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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]

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]

Holographic subsurface radar (HSR) is a promising geophysical electromagnetic technique to detect shallowly buried objects due to its high lateral resolution. However, the subsurface inspections and visualization of buried targets are prone to be impaired by strong clutter from the nonplanar surface reflections. In this article, a clutter mitigation method using dual-frequency cancellation and sparse feature enhancement is proposed for HSR data to distinguish the targets from background. The radar signals are received at two distinct frequencies under certain conditions to calculate the strong surface reflections. After cancellation of the estimated surface, a modified $\ell _{1}$ regularization is utilized to further mitigate the residual clutter and highlight the target signature. The effectiveness of the proposed method is evaluated on both numerical simulation and radar signals collected from real HSR systems. The visual and quantitative results demonstrate that the proposed method successfully removes the nonplanar surface clutter with the targets preserved. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Radar imaging of typical planet landforms can be used for planetary exploration such as landform recognition and geometry measurement. This communication realizes high-resolution imaging simulations of typical planetary landforms, such as craters, hills, and volcanoes, by using parametric scattering center (SC) models. Based on the geometric models of these typical landforms, parametric SC models are constructed from scattered solutions of the physical optics (POs) method. Because the SC models take geometric parameters as model parameters, these models can simulate the scattered waves of landforms of different sizes. These SC models are validated by the PO results. In addition, the landform types can be recognized and their geometrical parameters are further extracted by SC model matching. The extracted geometric parameters show good agreement with real landforms, demonstrating that the SC models of typical planetary landforms have potential applications in planetary geomorphologic exploration. [ABSTRACT FROM AUTHOR]

This article demonstrates the use of a neural network to replace the nonlinear optimization at the core of a fully adaptive radar (FAR) system. Use of the neural network is shown to reduce computational complexity, leading to faster updates of the radar parameters. The feed-forward neural networks used were trained using the Levenberg–Marquardt algorithm along with a generalized regression neural network architecture. Training data was obtained from an existing FAR optimization system using the system’s parameter selections and target states for single-target tracking in range and velocity. The trained networks were then used to perform radar parameter selection based on the predicted (forward propagation of prior probabilities) probabilities represented by the target state (target range, velocity, and signal-to-noise ratio) and error variance (target range and velocity variance). The trained neural network was able to reduce the average optimization time by an order of magnitude while obtaining on average a lower resource consumption and similar radar task performance. [ABSTRACT FROM AUTHOR]

Range/Doppler migration and velocity ambiguity are two well-known problems encountered in high-speed moving target detection using a linear-frequency-modulated continuous-wave automotive radar. To mitigate the problems, we introduce a simple Doppler–range processing (DRP) algorithm by first performing Doppler processing via fast Fourier transform (FFT) across slow-time samples, followed by a simple interpolation step, and then range processing via FFT along Doppler migration lines over fast-time samples. The proposed DRP algorithm can achieve full range and full velocity resolutions, as well as full coherent integration gains. It attains a computational complexity comparable to that of the conventional 2-D-FFT-based range–Doppler processing approach, computationally much more efficient than existing approaches. The proposed DRP algorithm can automatically resolve the velocity ambiguity problems. We analyze its velocity ambiguity mitigation capability in relation to the radar bandwidth and the number of slow-time samples within a coherent processing interval. The effectiveness and the computational efficiency of the proposed algorithm are demonstrated by numerical examples. [ABSTRACT FROM AUTHOR]

In this article, we propose a new signal-decomposition algorithm composed of three steps: principal components analysis to yield high-resolution range profiles with improved signal-to-noise ratio; estimation of whitening and mixing matrices using independent component analysis in distributed radar network; and signal decomposition to obtain inverse synthetic aperture radar (ISAR) images that correspond to rigid body and fast-rotating parts by using estimated two matrices regardless of complicated range migration. The proposed method efficiently removes image blur caused by micro-Doppler (MD) signals of rotating parts and reduces the sensitivity to noise. In simulations, our proposed method could perform accurate and robust removal of the MD signatures to obtain a focused ISAR image of an aircraft with fast-rotating parts. [ABSTRACT FROM AUTHOR]

