Radar Imaging Applications Research Articles

Pseudopolar Format Matrix Description of Near-Range Radar Imaging and Fractional Fourier Transform

Near-range radar imaging (NRRI) has evolved into a vital technology with diverse applications spanning fields such as remote sensing, surveillance, medical imaging and non-destructive testing. The Pseudopolar Format Matrix (PFM) has emerged as a promising technique for representing radar data in a compact and efficient manner. In this paper, we present a comprehensive PFM description of near-range radar imaging. Furthermore, this paper also explores the integration of the Fractional Fourier Transform (FrFT) with PFM for enhanced radar signal analysis. The FrFT—a powerful mathematical tool for signal processing—offers unique capabilities in analysing signals with time-frequency localization properties. By combining FrFT with PFM, we have achieved significant advancements in radar imaging, particularly in dealing with complex clutter environments and improving target detection accuracy. Meanwhile, this paper highlights the imaging matrix form of FrFT under the PFM, emphasizing the potential for addressing challenges encountered in near-range radar imaging. Finally, numerical simulation and real-world scenario measurement imaging results verify optimized accuracy and computational efficiency with the fusion of PFM and FrFT techniques, paving the way for further innovations in near-range radar imaging applications.

Polarization-insensitive, wideband reflective metasurface generating orbital angular momentum vortex beam

In this paper, a low-profile polarization-insensitive reflective metasurface is proposed to generate high gain and wideband orbital angular momentum (OAM) in X-band (in the frequency range of 8−12GHz). By adjusting a subwavelength pseudo-screw-shaped meta-atom’s size the required phase compensation has been achieved for designing a reflectarray antenna at 10GHz. The theoretical analyses and simulation results confirm that the proposed structure can effectively generate the first-, second-, third-, and fourth-order OAM vortex beams at the operating frequency. To verify the bandwidth of the suggested metasurface, the structure is implemented for first-order OAM mode generation at various frequencies. The outcomes of investigations show the OAM operational bandwidth and 1.5-dB gain bandwidth are attained to be 40 %, and 27.5 %, respectively. Meanwhile, approximately 14.35 % reflection efficiency was procured for the overall design at the operating frequency of 10GHz. The numerical aperture of the reflectarray metasurface is also conceptually defined and calculated for varying positions of the center-feed antenna. The admitted performance and substantial characteristics of the proposed OAM reflectarray antenna may make it a useful candidate for radar imaging and detection applications as well as broadband microwave communications.

A bifunctional metamaterial with broadband properties of absorption and linear-to-linear polarization conversion

This paper proposes, measures, and investigates a bifunctional metamaterial capable of achieving absorption and reflective linear-to-linear polarization conversion simultaneously, which both exhibit the characteristics of broadband. The unit cell consists of a metal pattern with resistors, a dielectric plate, an air layer, and a metal backplate. The simulation results demonstrate that the designed metamaterial acquires over 90% absorption in the microwave band of 6.5–9.3 GHz. Within the frequency range of 12.7 GHz–17.2 GHz, the polarization conversion rate exceeds 90%, effectively converting y-polarized incident waves into x-polarized reflected waves. The experimental results align with the simulation data. The surface current and electric field distributions are utilized to analyze the absorption and polarization conversion phenomena. This bifunctional metamaterial exhibits potential application in radar imaging, enhancing data transmission rates, and wireless communication.

The Data Compression Method and FPGA Implementation in the Mars Rover Subsurface-Penetrating Radar on the Tianwen-1 Mission

Since Mars is far away from Earth, the propagation delay between Mars and Earth is very large. To ensure the effective use of the link transmission bandwidth, China’s first Mars exploration mission has put forward a demand for data compression for all scientific payloads. The on-board mature algorithms for data compression are mainly focused on optical images and microwave imaging radar applications. No articles have been published on data compression methods that are applied to subsurface-penetrating radar. Based on the background of this application, this paper proposes a logarithmic lossy compression algorithm which can meet the mission requirements for high compression ratios of 4:1 and 2.5:1. Its compression error is less than that of the block adaptive quantization (BAQ) algorithm. The algorithm is not only easy to implement on field-programmable gate array (FPGA) platforms, but also offers simple ground decompression and fast imaging. The experimental results show that high compression ratios of 4:1 and 2.5:1 can be realized, even if the data in and between traces do not have a strong correlation. And its relative error is less than 2%, which is a new type of high-efficiency data compression method that can be implemented on-board to meet with the demand of subsurface penetrating radar.

