Detecting ships in synthetic aperture radar (SAR) images is a challenging task due to various factors, such as the diverse distribution of ships and the intricate nature of SAR images. In recent years, deep learning has made excellent progress in the field of SAR interpretation. Models that focus on extracting global semantic information can effectively achieve balanced detection of multiscale SAR targets, but their computational complexity is relatively high. Models that focus on processing local information have redundant calculations and poor robustness, but are prone to mistaking the background information of SAR images for targets. To address the above issues, we propose a real-time SAR ship detector based on global semantic information enhancement and local gradient information mining. The lightweight feature extraction backbone based on linear computing is designed, with the network structure of global information augmentation encoder (GIAE), local gradient information miner (LGIM), and decoder, which can quickly perform feature extraction. GIAE enhances the expression of image content through the long-sequence modeling capability of the state-space model (SSM). LGIM uses gradient modules composed of depthwise separable convolutions to extract local information of image and utilizes directed self-attention (DSA) to mine channel context information. Global-local detector (GLDet) can complete object detection, rotated object detection, and instance segmentation tasks by transforming the detection head. Excellent performance has been achieved on the SAR ship instance segmentation datasets: SAR ship detection dataset (SSDD) and high-resolution SAR image dataset (HRSID), as well as the SAR rotated ship datasets: rotated ship detection dataset (RSDD)-SAR and SSDD+. Meanwhile, GLDet demonstrated excellent generalization performance in large-scale SAR images captured by GF-3 and Terra-SAR satellites.

In this communication, we combine the facet-based bidirectional reflection distribution function (BRDF) and four-path model (FPM) to establish the attribute scattering center (ASC) model for composite scattering simulations. This hybrid method is beneficial for simulating scattering echoes of large composite scenes with high efficiency. The model consists of three parts: the ASC model for the target, the BRDF-ASC model for the rough dielectric background, and the FPM-BRDF-ASC model for the coupling scattering between the target and the background. Two simple composite scenes and two large composite scenes are taken as examples to verify the accuracy and feasibility of the proposed model. The validation results show that the radar cross sections (RCSs) of the proposed hybrid method are consistent with those of the multilevel fast multipole algorithm (MLFMA) with higher simulation efficiency. In addition, the ghosting image caused by the multipath effect can be well displayed in the simulated synthetic aperture radar (SAR) images.

Swarm targets consisting of multiple aircraft, such as unmanned aerial vehicles (UAVs), have become an emerging tool in modern applications due to their agility and vast coverage. Because of the complicated coupling and shading effects among different units, accurate, flexible, and computationally efficient scattering analysis for this novel target remains challenging. To overcome this difficulty, in this work, a so-called plane wave expansion (PWE) algorithm is proposed. Unlike the multilevel fast multipole algorithm (MLFMA), which treats swarm targets as a single entity, PWE deals with individual units separately, allowing for the reuse of data structures for identical targets. The low computational complexity and the effectiveness of the proposed method are achieved by combining three calculation strategies, a) evaluating the coupling effects between two close units using a rigorous fast plane wave expansion technique and ignoring those of well-separated units, b) determining the threshold defining "well-separated" through an adaptive procedure so that the overall accuracy is controllable, and c) quickly estimating the initial solution to accelerate convergence. Extensive numerical simulations, including one with up to 56 electrically large UAVs, are carried out to demonstrate the accuracy, effectiveness, and robustness of the proposed method.

Angular glints are induced essentially by the superposition of scattering responses from multiple scattering centers and usually fluctuate quickly and widely with as the aspect angles for complex targets change. This paper presents angular glint simulation of complex conducting targets using attributed scattering centers. The formulae for linear glint errors (LGEs) are derived based on parametric models of attributed scattering centers and can be applied for real-time LGE simulation. Our analyses and numerical experiments show that the LGE simulation requires more precise scattering center modeling than radar imaging simulation does. To ensure the accuracy of the LGE simulation, a forward approach that uses induced currents on conducting targets is used to precisely extract the scattering centers. The numerical results show that the LGE simulation based on the developed scattering center model has high accuracy. More importantly, the scattering center model developed for sparse aspect angles can be used to simulate LGEs for dense aspect angles, as required for complex targets, which leads to high efficiency in simulating LGEs.

In the traditional miss distance (MD) estimation, Doppler frequencies of radar echoes are used to estimate MD by curve fitting, which is based on the signal model of a single scattering center. Actually, scattering responses of an extended object are contributed by a series of scattering centers. Moreover, the attributes (i.e., amplitudes and locations) of scattering centers of different types change with the aspect angles of the measuring radar. The multiple scattering centers and their different attributes make the characteristics of Doppler frequencies quite complex, and subject the traditional MD estimation method to serious error. To tackle this problem, a parametric scattering center model is built to precisely characterize different types of scattering centers, and the primary characteristics of Doppler frequencies corresponding to various scattering centers shown in time-frequency representation (TFR) are used to estimate MD by image fitting. The performance of this method has been validated, and results show that this method is of high accuracy even in low signal-to-noise ratio (SNR).

Synthetic aperture radar (SAR) images of ships have complex background interference, multi-scale targets, and irregular distribution characteristics. However, existing mainstream SAR ship detection algorithms rely on manually designed hyperparameters. This results in poor robustness, which makes it difficult to effectively balance the detection accuracy and speed. To solve these problems, a novel anchor-free SAR ship detection algorithm based on self-balancing position attention is proposed. First, a lightweight feature extraction backbone (GhostVS-Net) is designed by FOCUS, Ghost, and separable convolution modules to extract feature information. This helps to extract the contour features of ships and suppress unrelated interference, making it more suitable for the scattering characteristics of SAR images. Second, a self-balancing position attention (SBPA) module is designed, which balances the local image features under multiple receptive fields, and aggregates the global position information and spatial context information. The proposed SBPA module considers the characteristics of background, scale, and distribution of SAR images to obtain greatly improved positioning accuracy. Finally, the feature pyramid network (FPN) is applied to fuse the scale context information, and further improve the accuracy of detection. Experimental results on the SSDD and HRSID datasets show that the proposed network bears feature of accurate detection and strong robustness, with the mean average precision (mAP) reaches 99.72% and 95.30%, which reveals that it outperforms all current state-of-the-art algorithms with super high performances.