Millimeter-wave radar plays a vital role in 3D object detection for autonomous driving due to its all-weather and all-lighting-condition capabilities for perception. However, radar point clouds suffer from pronounced sparsity and unavoidable angle estimation errors. To address these limitations, incorporating a camera may partially help mitigate the shortcomings. Nevertheless, the direct fusion of radar and camera data can lead to negative or even opposite effects due to the lack of depth information in images and low-quality image features under adverse lighting conditions. Hence, in this paper, we present the radar-camera fusion network with Hybrid Generation and Synchronization (HGSFusion), designed to better fuse radar potentials and image features for 3D object detection. Specifically, we propose the Radar Hybrid Generation Module (RHGM), which fully considers the Direction-Of-Arrival (DOA) estimation errors in radar signal processing. This module generates denser radar points through different Probability Density Functions (PDFs) with the assistance of semantic information. Meanwhile, we introduce the Dual Sync Module (DSM), comprising spatial sync and modality sync, to enhance image features with radar positional information and facilitate the fusion of distinct characteristics in different modalities. Extensive experiments demonstrate the effectiveness of our approach, outperforming the state-of-the-art methods in the VoD and TJ4DRadSet datasets by 6.53% and 2.03% in RoI AP and BEV AP, respectively.

In this paper, we consider reusing a rotary-wing UAV as both an airborne base station (BS) and radar to provide integrated sensing and communication (ISAC) wideband service to a ground mobile user. Specifically, the UAV transmits orthogonal frequency-division multiplexing (OFDM) signals where a part of the sub-carriers are assigned for communication purposes. We formulate an online optimization problem that jointly optimizes the UAV trajectory and power allocation of the OFDM sub-carriers to provide a balanced communication and localization service to the ground user. The problem is very challenging because of the non-convex localization accuracy metric with respect to the trajectory and transmit power. For this, we decouple the original problem into a sub-carrier power allocation sub-problem and a trajectory design sub-problem, and propose efficient algorithms to solve them respectively. Simulation results show that the proposed algorithm reduces the localization error by more than 66% at the cost of affordable decrease of communication rate compared to the representative benchmark method considered.

Feature-level fusion shows promise in collaborative perception (CP) through balanced performance and communication bandwidth trade-off. However, its effectiveness critically relies on input feature quality. The acquisition of high-quality features faces domain gaps from hardware diversity and deployment conditions, alongside temporal misalignment from transmission delays. These challenges degrade feature quality with cumulative effects throughout the collaborative network. In this paper, we present the Domain-And-Time Alignment (DATA) network, designed to systematically align features while maximizing their semantic representations for fusion. Specifically, we propose a Consistency-preserving Domain Alignment Module (CDAM) that reduces domain gaps through proximal-region hierarchical downsampling and observability-constrained discriminator. We further propose a Progressive Temporal Alignment Module (PTAM) to handle transmission delays via multi-scale motion modeling and two-stage compensation. Building upon the aligned features, an Instance-focused Feature Aggregation Module (IFAM) is developed to enhance semantic representations. Extensive experiments demonstrate that DATA achieves state-of-the-art performance on three typical datasets, maintaining robustness with severe communication delays and pose errors. The code will be released at https://github.com/ChengchangTian/DATA.

Automotive synthetic aperture radar (Auto-SAR) imaging can provide high-resolution images for vehicular environment perception and localization. Due to the severe motion errors associated with jerks and bumps of the vehicle, autofocusing plays a crucial role in Auto-SAR imaging. This paper aims to provide an integral Auto-SAR imaging workflow via the Omega-K algorithm and a contrast-based WPGA (CB-WPGA) method. Based on the analysis of phase error characteristics after sub-aperture (SA) imaging, CB-WPGA, which selects range cells and weights the PGA kernel based on the contrast, is proposed to enhance the robustness of PGA in automotive scenarios. Finally, the superiority of the proposed technique is showcased by employing experimental data obtained from a 77-GHz radar installed on a vehicle.

Hypersonic vehicle-(HSV) borne synthetic aperture radar (SAR) with spiral trajectory has significant advantages, such as wide angle coverage, flexible observation geometry, and rich spatial information acquisition. However, the high mobility of the platform can cause many problems for SAR imaging. To address these issues, this letter first establishes a suitable vector model for HSV SAR with spiral trajectory. We proposed an innovative method based on 2-D Lagrange interpolation to address cross–coupling and spatial variation caused by rapid altitude and velocity changes. The performance of the method is demonstrated through simulations and real data experiments. The proposed solution has significant advancements in the application of HSV SAR.

