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This paper proposes a radar signal transceiver framework that combines single-bit sampling and time division multiplexing receivers to satisfy the application requirements of low-cost lightweight radars. Firstly, this paper explains the advantages of saving the number of receivers by introducing the working principle of the framework. From the perspective of radar resource allocation, the importance of single-bit sampling in this framework was analyzed; additionally, the proposed framework can achieve better performance than a classical linear frequency modulation continuous wave radar using time and space exchange. Subsequently, the formulas for range, velocity and angle measurement were derived, along with the Cramér-Rao bound for estimating target parameters. Accordingly, the performance advantages of the proposed framework were verified, and the signal-to-noise ratio conditions for its stable operation were determined. Finally, this paper verifies the accuracy of the target acquisition principle of the proposed framework and the reliability of the performance analysis by using a velocity dimensional pairing algorithm based on single-bit two-dimensional multiple signal classification.

With the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing. This paper first outlines the fundamental theory of SR and then introduces the sparse characteristics in radar signal processing from the perspectives of scene sparsity and observation sparsity. Subsequently, it explores these sparse properties to provide an overview of CS applications in radar signal processing, including spatial domain processing, pulse compression, coherent processing, radar imaging, and target detection. Finally, the paper summarizes the applications of CS in radar signal processing.

Orthogonal Frequency Division Multiplexing (OFDM) waveform design is one of the key physical layer technologies for achieving joint radar-communication. OFDM waveforms usually have issues with high Peak to Average Power Ratio (PAPR) and high waveform autocorrelation sidelobe levels. This paper proposes an integrated waveform design method based on data distortion to address the communication rate degradation problem of existing joint PAPR and autocorrelation sidelobe reduction methods. The paper also takes the Error Vector Magnitude (EVM) of communication data as one of the optimization objectives, reducing the communication bit error rate caused by data distortion. Firstly, an optimization model was constructed to minimize the Integrated Sidelobe Level Ratio (ISLR) and EVM under PAPR constraints. Secondly, based on the characteristics of the modulation constellation, the multi-objective high-dimensional non-convex optimization problem is transformed into two single objective optimization subproblems by using the data distortion of outer constellation modulation and all modulation data distortion. Convex relaxation operation and Alternating Direction Method of Multipliers (ADMM) are respectively used to solve the simplified subproblems, resulting in low ISLR waveform and low EVM waveform under PAPR constraint. The simulation results show that the integrated waveform designed by the proposed method can meet the requirements of PAPR, and has good sensing and communication performance.

Time Domain Coding Metasurface (TDCM) is an emerging technology enabling dynamic modulation of electromagnetic waves. In response to the control characteristics of this technology, this paper presents a radar jamming method based on TDCM intrapulse and interpulse coding optimization. First, optimization models are established in fast and slow time domains. By optimizing intrapulse and interpulse phase coding, the energy redistribution of targets is achieved, thereby generating deceptive interference on the range-Doppler two-dimensional plot. Subsequently, a genetic algorithm is employed to solve this discrete optimization problem. Furthermore, this paper analyzes the effect of various modulation factors on interference effectiveness in terms of TDCM coding strategies, providing guidance for achieving optimal strategies for deceptive interference.

Automatic Target Recognition (ATR) is an interdisciplinary technological field related to pattern recognition, artificial intelligence, and information processing. ATR evaluation focuses on accessing ATR algorithms and systems. Due to the noncooperative targets, complex operating conditions, and multiple subjective preferences of the decision maker, ATR evaluation is performed for the entire ATR research process and shows its importance in guiding ATR development. This paper presents the connotation of ATR evaluation and briefly reviews ATR development. Furthermore, the conventional methods, applications, and latest developments in ATR evaluation are presented and discussed from the perspective of performance measures, test condition, inference and decision. Finally, several ATR evaluation research directions are summarized. This paper serves as a valuable reference for a better understanding of ATR evaluation and the effective adoption of various ATR evaluation methods.

Single snapshot forward-looking imaging technology with high performance and resolution is crucial for enabling the development of automotive radars. However, range migration issues can limit the implementation of coherent integration methods, and improving system resolution is generally difficult due to hardware parameter limitations. Based on the Time-Division Multiplexing Multiple-Input-Multiple-Output (TDM-MIMO) forward-looking imaging systems of automotive millimeter wave radar, this paper proposes Doppler domain compensation and point-to-point echo correction measures for achieving multidomain signal decoupling. However, the accuracy of traditional single-dimension range and angle imaging is limited by the number of finite array elements and significant noise interference. Therefore, this paper proposes a multidomain joint estimation algorithm based on the Improved Bayesian Matching Pursuit (IBMP) method. The Bayesian method is based on the Bernoulli-Gaussian (BG) model, and the estimated parameters and support domain are iteratively updated in this method while adhering to the Maximum a Posteriori (MAP) criterion constraint to achieve the high-precision reconstruction of multidimensional joint signals. The final set of simulation and actual measurement results demonstrate that the proposed method can effectively solve the problem of range migration and improve the angle resolution of radar forward-looking imaging while exhibiting excellent noise robustness.

Vehicle targets in urban scenes have the characteristics of random distribution and can be easily disturbed by environmental factors during the detection process. Given the above issues, this paper proposes a detection method that utilizes multi-aspect Synthetic Aperture Radar (SAR) images for stationary vehicle target extraction. In the feature extraction stage, a novel feature extraction method called Multiscale Rotational Gabor Odd Filter-based Ratio Operator (MR-GOFRO) is designed for vehicle targets in multi-aspect SAR images, where the original GOFRO features are improved from four aspects—filter form, feature scale, feature direction and feature level. The improvement allows MR-GOFRO to adapt to possible variations in the target direction, scale, morphology, etc. In the image fusion stage, a Weighted-Non-negative Matrix Factorization (W-NMF) method is developed to adjust the feature weights from various images according to the feature quality. This method can reduce the quality degradation of the fusion features due to mutual interference between different aspects. The proposed method is verified on various airborne multi-aspect image datasets. The experimental results revealed that the feature extraction and feature fusion methods proposed in this paper enhance the detection accuracy by an average of 3.69% and 4.67%, respectively, compared with similar methods.