Iteration-Based Temporal Subgridding Method for the Finite-Difference Time-Domain Algorithm

A novel temporal subgridding technique is proposed for the finite-difference time-domain (FDTD) method to solve two-dimensional Maxwell’s equations of electrodynamics in the TEz mode. Based on the subgridding FDTD algorithm with a separated spatial and temporal interface, our method focuses on the temporal subgridding region, as it is the main source of late-time instability. Different from other subgridding algorithms that work on the interpolation between coarse and fine meshes, our method stabilizes the solution by using iterative updating equations on the temporal coarse–fine mesh interface. This new method presents an alternative approach aimed at improving the stability of the subgridding technique without modifying the interpolation formulas. We numerically study the stability of the proposed algorithm via eigenvalue tests and by performing long-term simulations. We employ a refinement ratio of 2:1 in our study. Our findings indicate the stability of the conventional temporal subgridding FDTD algorithm with a magnetic field (Hz) interpolation. However, when electric fields (Ex and Ey) are utilized in interpolation, late-time instability occurs. In contrast, the proposed iteration-based method with an electric field interpolation appears to be stable. We further employ our method as the forward problem solver in the Through-the-Wall Radar (TWR) imaging application.

SMIFormer: Learning Spatial Feature Representation for 3D Object Detection from 4D Imaging Radar via Multi-View Interactive Transformers

4D millimeter wave (mmWave) imaging radar is a new type of vehicle sensor technology that is critical to autonomous driving systems due to its lower cost and robustness in complex weather. However, the sparseness and noise of point clouds are still the main problems restricting the practical application of 4D imaging radar. In this paper, we introduce SMIFormer, a multi-view feature fusion network framework based on 4D radar single-modal input. SMIFormer decouples the 3D point cloud scene into 3 independent but interrelated perspectives, including bird’s-eye view (BEV), front view (FV), and side view (SV), thereby better modeling the entire 3D scene and overcoming the shortcomings of insufficient feature representation capabilities under single-view built from extremely sparse point clouds. For multi-view features, we proposed multi-view feature interaction (MVI) to exploit the inner relationship between different views by integrating features from intra-view interaction and cross-view interaction. We evaluated the proposed SMIFormer on the View-of-Delft (VoD) dataset. The mAP of our method reached 48.77 and 71.13 in the fully annotated area and the driving corridor area, respectively. This shows that 4D radar has great development potential in the field of 3D object detection.

Design of ultra-wide band dual-polarized quad ridged horn antenna for obstacle penetrating radar imaging applications

In this study, a high-gain and dual-polarized Quad Ridged Horn Antenna (QRHA) is proposed and designed to be used in Obstacle Penetrating Imaging Radar (OPIR) applications. The quad ridged design is developed and then optimized by using the antenna design simulator software; CST Microwave Studio. The antenna parameters including the center frequency, frequency bandwidth and the antenna radiation pattern beam width are considered based on the requirements of OPIR applications. The final design is obtained for the center frequency of 2.785 GHz with a -10 dB bandwidth of at least 2.5 GHz. The final designed QRHA is able to provide a gain of 12.4 dB with a beam width of 36ᵒ at the center frequency of 2.785 GHz.

Detection of the Altitude and On-the-Ground Objects Using 77-GHz FMCW Radar Onboard Small Drones

Small drones are being utilized by researchers for applications such as object tracking, imaging and remote sensing as they have become more available, inexpensive and mobile with the advancements in sensor and UAV technologies. They can be equipped with sensors such as cameras and radars. Radars can be used onboard for navigation aid by detecting range and velocity, as well as for radar imaging applications. Although they are used in the latter commonly, radars can be used in navigation aid as well since they are barely affected by weather conditions or smoke. FMCW radars are suitable for drones since they can be lightweight and can detect range and velocity. Therefore, an FMCW radar-equipped drone system is designed and implemented for UAV positioning applications. A postprocessing algorithm is developed for detecting the altitude above ground level as well as the amount of reflection coming from the material on the ground surface, and a range compensation method is proposed to improve the performance of the algorithm. The results of a field experiment showed that the radar system and the postprocessing algorithm can be used for drone-based airborne positioning applications. Altitude values obtained with the radar postprocessing are in agreement with the altitude of the drone. Reflections coming from the metal objects are distinguished from those coming from the ground. With the range compensation method, minimum detectable altitude value is improved and the magnitude of ground reflections obtained from different altitudes became similar. The proposed system and algorithm can be utilized in navigation and in landing aid applications.

