Forward-looking multi-channel synthetic aperture radar (FLMC-SAR) is an important technical means for achieving forward-looking imaging and enhancing the visibility of the traditional SAR forward blind zone. Its significance extends across various applications, including military reconnaissance, geological exploration, disaster monitoring, and other related fields. However, motion errors, slant range errors, and other factors introduce Doppler errors, making FLMC-SAR imaging and Doppler ambiguity resolving a challenging task. In this paper, leveraging the physical characteristics that the maximum Doppler frequency within the imaging area is provided by targets in the direction of the radar platform’s velocity, we achieve pixel-level Doppler centroid correction. This holds significant implications for Doppler ambiguity resolving, distortion correction, and pixel localization in FLMC-SAR images. Initially, we established the space-time model of FLMC-SAR. Based on this model, we devised a two-step FLMC-SAR imaging processing: time-domain imaging followed by spatial-domain Doppler ambiguity resolving. Within this framework, we analyze the space-time characteristic representation of the maximum Doppler line in the two-dimensional range-Doppler image domain, and the estimation of Doppler error was provided by space-time characteristic. Furthermore, utilizing series inversion, we derived estimations of motion error and slant range error, thereby achieving pixel-level Doppler centroid frequency correction. Extensive simulations and real-data experiments demonstrate that the proposed algorithm is capable of FLMC-SAR imaging for Doppler ambiguity resolving and distortion correction.

In high-resolution and wide-swath synthetic aperture radar (SAR) systems, there are azimuth Doppler ambiguity and ghost ground moving targets (GMTs). Incorporated with an improved equivalent range model (IERM), this article explores a simultaneous scene imaging and GMT indication (GMTI) scheme with multiple beamforming for hypersonic vehicle-borne dive-trajectory high-squint multichannel SAR (HSV-DTHS-MC-SAR). First, the coarse-focusing imageries are recovered. Then, the CTV estimation with the dynamic-eigenspace-based adaptive sum and difference beams (EASDBs) is explored. After that, the SAR scene processing which enjoys “customized” beamforming, minimizes the ghost GMT and reconstructs the ambiguity-free scene image. Moreover, the proposed clutter suppression method generates the beamformer center in the desired GMT direction and reduces the beamformer sidelobe by using quadratic pattern constraints. Finally, the geometry and inverse projections are performed to calibrate the distortion arising from vertical velocity. Compared to the existing methods, the proposed scheme has innovations or improvements in CTV estimation, scene recovery, and cutter suppression. To be specific, it avoids the cumbersome CTV search and thus has lower computation, enjoys more concise and robust removal of ghost GMT in the scene, and frees the steering vector mismatch plus achieves a higher signal-to-clutter-and-noise ratio (SCNR). The extensive simulations confirm the effectiveness of our proposed scheme.

Radar jamming recognition aims to accurately recognize the type of jamming to provide guidance for radar countermeasures. Although previous deep learning-based methods have made promising performance, they mainly rely on convolutional neural network (CNN) based on local processing, which ignore the global information in the time-frequency domain data of the jamming signal and also require much inference time to obtain the final recognition results. In this paper, a novel few-shot jamming recognition network via time-frequency self-attention and global knowledge distillation (JR-TFSAD) is proposed by jointly considering the global information in the time-frequency spectrum of the jamming signal and the real-time performance of the recognition network. A time-frequency self-attention model (TFSA) is proposed to extract the global deep features of radar jamming signals by learning the correlation between two arbitrary points in the time-frequency spectrum of the jamming signal, thus improving the recognition accuracy. Moreover, to effectively reduce the inference time of the method while preserving the recognition accuracy, a global knowledge distillation model (GKD) is further constructed to perform jamming recognition by distilling the global knowledge from the TFSA model. The experimental results on the simulated and measured mixed dataset verify that the proposed method has higher recognition accuracy and shorter inference time compared to the state-of-the-art methods.

Due to the flexible configuration and maneuvering platform, bistatic synthetic aperture radar (SAR) plays an important role in modern remote sensing applications, but the non-ideal track simultaneously introduces model mismatch and spatial-variant phase problems. This paper proposes a sub-aperture parametric polar format algorithm (PFA) for high-resolution bistatic spotlight SAR imaging. First, a bistatic parametric polar format algorithm is proposed to focus on the generally configured bistatic SAR data. A high-precision range model in the bistatic range and ellipsoid parameter angle coordinate space is established to modify the PFA interpolation kernels. To further enhance the azimuth resolution, the PFA sub-images are fused in the image domain based on the coordinate transformation between the unified Cartesian coordinates and local imaging polar coordinates. During the sub-image fusion, in order to ensure the accurate projection and non-aliasing spectrum, we also consider the geometric deformation of the coarse PFA image and analyze the wavenumber support region along with the Nyquist sampling requirement. This novel sub-aperture method, which is theoretically more efficient than the fast back-projection algorithm, alleviates the limitation of full aperture resolution on PFA’s depth of focus. Finally, our method is applied to the general bistatic spotlight SAR data, involving the level-flight airborne transmitter and the dive hypersonic vehicle-borne receiver, and the results of both points and distributed targets demonstrate its effectiveness and efficiency.