Detection and tracking of small targets in sea clutter using high-resolution radar is a challenging problem. Recently, a Bernoulli track-before-detect (TBD) filter has been developed for an airborne scanning radar in the maritime domain, with the purpose of detection and tracking of short-exposure targets by using a fast scanning mode (i.e., short dwell-time and noncoherent integration). This article investigates the potential benefits of coherent radar processing and exploitation of Doppler information in TBD, when the radar operates in a slower scanning mode (i.e., using longer dwell times). For this purpose, a new Bernoulli TBD filter is developed, which is capable of processing full three-dimensional (range-azimuth-Doppler) radar data. The amplitude of sea clutter is modeled using the K distribution with Doppler-dependent shape and scale parameters. Numerical results indicate that the new Bernoulli TBD outperforms the fast scanning TBD filter, at signal-to-interference ratios below 6 dB. [ABSTRACT FROM AUTHOR]

This article presents a radar cross-section (RCS)-based statistical recognition system for identifying/classifying unmanned aerial vehicles (UAVs) at microwave frequencies. First, the article presents the results of the vertical (VV) and horizontal (HH) polarization RCS measurement of six commercial UAVs at 15 and 25 GHz in a compact range anechoic chamber. The measurement results show that the average RCS of the UAVs depends on shape, size, material composition of the target UAV as well as the azimuth angle, frequency, and polarization of the illuminating radar. Afterward, radar characterization of the target UAVs is achieved by fitting the RCS measurement data to 11 different statistical models. From the model selection analysis, we observe that the lognormal, generalized extreme value, and gamma distributions are most suitable for modeling the RCS of the commercial UAVs while the Gaussian distribution performed relatively poorly. The best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. The performance of the UAV statistical recognition system is evaluated at different signal noise ratio (SNR) with the aid of Monte Carlo analysis. At an SNR of 10 dB, the average classification accuracy of 97.60% or better is achievable. [ABSTRACT FROM AUTHOR]

This article reports the yield estimation of vineyards from using a mobile millimeter-wave radar system. The proximal radar sensor is a 122-GHz FM-CW radar mounted on a rover that moves along the furrows between vine plants. The proposed yield estimation is based on the classification of radar echoes measured for various grape varieties, for different meteorological conditions, and at different stages of grape development. As yield estimation errors are found to be lower than 10%, the proposed radar technique is suitable for precision viticulture applications. [ABSTRACT FROM AUTHOR]

Using a microwave or millimetre wave radar system, this study aims to achieve highly accurate Doppler velocity estimation and radar imaging that would be suitable for various remote‐sensing sensors, such as self‐driving, surveillance, or security applications. In particular, micro‐Doppler signatures are one of the most promising approaches for human recognition; however, the traditional limitations of both velocity and temporal resolution need to be addressed. The weighted kernel density (WKD) algorithm using super‐resolution Doppler velocity estimation has been one solution. Using WKD, this study incorporates the range points migration (RPM) method of radar imaging to enhance accuracy in both Doppler velocity and radar imaging using an iterative data selection scheme. Furthermore, to obtain more informative Doppler‐associated RPM‐based images using less data by exploiting a unique feature of WKD and RPM methods, this study introduces the image integration approach along the slow‐time profile. In this framework, bi‐directional data processing between Doppler velocity and imaging analysis is achieved. At each pulse hit sequence, both numerical and experimental tests demonstrate that the proposed method yields a more accurate Doppler‐associated radar image compared with the methods in previous studies. [ABSTRACT FROM AUTHOR]

We consider the problem of target parameter estimation in phase modulated continuous wave (PMCW) multiple-input multiple-output (MIMO) radar systems with quantized observations. We derive the Cramér-Rao bound (CRB) for jointly estimating targets’ amplitudes, time delays, Doppler shifts, and directions. The derived bound provides an efficient method to analyze the estimation performance achieved by different quantization schemes. We also devise the maximum likelihood (ML) estimator for the considered parameters, and a direct grid-based method (DGM) is introduced to obtain the ML estimates. To obtain the ML estimates more efficiently, a two-stage scheme is proposed. In the proposed scheme, we first formulate the estimation problem as a sparse signal recovery problem and modify the sparse learning via iterative minimization (SLIM) approach to solve it. Next, a RELAX-based iterative algorithm is proposed to refine the estimates. Simulation results show that the proposed scheme can approach the CRB. Simulation results also show that using quantized observations does not affect the estimation performance significantly when the targets’ amplitudes are similar. [ABSTRACT FROM AUTHOR]