Waveform Design for Watermark Framework Based DFRC System With Application on Joint SAR Imaging and Communication

In this article, the watermarking framework for electromagnetic systems is established with nonblind, semiblind, and blind watermarking demodulation processes mitigated to applications like radar detection, synchronization, integrated dual function, etc. Desired for similar advantages and trade-offs of watermarking technology, the dual function radar and communication (DFRC) system is specifically concerned for covert communication and low possibility of interception (LPI) radar sensing. In this respect, we propose a novel DFRC waveform design method where peak sidelobe level (PSL) of autocorrelation function (ACF) is considered as the figure of merit with information embedded via discrete Fourier transform (DFT) watermarking strategy. Meanwhile, the peak-to-average ratio (PAR) and energy constraints are forced to ensure compatibility with current hardware technique. To handle the resulting NP-hard design problems, the proximal method of multipliers (PMM) is employed with the overall computational burden linear with the amount of information per pulse and quadratic with respect to the code length. Finally, numerical and experimental results are provided to evaluate the effectiveness of the proposed DFRC waveform design scheme with application in joint synthetic aperture radar (SAR) imaging and communication.

A Cylindrical Antenna With a Conductively Loaded Slot for Broadband Imaging Radar Applications

A cylindrical antenna with a conductively loaded slot (CACLS) is designed for broadband imaging radar applications. Through design considerations, such as conductive loading, cylinder termination, and the insertion of an electromagnetic (EM) wave absorber into the cylinder, the proposed CACLS is shown to be able to radiate temporally short and nondispersive pulses. Based on the final design of the antenna, parametric studies pertaining to the time-domain radiation performances depending on the dimension of the cylinder are performed. The CACLS with the determined design parameters is then fabricated and its performances verified through antenna measurements and a radar experiment. In addition to its excellent pulse radiation and reception performance, the realized antenna has a compact and physically robust structure, which makes it potentially suitable for many broadband imaging radar applications.

Urban Traffic Imaging Using Millimeter-Wave Radar

Imaging technology enhances radar environment awareness. Imaging radar can provide richer target information for traffic management systems than conventional traffic detection radar. However, there is still a lack of research on millimeter-wave radar imaging technology for urban traffic surveillance. To solve the above problem, we propose an improved three-dimensional FFT imaging algorithm architecture for radar roadside imaging in urban traffic scenarios, enabling the concurrence of dynamic and static targets imaging. Firstly, by analyzing the target characteristics and background noise in urban traffic scenes, the Monte-Carlo-based constant false alarm detection algorithm (MC-CFAR) and the improved MC-CFAR algorithm are proposed, respectively, for moving vehicles and static environmental targets detection. Then, for the velocity ambiguity solution problem with multiple targets and large velocity ambiguity cycles, an improved Hypothetical Phase Compensation algorithm (HPC-SNR) is proposed and complimented. Further, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to remove outliers to obtain a clean radar point cloud image. Finally, traffic targets within the 50 m range are presented as two-dimensional (2D) point cloud imaging. In addition, we also try to estimate the vehicle type by target point cloud size, and its accuracy reaches more than 80% in the vehicle sparse condition. The proposed method is verified by actual traffic scenario data collected by a millimeter-wave radar system installed on the roadside. The work can support further intelligent transportation management and extend radar imaging applications.

SAR Image Matching Based on Local Feature Detection and Description Using Convolutional Neural Network

Feature detection is a vital step for the image registration process whose target is the misalignment correction among images to increase the convergency level. Deep learning (DL) in remote sensing has become a worldwide sensation. Despite its huge potential, DL has not reached its intended target concerning the applications of Synthetic Aperture Radar (SAR) images. In this study, we focus on matching SAR images using a Convolutional Neural Network. The big challenge in this study is how to modify a pretrained Visual Geometry Group model based on the multispectral dataset to act as a SAR image feature detector where it does not require any prior knowledge about the nature of the SAR feature. Since SAR images have different characteristics from optical images such as SAR dynamic range and imaging geometry, some problems arise and should be considered during the matching process. Despite all these difficulties, results demonstrate the robustness of the registration process where it can provide descriptors that preserve the localization data of features. Also, the proposed approach provides reasonable results compared to the state-of-the-art methods and outperforms the correlation approach and ORB descriptor under scaling. In addition, it may be considered an end-to-end image matching tool for SAR images, although the calculations of fine matching parameters are included.

