As the world’s first geosynchronous (GEO) orbit synthetic aperture radar (SAR) satellite, LuTan-4 was successfully launched on 13 August 2023. It was developed by the China Academy of Space Technology, with which the authors are affiliated. This study presents a comprehensive review of the recent advancements in GEO SAR technology. The review first begins by summarizing key considerations in GEO SAR system design, including orbital parameters and synthetic aperture time, transmit power and antenna aperture, two-dimensional beam-steering, imaging parameters and non-ideal factors. In terms of GEO SAR signal processing, the article focuses on two fundamental models, i.e., the high-order slant-range model and the coupled space-variant signal model. It also introduces the current GEO SAR imaging algorithms. Furthermore, the study presents an analysis between GEO SAR and low-Earth orbit SAR systems, highlighting the superior capability of GEO SAR for large-scale surface dynamic processes. Finally, the paper outlines the future development directions and potential applications of GEO SAR technology.

Weakly supervised object detection (WSOD) in remote sensing images (RSIs) aims to achieve high-value object classification and localization using only image-level labels, and it has a wide range of applications. However, existing popular WSOD models still encounter two challenges. First, these WSOD models typically select the highest-scoring proposal as the seed instance while ignoring lower-scoring ones, resulting in some less-obvious objects being missed. Second, current models fail to ensure consistency between classification and regression, limiting the upper bound of WSOD performance. To address the first challenge, we propose a feature-guided seed instance mining (FGSIM) strategy to mine reliable seed instances. Specifically, FGSIM first selects multiple high-scoring proposals as seed instances and then leverages a feature similarity measure to mine additional seed instances among lower-scoring proposals. Furthermore, a contrastive loss is introduced to construct a credible similarity threshold for FGSIM by leveraging the consistent feature representations of instances within the same category. To address the second challenge, a task-aligned focal (TAF) loss is proposed to enforce consistency between classification and regression. Specifically, the localization difficulty score and classification difficulty score are used as weights for the regression and classification losses, respectively, thereby promoting their synchronous optimization by minimizing the TAF loss. Additionally, rotated images are incorporated into the baseline to encourage the model to make consistent predictions for objects with arbitrary orientations. Ablation studies validate the effectiveness of FGSIM, TAF loss, and their combination. Comparisons with popular models on two RSI datasets further demonstrate the superiority of our approach.

Electromagnetic vortex waves have received widespread attention in many fields due to their unique physical characteristics. The information dimension provided by vortex electromagnetic waves brings possibilities for future breakthroughs in radar detection and imaging. This article proposes a multi-modal aggregated electromagnetic vortex wave generation method for the first time. Moreover, it conducts vehicle imaging experiments to verify the method’s practicality. The core element of the experiment is to simultaneously generate multiple-mode electromagnetic vortex wave signals with energy accumulation and perform fusion processing. Firstly, multiple orbital angular momentum (OAM) modes are superimposed to generate a mode group, and the initial phase of the modes in the mode group is further controlled to synthesize aggregated electromagnetic vortex waves. Based on the generation of aggregated vortex waves, imaging experiments were conducted using a vehicle-mounted setup. The experimental procedure and multi-modal fusion results were presented. It has been shown that the energy of the main lobe signal of the image target is enhanced by utilizing multi-modal vortex radar information fusion, which can improve the signal-to-noise ratio of the target imaging.

The main aim of the airborne early warning (AEW) system is to search the potential targets in a large surveillance area. The underlying assumption is that the desired target signals only exist in a few range cells for space-time adaptive processing (STAP), i.e., targets (with certain look direction and Doppler) are sparsely distributed in the entire range cells and most of the range cells are target-free. By utilizing the sparsity of the target distribution, we propose a new STAP method by minimizing the l1-norm of the output magnitude. Unlike conventional STAP methods, which exclude the cell under test from the training samples to avoid target self-nulling, our method processes the cell under test (CUT) and the training samples simultaneously without sample selection. Moreover, to achieve robustness against target steering vector mismatch, we constrain the l1-modulus of the response of any steering vector within a rhombus uncertainty set to exceed unity. Additionally, based on a new definition of the l1-norm of a complex-valued vector, the original nonlinear programming problem can be transformed into a linear programming problem. On the other hand, unlike the slide window processor (SWP) whose weights need to be updated for each range cell, the adaptive weight of our method for a block of samples requires no updating. Consequently, the computational complexity of the proposed method is much lower than that of conventional STAP methods. Finally, since the CUT is used to compute the STAP weights, our method can also suppress the discrete interference. The robustness, computational effectiveness and superiority of the proposed STAP method are verified based on simulated data and the MCARM data.

The current emphasis in the advancement of space-based synthetic aperture radar (SAR) is on lightweight payloads under 100kg with resolutions surpassing 1m. This focus is directed towards meeting the launch criteria for multiple satellites on a single rocket and cutting costs. This article discusses the creation and progress of a Ku-band SAR payload for the Taijing-4(03) Satellite, launched on January 23, 2024 accompanied by four other satellites. The SAR payload design was customized to meet the demands of a micro-nano satellite platform, resulting in a lightweight, flat design weighing less than 80kg, seamlessly integrated with the plate-shaped satellite platform. The article also introduces a beam optimization strategy for the phased array SAR antenna, significantly boosting the SAR system’s performance. The SAR payload provides various operating modes like slide-spot, strip, scan 1, scan 2, and others, with a maximum achievable resolution exceeding 1m. Extensive in-orbit testing of the payload produced numerous high-quality SAR images with potential uses in emergency disaster mitigation, safeguarding ecosystems, monitoring forests, managing crops, tracking sea ice, and more.