Robust perception for safety-critical Internet of Things (IoT) applications in dense jamming environments demands advanced radar sensing capabilities with enhanced interference mitigation and array-beampattern optimization. When the target direction overlaps with the jamming region, both a narrower mainlobe and a deeper sidelobe are desirable but hard to meet simultaneously since they both consume array degrees of freedom. This paper investigates the application of Movable Antenna Array (MAA) in aiding radar beampattern synthesis under dense jamming scenarios. Using the extra spatial degrees of freedom provided by MAA, this work jointly optimizes both weighting vectors and antenna element positions to achieve superior beampattern without adding array element number. The integrated sidelobe level is chosen as the optimization objective with practical constraints, which leads to a highly non-convex optimization. To address this, we propose an Alternating Optimization-based Sequential Approximation (AOSA) algorithm. In each iteration, the weighting vector subproblem is solved through convex approximations of the primary nonconvex constraints, while the antenna position vector subproblem is tackled indirectly with a specifically derived proposition. Simulation results verify that the joint optimization framework effectively improves target separability and jamming rejection in complex electromagnetic environments, demonstrating its promising potential for advancing radar detection capabilities.

The range-dependent phases induced by Frequency Diverse Array (FDA)-Multiple-Input Multiple-Output (MIMO) radar provide additional degrees of freedom for range related scenarios. This paper investigates the joint transmit waveform and frequency increment design of FDA-MIMO radar to enhance target detection in multipath environments. Specifically, we establish a generalized FDA-MIMO signal model for multipath reception and formulate a Signal-to-Interference-plus-Noise Ratio (SINR) maximization problem over the transmit waveform and frequency increment, while constraining the waveform modulus and increment upper-bound. Leveraging on an alternating optimization framework, we update the waveform via power-method-like implementation and the frequency increment via iterating an analytical expression based on a Majorization-Minimization (MM) scheme. Simulation results indicate that the proposed method achieves a gain up to 14dB over conventional MIMO, demonstrating superior energy focusing effect at target location.

As a novel configuration of along-track Multistatic SAR (Multi-SAR), the high frame rate Along-Track Swarm SAR (ATS-SAR) has garnered significant attention in recent years due to its exceptional efficiency in reducing data acquisition time. Motivated by its potential for high-resolution imaging of moving targets, this paper investigates the application of ATS-SAR in moving target imaging. However, high frame rate ATS-SAR-based moving target imaging confronts two critical challenges: time-space coupling and partial data loss in moving target echoes. To address these challenges, we first conduct a comprehensive analysis and theoretical derivation of the moving target echo model under the high frame rate ATS-SAR configuration. Subsequently, we propose an innovative motion parameter estimation algorithm that exploits unique echo characteristics to achieve high-performance imaging. Furthermore, we introduce the highresolution, high frame rate ATS-SAR Sub-Aperture Collaborative Imaging algorithm for Moving Targets (MT-SACIm-ATS). Extensive simulations and a real measured experiment validate the effectiveness of the MT-SACIm-ATS algorithm, demonstrating imaging performance that closely approximates reference imaging results. Comparative analysis with several state-of-the-art algorithms further highlights the superiority of the proposed approach in terms of resolution and robustness.

In order to adapt to various detection environments on the ground, airborne synthetic aperture radar (SAR) as a remote sensing platform usually arcs along nonlinear trajectories, and the large accumulation angle in the circling process also improves the imaging effect. However, the arc motion demands rigorous control of the flying platform and precise measurement of the motion. In some cases, there is a significant discrepancy between the track recorded by the flight platform and the actual track, which not only affects the imaging effect but also interferes with the positioning and navigation of the platform. This article presents a new method of arc-line SAR positioning and imaging based on a closed-loop structure. The echo history extracted from a 1-D range profile is corrected using an echo-history correction factor (EHCF), which reduces the self-positioning error of the platform caused by the motion measurement device. This allows for accurate positioning of the flying platform and the acquisition of imaging results with superior focusing performance. The effectiveness and stability of the proposed method are proven by simulation and experimental results.