Due to the wide bandwidth and large observation area, spaceborne synthetic aperture radar (SAR) is easily interfered by other electromagnetic signals, namely radio frequency interference (RFI), which can severely degrade SAR image quality and submerge useful information. Classic parametric and non-parametric methods are used to suppress RFI as much as possible without considering the useful information. To protect the real reflected signals, semi-parametric methods, based on low-rank and sparse recovery, are proposed to mitigate RFI, but they suffer from the singular-value over-shrinking problem when RFI is not strictly low-rank, resulting in interference residues in the recovered scene. Hence, in this paper, a robust interference self-cancellation (RISC) method is proposed to protect raw ground scenes from polluted data with better extraction accuracy of RFI. The proposed model can adaptively fit in different scenes and backgrounds by using adjacent homologous interference (HI) subregions instead of the low-rank constraints, thus better protecting SAR scenes and enhancing its robustness. Based on the alternating direction method of multipliers (ADMM), we design two different solvers for the proposed optimization model, and both are tested on four different scenes of Sentinel-1 measured data. All experiments demonstrate that the proposed method has excellent performance in RFI mitigation and SAR image recovery.

Due to the large diving trajectory, the design of the imaging algorithm remains an exceptionally difficult task for hypersonic synthetic aperture radar (SAR). In this article, an accurate range model that accounts for the impact of higher-order motion factors on imaging performance is established. Based on the model, the signal characteristics are analyzed, which indicates that the design of the imaging approach faces greatly challenges such as severe cross-couplings and significant spatial variations (SVs). In view of these issues, a time–frequency hybrid domain imaging algorithm (TFHDIA) is proposed for hypersonic vehicle (HSV)-borne SAR systems with a large diving trajectory. The cross-couplings are significantly reduced by azimuth preprocessing. The 2-D SV in the range cell migration (RCM) and the first-order SV in the Doppler parameters (DPs) are eliminated through the fractional Fourier transform (FrFT) and the extended keystone transform (EKT). Additionally, the higher-order SVs of DPs are removed by the phase filter bank, which includes range, azimuth, and cross-coupling spatially variant phase error compensation filters. The proposed algorithm exhibits strong capabilities in eliminating SVs and effectively reducing cross-coupling, making it particularly suitable for HSV SAR systems with large diving trajectories. Simulation and actual acquired data results validate the efficacy of the proposed algorithm.

As a wideband radar system, spaceborne synthetic aperture radar (SAR) has been widely applied in multiple applications, such as maritime surveillance and terrain observation. However, with the increase of electromagnetic devices, spaceborne SAR suffers from radio frequency interference (RFI) frequently. Many previous methods have been effective in interference mitigation, among which semiparametric methods demonstrate excellent performance and high efficiency. However, as a classic low-rank recovery method, robust principal component analysis (RPCA) usually suffers from the over-penalization problem of large singular values. Although some useful schemes were proposed to address this issue, their performance may still degrade if the low-rank characteristics of interference are not prominent. To overcome these obstacles, we first investigate the characteristics and distributions of different SAR signals and leverage kurtosis to differentiate interference and real echoes. Herein, interference tends to have a low kurtosis while the real echoes tend to have a high kurtosis. Then, we improve the low-rank recovery model with kurtosis and propose the kurtosis-based reweighted nuclear norm (KRNN) model to precisely extract interference components. Then, we derive the closed-form solution of the KRNN model via the alternating direction method of multipliers (ADMM) framework. Through the proposed KRNN method, we can effectively mitigate interference, preserve real echoes, and solve the problems of over-penalization and nonideal low-rank property. Finally, we conduct numerical experiments using the measured Sentinel-1 and LT-1 data to demonstrate the effectiveness and robustness of our proposed method.