As a biometric technology, gait recognition is usually considered a retrieval task in real life. However, because of the small scale of the existing radar gait recognition dataset, the current studies mainly focus on classification tasks and only consider the situation of a single walking view and the same wearing condition, limiting the practical application of radar-based gait recognition. This paper provides a radar gait recognition dataset under multi-view and multi-wearing conditions; the dataset uses millimeter-wave radar as a sensor to collect the time-frequency spectrogram data of 121 subjects walking along views under multiple wearing conditions. Eight views were collected for each subject, and ten sets were collected for each view. Six of the ten sets are dressed normally, two are dressed in coats, and the last two are carrying bags. Meanwhile, this paper proposes a method for radar gait recognition based on retrieval tasks. Experiments are conducted on this dataset, and the experimental results can be used as a benchmark to facilitate further research by related scholars on this dataset.

The performance of machine learning-based radar target recognition models is determined by the respective model and data to be analyzed. Currently, radar target recognition performance evaluation is based on accuracy metrics, but this method does not include the evaluation metrics regarding the impact of data quality on recognition performance. Data separability describes the degree of mixture of samples from different categories. Furthermore, the data separability metric is independent of the model recognition process. By incorporating it into the recognition evaluation process, recognition difficulty can be quantified, and a benchmark for recognition results can be provided in advance. Therefore, in this paper, we propose a data separability metric based on the rate-distortion theory. Extensive experiments on multiple simulated datasets demonstrated that the proposed metric can compare the separability of multivariate Gaussian datasets. Furthermore, by combining it with the Gaussian mixture model, the designed metric method could overcome the limitation of the rate-distortion function, capture the data’s local separable characteristics, and improve the evaluation accuracy of the overall data separability. Subsequently, we applied the proposed metric to evaluate the recognition difficulty in real datasets, the results of which validated its strong correlation with average recognition accuracy. In the experiments on evaluating the effectiveness of convolutional neural network modules, we first quantified and analyzed the separability trend of the feature extracted by each module during the testing phase. Further, we incorporated the proposed metric as a feature separability loss during the training phase to participate in the network optimization process, guiding the network to extract a more separable feature. This paper provides a new perspective for evaluating and improving the neural network recognition performance in terms of feature separability.

Multi-Radar Collaborative Surveillance (MRCS) technology enables a geographically distributed detection configuration through the linkage of multiple radars, which can fully obtain detection gains in terms of spatial and frequency diversity, thereby enhancing the detection performance and viability of radar systems in the context of complex electromagnetic environments. MRCS is one of the key development directions in radar technology and has received extensive attention in recent years. Considerable research on MRCS has been conducted, and numerous achievements in system architecture design, signal processing, and resource scheduling for MRCS have been accumulated. This paper first summarizes the concept of MRCS technology, elaborates on the signal processing-based closed-loop mechanism of cognitive collaboration, and analyzes the challenges faced in the process of MRCS’s implementation. Then, the paper focuses on cognitive tracking and resource scheduling algorithms and implements the technical summary regarding the connotation characteristics, system configuration, tracking model, information fusion, performance evaluation, resource scheduling algorithm, optimization criteria, and cognitive process of cognitive tracking. The relevance between multi-radar cognitive tracking and its system resource scheduling is further analyzed. Subsequently, the recent research trends of cognitive tracking and resource scheduling algorithms are identified and summarized in terms of five aspects: radar resource elements, information fusion architectures, tracking performance indicators, resource scheduling models, and complex task scenarios. Finally, the full text is summarized and future technology in this field is explored to provide a reference for subsequent research on related technologies.

Building layover detection is a crucial step in the 3D Synthetic Aperture Radar (SAR) imaging process in urban areas. It affects imaging efficiency and directly influences the final image quality. Currently, algorithms used for layover detection struggle to extract long-range global spatial characteristics and fail to fully exploit the rich features of layover in multi-channel SAR data. To address the issue of insufficient accuracy in existing layover detection algorithms to meet the requirements of urban 3D SAR imaging, this paper proposes a deep learning-powered SAR urban layover detection method that combines the advantages of the Vision Transformer (ViT) model and Convolutional Neural Network (CNN). The ViT model can efficiently extract global and long-range features through a self-attention mechanism, whereas the CNN has strong local feature extraction capabilities. Furthermore, the proposed method in this paper incorporates a module for investigating inter-channel layover features and interferometric phase layover features based on expert knowledge, which improves the accuracy and robustness of the algorithm while effectively decreasing the training pressure on the model in small-sample datasets. Finally, the proposed algorithm is tested on a self-built airborne array SAR dataset, and experimental findings revealed that the proposed algorithm achieves a detection accuracy of >94%, which is significantly higher than other layover detection algorithms, completely revealing the effectiveness of this method.

Cyclic prefixes in joint radar and communication systems based on Orthogonal Frequency Division Multiplexing (OFDM) and low probability of interception lead to weak radar echo masking on the battlefield. To address this problem, a low probability of interception waveform design scheme based on Filter Bank Multi-Carrier (FBMC) with Offset Quadrature Amplitude Modulation (FBMC-OQAM) is proposed in this paper. Mathematical models for the FBMC joint radar and communication waveform, target detection probability, and communication channel capacity are established. Under the radar and communication performance constraints required by the system, a joint optimization problem of minimizing the total transmitted power of the system is designed, and the subcarrier and power allocation scheme are optimized. Furthermore, the proposed algorithm can realize adaptive transmission where the parameters of the transmitting waveform can be optimally designed for the next pulse by utilizing the measured values of the current signal and the channel state information. Moreover, the feasibility and advantages of FBMC as the radar signal are analyzed based on the average ambiguity function. Theoretical analysis and simulation experiments show that the power allocation scheme proposed in this paper can effectively reduce the total transmitted power of the system, to achieve low interception performance compared with the equal power allocation. The FBMC waveform can effectively reduce the sidelobes caused by cyclic prefixes, which improves the radar resolution and information rate.

During the confrontation of electronic systems, it is challenging to deal with the enemy’s comprehensive electronic weapons by simply combining electronic equipment, such as radar, communication, surveillance, and jammers. Hence, to meet the requirements of a modern war environment, the comprehensive integration of various electronic equipment is an inevitable trend. Radar and communication equipment, which are viewed as forward eyes and ears, are very similar in hardware structure and signal processing methods. In this regard, the organic union of these two is plausible. As a result, the Dual-Function Radar and Communication (DFRC) system has received a lot of attention, where integrated waveform design is one of the key scientific issues. The DFRC waveform primarily refers to the transmit waveform that realizes radar detection and information communication functions simultaneously in multiple dimensions, such as space, time, and frequency domains, through electromagnetic spectrum sharing. This paper provides a fundamental review of the radar-centric DFRC waveform design. Initially, this paper presents a brief overview of the radar-centric DFRC system’s application scenarios. Then, the progress of radar-centric integrated waveform design research is discussed. Finally, some closing remarks and potential future research directions are provided.