Sharing a multiple-input multiple-output (MIMO) radar, the single platform can achieve dual functions of radar and communication (DFRC) within the same frequency spectrum, via the same transmit waveforms. In this paper, a space-time coding scheme is developed for transmit beamforming of DFRC and embedding communication information, without their cross-interference. For transmit beampattern design of DFRC, the shape approximation and integrated power approximation criteria are adopted respectively for waveforms optimization with the constant-envelope constraints of transmit waveforms and the equivalent signal in the communication direction. Based on the space-time coding scheme, the direct constellation mapping (DCM) and phase-rotation constellation mapping (PRCM) methods are proposed to embed information symbols. It turns out that the proposed space-time coding scheme for information constellation mapping can prevent missing information symbols and have better performance in bit-error rate (BER), compared to the existing information-embedding techniques. Moreover, the scheme can reduce the dependence of the communication data rate on radar pulse repetition frequency (PRF). Simulation results are presented to demonstrate the effectiveness of the proposed methods.

Under the special geometric model of bistatic forward-looking synthetic aperture radar (SAR), the 2-D spatial variation problem makes it difficult for traditional algorithms to achieve well-focused imaging, which limits the imaging area. In this letter, from the perspective of range-azimuth decoupling of signal, dechirping processing and keystone transform are used to realize range-variant phase compensation and range cell migrations (RCMs) correction. Based on the range-azimuth decoupled signal, the overlapped subaperture processing can eliminate the azimuth-variant quadratic chirp rate phase in each range cell. Experimental results demonstrate that the proposed method is suitable for the bistatic forward-looking SAR (BFSAR) system to solve the 2-D spatial variation problem and achieve well-focused imaging.

This letter proposes a modified polar format algorithm (PFA) applied to the highly squinted synthetic aperture radar (SAR) onboard an accelerating hypersonic platform. In conventional PFA, the range wavenumber is orthogonally decomposed to obtain the 2-D wavenumbers corresponding to the imaging Cartesian coordinates. This operation, however, is inadequate when facing with more complex motion trajectory. In this letter, the essence of the proposed method is to homogenize the spatially nonuniformly sampled echoes by combining interpolation with the generalized wavenumber definition. The imaging polar coordinate system in the slant plane provides new space-wavenumber Fourier transform pair, and the 1-D interpolation compensates for the range-variant phase errors. The proposed algorithm, thus, enables fast and effective imaging of hypersonic SAR in large squint angles and high maneuverability dive mode. Its performance is discussed and analyzed in this letter, including the resolution and the size of the imaging scene. The effectiveness, superiority, and application value of the proposed algorithm are verified by simulation.

Forward-looking multichannel synthetic aperture radar (FLMC-SAR) is an important tool for modern remote sensing applications, which has the capability to reconstruct the high-resolution image of the front area. However, due to the azimuth-variant characteristics of the motion errors over a long aperture, FLMC-SAR data processing is usually a challenging task, especially when involving the motion compensation (MOCO) coupled with Doppler ambiguity resolving. To accomplish an accurate MOCO for FLMC-SAR, a novel parametric azimuth-variant MOCO approach is proposed in this article. Aiming at the coupling problem of MOCO and Doppler ambiguity resolving over the full aperture, we can decouple them through the subaperture division. As a full synthetic aperture is decomposed into several subapertures, the high-order motion errors of the full aperture can be decomposed into the first-order motion errors of the subaperture. On this basis, the mismatch of the space–time spectrum caused by the motion errors can be solved by spectral estimation, yielding Doppler ambiguity resolving for each subaperture. Meanwhile, the azimuth-variant characteristic of motion errors in FLMC-SAR system is characterized by a parametric angle-dependent quadratic phase error (QPE) model. The motion parameters are estimated by a joint multichannel angle estimation-based signal quadratic decomposition method. Immediately, the MOCO for ambiguous targets with different motion errors can be processed separately to improve the imaging performance. Experimental results based on both simulated and real data demonstrate that the proposed method is suitable for FLMC-SAR system.