The article presents a monostatic $D$ -band frequency-modulated continuous-wave (FMCW) radar based on a fully integrated monostatic single-channel silicon-germanium (SiGe) transceiver (TRX) chip. The chip is fabricated in Infineon’s bipolar-complementary metal–oxide–semiconductor (BiCMOS) production technology B11HFC which offers heterojunction bipolar transistors (HBTs) with an $f_{\mathrm {T}}/f_{\mathrm {max}}$ of 250 GHz/370 GHz. The monolithic microwave integrated circuits (MMICs) output signal is coupled by a fully differential substrate integrated waveguide (SIW) based coupling network. The output power at the WR-6.5 antenna flange is more than −10 dBm over a bandwidth of 37.5 GHz. For a sweep within a single-loop phase-locked loop (PLL) circuit from 174.5 to 121.5 GHz, a spatial resolution of almost 3 mm with a metallic plate as the target is achieved. The radar provides a small form factor of $2 \times 4 \times 5$ cm3 and low power consumption of 2.2 W at 5 V. Finally, the capabilities of the sensor for non-destructive testing (NDT) are demonstrated using a millimeter scanner. With radar imaging, it was possible to measure the orientation of the fiber layers up to a depth of 7.03 mm. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of adaptive moving target detection, embedded in homogeneous Gaussian noise with unknown covariance matrix, for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar operating in interference-dominant environment. Unlike traditional adaptive moving target detectors that need training data to estimate the jamming covariance matrix (JCM), we present the Rao and Wald test based adaptive detector, which requires no training data. Furthermore, we proposed a two-stage approach to obtain maximum likelihood estimate (MLE) of the joint range, angle and Doppler, respectively. The corresponding signal-to-jamming-plus-noise ratio (SJNR) is derived to evaluate the FDA-MIMO radar performance. All proposed methods are validated by numerical results, which show that the proposed detector outperforms the existing generalized likelihood ratio test (GLRT). [ABSTRACT FROM AUTHOR]

The 3-D inverse synthetic aperture radar (ISAR) tomography is an enabling technique for applications such as the exact diagnosis of scattering mechanisms for complex targets. Nevertheless, current ISAR tomography solutions still suffer from problems such as the great computational complexity and optimal utilization of the signatures acquired from limited baselines. In this work, we propose a fast ISAR tomography technique for fully polarimetric 3-D imaging of man-made targets. A stack of 2-D complex-valued images with different baselines and polarizations is first obtained through a phase error calibration (PEC) process and graphic processing unit accelerated polarimetric filtered backprojection. A polarimetric state-space decomposition (P-SSD) algorithm is then developed which could provide joint 3-D reconstruction results with low computational complexity. Examples from both numerical multibaseline data for the Sandia laboratories implementation of cylinders (SLICY) benchmark model and the outdoor range dataset collected by the Georgia Tech Research Institute (GTRI) are presented to demonstrate the superior performance and the usefulness of the proposed technique. [ABSTRACT FROM AUTHOR]

Buried ice in the Martian mid-latitudes is of high interest for In-Situ Resource Utilization (ISRU) by future crewed missions, as well as for its scientific value as a record of climate history on another planet. “Lobate debris aprons,” widely considered to be debris-covered glaciers, constitute a large reservoir of such ice. Many have been probed by Mars Reconnaissance Orbiter’s Shallow Radar (SHARAD) sounder, which imaged their base and constrained their internal composition to >80% water ice. The basal contact is not imaged by SHARAD for all aprons, however, which introduces the possibility that these features contain significantly less ice. In this study, we used SHARAD survey results in tandem with high-resolution digital terrain models to show that these “non-detections” can be attributed to surface roughness, which through scattering reduces the coherent signal that penetrates the aprons. We quantified this effect using a fractal backscattering model and found that the roughest surfaces coincided with the location of basal non-detections. There is thus no need for reduced ice content to explain nondetection of lobate debris apron bases with SHARAD. This result leads us to interpret all lobate debris aprons as likely debris-covered glaciers, due to the similarities in their gross morphology and surface textures to aprons with basal detections. [ABSTRACT FROM AUTHOR]

