With the growing demand for High-Resolution and Wide-Swath (HRWS) imaging in Synthetic Aperture Radar (SAR) missions, digital beamforming (DBF) in the range dimension has become a key enabling technology. However, under wide-swath imaging conditions, existing DBF algorithms—such as SCORE, single/multi-delay DBF, multi-frequency DBF, and scaling-function DBF—often suffer from performance degradation near the swath edges, including target displacement, mainlobe broadening, and signal-to-noise ratio (SNR) reduction, which decrease imaging quality. This paper proposes an enhanced DBF algorithm based on multi-delay and scaling compensation. The proposed method introduces multi-delay correction, scaling compensation, and a data fusion strategy to effectively suppress mainlobe broadening while enhancing image SNR, all with a relatively low computational complexity ofO(N×Nr×log(Nr)). Extensive validation using both satellite simulation data and airborne measured data demonstrates the superiority of the proposed method: compared to existing algorithms, it achieves up to 6 dB SNR improvement and 0.4098 m mainlobe suppression in the near range, and up to 2 dB SNR improvement and 0.0819 m suppression in the far range. This method offers an important technical reference for efficient beam shaping and robust imaging in future wide-swath SAR systems.

As synthetic aperture radar (SAR) technology continues to evolve with a focus on miniaturization, reduced weight, and lower costs, its range of applications has broadened to encompass lightweight and slow-moving platforms, such as small autonomous aerial vehicles (AAVs) and airships. However, this advancement introduces a new challenge for SAR systems in the form of excessively long synthetic aperture time (LSAT). LSAT-SAR faces significant challenges caused by its longer integration time (10–1000 times more than conventional airborne SAR’s synthetic aperture time), including more stringent error tolerance of navigation system and more demanding trajectory control requirements. These issues often result in severe 2-D defocusing. Existing high-precision navigation systems and traditional autofocus algorithm, though adequate for conventional airborne SAR, often fail to meet the stringent requirements of LSAT-SAR, especially in low-contrast scenarios, necessitating more efficient and more robust compensation methods. To address these challenges, we propose a 2-D autofocus algorithm for LSAT-SAR using deep neural networks (DNNs). The proposed approach treats the 2-D focusing problem as a series of coupled 1-D curve estimation tasks, employing a weighted entropy loss function. The solution is optimized in an unsupervised manner using a DNN. Finally, the additional refinement is performed through a specialized fine-tuning correction module. Experiments on real LSAT-SAR images with synthetic aperture times of 150–270 s show that the proposed method substantially exceeds the performance of traditional approaches in low-contrast scenarios. It demonstrates excellent robustness, offering valuable insights and technical guidance for LSAT-SAR autofocusing.

With synthetic aperture radar (SAR) systems being developed with miniaturization, lightweight, low-cost, high-resolution, and real-time capabilities, the scope of their application has expanded to some lightweight platforms that cannot carry a high-precision position and orientation system (POS) and whose trajectories are susceptible to interference. This often results in residual range cell migration (RCM) exceeding several or even dozens of range resolution cells, which notably degrades the performance of traditional autofocus algorithms and seriously affects the quality of real-time imaging. Considering this, we propose a real-time residual RCM correction scheme based on the autocorrelated normalized cross-power spectrum (ANCPS). First, the range-compressed image is segmented into blocks based on the azimuth and range. The ANCPS is then used to estimate the optimal residual RCM for each subblock. Second, the optimal segment is selected from the residual RCM curve fragments estimated by various subblocks. Finally, the residual RCM fragments are spliced and filtered to correct the complete data. When the algorithm is deployed on a GPU platform, it only takes 1.39 s to process an$8\times 40$K high-resolution original SAR image, and the calculation time is reduced by 94.6% compared with only using a CPU for calculation. The experimental results demonstrate that the proposed residual RCM correction scheme has good convergence, can achieve subpixel residual RCM compensation without iteration and interpolation, has a simple calculation process, is efficient, and has strong parallelism. It is thus suitable for deployment in GPUs and is conducive to realizing real-time SAR autofocus at a low cost and with a high resolution.