Compared with conventional synthetic aperture radar (SAR) systems, hypersonic vehicle-borne (HSV) SAR with a spiral trajectory offers unique advantages, including high maneuverability, large detection range, and diverse observation views, enabling precise three-dimensional (3-D) SAR coordinate positioning. However, the high maneuverability of the spiral trajectory poses significant challenges, such as model mismatch, image defocusing, and positioning algorithm failures. To address these challenges, a geometric vector model of HSV SAR with a spiral trajectory is established, and the corresponding signal and trajectory characteristics are analyzed. Subsequently, the 3-D coordinate positioning approach based on HSV SAR with spiral trajectory is proposed. The proposed method achieves target positioning via two-dimensional (2-D) multi-view imaging, SAR target matching, and 3-D coordinate calculation. This method is relatively novel and provides superior imaging and positioning performance. Simulation, real data, and semi-real data experiments are given to validate the effectiveness of the proposed method.

As an advanced remote sensing technology, synthetic aperture radar (SAR) generates high-resolution images by transmitting continuous electromagnetic waves toward the target area. SAR has played a pivotal role in both contemporary research and practical applications. This underscores the importance of maintaining imaging integrity. However, the performance of SAR systems is severely affected by the increasingly prevalent radio frequency interference (RFI). RFI not only degrades the quality of SAR images but also hinders the accurate interpretation of SAR data. The rapid development and widespread use of modern electromagnetic devices have led to a diversification of interference types, resulting in complex mixed-mode interference. Traditional interference mitigation techniques struggle to effectively alleviate these issues. Moreover, varying terrains add significant difficulty to mitigating interferences, often resulting in residual interference in processed images and the loss of substantial scene information. To tackle these challenges, this article proposes a novel interference mitigation method called the group-parametric method. Unlike previous semiparametric methods, the group-parametric method refines both the interference and target models and achieves more effective interference mitigation and scene preservation by applying distinct regularizations to the refined models. Based on the new model, we have designed a structured trifactorization (STF) algorithm across frequency and time domains, which achieves data recovery through regularizations of low-rank and sparsity applied to the interference. Experimental verification with Level-1 data from LuTan-1 (LT-1) and Sentinel-1 confirms the effectiveness and superiority of our proposed model and method.

Maneuver provides flexibility for highly squinted synthetic aperture radar (SAR) and also means complicated signal characteristics in the echo, especially for ground moving target imaging (GMTIm). This article analyzes the interaction of parameters between the maneuvering platform and the moving target. The analysis suggests that three key factors should be taken into account: azimuth spectrum aliasing, Doppler centroid ambiguity, and high-order errors. To deal with these challenges, a novel GMTIm methodology for maneuvering platform is presented. The proposed approach employs an advanced parameter estimation method based on the extended generalized high-order ambiguity function (EGHAF), which enables simultaneous estimation of high-order phase coefficients across all orders in low signal-to-noise ratio (SNR) conditions through 2-D extension. Due to the estimation and compensation for higher order phases, which are usually ignored in conventional methods, the proposed method is more suitable for moving target imaging with maneuvering platforms. The superiority of the proposed approach is verified by simulation and real data results.

Time-domain modulated metasurfaces (TMMs) enable passive radar jamming by dynamically altering incident waveforms. We introduce the concept of passive barrage jamming, where a TMM redistributes target energy to suppress dominant features in the synthetic aperture radar (SAR) imaging without active radiation. Traditional approaches, such as random phase modulation, can disperse target energy but lack a principle framework for shaping the SAR response, leaving detectable residual signatures. In this work, the design of TMM modulation sequences is formulated as a non-convex minimax optimization problem to improve the energy distribution of the SAR imaging. An alternating direction method of multipliers framework is developed to solve the problem efficiently under the constant-modulus constraint, with theoretical guarantees of monotonic convergence under suitable parameter settings. In SAR simulations, the optimized TMM sequences achieve a peak reduction of 70.18 dB for a point target and 48.29 dB for an extended target compared to the unmodulated baseline, outperforming both random and structured coding schemes. Experimental validation on a practical 1-bit TMM platform confirms a 12.87 dB peak reduction in one-dimensional matched filtering, despite phase quantization and hardware nonidealities. These results highlight the effectiveness of the proposed optimization approach in enhancing TMM-based barrage jamming performance, providing a robust and practical solution for radar countermeasures.