High-resolution wide-swath synthetic aperture radar (HRWS-SAR) imaging is highly desirable since it allows one to produce high-resolution SAR images of large areas during a short visit time. In this article, staggered coprime pulse repetition frequencies synthetic aperture radar (SCopSAR) is proposed. It divides the time during which a scatterer is illuminated by the antenna beam pattern into two halves where, in each half, pulses are transmitted at the rate of one of two sub-Nyquist pulse repetition frequencies (PRFs). Such PRFs are related to the Nyquist PRF using two coprime subsampling factors. This allows extending the maximum range swath width that can be imaged by a number of times that equals the smaller subsampling factor at the expense of a reduction in the azimuth resolution by half. It further allows for a reduction in the amount of data to be stored and communicated. SCopSAR is an imaging modality suitable for scenes that contain a small number of bright scatterers over a dark background which, for instance, is the case when imaging ships in a calm sea background. Compared with the techniques recently proposed in the literature, SCopSAR simplifies the radar requirements since it uses only one carrier frequency, one waveform, and one channel. Simulations and real ERS-2 satellite raw data are used to validate the theoretical findings presented in this article. [ABSTRACT FROM AUTHOR]

Scatterer trajectory association is a critical step of 3-D target reconstruction from the inverse synthetic aperture radar (ISAR) image sequence. To cope with the complex scatterer trajectory association of the noncooperative target, a novel method based on multiple hypothesis tracking (MHT) algorithm is proposed. First, the scatterer trajectory association situation is modeled as a multiple hypothesis tree, in which each branch represents a possible association. Then, to generate the hypothesis in each specific branch, a general trajectory motion model is constructed and the parameters are estimated based on the current trajectory association situation. The parameter estimation precision will increase with the growth of the trajectory length. Besides, to eliminate the influence of inaccurate association initialization and direction selection, a fusion algorithm is proposed to merge the forward and backward associated trajectories. Finally, experimental results based on the simulated data and electromagnetic data verify the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) human-imaging technology has been widely used in the fields of security screening and activity recognition. However, most of the existing methods aim at stationary human targets, resulting in severe restrictions on their potential applications. In this article, an SAR imaging method for walking human is proposed with short aperture terahertz (THz) radar. This method is based on a modified wavenumber domain approximate algorithm ($\text{m}{\omega }$ ka), whose interpolation mapping is modified according to the approximation of region of interest (RoI). Because the nonrigid motion phase error caused by the walking human would lead to severe deformation and blur in imaging results, a compensation processing is essential. To figure it out, the fractional Fourier transform combined with the maximum and minimum generalized likelihood ratio test (FrFT-mmGLRT) is devised in this article. Within a short aperture time, the nonrigid motion of walking human can be approximately assumed as a superposition of the rigid movements of different human body parts. Particularly, by taking advantage of the superposition property of FrFT, the motion phase errors of different human body parts are distinguished and estimated by jointly searching for the peaks of FrFT energy spectrum. Furthermore, the mmGLRT-based composite imaging technique is utilized to obtain a whole body result from the compensated results of different body parts. Finally, numerical simulation and real experiments are conducted to verify the feasibility and capability of the proposed method for walking human SAR imaging. [ABSTRACT FROM AUTHOR]

This article develops and examines methods for the production of real-world, very high-resolution imagery using a high-frequency drone-borne synthetic aperture radar (SAR) operating at short ranges. The significance of motion errors which lead to space-invariant/variant phase errors is discussed. Subsequently, an imaging algorithm capable of handling these errors is proposed and presented. The validity of the approach is tested through both simulation and experiment. We present novel short-range, fine-resolution imagery (less than 2 cm in cross-range) of an extended target area generated using a low-cost, drone-borne vehicular frequency-modulated continuous-wave (FMCW) radar operating at 77 GHz, without using a dedicated inertial navigation system (INS) or Global Positioning System (GPS). [ABSTRACT FROM AUTHOR]

Comparing the observed brightness of various buried objects is a straightforward way to characterize the performance of a ground penetrating radar (GPR) system. However, a limitation arises. A simple comparison of buried object brightness values does not disentangle the effects of the GPR system itself from the system’s operating environment and the objects being observed. Therefore, with brightness values exhibiting an unknown synthesis of systemic, environmental, and object factors, GPR system analysis becomes a convoluted affair. In this work, we use an experimentally collected dataset of over 25000 object observations from five different multistatic radar arrays to develop models of buried object brightness and control for these various effects. Our modeling efforts provide a means for quantifying the relative brightness of GPR systems, the objects they detect, and the physical properties of those objects that influence observed brightness. To evaluate the models’ performance on new object observations, we repeatedly simulate fitting them to half the dataset and predicting the observed brightness values of the unseen half. In addition, we introduce a method for estimating the probability that individual observations constitute a visible object, which aids in failure analysis, performance characterization, and dataset cleaning. [ABSTRACT FROM AUTHOR]