Point clouds from real-world scenarios inevitably contain complex noise, significantly impairing the accuracy of downstream tasks. To tackle this challenge, cascading encoder-decoder architecture has become a conventional technical route to iterative denoise. However, circularly feeding the output of denoiser as its input again involves the re-extraction of underlying surface, leading to unstable denoising process and over-smoothed geometric details. To address these issues, we propose a novel denoising paradigm dubbed PD-Refiner that employs a single encoder to model the underlying surface. Then, we leverage several lightweight hierarchical Underlying Surface Inheritance Refiners (USIRs) to inherit and strengthen it, thereby avoiding the re-extraction from the intermediate point cloud. Furthermore, we design adaptive edge-aware supervision to improve the edge awareness of the USIRs, allowing for the adjustment of the denoising preferences from global structure to local details. The results demonstrate that our method not only achieves state-of-the-art performance in terms of denoising stability and efficacy, but also enhances edge clarity and point cloud uniformity.

Special attention has been devoted to joint wireless communication and radar sensing concepts over the past decades. Considerable confusion arose, however, with regard to waveform designs. In this article, various joint wireless communication and radar sensing waveform schemes are reviewed and classified into two categories. Disadvantages and limitations of each category are clarified theoretically. It is shown that waveforms designed merely in conventional time and frequency domains cannot fulfill the conflict requirements posed by radar and wireless communication. As a solution to this fundamental challenge, the multidimensional waveform concepts, along with a generalized correlation model, are proposed for joint wireless communication and radar sensing. To showcase our current research advances in this emerging area, several concrete waveforms designed in multiple dimensions are introduced. Theoretical analysis is validated by simulations and outfield demonstration experiments. It is shown that the suggested waveforms can simultaneously achieve superior detection and communication performances with shared spectrum resources.

Circular synthetic aperture radar (CSAR) offers multi-angle scattering for strong directional targets, benefiting urban surveying. However, the CSAR three-dimensional (3D) imaging of buildings is difficult. The reference height mismatch problem during imaging can lead to a defocused target, and a significant layover phenomenon exists in urban environments. To solve these problems and realize the CSAR 3D imaging of buildings, this study adopts the airborne circular flight array synthetic aperture radar (CFASAR) system for data acquisition and proposes a 3D processing method for airborne CFASAR based on layered phase compensation in the wavenumber domain. CFASAR has advantages over single-baseline and multi-baseline CSAR, as 3D imaging is independent of target azimuth scattering consistency and reduces flight experiment complexity. 3D imaging of buildings consists of two key steps: layered focusing based on phase compensation in the wavenumber domain and super-resolution imaging, which solves the defocusing problem and contributes to high-dimensional resolution. Compared with the traditional layered back projection (BP) imaging, the layered focusing method based on phase compensation in the wavenumber domain has a lower computational complexity. Layered focusing combined with super-resolution processing effectively suppresses conical sidelobes, addresses layover issues, and establishes an accurate 3D scattering model. The proposed method was validated using X-band airborne CFASAR data obtained in Rizhao, Shandong Province, China, in 2021. The experimental results indicate that the proposed method has low time complexity, and it can effectively suppress the conical sidelobe of CSAR and realize high-quality 3D reconstruction of buildings.

Array interferometric synthetic aperture radar (Array InSAR) has a 3-D resolution capability and solves the layover problem in interferometric SAR (InSAR) by arranging multiple antennas in the cross-orbit direction. Airborne Array InSAR point clouds are obtained from two scans for complete building information in urban areas, resulting in very low overlapping point cloud. The existing methods are difficult to extract the identical features for the registration of Array InSAR point clouds. To this end, a robust registration approach Array InSAR point clouds in urban areas is proposed in this study. The main contribution of this article is raising the theoretically optimal transformation for achieving point cloud registration, considering the constraint from parallel facades of a certain building. Point density estimation is adopted to retain building facade points for initial registration. The facade pairs of a specific building are then matched and divided into two categories by judging whether one contains the concave–convex features or not, for performing rotation rectification and fine shift fixation, respectively. Experimental results of both simulated and real data validate the feasibility and reliability of our approach. For the simulated data, the results reach an average rotation error of about 0.01° and an average translation error of less than 0.8 m. For the real data, two evaluation criteria are designed for the lack of reference data. The results reach an average of 0.4° of the defined angle difference and less 0.8-m distance difference from the source facades center to the normal extension of the target facades.

