processing chain. This article also introduces a labeled range-Doppler images dataset obtained from real-world air traffic control radar data. Experimental results demonstrate the superior performance of the proposed technique over 1-D and 2-D CFAR and peak detection algorithms. [ABSTRACT FROM AUTHOR]

Radio-frequency-based noncooperative monitoring of humans has numerous applications ranging from law enforcement to ubiquitous sensing applications such as ambient assisted living and biomedical applications for nonintrusively monitoring patients. Large training datasets, almost unlimited memory capacity, and ever-increasing processing speeds of computers could drive forward the data-driven deep-learning-focused research in the abovementioned applications. However, generating and labeling large volumes of high-quality, diverse radar datasets is an onerous task. Furthermore, unlike the fields of vision and image processing, the radar community has limited access to databases that contain large volumes of experimental data. Therefore, in this article, we present an open-source motion capture data-driven simulation tool, SimHumalator, that can generate large volumes of human micro-Doppler radar data in passive WiFi scenarios. The simulator integrates IEEE 802.11 WiFi Standards (IEEE 802.11 g, n, and ad) compliant transmissions with the human animation data to generate the micro-Doppler features that incorporate the diversity of human motion characteristics and the sensor parameters. The simulated signatures have been validated with experimental data gathered using an in-house-built hardware prototype. This article describes simulation methodology in detail and provides case studies on the feasibility of using simulated micro-Doppler spectrograms for data augmentation tasks. [ABSTRACT FROM AUTHOR]

The Ionospheric Continuous‐wave E region Bistatic Experimental Auroral Radar (ICEBEAR) has been reconfigured using a phase error minimization and stochastic antenna location perturbation technique. The resulting 45‐baseline sparse non‐uniform coplanar T‐shaped array, ICEBEAR‐3D, is used for aperture synthesis radar imaging of low elevation targets. The reconfigured receiver antenna array now has a field of view ±45° azimuth and 0°–45° elevation at 0.1° angular resolution. Within this field of view no aliasing occurs. Radar targets are imaged using the Suppressed Spherical Wave Harmonic Transform (Suppressed‐SWHT) technique. This imaging method uses precalculated constant coefficient matrices to solve the integral transform from visibility to brightness through direct matrix multiplication. The method then suppresses image artefacts (dirty beam) due to undersampling by combining brightness maps of differing harmonic order. Measuring elevation angles of targets at low elevations with radar interferometers has been a long standing problem. ICEBEAR‐3D elucidates the underlying misinterpretations of the conventional geometry for vertical interferometry especially for low elevation angles. The proper phase reference vertical interferometry geometry is given which allows radar interferometers to unambiguously measure elevation angles from zenith to horizon without special calibration. The receiver antenna array reconfiguration, Suppressed‐SWHT imaging technique, and proper geometry for vertical interferometry are validated by showing agreement of the meteor trail altitude distribution with numerous data sets from other radars. Key Points: Ionospheric Continuous‐wave E region Bistatic Experimental Auroral Radar receiver antenna array reconfiguration design based on a phase error minimization techniqueAperture synthesis imaging using the Suppressed Spherical Wave Harmonic Transform (Suppressed‐SHWT)Application of the proper geometry for vertical interferometry resolves low elevation angles unambiguously [ABSTRACT FROM AUTHOR]

The benefits of terahertz (THz) radiation have increased its use, especially in imaging systems. Recently, the use of a linear sparse periodic array (SPA) has been proposed as an effective solution for two-dimensional (2D) scanning in THz imaging systems. However, the special multistatic structure of the SPA is such that it is not possible to apply fast Fourier transform-based techniques directly in the near-field (NF). Therefore, in this paper, a fast processing approach based on two Fourier techniques compatible with linear SPA is presented for NF THz imaging. In this approach, we first employ a multistatic-to-monostatic conversion to reduce phase errors due to NF multistatic imaging. Then, to improve the quality of the results, we mathematically derive an interpolation formula to counteract the non-uniform spacing of the virtual array. The modified data is then processed by three rapid techniques (fast Fourier transform (FFT)-inverse fast Fourier transform, matched filtering and a novel 1D FFT-based technique with low computational complexity) to obtain reconstructed images of the scene. Numerical and experimental results confirm the satisfactory performance of the proposed approach in terms of both the computational time and the quality of the reconstructed images. [ABSTRACT FROM AUTHOR]