With the advancement of self-driving vehicles, autonomous driving systems depend on multimodal data to achieve a dynamic perception of the surrounding environment. Synthetic aperture radar (SAR) techniques can enhance azimuth resolution by utilizing the relative motion between the vehicle and targets, requiring a precise trajectory of the vehicle, normally without the assistance of automotive-grade navigation systems. In this case, data-driven autofocus-based algorithms are typically used to implement compensation for non-systematic motion errors. Despite demonstrating robust autofocus capabilities in numerous scenarios, their potential for application in automotive scenarios still needs to be exploited. This paper aims to provide a comprehensive automotive SAR imaging with autofocus workflow and to analyze the performance of autofocus algorithms based on phase gradient autofocus (PGA) in typical automotive scenarios. We rigorously derive the Omega-$\boldsymbol {K}$algorithm based on the system-grade waveform of frequency modulated continuous wave (FMCW) signals. Based on the analysis of motion error and phase error characteristics, a sub-aperture contrast-based weighted PGA (SAC-WPGA) method, a contrast-based selection strategy (CBSS), and a contrast-based WPGA kernel are proposed to improve the robustness of autofocus for automotive scenarios. In addition, we theoretically discuss the impact of the selection strategy, the PGA kernel, and the selection threshold in detail, highlighting the validity of the proposed method. Finally, we showcase the superiority of the proposed technique by employing experimental data in two typical automotive scenarios, i.e., a simple scenario with isolated dominant points and a complex scenario with strong clutter.

Due to the strong consistency with the synthetic aperture radar (SAR) system, deceptive jamming can be well integrated with SAR images and has high concealment. Therefore, deceptive jamming suppression in SAR is an urgent problem that needs to be solved. This article proposes a slow-time group phase coding (GPC) scheme for deceptive jamming suppression. Specifically, the proposed method can be divided into three steps: first, by using the slow-time GPC, the SAR transmitted signals are encoded separately in pulses and divided into two groups. Second, based on each group of signals, we propose a new optimization problem to reconstruct the SAR images and eliminate the unmatched deceptive jamming, i.e., the deceptive jamming combined with the second kind of GPC is unmatched with the first kind of GPC. Third, due to the design of GPC, each group of signals generates a SAR image where the scene stays almost the same, while the residual matched deceptive jamming is located at different azimuths. In this context, this difference is successfully used to eliminate the remaining deceptive jamming. Finally, the RADARSAT-1 and MiniSAR datasets are used to evaluate the effectiveness of the proposed method.

The typical issues are that the existing methods of moving target detection in spaceborne distributed aperture radar (SBDAR) suffer from the range cell migration (RCM) and Doppler frequency modulation (DFM) problems in space-time two-dimensional (2D) domain. To deal with these issues, a new SBDAR moving target detection method based on space–time hybrid integration (STHI) is developed in this paper. Firstly, the RCM and DFM in time dimension are removed by the second-order Keystone transform (SKT), a novel range frequency reversal process (NRFRP) and a modified scaled Fourier transform (MSCFT), to achieve the time dimensional coherent integration. Secondly, the spatial projection method is utilized to achieve the space dimensional integration of moving target by gridding the radar detection area, and the moving target is finely focused and detected. Finally, the effectiveness of the proposed method is verified by simulations.

As a frontier technology in radar imaging, hypersonic vehicle-borne (HSV) synthetic aperture radar (SAR) has several practical issues to be dealt with, namely, ground resolution capability, pulse repetition frequency (PRF) selection, and beam pointing description, especially for the near-vertical diving trajectory because of the extremely small angle between the velocity and slant range vectors. Moreover, its focusing approach design is greatly challenged by very large cross-couplings and spatial variations. Considering these practical problems, the constraints between system performance and parameter selection are analyzed firstly to obtain the parameter optimization procedure and avoid system design deviation. Then, a frequency radius/angle algorithm (FRAA) is devised, which is an extension of the radius/angle algorithm (RAA) performed in two-dimensional (2-D) frequency domain. In the FRAA, new range equation and 2-D frequency interpolation function are reconstructed with high accuracy by quadratic fitting and 3-D expansion. Compared with RAA, FRAA is more suitable for the HSV SAR with near-vertical diving trajectory. Simulation results verify the effectiveness of the proposed approach.