GreyWolfLSM: an accurate oil spill detection method based on level set method from synthetic aperture radar imagery

ABSTRACT Oil spill detection (OSD) in marine areas is an application of synthetic aperture radar (SAR) images to protect aquatic life. In this paper, a new oil spill detection algorithm based on level set method (LSM) is presented. Dark spot detection, feature extraction, and classification are the main steps in the proposed method. In the first step, a new dark spot detection method in SAR images is presented, which introduces a combination of multi-objective grey wolf optimization (MOGWO) and K-means clustering to find the best threshold level for image segmentation. This method overcomes the K-means clustering by choosing optimal number of clusters and centers. In the feature extraction step, a vector consisting of 45 arrays of calculated Legendre moments in the main, gradient and radon transform of the image is used. The oil-suspicious areas are also discriminated by the supported vector machine (SVM) classifier, and their boundaries are applied as an initial contour to a new hierarchical region-based level-set method (HRLSM). Experiments are performed on the images acquired by Envisat, UAVSAR, TerraSAR-X, and Sentinel1 satellites. The results showed the reliability and robustness of the proposed method with high accuracy, even for noisy images with heterogeneous and weak boundaries.

Brief communication: Radar images for monitoring informal urban settlements in vulnerable zones in Lima, Peru

Abstract. Lima, Peru's capital, has about 9.6 million inhabitants and keeps attracting more residents searching for a better life. Many citizens, without access to housing subsidies, live in informal housing and shack settlements. A typical social phenomenon in Lima is the sudden illegal occupation of areas for urban settlements. When such areas are unsafe against natural hazards, it is important to relocate such a population to avoid significant future losses. In this communication, we present an application of Sentinel-1 synthetic aperture radar (SAR) images to map the extension of a recent occupation of an area with unfavorable soil conditions against earthquakes.

Fast, super‐resolution sparse inverse synthetic aperture radar imaging via continuous compressive sensing

Conventional inverse synthetic aperture radar (ISAR) imaging with sparse aperture usually suffers from high side lobes and wide main lobes, which limit the applications of radar super-resolution imaging, multi-target resolution, and cognitive reconfiguration. This paper proposes a fast, super-resolution imaging method employing continuous compressive sensing for sparse-aperture ISAR. First, the received echo in each range bin is characterised as a linear combination of multiple frequencies shown in a continuous atomic set, established into an atomic norm minimisation (ANM) mode. Second, to improve the resolution and reduce the computational burden significantly, a locally convergent iterative algorithm based on the alternating direction method of multipliers, which iteratively performs ANM with a sound reweighting strategy, is implemented. Then, the low-rank Toeplitz covariance matrix, which contains the information of the target, is obtained. Subsequently, the Vandermonde decomposition of the Toeplitz covariance matrix is performed to acquire the locations and intensities of the scattering points. Finally, the super-resolution result is generated by depicting the estimated scatterers in the image. Extensive numerical experiments demonstrate that the proposal is highly effective in recovering the super-resolution image and shows better performance than state-of-the-art methods.

An Up-Sampled Particle Filter Fusion Technique and Its Application in Synthetic Aperture Radar Imaging

Global positioning system (GPS) and inertial measurement units (IMUs) are often combined to produce navigation systems for airborne imaging platforms. The current state-of-the-art radar technology allows for radars to pulse at very high rates. GPS and IMU update rates are not fast enough to accurately report the platform position for each radar pulse. Independent GPS and IMUs cannot provide positional accuracy for long term stability. Traditional techniques, such as the Kalman and particle filter, are used to fuse GPS and IMU measurements. The Kalman filter excels for linear and Gaussian systems whereas the particle filter excels at non-linear and non-Gaussian systems. Sensor fusion techniques are used to help correct for IMU errors and provide the positional accuracy required for synthetic aperture radar (SAR) imaging applications. However, SAR requires the fusion algorithms to provide faster update rates. This paper explores the use of an up-sampled particle filter (UPF) for SAR to provide highly accurate position estimates at sampling frequencies comparable to radar pulse rates and overcome the limitations of standard interpolation techniques. This up-sampled particle filter is proven through simulations and instrumentation with a NovAtel GPS and IMU. The UPF technique allows for the GPS/IMU sampling rate to be different from the radar pulse repetition frequency (PRF) while providing accurate position solutions for each radar pulse capable of compensating for the phase history required for focusing a SAR image. The algorithms are instrumented in a SAR system and the position estimates are further validated and demonstrated through captured SAR images.