The hardware imperfection can generate a unique fingerprint of the trasmitter, and it is attached to the radio signal. The unique attribute of transmitter can be used for Radio Frequency Fingerprinting (RFF). Due to the unknown channel conditional and the lack of prior information such as modulation scheme, the traditional method of RFF faces huge challenges to non-cooperative conditions. On the contrary, RFF methods based on Deep Learning (DL), especially those that can directly process raw I/Q, show great potential. However, the research results of this direction are scattered, which seriously hinders researchers from grasping the key issues. This paper first classifies and compares the RFF methods based on DL according to the utilization of prior knowledge, and focuses on the RFF methods based on raw I/Q and DL. Then, this paper focuses on the classification and discussion of the deep neural network model of RFF using raw I/Q, and summarizes the open source data sets, data representation methods and data augmentation methods related to RFF. Finally, this paper discusses the difficulties and research directions of the RFF based on DL, hoping to help the research and application of the RFF.

As a novel radar system, the Multiple-Input Multiple-Output (MIMO) radar with waveform diversity has demonstrated excellent performance in several aspects, including target detection, parameter estimation, radio frequency stealth, and anti-jamming characteristics. After nearly 20 years of in-depth research by scholars, the MIMO radar theory based on orthogonal waveforms has significantly improved. It has been widely applied in fields such as automobile-assisted driving and safety defense. In recent years, with the introduction of the concepts of electromagnetic environment perception and knowledge aid, and the application requirements of radar-active anti-jamming, radio frequency stealth, and detection-communication integration, multiple new theories and methods have been generated for the MIMO radar in system architecture, transmit waveform design, and signal processing. This paper aims to review and summarize the research works on MIMO radar published in the past 20 years, including: the principle of the orthogonal-waveform MIMO radar, its target detection performance analysis and typical applications; waveform design and characteristics of the orthogonal-waveform MIMO radar; knowledge-aided cognitive MIMO waveform design and algorithm; MIMO detection-communication integrated waveform design and algorithm; MIMO radar parameter estimation; MIMO radar target detection; and MIMO radar resource management and scheduling. Finally, the paper discusses the clutter suppression and Space-Time Adaptive Processing (STAP) of MIMO radar in airborne applications, the signal processing of MIMO radar in imaging, and the signal processing of chirp millimeter-wave (mmWave) MIMO radar based on time division multi-waveform diversity.

This paper releases a rotated SAR ship detection dataset, named Rotated Ship Detection Dataset in SAR Images (RSDD-SAR), to address the problem that the existing rotated SAR ship detection datasets are not enough to meet the requirements of algorithm development and practical application. This dataset consists of 84 scenes of GF-3 data slices, 41 scenes of TerraSAR-X data slices, and 2 scenes of large uncropped images, including 7,000 slices and 10,263 ship instances of multi-observing modes, multi-polarization modes, and multi-resolutions. This dataset is effectively annotated by automatic annotation with manual correction. Meanwhile, experiments were conducted for several popular rotated object detection algorithms in optical remote sensing images and rotated ship detection algorithms in SAR images, and the one-stage algorithm S2ANet achieved the highest average precision of 90.06%. When using this dataset, scholars can reference the experimental results, and corresponding analysis can be used. Finally, this paper conducts generalization ability testing experiments on other datasets and large uncropped images to analyze and discuss the performance of the model trained on RSDD-SAR. The experimental results show that the model trained on RSDD-SAR has decent performance and confirms the application value of this dataset. The RSDD-SAR dataset is available at https://radars.ac.cn/web/data/getData?dataType=SDD-SAR.

Radar emitter signal deinterleaving is a key technology for radar signal reconnaissance and an essential part of battlefield situational awareness. This paper systematically sorts out the mainstream technology of radar emitter signal deinterleaving. It summarizes the main research progress in radar emitter signal deinterleaving from three directions: interpulse modulation characteristics-based, intrapulse modulation characteristics-based, and machine learning-based research. Particularly, this paper focuses on explaining the principle and technical characteristics of the latest deinterleaving technology, such as neural network-based and data stream clustering-based techniques. Finally, the shortcomings of the current radar emitter deinterleaving technology are summarized, and the future trend is predicted.

High-precision extraction of river boundaries in Synthetic Aperture Radar (SAR) images is of great significance in monitoring rivers. In this paper, the detection of the health of the Yellow River after the rainstorm in 20 July, 2021 in Zhengzhou is the focus of this paper. The refined-Lee filtering concept and the filtering characteristics of the convolution operation are combined, and an optimized internal weight convolution kernel Refined-Lee Kernel is proposed according to the geometric characteristics of the river channel. A novel river extraction deep neural network model, the River-Net, is also proposed. To verify the effectiveness of the proposed model, this article utilized 20 m resolution Interferometric Wideswath (IW) image data obtained from the European Space Agency Sentinel-1 satellite before and after the 20 July rainstorm in Zhengzhou, employing the images before the rainstorm to train the model. The model, after training, was used to extract the Yellow River channel and analyze the rise of the river after the rainstorm. Experimental results show that the proposed model can extract river channels from SAR images more accurately than trendy semantic segmentation models. The model has important application value for flood disaster detection and evaluation.

High-resolution SAR images contain rich information about targets and their surroundings, but the complex electromagnetic scattering mechanism makes intuitive interpretation difficult, leading to an important research topic in SAR image interpretation. This paper summarizes the typical geometric primitives modeling method of high-frequency scattering, which is reviewed in detail with respect to surface, wedge, and vertex scatterings. Besides the classical expressions of these typical scattering mechanisms, some simulation results are presented. The difficulties in characterizing typical scattering mechanisms and key scientific problems applied to SAR image interpretation are analyzed. Furthermore, this paper proposes a complete and extensive scattering characteristic characterization system by combining and interacting with the scattering primitives based on the corresponding geometric primitives. Finally, the feasibility of developing a scattering mechanism dictionary for use in interpreting SAR image scattering information is discussed.

The vortex ElectroMagnetic (EM) wave has a unique spiral phase wavefront. The rotational Doppler effect, induced by the lateral micromotion of a target is expected to provide a new mode of radar target detection. Under the illumination of vortex EM waves, the micromotion of a cone-shaped target periodically modulates the instantaneous frequency of the radar echo; this can effectively reflect the geometric characteristics and micromotion parameters of a cone-shaped target. This paper focuses on the estimation of cone-shaped target parameters under forward-looking radar conditions. First, based on the principle of vortex EM wave target rotational Doppler detection, a mathematical equation describing the vortex EM wave of a cone-shaped target echo is derived, and an echo rotational Doppler model is established. Second, a method for cone-shaped target parameter estimation under forward-looking conditions is proposed. Using the two-dimensional rotational Doppler information of the scattering points at the top and bottom of a cone-shaped target, the micromotion and geometric parameters of the target can be effectively estimated. The simulation results verify the effectiveness and robustness of the method proposed in this paper.