Although space-time adaptive processing (STAP) is recognized as the optimal clutter suppression way for synthetic aperture radar (SAR) in theory, the deficient of independent and identically distributed range samples in real scenario limits its application. The reduce-dimension STAP methods can decrease the demand for range samples, but the assumption of moving target-free is always unsatisfied. The direct data domain methods only use the data of the range cell under test (RCUT) to avoid the assumption, but they are conducive to interference suppression than clutter suppression and have huge computational burden. Thus, in this letter, a single range data-based STAP method is proposed not only exploring the space-time statistical properties of clutter to suppress it, but also operating solely on the RCUT without recourse to range samples. Theoretical analyses and simulation results verify the effectiveness of the proposed method.

In the above article[1], the corresponding authors should be Yan Huang and Jie Li.

Inverse synthetic aperture radar (ISAR) imaging for maneuvering targets has always been a challenging task due to azimuth time-varying Doppler frequency modulation, especially under moving platform condition. In this case, the common assumption that the image projection plane (IPP) of the radar line-of-sight (LOS) direction is constant during coherent processing interval (CPI) is invalid. To address this issue, a novel ISAR imaging approach for maneuvering targets is proposed by exploiting nonstationary IPP in this article. First, considering time-varying LOS direction, the new geometric and signal models are developed, where 2-D spatial-variant phase error is mainly deduced. After that, a parametric image entropy minimum optimization combined with efficient particle swarm optimization (PSO) is used to obtain optimal motion parameters. In doing so, 2-D spatial-variant phase error terms are compensated accurately to produce well-focused ISAR image. Finally, the effectiveness and superiority of the proposed algorithm are verified by the simulation results and electromagnetic scattering data.

Unknown motions will make the synthetic aperture radar (SAR) images of ground moving targets defocused. The target signal easily exhibits Doppler ambiguity due to the limitation of pulse repetition frequency, which leads to the focusing difficulty of moving targets. To address these issues, a fast approach for SAR imaging of ground moving target with Doppler ambiguity is proposed. In this method, the first-order and quadratic phase are initially estimated by using proposed operations based on 2-D scaled Fourier transform and improved range frequency cross correlation function, respectively. With the estimated parameters, the moving target is then focused in the range–azimuth time domain by the matched filtering. The presented approach is fast, because its realization procedure does not have any parameter-searching step and can be sped up by nonuniform fast Fourier transform. Moreover, the proposed approach can handle Doppler ambiguity (including Doppler center blur and spectrum ambiguity), blind speed sidelobe, and scaled frequency spectrum aliasing. Both spaceborne and airborne real data-processing results are presented to confirm the effectiveness of the proposed method.

As a wideband radar system, the synthetic aperture radar (SAR) usually conflicts with several electromagnetic systems, such as frequency modulation (FM), TV, and other communication systems. These signals, which are radio frequency interference (RFI) for radar systems, severely interfere with SAR systems to generate a high-resolution image. Some previous parametric methods focused on the time-varying RFI model; however, they cannot realize the comparable effectiveness and efficiency against semi-parametric methods. However, previous semiparametric methods did not focus on the time-varying RFI case. Hence, in this letter, a graph Laplacian clustering (GLC) semiparametric algorithm is proposed to suppress RFIs by constructing the Laplacian embedding connections between different pulses of signals. As a result, locally time-varying interferences are clustered in a nonlinear low-dimensional manifold and can be effectively mitigated. The real SAR data with measured RFIs are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

Synthetic aperture radar raw data simulation (SAR-RDS) is beneficial to the SAR system design, signal processing method verification, and radar parameter optimization. Most SAR-RDS methods are based on the flat terrain assumption. However, the fluctuant terrain in real scene will induce severe SAR beam occlusion effect and produce radar shadow, leading to incorrect RDS results. Thus, a dynamic elevation angle interpolation (DEAI) algorithm is proposed for SAR shadow judgment by considering the actual SAR working process. The key of the proposed DEAI algorithm is the 1-D EAI and shadow visualization update, which avoids the problem that the existing methods cannot judge the shadow of partial areas due to the insufficiently refined mesh grid or the mismatch of the judgment model. Moreover, an evaluation framework named as joint image and signal criteria (JISC) is proposed from the perspectives of SAR imaging and signal processing results to objectively evaluate the SAR-RDS results and solve the problem that the existing evaluation methods cannot be compatible with fluctuant terrain. Finally, the numerical experiment verified our theoretical analyses.