Robust Matching for SAR and Optical Images Using Multiscale Convolutional Gradient Features

Image matching is a key preprocessing step for the integrated application of synthetic aperture radar (SAR) and optical images. Due to significant nonlinear intensity differences between such images, automatic matching for them is still quite challenging. Recently, structure features have been effectively applied to SAR-to-optical image matching because of their robustness to nonlinear intensity differences. However, structure features designed by handcraft are limited to achieve further improvement. Accordingly, this letter employs the deep learning technique to refine structure features for improving image matching. First, we extract multiorientated gradient features to depict the structure properties of images. Then, a shallow pseudo-Siamese network is built to convolve the gradient feature maps in a multiscale manner, which produces the multiscale convolutional gradient features (MCGFs). Finally, MCGF is used to achieve image matching by a fast template scheme. MCGF can capture finer common features between SAR and optical images than traditional handcrafted structure features. Moreover, it also can overcome some limitations of current matching methods based on deep learning, which requires solving a huge number of model parameters by a large number of training samples. Two sets of SAR and optical images with different resolutions are used to evaluate the matching performance of MCGF. The experimental results show its advantage over other state-of-the-art methods.

Automatic Extraction of Layover From InSAR Imagery Based on Multilayer Feature Fusion Attention Mechanism

Layover is a kind of geometric distortion in radar systems with side-look imaging, especially in mountainous and dense urban areas. It causes phase distortion and alters target characteristics in the acquired images, which directly hinders the application of radar images. In this letter, the multilayer feature fusion attention mechanism (MF2AM) is proposed to extract layover from interferometric synthetic aperture radar (InSAR) imagery automatically. First, the SAR image, the corresponding coherence map, and interferometric phases are channel-fused to enhance semantic information of layover areas. Then, the fused image is fed into MF2AM to extract the essential features of layover. Finally, the detection results are produced via MF2AM. MF2AM consists of the encoder and the decoder. The encoder contains three parts: the resnet101, attention-based atrous spatial pyramid (AASP), and the semantic embedding branch (SEB). In the decoder, step decoding is used to better fuse high- and low-level features and improve the effect of edge segmentation. To verify the proposed method, the images of millimeter wave InSAR system are used for the experiment, and the performance is compared with DeepLabV3+ and Geospatial Contextual Attention Mechanism (GCAM). The results show that the MF2AM has achieved obvious performance advantages. The average pixel accuracy and average intersection over union (IOU) are 0.9601 and 0.9310, respectively, and the average test time is only 7.97 s.

On Dispersion and Multipath Effects in Harmonic Radar Imaging Applications

In harmonic radar applications, images produced using algorithms of conventional radar applications experience some defocusing effects of the electronic targets’ impulse responses. This is typically explained by the dispersive transfer functions of the targets. In addition, it was experimentally observed that objects with a linear transfer behavior do not contribute to the received signal of a harmonic radar measurement. However, some signal contributions based on a multipath propagation can overlay the desired signal, which leads to an undesired and unusual interference caused by the nonlinear character of the electronic targets. Here, motivated by the analysis of measured harmonic radar data, the effects of both phenomena are investigated by theoretical derivations and simulation studies. By analyzing measurement data, we show that the dispersion effects are caused by the target and not by the measurement system or the measurement geometry. To this end, a signal model is developed, with which it is possible to describe both effects, dispersion and multipath propagation. In addition, the discrepancy between classic radar imaging and harmonic radar is analyzed.

Research Advances on the Rotational Doppler Effect of Vortex Electromagnetic Waves

Traditional radars can detect moving targets using the Doppler effect. However, traditional radars have shadow areas in detecting the angular motion of the rotating targets. The discovery of the rotational Doppler effect based on vortex electromagnetic waves helps solve the problem of detecting the angular motion of the rotating targets under direct vision, which has attracted considerable attention from domestic and foreign scholars. In this study, we discussed the recent research progress on the rotational Doppler effect of vortex electromagnetic waves, particularly for related results in the microwave band, including the rotational Doppler effects on the target under on-axis and off-axis cases; decoupling linear Doppler, micro-Doppler and rotational Doppler effects under complex motion cases; and rotational Doppler effects on the applications of radar imaging and velocity measurement. We summarized and analyzed the existing problems demanding prompt solutions in this field, and proposed future research directions and relative applications.