Vortex electromagnetic wave carries the Orbital Angular Momentum (OAM), and thus has a spiral wavefront structure, which contains the azimuthal information of the target in the echo signal when performing radar imaging compared with planar waves. Hence, this kind of electromagnetic wave shows great potential for various applications in the field of radar detection and imaging, and it is expected to become the development direction of new-system radars. This paper describes in detail the research progress in vortex radar imaging technology in recent years. First, the characteristics of the vortex electromagnetic wave and the principles of imaging using a uniform circular array are introduced. Then the paper reviews the developmental history and research status of vortex radar imaging technology, according to three research directions: vortex radar imaging model, vortex radar gaze imaging algorithm, and vortex radar motion imaging. Finally, the prospects for the development of vortex radar imaging have been presented, along with the key scientific issues and trends for the future developments of vortex radar imaging.

The Bilateral Constant False Alarm Rate (BCFAR) detection algorithm calculates the spatial information of Synthetic Aperture Radar (SAR) image by the Gaussian kernel density estimator, and combines it with the intensity information of image to obtain the joint image for target detection. Compared with the classical CFAR detection algorithm which uses only intensity information for target detection, bilateral CFAR has better detection performance and robustness. However, with continuous high-intensity heterogeneous points (such as breakwater, azimuth ambiguity and phantom) in a complex environment, spatial information calculated by kernel density estimator will have more errors, which will lead to many false alarms in detection results. In addition, when it comes to a weak target with less similarity between adjacent pixels, it will miss detection. To effectively improve these problems, this paper designs an Improved Bilateral CFAR (IB-CFAR) algorithm in complex environment. The IB-CFAR proposed in this paper is mainly divided into three stages: intensity level division based on the nonuniform quantization method, intensity spatial domain information fusion and parameter estimation after clutter truncation. The intensity level division based on the nonuniform quantization method can improve the similarity and contrast information of weak targets, leading to improved ship detection rate. The information fusion of strength spatial domain is to fuse the spatial similarity, distance direction and strength information, which can further improve the detection rate and describe the ship structure information. Parameter estimation after clutter truncation can remove continuous high-intensity heterogeneous points in the background window and retain the real sea clutter samples to the maximum extent, which makes parameter estimation more accurate. Finally, according to the estimated parameters, an accurate sea clutter statistical model is established for CFAR detection. In this paper, the effectiveness and robustness of the proposed algorithm are verified by using GaoFen-3 and TerraSAR-X data.The experimental results show that the proposed algorithm performs well in the environment with more dense distribution of weak targets, and can obtain 97.85% detection rate and 3.52% false alarm rate in such environment. Compared with the existing detection algorithms, the detection rate increased by 5% and the false alarm rate reduced by 10%. However, when the number of weak targets is small and the background is very complex, few false alarms will appear.

Deep convolutional neural networks have achieved great success in recent years. They have been widely used in various applications such as optical and SAR image scene classification, object detection and recognition, semantic segmentation, and change detection. However, deep neural networks rely on large-scale high-quality training data, and can only guarantee good performance when the training and test data are independently sampled from the same distribution. Deep convolutional neural networks are found to be vulnerable to subtle adversarial perturbations. This adversarial vulnerability prevents the deployment of deep neural networks in security-sensitive applications such as medical, surveillance, autonomous driving and military scenarios. This paper first presents a holistic view of security issues for deep convolutional neural network-based image recognition systems. The entire information processing chain is analyzed regarding safety and security risks. In particular, poisoning attacks and evasion attacks on deep convolutional neural networks are analyzed in detail. The root causes of adversarial vulnerabilities of deep recognition models are also discussed. Then, we give a formal definition of adversarial robustness and present a comprehensive review of adversarial attacks, adversarial defense, and adversarial robustness evaluation. Rather than listing existing research, we focus on the threat models for the adversarial attack and defense arms race. We perform a detailed analysis of several representative adversarial attacks on SAR image recognition models and provide an example of adversarial robustness evaluation. Finally, several open questions are discussed regarding recent research progress from our workgroup. This paper can be further used as a reference to develop more robust deep neural network-based image recognition models in dynamic adversarial scenarios.

Deep-learning technology has enabled remarkable results for ship detection in SAR images. However, in view of the complex and changeable backgrounds of SAR ship images, how to accurately and efficiently extract target features and improve detection accuracy and speed is still a huge challenge. To solve this problem, a ship detection algorithm based on multiscale feature fusion and channel relation calibration of features is proposed in this paper. First, based on Faster R-CNN, a channel attention mechanism is introduced to calibrate the channel relationship between features in the feature extraction network, so as to improve the network’s expression ability for extraction of ship features in different scenes. Second, unlike the original method of generating candidate regions based on single-scale features, this paper introduces an improved feature pyramid structure based on a neural architecture search algorithm, which helps improve the performance of the network. The multiscale features are effectively fused to settle the problem of missing detections of small targets and adjacent inshore targets. Experimental results on the SSDD dataset show that, compared with the original Faster R-CNN, the proposed algorithm improves detection accuracy from 85.4% to 89.4% and the detection rate from 2.8 FPS to 10.7 FPS. Thus, this method effectively achieves high-speed and high-accuracy SAR ship detection, which has practical benefits.

Waveform design of joint radar and communication has become a focus of intense research in recent years. Some scholars have proposed to use the odd and even carrier of Orthogonal Frequency Division Multiplexing (OFDM) signal to modulate the radar and communication functions, respectively, to realize the integration. However, OFDM systems generally use cyclic prefix to avoid Inter-Carrier Interference (ICI) and Inter-Symbol Interference (ISI) caused by multipath effects, reducing energy utilization and creating false targets, which affect radar performance. In addition, the traditional OFDM integrated signal is more sensitive to Doppler shift. A small Doppler frequency offset will also cause a considerable drop in orthogonal performance. On this basis, this paper proposes a new waveform design and processing method. This method uses blank guard intervals to replace cyclic prefixes, which can resist multipath effects while avoiding false targets introduced by cyclic prefixes, effectively preventing ICI and ISI. In terms of signal processing methods, this paper proposes a method for channel estimation and Doppler compensation using the priori information of the radar signal. Compared with the traditional method, this new method reduces the system’s resource overhead, such as pilot frequency and training sequence. It improves energy utilization and spectrum efficiency. The peak side lobe ratio, integration side lobe rate, and bit error ratio are also improved. Simulation experiments verify the effectiveness of this method.

