Pucheng Li

To achieve multistatic phase synchronization of distributed unmanned aerial vehicle synthetic aperture radar (UAV SAR), the two-way direct wave links are established between every two radar units and the time-varying phase fluctuations in bistatic echoes can be eliminated utilizing the traditional two-way phase synchronization method. However, the vibration environment of the UAV platform causes the degradation of the frequency source stability. Especially at high carrier frequencies, the phase fluctuations doubling to RF terminal break the phase unwrapping condition, and the time-varying π-ambiguity is introduced in phase synchronization error estimation value which causes the SAR image defocused. In addition, to satisfy the general baseline design requirements, there may be direct wave link obstruction between stations, resulting in interruption of direct wave reception and subsequent failure of phase synchronization. Even if the direct wave link remains unobstructed, potential multipath effects can degrade synchronization accuracy. To address the above issues, a reliable distributed UAV SAR high-band phase synchronization approach is proposed, in which a coarse-to-fine two-stage phase synchronization is performed based on two-way direct waves and echoes, respectively. In the coarse synchronization stage, the time-varying π-ambiguity after unwrapping is effectively suppressed using two-way multiplication and local unwrapping techniques, and full-link phase synchronization is achieved utilizing the phase triangle under conditions of partially obstructed direct-wave links. In the fine synchronization stage, the two-way echo of the calibration target is utilized instead of the full scene echo to avoid multipath effects in the direct-wave link, thereby achieving higher phase synchronization accuracy. This approach has been applied in the high-band distributed UAV SAR system, and the real measured data shows the effectiveness of the approach.

Traditional radar imaging methods based on point target models fail to achieve ideal results for extended targets, while simultaneously using multiple models introduces redundancy. This letter first treats point targets and line segments as special cases of rectangular plate targets, unifying the three target types under a single scattering model based on rectangular plates. Subsequently, a two-step orthogonal matching pursuit (OMP) algorithm is proposed to achieve sparsity-driven radar imaging. In the first OMP step, an observation matrix is constructed using the ideal point target model and the observation geometry, enabling coarse estimation of target position parameters in a greedy manner. In the second step, a unified rectangular plate model is employed along with the positional information obtained from the first OMP step to reconstruct the observation matrix. This allows for the simultaneous greedy estimation of both the rectangular plate model parameters and positional parameters, thereby reconstructing the target structure. Finally, the effectiveness of the proposed algorithm is validated through simulated experiments and real anechoic chamber tests.

High-resolution radar range profile (RRP) is crucial for accurate target recognition and scene perception. To get a high-resolution RRP, many methods have been developed, such as multiple signal classification (MUSIC), orthogonal matching pursuit (OMP), and a few deep learning-based approaches. Although they break through the Rayleigh resolution limit determined by radar signal bandwidth, these methods either get limited super-resolution capability or work well just in high signal to noise ratio (SNR) scenarios. To overcome these limitations, in this paper, an interpretable unfolded neural network for super-resolution RRP (DSSR-Net) is proposed by integrating the advantages of both model-guided and data-driven models. Specifically, DSSR-Net is designed based on a sparse representation model with dimension scaling, and then trained on a training dataset. Through dimension scaling, DSSR-Net lifts the radar signal into high-dimensional space to extract subtle features of closely spaced objects and suppress the noise of the high-dimensional features. It improves the super-resolving power of closely spaced objects and lowers the SNR requirement of radar signals compared to existing methods. The superiority of the proposed algorithm for super-resolution RRP reconstruction is verified via experiments with both synthetic and measured data.

Numerous sparse inverse synthetic aperture radar (ISAR) imaging methods based on unfolded neural networks have been developed for high-quality image reconstruction with sparse measurements. However, their training typically requires paired ISAR images and echoes, which are often difficult to obtain. Meanwhile, one property can be observed that for a certain sparse measurement configuration of ISAR, when a target is rotated around its center of mass, only the image of the target undergoes the corresponding rotation after ISAR imaging, while the grating lobes do not follow this rotation and are solely determined by the sparse-sampling pattern. This property is mathematically termed as the equivariant property. Taking advantage of this property, an unfolded neural network for sparse ISAR imaging with self-supervised learning, named SS-ISAR-Net is proposed. It effectively mitigates grating lobes caused by sparse radar echo, allowing highquality training to be achieved using only sparse radar echo data. The superiority of the proposed SS-ISAR-Net, compared to existing methods, is verified through experiments with both synthetic and real-world measurement data.