Traditional synthetic aperture radar (SAR) imaging is limited to achieve the high-resolution image of the side-looking areas. Nevertheless, equipped with a small size linear array across the trajectory, forward-looking multichannel SAR (FLMC-SAR) is capable of reconstructing the high-resolution image of the front area. In FLMC-SAR imaging framework, the left-right Doppler ambiguity is expected to resolve with beamforming approaches using the multichannel system diversity. However, beamforming-based Doppler ambiguity resolving is sensitive to the array deviation angle, which causes a mismatch between the azimuth angle and Doppler frequency. In this article, we propose an array deviation angle calibration and imagery algorithm for FLMC-SAR. The space-time characteristic of FLMC-SAR is explored and the range-dependent array deviation angle model is established. Following the Doppler beam sharpening imaging, strong targets are selected to derive the mismatch of the space-time characteristic. A maximum likelihood estimation of the array deviation angle is developed to modify the matching between the azimuth angle and Doppler frequency. Therefore, the left-right Doppler ambiguity can be solved correctly, yielding high-resolution FLMC-SAR imagery. Extensive simulation and real data experiments are performed to demonstrate the effectiveness of the proposed method.

Dynamic estimation of spin spacecraft is a challenge and plays a significant role in space situation awareness applications like potential space collision warning. Based on remote sensing technologies of laser and radar sensors, current methods almost adopt a match strategy to estimate the dynamic parameters of a particular target with the long-term measurement collection. These kinds of data-driven methods merely consider the inherent connection between the measured characters and target dynamic patterns, and can hardly be expanded to other spacecraft when the measurement collection is insufficient. Therefore, this article presents a novel approach to interpreting multiple-station inverse synthetic aperture radar (ISAR) images for the dynamic estimation of spin spacecraft. As a unique phenomenon of radar imaging, the imaging plane of ISAR observation not only depends on the change of the relative position between the target and radar, but also changes with the spin of the target. In order to decouple the target dynamic estimation from the determination of the imaging geometry, the angular diversity of multiple-station images is employed. The proposed algorithm deduces an explicit expression of target dynamic parameters under the imaging projection model of the multiple-station observation. By utilizing the chaotic grasshopper optimization algorithm (CGOA), it determines three crucial elements of the target spin motion with a two-step optimization, including instantaneous attitude, rotation shaft and rotation speed. Simulation experiments of a typical spin spacecraft, Tiangong-I (TG-I), illustrate the feasibility of the proposed method under different motion patterns.

As more and more satellites are launched into the space, dynamic estimation of spin satellites has become a critical component of the space situation awareness application. Some explored studies using exterior measurements from different sensors such as optical device and inverse synthetic aperture radar (ISAR) to estimate dynamic parameters of spin satellites. As a single sensor normally provides two-dimensional observation, three-dimensional estimations resulting from these algorithms are strictly related to the prior knowledge of targets characteristics. As a result, it is difficult to expand these methods to other satellites. In order to support the dynamic estimation of most spin satellites, this paper presents a novel dynamic estimation approach which employs synchronized optical-and-radar images. The optical-and-radar fusion strategy has demonstrated its superiority in image analysis field, and breaks down the dynamic estimation of spin satellites into two sub-problems: target attitude estimation and spin parameters estimation. In this work, the proposed algorithm deduces two explicit expressions of target dynamic parameters under the imaging projection model of the joint optical-and-radar observation. Through the particle swarm optimization (PSO), target dynamic parameters are determined in two stages. This paper presents some experiments illustrating the feasibility of the proposed method and subsequent conclusions, which reflect advantages of the joint optical-and-radar observation mode in image interpretation.

This paper proposes a novel approach to interpreting the satellite attitude based on inverse synthetic aperture radar (ISAR) images. In the conventional viewpoint, quadratic and higher order phase terms of ISAR imagery are regarded as negative factors causing the defocusing phenomenon. In this paper, we introduce how to apply quadratic phase coefficients to estimate target attitude from the ISAR imagery. A geometric projection model of ISAR imaging is built according to radar line of sight, and an explicit expression is also derived to connect target attitude parameters and the image defocusing property. With the accommodation of Broyden-Fletcher-Goldfarb-Shanno algorithm, spatial-variant quadratic phase coefficients together with attitude parameters are determined by an image contrast maximization. We also extend the proposed algorithm to multistatic ISAR applications, where the quadratic phase information lying in simultaneous multistatic ISAR images can be mined to enhance the performance of target attitude estimation. Experimental results illustrate the feasibility of the proposed algorithm.

This study proposes a clutter suppression approach and the corresponding ground moving target imaging algorithm for hypersonic vehicle (HSV) borne synthetic aperture radar (SAR) system with multiple‐input–multiple‐output (MIMO) antenna. HSV‐borne radar platforms fly with a high speed, which can lead to severe Doppler ambiguity, and the radar system usually cannot provide enough channel freedom degree for clutter suppression. In this study, an SAR ground moving target indication (GMTI) approach with MIMO antenna is presented for HSV‐borne radar. Compared with the traditional multichannel SAR GMTI methods, the proposed approach can provide more space freedom degree and obtain a wider imaging swath without decreasing pulse repetition frequency. Besides, the improved deramp space‐time adaptive processing method decreases the ambiguity times of the ground clutter and focuses the moving target. The simulation results validate the effectiveness of the proposed method.