Synthetic aperture radar (SAR) systems have extensively contributed to diverse scientific applications in the recent past decades. For the next generation of top-level SAR systems, the multimodal operation of wide swath imaging, sliding spotlight, and highly sensitive ground moving target indication (GMTI) is becoming increasingly crucial. Following this concept, in this article, a novel airborne multiple-input–multiple-output (MIMO) SAR system, along with the space–time coding (STC) waveform scheme and the phased array, is developed to realize the multimodal operation. In particular, the airborne MIMO SAR system consists of two transmitters and four receivers. The signals of the multiple modes, which overlap in the frequency domain, are interpulse modulated and transmitted by different subarrays concomitantly. The superposed echoes are separated by range-Doppler filtering. Based on the separated echoes, we can obtain eight equivalent transceiver channels and, as a result, enhance the signal-to-noise ratio (SNR) for airborne multimodal operation. In the future, the STC waveform scheme and flight experiments of this novel airborne MIMO SAR system will be further investigated to reduce the pulse repeat frequency (PRF) for spaceborne multimodal operation.

Multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) has been intensely investigated in recent years, due to its potential to achieve new system concepts such as high-resolution wide-swath imaging and multimodal operation. Among the proposed orthogonal waveforms, inter-pulse phase modulation outperforms by the immunity to the impact of Doppler shift arising from the movement of the platform, thus no compensation of the Doppler effect is needed before SAR imaging. However, the demodulation procedure requires oversampling in Doppler domain, which poses strict restriction on the pulse repetition frequency (PRF) and seriously limits its applications. In this paper, a novel azimuth discrete periodic phase coding scheme and the corresponding demodulation method is proposed. By using this method, the undersampled echoes could be separated from each other after multichannel reconstruction, thus feasible for high-resolution wide-swath imaging. Theoretical derivations and simulation results validate the effectiveness of the proposed coding scheme.

It has become a field of intensive research to exploit SAR tomography to reconstruct a 3-D model of the buildings. However, in multiple-input multiple-output (MIMO) SAR tomography, the double-bounce reflections of the building corner will cause symmetric virtual scatterers, affecting both the scattering coefficient and structure of the 3-D model. To solve this problem, the building corner reflection is discussed and compressive sensing (CS)-based corner reflection suppression (CSCRS) is proposed. In the final part of this letter, the effectiveness of the proposed method is validated using array InSAR data. It is found that the proposed method leads to considerable improvements with regard to suppression ratio and reconstruction accuracy.

Special attention has been devoted to joint wireless communication and radar sensing systems in recent years. However, since wireless communication and radar have conflict requirements in terms of waveforms, transceiver developments and signal processing algorithms, realization of this system concept is still a great challenge. In this paper, we introduce airborne multiple-input multiple-output (MIMO) radar, along with the space time coding (STC) waveform scheme, for the implementation of joint wireless communication and synthetic aperture radar (SAR) imaging. The proposed system, which simultaneously transmits mutually orthogonal waveforms with reconfigurable channels, can acquire adequate degrees of freedom. Thereby, it becomes a feasible method to perform both data transmission and SAR imaging at the same time without intra-modal interference. Theoretical analysis is validated by experimental flight results. Through our analysis, we aim to open up a new perspective of using MIMO radar to realize joint wireless communication and SAR imaging.

Airborne multiple-input multiple-output array synthetic aperture radar (MIMO array SAR) can be used to directly obtain the three-dimensional (3-D) imagery of the illuminated region with a single track. Different sparse reconstruction algorithms within the framework of compressive sensing (CS) have been conceived to reconstruct the cross-track signal because of its inherent spatial sparsity. However, the computational complexity and the number of two-dimensional (2-D) SAR images needed for sparse reconstruction of the existing algorithms are usually high. To overcome this problem, the same range line cells based 2-D real-valued CS is proposed in this paper. In this new algorithm, the original signal model is transformed from the complex domain to the real domain by means of unitary transformation. Finally, airborne MIMO array SAR real experimental results are illustrated to validate the prominent advantages of the proposed method.