High-frequency surface wave radar (HFSWR) has become the cornerstone of maritime surveillance because of its low-cost maintenance and coverage of wide area. However, when it comes to the extraction of key areas, such as vessel-target detection and vessel-path tracking, the HFSWR signal is strongly interfered by clutters and noise, which makes maritime surveillance a challenging task. This article proposes a hierarchical key-area perception model for maritime surveillance harnessing range-Doppler (RD) image from HFSWR, Laplacian kernel, a linear classifier (LC), and a subnet-based multilayer representation learning framework (SMRLF). First, a weak LC with a Laplacian kernel is utilized to capture the plausible vessel regions (PVRs). Then, a novel SMRLF is proposed to localize the vessel targets from the PVRs. To handle the noise, a maximum correntropy criterion with variable centers (MCC-VC) is incorporated in the subnet-based learning model. A thorough experimental analysis on cross-domain samples from radar dataset to scene classification dataset shows that the proposed HKPM performs competitively. The model shows a superior performance over most of the state-of-the-art vessel-target detection algorithms with a vessel-target detection accuracy of 94%. The extended analysis on image classification problem proves that the proposed model has great adaptivity and scalability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article presents an universal circular synthetic aperture radar (SAR) (UCSAR) by which the targets to be observed at any radial distance can be imaged, thus making SAR imaging possible in a more general scenario with a circular movement of the radar platform. The UCSAR point spread function (PSF) is firstly analyzed based on the time-domain correlation imaging approach, and thus a three-dimension (3-D) spatial variant PSF of the target can be formulated. The closed-form PSF expressions with single-frequency and frequency-modulated continuous wave (FMCW) transmitted signals are derived respectively to quantify the imaging resolutions, showing that the PSF is a product of a sinc function and a zeroth-order Bessel function when using a wideband FMCW signal. Secondly, a fast UCSAR imaging algorithm and its further simplified version are proposed to reduce the computational cost significantly based on the piecewise constant Doppler (PCD) principle. To quantify the imaging performance, we derive an error function of the slant range approximation for the proposed algorithm, serving as a practical guideline for the UCSAR parameter selection. Finally, the simulation and experimental results are provided to validate the PSF analysis, the fast imaging algorithm, and the implementation of the proposed UCSAR. [ABSTRACT FROM AUTHOR]

To suppress background clutter in short-range millimeter wave multiple-input multiple-output (MIMO) imaging, an adaptive and cost-efficient approach is proposed based on the availability of the background observation. By analyzing the origins of the background clutter, gain variation, and blocking effect are identified as the primary culprits responsible for the background clutter variation, which are treated separately in the proposed method. The gain variation is compensated with an adaptive power weighting (APW) procedure, where the gain variance is accurately and rapidly measured using the short-range profile segment associated with the interelement direct coupling. The clutter residual due to the blocking effect after APW is further removed by the combination of two zero-thresholding processes and coherence-factor-based image enhancement. The proposed method entails relatively light computational burden at the cost of acceptable memory increment, and is, thus, more amenable to real-time imaging requirement. Both simulations and experimental imaging tests on an MIMO array demonstrate the effectiveness and cost-efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Cross-resolution enhancement for airborne high-squint radar (AHSR) imagery is mathematically equivalent to the ill-conditioned problem of inverse-scattering reconstruction. Although a variety of inversion methods with regularization can be introduced to advance the field of AHSR imagery, they turn out to be computationally intensive when extended to 2-D (range and cross-range dimension) image formulation due to the range-by-range calculation for the space-variant inversion operators over the full range swath. To tackle the problem of efficiency, this article presents a low-complexity inverse-scattering strategy. Our underlying idea is to equalize the space-variant Doppler centroid embedded in an inversion operator for a reference range cell using Doppler centroid compensation. With the proposed strategy, the necessary computational complexity required for 2-D AHSR inverse-scattering reconstruction can be significantly reduced by requiring only the calculation of the inversion operator, independently of the number of range cells. Our experimental assessment, conducted using both the simulation and real data, demonstrates that our proposed inverse-scattering strategy offers preferable computational reduction in the task of inverse-scattering reconstruction for 2-D AHSR imagery without resolution loss. [ABSTRACT FROM AUTHOR]