As a wideband radar system, a synthetic aperture radar (SAR) may conflict with several electromagnetic systems, such as frequency modulation (FM), TV, and other communication systems. These signals, termed as radio frequency interference (RFI), may severely interfere SAR systems from generating a high-resolution image. Numerous previous researches focused on the RFI suppression problem, among which the semiparametric methods have been verified to have the state-of-the-art performance. However, most of the semiparametric methods are computationally expensive and can hardly be used on wide-swath SAR imaging processing. In this article, an efficient semiparametric algorithm is proposed to suppress RFIs via alternating projections. It has comparable performance as the other methods but significantly improves the computational efficiency a lot. It is able to remove both narrowband and wideband RFIs and can be used directly on the Level-1 SAR data. Finally, multiple real SAR data are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

Synthetic aperture radar (SAR), as a wideband radar system, may be subject to strong interferences with a variety of signals. The narrowband interference (NBI) exemplifies the most typical of its kind, such as the form of radio frequency interference (RFI). With the development of SAR imaging technology, the high-resolution wide-swath (HRWS) imagery technology now reaches its maturity to finally take shape in current SAR systems. To obtain HRWS images, the multichannel SAR (MC-SAR) system has been employed to tackle the contradictory requirements for both high resolution and low pulse repetition frequency (PRF). Previous interference methods focused on single-channel SAR systems and few research works for MC-SAR systems. In this article, we first derive a new interference-mitigation model for HRWS SAR systems and conclude that the low-rank property of the NBI is suitable for MC-SAR systems. Then we employ an image-domain sparse regularization to protect the real echoes of the SAR system and mitigate the NBIs by solving the low-rank recovery problems of NBIs. Also, the MC-SAR system errors are further taken into account as a measure for our method’s practical applicability. Finally, the real SAR data is used to demonstrate the effectiveness of the proposed method.

Unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) is usually sensitive to trajectory deviations that cause severe motion error in the recorded data. Because of the small size of the UAV, it is difficult to carry a high-accuracy inertial navigation system. Therefore, in order to obtain a precise SAR imagery, autofocus algorithms, such as phase gradient autofocus (PGA) method and map-drift (MD) algorithm, were proposed to compensate the motion error based on the received signal, but most of them worked on range-invariant motion error and abundant prominent scatterers. In this letter, an improved MD algorithm is proposed to compensate the range-variant motion error compared to the existed MD algorithm. In this context, in order to solve the outliers caused by homogeneous scenes or absent prominent scatterers, a random sample consensus (RANSAC) algorithm is employed to mitigate the influence resulting from the outliers, realizing robust performance for different cases. Finally, real SAR data are applied to demonstrate the effectiveness of the proposed method.

Synthetic aperture radar (SAR) as a wideband radar system is subject to complicated interferences, such as radio frequency interference or other narrowband interferences (NBIs). In order to suppress the NBI, voluminous literature focused on its signal models and characteristics, such as the sinusoidal model and relatively constant frequencies. However, in practice, the interference environment is commonly complicated. It is hard to model the interferences accurately and mitigate them clearly in an easy way, especially for the time-varying interferences. In this article, a novel graph-based algorithm is proposed to mitigate the time-varying NBIs by using graph theory, which constructs the connections between different azimuth samples of NBIs. As a result, the locally time-varying interferences can be clustered in a nonlinear low-dimensional manifold and effectively removed by the proposed algorithm. In addition, the case of the globally time-varying interference is also analyzed in detail with strict derivations to demonstrate its low-rank property. Furthermore, the matrix factorization scheme is introduced to improve the efficiency of the proposed algorithm, and the closed-form solutions are derived for each iteration. The real SAR data with measured NBIs are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

This paper mainly focuses on synthetic aperture radar (SAR) ground moving target imaging. Although there exists many excellent SAR moving target imaging algorithms, two issues, the maneuverability of the SAR platform and the type of data used for moving target imaging, are not discussed by most of them. Thus, a novel SAR image domain-ground moving target imaging method is proposed to preliminarily handle the aforementioned two issues. The method proposed contains two main steps. The first one is the pre-imaging of the raw data, and the second one is focusing the ground moving target's image data by a proposed one-dimensional parameter traversal approach. Numerical experiments are finally presented to verify the effectiveness of the proposed ground moving target imaging method.