Recent advances in very high-resolution tomographic synthetic aperture radar (SAR) inversion using multiple data stacks from different viewing angles enable us to reconstruct the reflectivity function along the elevation direction by means of spectral analysis for every azimuth-range pixel. They can be potentially used for facade reconstruction and deformation monitoring in an urban area. When SAR tomographic processing is used to form 3-D point clouds, some points that we simply omitted in the traditional 3-D reconstruction are difficult to understand. It comes to our attention that this special detail which seems violated to a common practice is due to the mechanism of multipath (MP) scattering. Dominated by SAR tomography process, MP scattering reveals many interesting phenomena in 3-D point clouds. In this paper, a theoretical model of the MP scattering of the structure of dihedral corner which often appears in the urban area is given. There are two groups of points distributed symmetrically and compactly around the base angle of the building. The positions of these two groups of the points can be predicted by our model. Corresponding experiments are carried out to show the validity. The position is relative to the height of the building which indicates a direct utilization of our model to estimate the height of the building directly without 3-D reconstruction.

Special attention has been devoted to joint wireless communication and radar sensing systems in recent years. However, since communication and radar have conflict requirements in terms of waveforms, transceiver developments, and signal processing algorithms, realization of this system concept is still a great challenge. In this paper, we introduce an airborne multi-input multi-output (MIMO) radar, along with the modified orthogonal frequency-division multiplexing (OFDM) and space-time coding (STC) waveform schemes, for the implementation of the joint wireless communication and synthetic aperture radar (SAR) imaging. The proposed system, which simultaneously transmits multidimensional waveforms with reconfigurable channels, can acquire adequate degrees of freedom. Thereby, it becomes a feasible method to perform both data transmission and high-resolution SAR imaging at the same time without intramodal interference. Theoretical analysis is validated by laboratory and flight experiments. Through our analysis, we aim to open up a new perspective of using MIMO radar to realize joint wireless communication and SAR imaging.

Multiple-input multiple-output (MIMO) array synthetic aperture radar (SAR) with array antennas distributed along the cross-track direction can obtain 3-D scene information of the surveillance region. However, the cross-track resolution is unacceptable due to the length limitation of the MIMO antenna array. The superresolution algorithms within the framework of compressive sensing (CS) have been introduced to recover the cross-track signal because of its inherent spatial sparsity. The existing sparse recovery algorithms for 3-D SAR are attempted to find the sparse solution in the complex domain directly, which requires a very high computational complexity. To overcome this problem, a new fast 3-D imaging algorithm based on real-valued sparse representation is proposed in this paper. In this new algorithm, unitary transformation can be employed to transform the sparse signal recovery model of uniform/nonuniform MIMO array SAR from the complex domain to the real domain. Thus, a real-valued reweighted 12,1-norm minimization model is established. In addition, a modification of the fast iterative shrinkage-thresholding algorithm (FISTA) is used to reconstruct the 3-D image for further improving the computational efficiency. Moreover, the theoretical analysis of computational complexity of the proposed algorithm is derived when compared with an existing complex domain algorithm. Finally, numerical simulations and MIMO array SAR real experimental results are illustrated to validate that the proposed algorithm can reduce the computational complexity significantly in terms of CPU time while still maintaining the inherent advantages of superresolution and robustness against the noise.

Synthetic aperture radar (SAR) systems in forthcoming surveillance and reconnaissance tasks have to meet increasingly severe demands. The next generation of toplevel SAR systems will comprise high resolution wide swath (HRWS) imaging capability, highly sensitive ground moving target indication (GMTI) and a multitude of sophisticated operational modes. Multi-input multi-output (MIMO) radar systems, which can acquire more degrees of freedom, offer new potential solutions to future missions. In this paper, we introduce a novel airborne MIMO-SAR system built in IECAS. The proposed system contains a reconfigurable phased array, two transmit channels, and four receive ones. Thereby, it can realize 0.3m resolution for 30km swath, multimodal operation of wide swath imaging, spotlighting, ground-moving target indication (GMTI) et al. The system design and the orthogonal waveform scheme are detailed, along with the flight experimental results.