The need of extra wireless spectrum is on the rise, given the rapid development of global wireless communication industry. To this end, Radar and Communication Spectrum Sharing (RCSS) has gained considerable attentions recently from both industry and academia. In particular, RCSS aims not only at enabling the spectral cohabitation of radar and communication systems, but also at designing a novel joint system that is capable of both functionalities. In this paper, a systematic overview of RCSS by focusing on the two main research directions are provided, i.e., Radar-Communication Coexistence (RCC) and Dual-Functional Radar-Communication (DFRC). We commence by discussing the coexistence examples of radar and communication at various frequency bands, and then elaborate on the practical application scenarios of the DFRC techniques. As a further step, the state-of-the-art approaches of both RCC and DFRC are reviewed. Finally we conclude the paper by identifying a number of open problems in the research area of RCSS.

This paper introduces a W-band phased electromagnetic surface radar system. This phased electromagnetic surface works at 92～96 GHz and is manufactured with general PCB technology and processing accuracy requirements. The appropriate unit design makes the DC bias voltage of each PIN diode on the phased electromagnetic surface controlled for the current reversal purpose at a low cost and portability. A 180° phase shift of the unit cell can be provided as the current direction on the unit cell is reversed. Beam scanning in different directions can be formed when inputting the right spatial codes. This transmission type phased electromagnetic surface with the ability of beam scanning is used as the receiving antenna of the radar system. This paper presents a W-band phased electromagnetic surface radar system and its manufacture and measurement results, which are fundamental to further research of precision guidance, target identification, and imaging.

Traditional Synthetic Aperture Radar (SAR) imaging is the projection of a real three-dimensional scene onto a two-dimensional domain of azimuth and slant range, which results in the loss of the high-dimensional information. With the advancement of SAR system and its processing technology, tomographic SAR systems obtain multiple data along the height direction to construct the high-dimensional synthetic aperture, and use array signal processing methods to achieve high-resolution three-dimensional images. It can reconstruct the observation scene and extract vertical structure information of the ground target, which is very important for vegetation monitoring, snow and ice detecting, and urban modeling. This paper analyzed the key steps of three-dimensional imaging, such as image registration, flat-earth phase removal, phase compensation, and the three-dimensional focusing, as well as the current research status of each step based on the observation mechanism of tomographic SAR system. This paper particularly focuses on using tomographic SAR on the application of vegetation, glacier, snow, and urban information. The most relevant experimental results in the past two decades were introduced. Further, the application potential and existing problems related to the vegetation height with canopy structure, glacier thickness with internal structure, snow thickness with stratification, and urban three-dimensional reconstruction with deformation monitoring under different platforms are discussed. Finally, the prospects of TomoSAR in the primary applications field are presented.

Multi-Target Tracking (MTT) is a difficult task in radar data processing. When compared to tracking in various fields or scenario, Maritime MTT (MMTT) is a challenging one and also a daunting task. On one hand, low signal-to-clutter ratio in the highly complex marine environment limits the detection performance for small targets at sea, and the plots obtained by the detector contain missing detections and a large number of false alarms, which make MTT much more difficult. On the other hand, when marine targets are moving in the form of multiple groups, or when the high resolution radar is used in marine detection applications, the measurements of the target pave the way to show efficiently the distribution characteristics of occupying multiple cells. In this case, using of conventional MTT methods is not ideal as their performance is not effective as desired. Currently, the number of papers on MMTT at home and abroad is very limited, and most of them only focus on a single target. This paper summarizes the use of MMTT algorithms based on four methods: conventional MTT method, amplitude aided MTT method, multi-target track-before-detect method, and multiple extended target-tracking method. In addition, this paper also considers and analyzes the future perspective of MMTT comprehensively.

False targets caused by multichannel Synthetic Aperture Radar (SAR) are similar to a defocused ship in both shape and texture, making it difficult to discriminate in the full-aperture SAR image. To address the issue of false alarms caused by such false targets, this paper proposes a multichannel SAR false-target discrimination method based on sub-aperture and full-aperture feature learning. First, amplitude calculation is performed on complex SAR images to obtain the amplitude images, and transfer learning is utilized to extract the full-aperture features from the amplitude images. Then, sub-aperture decomposition is performed on complex SAR images to obtain a series of sub-aperture images, and the Stacked Convolutional Auto-Encoders (SCAE) are applied to extract the sub-aperture features from the sub-aperture images. Finally, the sub-aperture and the full-aperture features are concatenated to form the joint features, which are used to accomplish target discrimination. The accuracy of the method proposed in this paper is 16.32% higher than that of the approach only using the full-aperture feature on GF-3 UFS SAR images.

Given the functions and performance advantages of passive radar, this paper first reviews the research history of passive radar for more than 80 years and then examines the research progress of related key technologies, including reference signal reconstruction, multipath clutter suppression, target detection, target tracking, and passive radar imaging. On this basis, the latest research results of typical experimental systems of passive radar abroad (particularly in European countries) are presented in terms of system structures, technical parameters, and performance indices. Then this paper focuses on the Multi-Illuminator-based PAssive Radar (MIPAR) series of Wuhan University in China. The target detection results of MIPAR in different frequency bands (HF/VHF/UHF/L) are given, that show the application potential of the MIPAR system in long-range early warning and close-range high-precision monitoring. Finally, the development trends of passive radar, including the integration of multiple illuminators, system network configuration, and intelligent signal processing, are discussed.

Specific emitter identification is a technique of extracting the radio frequency fingerprints of the received electromagnetic signal only using external feature measurements to determine the specific emitter that transmits the signal. In recent years, the related theories and practical applications of specific emitter identification have been continuously improved, and research on radio frequency fingerprinting feature extraction methods has made great progress. Based on the domestic and foreign academic achievements, this paper systematically reviews the status quo of the fingerprint feature extraction method of specific emitter identification. In addition, a new feature classification framework is proposed based on the inherent logic of fingerprint feature extraction. The classification framework combines the description characteristics of different radio frequency fingerprinting features and the correlation between them. It divides the existing radio frequency features into two main categories: direct measurement features and dimensionality reduction transform features, which have three levels. Finally, this paper analyzes and explores several potential research directions of fingerprint feature extraction, aiming to benefit the research and application of specific radiation source identification.

