Existing inverse synthetic aperture radar (ISAR) imaging methods under sparse aperture (SA) conditions are usually based on the assumption that the target motion is stationary, which limits their performance in practical applications. However, traditional ISAR imaging methods of maneuvering targets cannot be applied directly because they usually suffer from performance loss and computational burden under SA conditions. In this study, an efficient SA-ISAR imaging framework of maneuvering targets is proposed, which decomposes the multifactorial imaging problem into easy-solving sub-problems. A fast phase compensation method that is a combination of modified eigenvector method and interpolation-based compensation matrix is proposed to efficiently remove the translational phase error and the non-uniform rotation induced phase error. Meanwhile, the target rotation parameter is estimated by minimizing the image entropy to guarantee the compensation accuracy, where particle swarm optimization (PSO) is used achieve the global optimum efficiently. Within each iteration, alternating direction method of multipliers (ADMM) algorithm is used to achieve fast image reconstruction, which suppresses the effects of SA to ensure the estimation accuracy. Experiments on simulated and measured data have verified that our proposed SA-ISAR imaging framework can achieve focused images of maneuvering targets. It outperforms in simple structure, high extensibility and less computational burden.

Built-up area (BA) extraction using synthetic aperture radar (SAR) data has emerged as a potential method in urban research. Currently, typical deep-learning-based BA extractors show high false-alarm rates in the layover areas and subsurface bedrock, which ignore the surrounding information and cannot be directly applied to large-scale BA mapping. To solve the above problems, a novel transformer-based BA extraction framework for SAR images is proposed. Inspired by SegFormer, we designed a BA extractor with multi-level dual-attention transformer encoders. First, the hybrid dilated convolution (HDC) patch-embedding module keeps the surrounding information of the input patches. Second, the channel self-attention module is designed for dual-attention transformer encoders and global modeling. The multi-level structure is employed to produce the coarse-to-fine semantic feature map of BAs. About 1100 scenes of Gaofen-3 (GF-3) data and 200 scenes of Sentinel-1 data were used in the experiment. Compared to UNet, PSPNet, and SegFormer, our model achieved an 85.35% mean intersection over union (mIoU) and 94.75% mean average precision (mAP) on the test set. The proposed framework achieved the best results in both mountainous and plain terrains. The experiments using Sentinel-1 shows that the proposed method has a good generalization ability with different SAR data sources. Finally, the BA map of China for 2020 was obtained with an overall accuracy of about 86%, which shows high consistency with the global urban footprint. The above experiments proved the effectiveness and robustness of the proposed framework in large-scale BA mapping.

Manifold learning is a widely used technique for dimensionality reduction as it can reveal the intrinsic geometric structure of data. However, its performance decreases drastically when data samples are contaminated by heavy noise or occlusions, which leads to unsatisfying data processing performance. We propose a novel robust dimensionality reduction method via low-rank Laplacian graph learning for classification and clustering tasks to solve the above problem. First, we construct a low-rank Laplacian graph by combining manifold learning and subspace learning. This graph can capture both global and local structural information of the data. And we introduce rank constraints for the Laplacian graph to make it more discriminative. Second, we put the learning of projection matrix and sample affinity graph into a unified framework. The projection matrix is embedded into a robust low-rank Laplacian graph so that the low-dimensional mapping of data can maintain the structural information in the graph well. Finally, we add a regularization term to the projection matrix to make it have the ability of both feature extraction and feature selection. Therefore, the proposed model can resist the interference of noise or data damage to learn the optimal projection to achieve better performance in dimensionality reduction through such a data dimensionality reduction joint framework. Comprehensive experiments on various benchmark datasets with varying degrees of occlusions or corruptions are carried out to evaluate the performance of the proposed method. Compared with the state-of-the-art dimensionality reduction methods in the literature, the experimental results are inspiring, showing our method’s effectiveness and robustness in classification and clustering, especially in object recognition scenarios with noise or occlusions.

In conventional colocated multiple-input multiple-output (MIMO) radars, practical waveform constraints including peak-to-average power ratio, constant or bounded modulus lead to a significant performance reduction of transmit beampattern, especially when the element number is limited. This paper adopts an active reconfigurable intelligent surface (ARIS) to assist the transmit array and discusses the corresponding beampattern synthesis. We aim to minimize the integrated sidelobe-to-mainlobe ratio (ISMR) of beampattern by the codesign of waveform and ARIS reflection coefficients. The resultant problem is nonconvex constrained fractional programming whose objective function and plenty of constraints are variable-coupled. We first convert the fractional objective function into an integral form via Dinkelbach transform, and then alternately optimize the waveform and ARIS reflection coefficients. Three types of waveforms are unifiedly optimized by a consensus alternating direction method of multipliers (CADMM)-based algorithm wherein the global optimal solutions of all subproblems are obtained, while the ARIS reflection coefficients are updated by a concave-convex procedure (CCCP)-based algorithm. The convergence is also analyzed based on the properties of CADMM and CCCP. Numerical results show that ARIS-aided MIMO radars have superior performance than conventional ones due to significant reduction of sidelobe energy.

Low-altitude slow-moving small (LSS) targets are defined as flying at altitudes less than 1000 m with speeds less than 55 m/s and a radar crossing-section (RCS) less than 2 ${\mathrm{m}}^2$. The detection performance of ground-based radar using the LSS target detection technique can be significantly deteriorated by the diversity of LSS targets, background clutter, and the occurrence of false alarms caused by multipath interference. To address the LSS target detection problem, we have devised a novel two-dimensional electronic scanning active phased array radar system that is implemented in the software-defined radar architecture and propose a transmit beam control algorithm based on the low peak-to-average ratio (PAPR). Meanwhile, we devised a flexible arbitrary radar waveform generator to adapt to complex environmental situations. Field experiment results effectively demonstrate that our radar can be used to detect LSS targets. Moreover, an ablation experiment was conducted to verify the role played by transmit beam control and adaptive waveform optimization and generation in improving the system performance.

Limited by the number of antenna elements, conventional array radars usually have insufficient ability of signal-dependent interference suppression in harsh environments, even though transmit and receive beamformers are jointly designed. This article deploys an active reconfigurable intelligent surface (RIS) to assist the receive array, which provides numerous extra degrees-of-freedom to suppress interferences and to enhance the beamforming gain of target echo simultaneously since the active RIS has the capability of adjusting and amplifying incident signals. Aiming to maximize the output signal-to-interference-plus-noise ratio (SINR), we jointly design the phase-only or power-limited transmit beamformer, receive beamformer, and active RIS reflection coefficients. We devise an alternating optimization-based algorithm to handle the resultant nonconvex-constrained fractional programming problem. Specifically, the phase-only transmit beamformer is determined by the Riemannian gradient descent (RGD)-based method while the power-limited one is given as a closed-form optimal solution, and the active RIS reflection coefficients are updated by the concave-convex procedure (CCCP)-based method. Moreover, we derive the convergence condition of the proposed algorithm based on the properties of RGD and CCCP. Numerical results reveal that the proposed active RIS-aided array radar significantly outperforms the passive RIS-aided and RIS-free ones in terms of output SINR.