Detecting power lines accurately remains a challenging task for low-altitude aircraft due to their low radiation and complex background, making them one of the most perilous impediments for such aircraft. To overcome this issue, in this letter, a knowledge-guided rotated network (KRNet) is proposed for power line detection in complex infrared scenes. The proposed method consists of the following main three steps. First, we develop a linear-distribution perception module (LDPM), which enhances feature representation by extracting and exploiting important linear and spatial distributional prior knowledge. Second, to more accurately locate the power line, we design a novel loss function rotation-aware complete intersection-over-union (RACIoU), which utilizes the prior knowledge of power line non-directionality to guide model learning. Finally, using this prior knowledge of the power line, we obtain good detection results and verify the effectiveness and advantages of the proposed method using the power line rotation detection dataset (PRDD) that we built. Our method outperforms other state-of-the-art benchmarks for power line detection.

With the increase of spaceborne synthetic aperture radar (SAR) platforms, numerous SAR images are available for ship detection applications. Traditional deep learning-based detection methods struggle with the distributional disparities in SAR images acquired from different platforms, arising from differences in radar characteristics and data acquisition conditions. Existing approaches employ domain adaptation (DA) techniques to align domain distribution and thus mitigate distribution divergence. However, due to the inherent specificity of SAR images, i.e., ship targets and background environments exhibit highly visual similarity, these methods may inadvertently destroy the discriminative representations of ship targets, resulting in poor cross-domain detection performance. To alleviate this dilemma, we propose a source-assisted hierarchical semantic calibration (SHSC) framework for ship detection across different satellite SAR images. First, a source-assisted semantic calibration module (SSCM) is designed, which performs multilevel semantic calibration by constructing a source-assisted (SA) detector as a guiding mechanism to preserve the discriminative semantics of ship targets. Then, the uncertainty-aware guided feature-level alignment module (UG-FAM) and instance-level alignment module (UG-IAM) are developed, which effectively capture the crucial ship target attributes by emphasizing the learning of those discriminative samples. Extensive experiments are conducted on the datasets obtained from the TerraSAR, Gaofen-3, Sentinel-1, and RadarSat-2 satellites. The experimental results show that the proposed SHSC method outperforms the other UDA approach by an average of more than 3% on AP in ship target detection accuracy across different satellite SAR images.

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.

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.

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.

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.

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.

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.