To achieve high-resolution imaging of distant space target, stepped frequency carrier linear frequency modulation (SFCLFM) radar can be used for prolong observation. In such circumstances, the target’s motion is intricate, the signal-to-noise ratio (SNR) of the echoes is low, and subband phase errors are present. This complexity renders traditional imaging methods susceptible to failure. Therefore, an imaging algorithm of space target based on multiple subbands’ generalized radon Fourier transform (MS-GRFT) is proposed. Initially, the signal echo from space target in SFCLFM radar is modeled, incorporating subband phase errors and noise into the model. Subsequently, motion parameter coarse estimation is achieved using the GRFT of individual subband data, compensating for translational components using peak values in the parameter domain. Simultaneously, obtaining the parameter range of the actual target motion. Next, utilizing parallel GRFT to obtain parameter estimation results for each subband data, detecting partial motion parameters using constant false alarm ratio (CFAR) technology after non-coherent accumulation across multiple subbands. Sharpness is employed as the objective function to estimate subband phase errors, and analytical solutions are then derived. Subsequently, error compensation is applied to the GRFT results of multiple subbands, and wideband synthesis is performed in the parameter domain. Finally, CFAR is employed for parameter detection, and point-wise motion compensation is applied to achieve high-resolution range-instantaneous Doppler (RID) images of space targets. Computer simulations and real experimental results validate the effectiveness of the proposed algorithm.

Stepped-frequency chirp radar achieves range highresolution through wideband synthesis, yet it harbors systemic errors. These errors coupled with the motion errors of highspeed space target, render error calibration more challenging and compromise the quality of imaging. To tackle this problem, this paper proposes the coupled error self-calibration method for highspeed space target imaging in stepped-frequency based on minimum entropy. Firstly, a parameterized model of echo signals incorporating complex coupled errors is established. This model not only takes into account the errors introduced by the amplitude-phase response characteristics of the stepped-frequency radar system, but also considers errors arising from the motion of high-speed targets. Then, analytical relationships between entropy and errors after range pulse compression of sub-band data, after two-dimensional imaging of sub-band data, and after high-resolution synthesis of all sub-band images are constructed. This stepwise processing strategy decomposed complex errors into three distinct components. Subsequently, employing an adaptive matrix estimation method to separately estimate and calibrate the three decoupled error components ensures a gradual improvement in imaging quality. Finally, the effectiveness of the proposed method is verified through computer simulation and a real experiment.

The distributed coherent aperture radar (DCAR) utilizes full coherent processing (FCP). Compared to single radar unit observations, N radar units can achieve an N ³ times increase in signal-to-noise ratio (SNR), providing an advantage in observing distant targets. However, its stringent requirements for time and phase of multiple radar units make coherent parameters (CPs) estimation the crucial aspect of FCP. The paper introduces a satellite data-driven CPs estimation method via hierarchical and hybrid generalized Radon-Fourier transform (HHGRFT). Firstly, the FCP procedure is outlined, and the signal model with CPs is established. Secondly, the principles for selecting satellites and observation time in the experimental setup are induced, along with an analysis of the SNR variations during processing. Furthermore, through a hierarchical processing approach using the generalized Radon-Fourier transform (GRFT), inter-pulse coherence processing, inter-unit-radar coherence processing, and inter-sub-aperture coherent or non-coherent processing are sequentially conducted. The utilization of generalized sharpness and gradient descent method facilitated CPs estimation, subsequently enhancing the SNR post-coherence processing. Finally, the proposed method has been validated through simulation and successfully applied to a real DCAR system.

Most research on sparsity-driven synthetic aperture radar (SAR) imaging has been carried out in $\ell _{1}$ -norm regularization and considers that the SAR image contains only targets and noise, which ignores the clutter and seriously degrades classical algorithms. To address this problem, we propose an integrated detection and imaging algorithm for radar sparse targets with constant false alarm rate (CFAR) regularization by alternating direction method of multipliers (ADMM), called CFAR-ADMM, and we further introduce total variation (TV) regularization and propose the more robust CFAR-TV-ADMM. First, a more complete echo signal model, which considers targets, the clutter, and the noise simultaneously, is established. Then, inspired by the CFAR detection, a novel regularization with sparse target awareness is proposed. The proposed regularization can obtain the statistical characteristics of clutter and noise region by region, and distinguish whether the current cell contains the target effectively and accurately. Benefiting from this novel regularization, CFAR-ADMM and TV-CFAR-ADMM can not only realize the sparse imaging but also detect sparse targets simultaneously, which can reduce the propagation error caused by cascading processing and improve the solution accuracy. Finally, the proposed algorithm is verified by simulation data results, phase transition analysis, and real data experiments.