The synthetic aperture radar (SAR) image of ground moving targets will be typical smeared given the range migration (RM) and Doppler frequency migration (DFM). To deal with these issues, a new SAR ground moving target imaging method based on modified second-order keystone transform (MSOKT) and keystone transform (KT) is developed in this paper. Firstly, the time reversing process is utilized to separate the second-order phase. Secondly, the range curvature migration and DFM are removed by MSOKT, and then the second-order phase is estimated. Finally, the moving target is finely focused after eliminating residual RWM by KT. The main contributions of this paper are listed as follows: 1) the proposed method can effectively focus moving targets without residual errors; 2) the effects of Doppler ambiguity and blind speed sidelobe are further handled. The effectiveness of the proposed method is confirmed by the simulation and real data-processing results.

For the interference suppression problem on synthetic aperture radar (SAR) systems, traditional methods have focused on how to remove one kind of interference through nonparametric methods and parametric methods. However, complicated interferences, including both narrowband interferences (NBIs) and wideband interferences (WBIs), severely affect SAR imaging in practical scenarios. Also, the spectra of the complicated interferences can be continuously distributed, which are even harder to mitigate from the received signal. Hence, in this article, we propose a smoothing multiview (SMV) tensor model in range-azimuth-space domain to represent the intrinsically unified characteristics of the NBIs and the WBIs for SAR systems, reserving more azimuth degrees-of-freedom (DOFs) than the previous MV tensor model. The proposed SMV tensor model can enhance the potential low-rank property of the complicated interferences, even though the interferences may be continuously distributed in low-dimensional domains. Moreover, due to the larger scale of the SMV model than those of the traditional models, a complex reweighted tensor factorization (CRTF) algorithm is proposed to factorize the large-scale tensor into the product of two small-scale tensors, achieving both better computational efficiency and better low-rank approximation of complicated interferences. Finally, the measured SAR data with different kinds of simulated complicated interferences are employed to demonstrate the effectiveness and efficiency of the newly designed SMV model and the proposed method compared with the MV model and the complex tensor robust principal component analysis (CT-RPCA) method.

Nowadays, in the complicated electromagnetic environment, the complex interferences, including the narrowband interferences (NBIs) and wideband interferences (WBIs), may severely affect the imaging quality of synthetic aperture radar (SAR) systems. Most traditional methods can only tackle with one kind of isolated interferences, NBIs or WBIs. In this paper, we first strictly derive the low-rank property of both NBIs and WBIs and then employ the robust principal component analysis (RPCA) to simultaneously suppress them. Unlike the traditional methods, the proposed method is capable to tackle with complicated interferences, not only the isolated NBIs or WBIs. The real X-band SAR data is provided to demonstrate the effectiveness of the proposed method.

A non‐adaptive space‐time clutter canceller (NSCC) for multi‐channel (MC) synthetic aperture radar (SAR) was proposed. First, a new three‐part range equation was derived on the basis of the two‐dimensional Taylor series expansion. Then, each part of the model was analysed. By compensating of the high‐order coupling part and the compression of the Doppler extension part of the derived equation, the interlaced signal of a moving target and a clutter patch was easily separated in a space‐time domain. The clutter signal in different pulses only contained a constant phase difference. Using radar parameters, the authors constructed a non‐adaptive clutter canceller that prevented traditional space time adaptive processing (STAP) issues, such as secondary sample support, computational complexity burden, and unknown moving target information. Compared with the representative non‐adaptive method, that is displaced phase centre antenna (DPCA), NSCC is robust to a small degree of parameter error. It can be applied when DPCA condition is not satisfied. The effectiveness of the proposed method was validations through simulation.

In this study, a ground moving target focusing and motion parameter estimation method based on modified second‐order keystone transform (MSOKT) have been proposed for a synthetic aperture radar. Firstly, a cross‐track velocity matching compensation function is derived to remove range walk migration and estimate the cross‐track velocity of the moving target. Secondly, an MSOKT is proposed to remove the range curvature and Doppler frequency migration simultaneously. Lastly, a well‐focused result of the moving target is obtained, and the motion parameters of the moving target are estimated. Compared with the traditional KT‐based method, the proposed method works well in situations where Doppler centre blur and Doppler spectrum ambiguity are present. The computational complexity of the proposed method is considerably lower than that of the traditional optimum method, such as Radon‐Lv's distribution. Simulation and real data processing results validate the effectiveness of the proposed method.