Due to its rapid data acquisition capabilities, the Along-Track Swarm SAR (ATS-SAR) system offers a distinct advantage in simplifying the motion states of moving target. This paper addresses challenges encountered in ATS-SAR moving target imaging, specifically focusing on issues related to partial aperture data loss and the spatiotemporal non-equivalence of the ATS-SAR moving target echo. A novel ATS-SAR imaging method for moving target is introduced. Initially, the ATS-SAR moving target echo model is established. Subsequently, the proposed algorithm designs a phase compensation function that contributes to the accurate reconstruction of the complete echo for ATS-SAR moving target, resulting in outstanding imaging performance.

Verifying the neighborhood consensus to remove false correspondence is a popular idea in feature matching. However, traditional neighborhood consensus only considers spatial neighborhoods, which is not robust in challenging remote sensing tasks. This paper extends the traditional neighborhood consensus for improving robustness to the two key issues — significant geometric transformation and repetitive patterns. First, we introduce a novel matching neighborhood that extends the one-to-one correspondence in traditional neighborhood consensus to one-to-multiple structure to address the repetitive patterns, where one-to-multiple means that multiple matching candidates are preserved in calculating descriptor similarity. Second, the traditional spatial neighborhood is also extended using affine correspondence, which can adaptively address the significant geometric transformations without multi-scale processing. On the two bases, we construct a novelextended neighborhoodby combining theextended spatial neighborhoodwith thematching neighborhood. And consequently, the false feature correspondences are filtered by measuring the consensus between the extended neighborhoods. Numerous experiments demonstrate that the proposed method is state-of-the-art in comparison with recent learning and traditional methods, especially for the UAV localization task. We also show that the proposed method is robust to the basic settings, such as the the pre-filtering threshold and the type of local features.

The ambiguity function (AF) is a crucial tool in characterizing the range-angle response of a multiple-input multiple-output (MIMO) radar system, which is intricately influenced by the transmit waveforms, receiving filters and also antenna configurations. Notably, the role of antenna configurations is less explored compared to the well-studied areas of waveforms and filters. In this article, we incorporate antenna positions as an additional design parameter alongside waveforms and filters to optimize the AF in a specific range-angle bin. We employ the mainlobe-to-integrated-sidelobe-level-ratio (MISLR) as a quantitative metric to assess the performance. The resulting optimization problem is inherently nonconvex, encompassing binary and unimodular constraints. To address this challenge, we reformulate the problem, enabling an alternating optimization approach for antenna positions and waveforms. Each iteration involves solving a sequence of quadratic constrained quadratic programming problems for the binarily constrained antenna position optimization and updating the waveforms iteratively via an analytical expression. Our simulation results validate the effectiveness of the proposed method as it achieves higher MISLR with the same number of antennas compared to conventional approaches. Moreover, the optimized antenna configurations notably enhance the balance between angular ambiguity and resolution.

Feature matching refers to the establishment of reliable correspondence between two sets of local features, which is an essential approach in remote sensing applications such as image registration and mosaicking. In this paper, a simple yet effective method, called frame-based locality preservation matching, is proposed for robust remote sensing image matching. We primarily focus on those images pairs that involve large-scale geometric transformations (e.g., extreme zoom). The key idea of our approach is to dig up the frame knowledge, such as the feature orientation and scale implied by common features like SIFT. The frame knowledge is free to obtain, and we find it to be of great significance in feature matching, especially for our focus -- large-scale geometric transformations. The proposed method can easily handle the geometric challenges and high outlier proportions, and significantly improves the performance compared to other state-of-the-art methods.

Feature matching is a fundamental technique in remote sensing image processing. This article proposes a new formulation of affine covariant feature matching for remote sensing images, where we suggest matching features by matching two sets of triplets. Compared with previous works, the formulation exploits the whole feature frame rather than the 2-D location to reject outliers. Besides, we also develop a new latent variable model to combine the feature frame and the SIFT ratio values, to enhance the convergence speed and success rate in challenging cases. We evaluate our model on three challenging datasets in terms of both qualitative and quantitative experiments. We also study the robustness to outliers since remote sensing images are typically affected by mismatches. The results demonstrate that the proposed method provides excellent matching performance with satisfying runtime and shows good robustness to outliers.