Marine radar plays a significant role in ship navigation. However, when contending with interference among cosailing navigation radars, the echo data may be unintentionally corrupted, and it becomes challenging to obtain high-quality imagery using current radar imaging methods. To overcome this problem, an efficient anti-interference imaging framework is presented in this article based on the theory of nonuniform sampling. First, a beam-recursive anti-interference method based on the signal-to-interference-plus-noise ratio (SINR) estimation is proposed to compensate for the shortcoming of the traditional interference rejection method. Second, a nonuniform sampling model is established to well model the echo data with missing samples, which facilitates reconstructing the marine radar imagery from the missing echo data. Finally, a fast super-resolution method based on the dimension-reduction iterative adaptive approach (DRIAA) is proposed to reconstruct the distribution of sea-surface targets at a much lower computational complexity. Simulated and experimental results demonstrate that our anti-interference imaging framework can provide radar imagery with higher quality and lower computational complexity than the existing radar imaging methods in the presence of unintentional interference. [ABSTRACT FROM AUTHOR]

In a low signal-to-noise ratio (SNR) environment, conventional range profiles-based motion compensation (MoCo) methods become inapplicable for inverse synthetic aperture radar (ISAR) imaging. In this article, a novel method for range profile alignment along with phase adjustment is proposed. The method is applied in the uncompressed phase history domain without the need for a prominent point. Motion errors are estimated from a phase map using only fast Fourier transform (FFT) and Hadamard multiplication operations, making the method suitable for real-time applications. The proposed method strives to aggregate the energy of all scatterers into a single peak by 2-D coherent accumulation incorporating the product of the radar pulse backscattering and its subsequent pulse, hence the reference to phase gradient processing (PGP). This process strengthens SNR and overcomes the problems associated with low signal power, even when the range profiles are submerged in noise. Experimental results are provided to demonstrate the performance of the proposed method compared with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This article deals with the problem of angle and incremental range (i.e., the target range offset with respect to the center of the cell under test) estimation with a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar exploiting a single data snapshot as observable. Starting from the observation that the maximum likelihood (ML) estimation entails a 2-D grid search over the parameters of interest, three approximated ML techniques are designed resorting to the coordinate descent algorithm and the adaptive monopulse criterion (employing either real or complex slope/bias corrections). At the analysis stage, the estimation performance of the proposed methods, including the tapered and double-step monopulse versions, is also assessed in comparison with the Cramér–Rao lower bound. Numerical results corroborate the effectiveness of the considered estimation strategies in some diverse simulated scenarios. [ABSTRACT FROM AUTHOR]

The compressed sensing (CS)-based synthetic aperture radar (SAR) imaging methods have emerged as the standard approach to obtain super-resolution (SR) SAR images and achieve extraordinary performances. However, they face three challenges. First, this kind of method is mainly based on the point scattering model and not suitable for characterizing the line-segment-scattering and surface-scattering features of distributed targets. Second, the hyperparameters in these methods are hard to tune to optimal values. Third, due to a large amount of calculation, these methods are difficult to apply in practice. In this article, to solve these problems, we introduce the line-segment-scatterers (LSSs) and rectangular-plate-scatterers (RPSs) in SAR echo model to develop the SAR hybrid echo model and propose two SAR parametric SR image reconstruction methods based on solving a CS problem, where three penalties are utilized to exploit the sparsity of the point scatterers, LSSs, and RPSs, respectively. At the core of the first method is a direct solver called multicomponent alternating direction method of multipliers (MC-ADMM) solver that solves the CS problem quickly and iteratively based on closed derivative expressions. In contrast, the second method maps the MC-ADMM solver into a deep unfolded neural network, i.e., the parametric SR imaging network (PSRI-Net), which is faster, and the parameters can be automatically set to the optimum. Since all the parameters of the MC-ADMM solver are learned discriminatively through end-to-end training in PSRI-Net. Extensive simulation and practical experiments are carried out to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]