The Multipath Exploitation Radar (MER) target detection technology is primarily based on the Non-Line-Of-Sight (NLOS) multipath propagation characteristics of electromagnetic waves, such as reflection and diffraction on the surface of the medium, enabling the effective detection of targets hidden in the “visually” blind area, such as urban street corners and vehicle occlusion. Thus, the technology can be feasible for various applications, including urban combat and intelligent driving. Further, it has significant practical and research implications. This paper summarizes the domestic and foreign literature in this field since the beginning of the 21st century to keep abreast of developments in this field and predict future development trends. The literature review revealed that according to the different types of detection platforms, MER target detection technology primarily consists of multipath detection technologies based on air and ground platforms. Both these technologies have achieved certain produced research results of practical significance. For air platforms, the following aspects are discussed: feasibility verification, analysis of influencing factors, architectural environment perception, and NLOS target detection. Further, for ground platforms, these four aspects are covered: target detection and recognition, two-dimensional target positioning, three-dimensional target information acquisition, and new detection methods. Finally, the prospects of MER target detection technology are summarized, and the potential issues and challenges in the current practical application of this technology are highlighted. These results show that MER target detection technology is evolving toward diversification and intelligence.

This paper proposes a novel multimodal collaborative perception framework to enhance the situational awareness of autonomous vehicles. First, a multimodal fusion baseline system is built that effectively integrates Light Detection and Ranging (LiDAR) point clouds and camera images. This system provides a comparable benchmark for subsequent research. Second, various well-known feature fusion strategies are investigated in the context of collaborative scenarios, including channel-wise concatenation, element-wise summation, and transformer-based methods. This study aims to seamlessly integrate intermediate representations from different sensor modalities, facilitating an exhaustive assessment of their effects on model performance. Extensive experiments were conducted on a large-scale open-source simulation dataset, i.e., OPV2V. The results showed that attention-based multimodal fusion outperforms alternative solutions, delivering more precise target localization during complex traffic scenarios, thereby enhancing the safety and reliability of autonomous driving systems.

The aiming jamming emitted by self-defense jammers renders various passive anti-jamming measures based on signal processing ineffective, posing severe threats to modern radars. Frequency agility, as an active countermeasure, enables the resistance of aiming jamming. In response to issues such as the unstable anti-jamming performance of traditional random frequency hopping, limited freedom in frequency selection, and the long time required for strategic learning, the paper proposes a fast-adaptive frequency-hopping strategy for a frequency agile radar. First, a frequency agile waveform with repeatable frequency selection is designed, providing more choices for an optimal solution. Accordingly, using the data collected through continuous confrontation between a radar and a jammer, and the exploration and feedback mechanism of deep reinforcement learning, a frequency-selection strategy is continuously optimized. Specifically, considering radar frequency from the previous time and jamming frequency perceived at the current time as reinforcement learning inputs, the neural network intelligently selects each subpulse frequency at the current time and optimizes the strategy until it is optimal based on the anti-jamming effectiveness evaluated by the target detection result and Signal-to-Jamming-plus-Noise Ratio (SJNR). To improve the convergence speed of the optimal strategy, the designed input state is independent of the historical time step, the introduced greedy strategy balances the search-utilization mechanism, and the SJNR differentiates rewards more. Multiple sets of simulations show that the proposed method can converge to the optimal strategy and has high convergence efficiency.

The Back Projection (BP) algorithm is an important direction in the development of synthetic aperture radar imaging algorithms. However, the large computational load of the BP algorithm has hindered its development in engineering applications. Therefore, techniques to enhance the computational efficiency of the BP algorithm have recently received widespread attention. This paper discusses the fast BP algorithm based on various imaging plane coordinate systems, including the distance-azimuth plane coordinate system, the ground plane coordinate system, and the non-Euclidean coordinate system. First, the principle of the original BP algorithm and the impact of different coordinate systems on accelerating the BP algorithm are introduced, and the development history of the BP algorithm is sorted out. Then, the research progress of the fast BP algorithm based on different imaging plane coordinate systems is examined, focusing on the recent research work completed by the author’s research team. Finally, the application of fast BP algorithm in engineering is introduced, and the research development trend of the fast BP imaging algorithm is discussed.

Random Stepped Frequency (RSF) radars can achieve high-range resolution with relatively low hardware complexity by synthesizing a wide bandwidth. Moreover, because of the random frequency agility of each pulse, the radars possess robust anti-interference and electromagnetic compatibility capabilities, rendering them invaluable for high-precision detection in complex electromagnetic environments. However, the inherent sparsity sensing of the radar waveform in the time-frequency domain, causes a lack of echo coherence information, leading to an underdetermined estimation of the traditional matched filter, which results in fluctuating high side lobes in the estimation spectrum and adversely deteriorating detection performance. This paper proposes a sparse recovery method based on Hankel matrix completion for the high-resolution range-Doppler spectrum of the RSF radars. Using the low-rank matrix completion concept, this method fills in the missing samples caused by sparse sensing for RSF radars, thereby restoring continuous coherence information and effectively addressing the underdetermined estimation issue. First, an undersampled data matrix of a single coarse-resolution range for RSF radar is constructed. Subsequently, the time-frequency data matrix is reconstructed into a double Hankel form, and its low-rank prior characteristics are analyzed and proven. Finally, the Alternating Direction Method of Multipliers (ADMM) algorithm is applied to restore the unsampled time-frequency data, ensuring sparse recovery of the high-resolution range-Doppler spectrum with low sidelobes. Simulations and real tests demonstrate the effectiveness and superiority of the proposed method.

Distinguishing multiple targets in the same resolution cell is an important and challenging task in the forward-looking imaging process of monopulse radar. Although Doppler processing can improve the recognition performance for multiple targets at high squint angles, the precise estimation of Doppler frequency remains challenging under conditions with unknown target numbers and energy leakage from strong point targets. To address these issues, this paper proposes a Fast Iterative Interpolated Beamforming (FIIB) algorithm with model order estimation and single snapshot processing for monopulse forward-looking imaging, which combines information theory to unbiasedly estimate the number of targets and Doppler frequencies. The simulation results show the superiority of the proposed FIIB algorithm over the Chirp-Z Transform (CZT) algorithm for estimating target numbers and Doppler frequencies within the same resolution cell in the presence of multiple point targets. In addition, the proposed FIIB algorithm can accurately estimate point targets beyond a ±5° azimuth angle in monopulse angle measurement tasks. Real-data experiments also reveal that FIIB-based monopulse forward-looking imaging has high focusing capability and imaging contrast and can effectively suppress background clutter.