Attention has been devoted to multi-input-multi-output (MIMO) synthetic aperture radar (SAR) systems in recent years. The applications of MIMO-SAR systems that involve high-resolution wide-swath remote sensing, 3-D imaging, and multi-baseline interferometry are seriously limited by the ambiguous energy introduced by the imperfect orthogonal waveforms. The ambiguous energy degrades SAR image severely, because it can be accumulated from closely spaced scatters. In order to suppress the ambiguous energy, the space-time coding (STC) scheme using the Alamouti technique has been proposed recently. However, this scheme is based on the assumption that the channel responses are constant within the consecutive transmissions. For the time-variant channel responses caused by motion errors or other factors, there will be residual ambiguous energy and “ghost targets.” In this letter, a novel STC scheme suitable for MIMO-SAR systems is proposed. The performance of the proposed scheme is immune to the time-variant channel responses. Moreover, it can get more spatial diversities to enhance the MIMO SAR imaging applications. The proposed scheme is validated by numerical simulation results.

Attention has been devoted to multi-input multioutput (MIMO) synthetic aperture radar (SAR) systems in recent years. The applications of MIMO-SAR systems which involve high-resolution wide-swath remote sensing, 3-D imaging, and multibaseline interferometry are seriously limited to the orthogonal waveforms. This restriction is mainly caused by the imperfect orthogonal waveform-introduced ambiguous energy. For a single point target, the impact of the ambiguous energy can be neglected. However, when it comes to the spatially distributed scattering scenarios, the ambiguous energy from closely spaced scatterers, which would be accumulated, degrades the SAR image seriously. In order to suppress the ambiguous energy, a novel energy cancellation scheme using the Sequence CLEAN technique in the range direction is proposed in this letter. By employing the proposed novel scheme on the raw data of MIMO-SAR systems, the ambiguous energy from both point and distributed targets can be suppressed. The proposed scheme is validated by simulations and practical experiments.

Attention has been devoted to multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) systems in recent years. The applications of MIMO SAR systems, which involve high-resolution wide-swath remote sensing, 3-D imaging, and multibaseline interferometry, are seriously limited by the available sets of orthogonal waveforms. Although orthogonal frequency-division multiplexing (OFDM) chirp waveforms are proposed to avoid intrapulse interferences, this waveform scheme has not been investigated for practical implementation. In this paper, challenges in implementing the OFDM chirp waveforms on practical systems are analyzed and solved. First, the small extra carrier frequency between the mutually orthogonal waveforms, which renders the OFDM chirp waveforms not strictly on common spectral support, is avoided by improving the modulation of the OFDM chirp waveform. Second, the tedious demodulation, which is realized by circular-shift addition in the time domain and subcarrier extraction in the frequency domain, is improved. Third, the radar systematic error and the Doppler shift, which introduce bandwidth leakage and degrade the waveform orthogonality significantly, are compensated. Finally, taking all these challenges into consideration, a novel signal processing algorithm along with a MIMO SAR system model is proposed. Theoretical analysis is validated by simulations and systematic calibration measurements based on a C-band system.

A multi-frequency polarimetric SAR named “IECAS-SAR” has been developed in Institute of Electronics, Chinese Academy of Sciences (IECAS). It is composed of four subsystems, namely, P-, L-, C- and X-band SAR sensors. It would play the role of a powerful remote sensing instrument and support scientific researches on the mechanism of scattering. The first-stage test flights have been flown, validating the SAR sensors and getting valuable results. An overview of this system and processing results are presented in the paper.

This paper designs a multi-input multi-output (MIMO) synthetic aperture radar (SAR) system using digital implemented orthogonal frequency division multiplexing (OFDM) waveforms, which can not only avoid ambiguous energy caused by the nonideal orthogonality of current-taken MIMO SAR waveforms, but also reduce the pulse repetition frequency (PRF) by a factor of 2Nl-1, where l N denotes the number of subapertures. One linearly frequency modulated (LFM) signal sampled in frequency domain is modulated into the odd subcarrier frequencies of OFDM waveforms, and another as the weights of even subcarrier frequencies. In order to avoid aliasing in time domain caused by extracting the weights of subcarrier frequencies, the sliding discrete Fourier transform (SDFT) is used for demodulation. The procedure of MIMO-OFDM SAR imaging is detailed. Theoretical analysis and simulation results illustrate the feasibility of this system.