In modern electronic warfare, the jamming environment of radar is more complex than ever. The airborne jammer adapts its jamming method based on diverse raid missions and stages. Recently, the reinforcement learning–based radar anti-jamming method has made some progress in the confrontation scenario of single jamming; however, the gap with respect to actual complex multi-jamming scenarios is large. To address this issue, this paper proposes a multi-jamming scenario radar anti-jamming method based on deep reinforcement learning in the complex domain to optimize the anti-jamming strategy of frequency agile radar. First, according to the stage characteristics of the raid mission, noise spot jamming, range deception jamming , and dense false target forwarding jamming models are established. The three jamming sequence strategies were designed to simulate actual jamming scenarios. Second, a reinforcement learning reward function that integrates the signal-to-noise ratio and target trajectory integrity is constructed for the multi-jamming scenario model. Thus, a multi-jamming scenario radar anti-jamming method based on deep reinforcement learning in a complex domain is proposed, which is based on the complex domain characteristics of the jamming signal. Finally, radar anti-jamming simulation experiments are performed based on the three jamming sequence strategies. The results show that the proposed method can effectively deal with the main-lobe jamming problem of complex multi-jamming scenarios under time-sequence conditions. Moreover, the average decision-making accuracy was improved, and the average decision-making time was reduced to 405.3 ms compared with the two classical reinforcement learning algorithms.

Radio Frequency-screen is one of the earliest radar active antijamming measures. It achieves antijamming by transmitting cover pulses of different frequencies before the radar pulse signal to induce enemy jammers. As the demand for antijamming measures has become increasingly urgent in recent years, Radio Frequency-screen technology has been further developed. The most representative is the use of discontinuous spectrum signals as a cover signal. However, energy utilization for sending the cover signal can be improved further. To address this problem, this paper proposes a discrete spectrum cover signal based on the discontinuous spectrum cover signal and establishes the waveform design function under the joint constraint of constant modulus and spectral amplitude. The cover signal with discrete spectrum and energy aggregation is generated using the Alternating Direction Method of Multipliers (ADMM) and spectrum shaping algorithm solution. The simulation results show that the discrete spectrum cover signal has a higher spectral amplitude of approximately 5～12 dB than the discontinuous-spectrum cover signal for the same energy and bandwidth. Moreover, the discrete spectrum cover signal can cover a larger spectral range with the same energy and close spectral amplitude, realizing a better antijamming cover effect.

Target detection and recognition are popular issues in the field of high-resolution Synthetic Aperture Radar (SAR). As a typical target, aircraft detection and identification has certain uniqueness. This paper reviews the development of detection and recognition techniques for a typical target in SAR imagery, analyzes the scattering mechanism and technical difficulties of aircraft in SAR imagery, describes the system flow, technical routes, and key scientific problems of target aircraft detection and recognition in SAR imagery, summarizes the research progress from traditional methods to deep-learning-based methods for aircraft detection and recognition, discusses the characteristics and existing problems of various methods, and predicts the future development trend. This paper proposes that combining target electromagnetic scattering mechanism with deep convolutional neural network to improve the generalization capability of the model is the key to improve SAR detection and recognition performance. Moreover, this paper establishes an aircraft detection method based on the fusion of scattering information and deep convolutional neural network.

The flooded area detection method based on the fusion of optical and Synthetic Aperture Radar (SAR) images is applicable for all weather conditions and times. However, due to the large number of randomly distributed intensive speckle noise in SAR images, the conventional methods of detection often trigger high false alarm rates at flood-stricken zones. Inspired by the Fuzzy C-Means (FCM) clustering method, a hierarchical clustering algorithm (Hierarchical Fuzzy C-Means, H-FCM) is proposed in this paper. This method fuses the SAR image captured after the flood with the optical image captured before the flood. Based on the fused image, this method uses the proposed hierarchical clustering model to obtain the preliminary detection results of the flooded area. Additionally, the algorithm uses the proposed region-growing algorithm to obtain the river location before the flood and uses it as a spatial constraint for the preliminary detection results to further screen out suspected flooded areas and significantly improve detection performance. The experimental data used in this paper include the remote sensing images captured before and after the Gloucester floods in the United Kingdom in 1999, as well as the remote sensing images captured before and after the Nanchang floods in China in 2019. The effectiveness and validity of the H-FCM algorithm are also supported by comparison experiments.

Active radar remote sensing technology, with its capability of acquiring all-weather data, has great potential for agricultural monitoring. This technology can penetrate vegetation cover more deeply than optical sensors and has sensitivity to the shapes, structures, and dielectric constants of vegetation scatterers. In this paper, we discuss the applications of radar remote sensing in crop identification, cropland soil moisture inversion, crop growth parameter inversion, crop phenology retrieval, agricultural disaster monitoring, and crop yield estimation. We review several specific papers focusing these fields, and then describe the results obtained using information extracted from radar scatterometers and Synthetic Aperture Radar (SAR). Extracted SAR data include characterizations of backscattering, polarimetry, interferometry, and tomography. Lastly, we summarize the problems faced by radar applications in agriculture and consider the future trend of these applications.

Circular Synthetic Aperture Radar (CSAR) is a novel imaging mode, which has the advantages of alldirectional observation, high spatial resolution, and three-dimensional imaging. With the development of airborne CSAR imaging techniques, it has become one of the effective methods for key point area observation. This paper introduces works on airborne CSAR imaging techniques performed by our research team in recent years, including airborne CSAR imaging mode, spatial resolution evaluation, two-dimensional CSAR imaging, three-dimensional target image reconstruction based on a single CSAR, and three-dimensional holographic SAR imaging. In this paper, experimental results based on raw data acquired using airborne CSAR systems with P and X bands are presented. The obtained research results prove the effectivity and practicability of the airborne CSAR imaging mode. The content of this paper is based on a keynote speech presented by the author at the Fifth Young Scientists Forum of Journal of Radars on August 15, 2019.

Compact Polarimetric (CP) mode is a new dual-pol mode introduced in the last decade. The main current CP mode transmits circular polarized waves. Data in the form of Stokes parameters obtained by this mode has rotational invariance. In real engineering applications, transmit distortions in all dual-pol modes, including the CP mode, cannot be directly compensated with external calibration methods. Therefore, it is necessary to analysis the influences caused by transmit distortions. Until now, the Maximum Normalized Error (MNE) parameter has already been proposed by existing researches to analyze polarimetric quality of the Polarimetric SAR (PolSAR) system. This paper has proposed an analysis method to analysis the influence of transmit distortions in polarimetric modes with circular polarimetric wave in transmission, based on the Axial Ratio (AR) parameter of real transmitted wave. Firstly, this paper has analyzed the influence of different transmit distortion sources to AR parameter with simulations. Meanwhile, this part has also demonstrated the influence of same distortion sources to the MNE parameter. Through comparison of this two results, this paper has concluded three advantages of the AR parameter over the MNE parameter. At last, the effectiveness of the proposed evaluation method has been verified using real measured GF-3 distortion data and test data obtained by experimental system, which transmit circular polarized waves.

Synthetic Aperture Radar (SAR) has attracted much attention in the recent decades owing to its allweather and high-resolution working mode. As an active radar system, the high-resolution imaging process of SAR systems is affected by different types of strong, complex, and variable electromagnetic interferences that can severely affect the final high-resolution SAR imaging results. Thus, developing ways to effectively suppress complex electromagnetic interferences is a major challenge and focus of SAR detection. In this paper, we summarize the key elements and main concepts underlying interference suppression in high-resolution SAR imaging, including different interference patterns, interference sources, interference scattering mechanisms, radar antenna configurations, and target characteristics. We then consider the essential task of interference suppression algorithms. Recent papers that detail the representative SAR algorithms used to mitigate suppressed and deceptive jamming are introduced and summarized to provide references for future research.

Array signal processing is an essential tool in broad radar applications. The coprime array has recently been proposed to overcome the bottleneck caused by the Nyquist spatial sampling rate. The coprime array, whose sparse structure and undersampling feature drastically decrease necessary computational and hardware cost, provides a theoretical foundation and technical basis for the increasing demands of its practical applications. Considering its superior performance in degrees-of-freedom, spatial resolution, and computational complexity, research on coprime array signal processing has attracted much attention. This paper reviews recent research progress on coprime array signal processing, which has focused on both the Direction-of-Arrival (DOA) estimation and adaptive beamforming. From the perspective of coprime array DOA estimation, this paper summarizes two typical approaches, namely the coprime subarray decomposition-based approach and the virtual array signal processing-based approach. Moreover, recent work on low-complexity and super-resolution DOA estimation via compressive sensing and gridless techniques is also introduced. From the perspective of coprime array adaptive beamforming, the differences and relationships between DOA estimation and beamforming in the framework of coprime array signal processing are discussed, and an efficient, robust, and adaptive beamformer design tailored for the coprime array is introduced. Advantages and the future directions of coprime array signal processing are discussed, along with the theoretical basis and a technical reference for practical radar applications.

Recently, Netted Radar System (NRS) has received much attention due to its robust performance gain. Usually, the NRS Detect Before Track (DBT) method detects the received data at each time, acquiring a set of alarm plots, and then transmits these plots or the trajectories obtained based on them to the fusion center, thus generating a global estimated result. However, when the Signal-to-Noise Ratio (SNR) is low, the performance becomes highly degraded because the targets cannot pass the single-frame detection threshold of DBT. To solve this problem, a netted radar Multi-Frame Track Before Detect (MF-TBD) method is proposed in this paper. First, MF-TBD is performed in local radar nodes, and then it acquires estimated target plot sequences and transmits them to the center for further fusion. MF-TBD can take advantage of NRS, and also can utilize target space time correlation through MF-TBD processing and enhance the target SNR. Thus, it can improve detection performance. However, the outputs of MF-TBD are different from that of DBT. Therefore, the current fusion methods for DBT are not suitable for MF-TBD. To solve this problem, this paper first derives a fusion method for plot sequences, then reports its processing steps in radar system, and finally proposes an implementation method based on the particle filter. The simulation results show that the proposed method has a detection performance gain of 4 to 6 dB than the traditional method based on DBT, and a 50% gain on estimation accuracy than single-sensor MF-TBD.

As one of the core components of Advanced Driver Assistance Systems (ADAS), automotive millimeter-wave radar has become the focus of scholars and manufacturers at home and abroad because it has the advantages of all-day and all-weather operation, miniaturization, high integration, and key sensing capabilities. The core performance indicators of the automotive millimeter-wave radar are distance, speed, angular resolution, and field of view. Accuracy, cost, real-time and detection performance, and volume are the key issues to be considered. The increasing performance requirements pose several challenges for the signal processing of millimeter-wave radar systems. Radar signal processing technology is crucial for improving radar performance to meet more stringent requirements. Obtaining dense radar point clouds, generating accurate radar imaging results, and mitigating mutual interference among multiple radar systems are the key points and the foundation for subsequent tracking, recognition, and other applications. Therefore, this paper discusses the practical application of the automotive millimeter-wave radar system based on the key technologies of signal processing, summarizes relevant research results, and mainly discusses the topics of point cloud imaging processing, synthetic aperture radar imaging processing, and interference suppression. Finally, herein, we summarize the research status at home and abroad. Moreover, future development trends for automotive millimeter-wave radar systems are forecast with the hope of enlightening readers in related fields.

In recent years, millimeter-wave radar has been widely used in safety detection, nondestructive detection of parts, and medical diagnosis because of its strong penetration ability, small size, and high detection accuracy. However, due to the limitation of hardware transmission bandwidth, achieving ultra-high two-dimensional resolution using millimeter-wave radar is challenging. Two-dimensional high-resolution imaging of altitude and azimuth can be realized using radar platform scanning to form a two-dimensional aperture. However, during the scanning process, the millimeter-wave radar produces sparse tracks in the height dimension, resulting in a sparse sampling of the altitude echo, thus reducing the imaging quality. In this paper, a high-resolution three-dimensional imaging algorithm for millimeter-wave radar based on Hankel transformation matrix filling is proposed to solve this problem. The matrix filling algorithm restores the sparse sampling echo, which guarantees the imaging accuracy of the millimeter-wave radar in the scanning plane. First, the low-rank prior characteristics of the millimeter-wave radar's elevation-range section were analyzed. To solve the problem of missing whole rows and columns of data during sparse trajectory sampling, the echo data matrix was reconstructed using the Hankel transform, and the sparse low-rank prior characteristics of the constructed matrix were analyzed. Furthermore, a matrix filling algorithm based on truncated Schatten-p norm combining low-rank and sparse priors was proposed to fill and reconstruct the echoes to ensure the three-dimensional resolution of the sparse trajectory millimeter-wave radar. Finally, using simulation and several sets of measured data, the proposed method was proved to achieve high-resolution three-dimensional imaging even when only 20%–30% of the height echo was used.