Unmanned aerial vehicle (UAV) swarms-enabled mobile edge computing (MEC) system can be deployed in critical industrial zones for monitoring. Meanwhile, its malicious use may bring great threat to the security, and the accurate detection and localization are important. UAV swarms show characteristics of the high density, small radar cross section (RCS), far range and time-varying motion, and have posed formidable challenges to the accurate detection and localization. In this paper, the accurate detection and localization of UAV swarms are investigated and an effective method is proposed based on the Dechirp-keystone transform (KT) and frequency-selective reweighted trace minimization (FS-RWTM). It inherits high robustness of the long-time integration (LTI) technique and super resolution of the gridless sparse technique. Mathematical analyses and numerical simulations validate its superiorities in accurate detection and localization of UAV swarms.

Multiple-input-multiple-output (MIMO) synthetic aperture radar (SAR) (MIMO-SAR) has numerous potential applications in fields, such as medical diagnosis and security screening. In near-field MIMO-SAR imaging, the virtual array is typically defined as the midpoint of the transceiver array. Based on the virtual array, the multistatic data can be converted to monostatic data for imaging. However, these approaches suffer from low accuracy and cannot reconstruct large scenes, despite their small computational load. In this article, we propose a novel near-field MIMO-SAR imaging algorithm based on range compensation. The convolution between transmitting and receiving elements of the MIMO array defines the positions of virtual array's elements, with the center of the small scene selected as the reference point. The range difference between the transceiver array and the corresponding virtual array to the reference point is calculated. An approximate expression of the range difference is also derived, which can effectively reduce the computational load associated with the range compensation. The conditions for partitioning large scenes are derived by analyzing the range difference between the reference point and other scattered points with respect to the antenna elements. Based on the conditions, the large scenes are divided into blocks. The final image is obtained by a weighted sum of each block scene image. Simulation and experimental results on the designed MIMO radar near-field imaging system demonstrate that the proposed algorithm can effectively reconstruct high-resolution scene images of arbitrary size.

Neural networks for synthetic aperture radar (SAR) automatic target recognition often encounter overfitting challenges owing to limited training samples. Moreover, the azimuth angle of SAR, a vital parameter for improving network generalization, is frequently disregarded in most models. In response, we propose MIGA-Net, a classification neural network that effectively perceives azimuthal information using multi-view images to improve classification performance. Specifically, we quantize low-dimensional azimuthal values for sample-limited scenarios. Then, we utilize encoded image sequences as training data because they encompass spatial context information compared to individual images. After extracting features of the sequence samples through convolutional layers, we design a two-layer output module. One layer converts these sequence features into graph data. Then the dense graph attention network (GAT) extracts contextual features from the graph data for angle estimation. Simultaneously, another layer combines these features for target classification. During the network training, the GAT module can extract image azimuth features with powerful information aggregation capabilities. It supervises the convolutional layers to learn azimuth features, which are fused with class features from another layer to obtain a more structured feature domain. This feature domain significantly enhances the classification performance of the network. Experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset have proven the superior performance of the proposed method, achieving at least 1% higher accuracy compared to other state-of-the-art algorithms.

Energy-efficient, highly integrated lens antenna arrays (LAAs) have found widespread applications in wideband millimeter wave or terahertz communications, localization and tracking, and wireless power transfer. Accurate estimation of angle-of-arrival (AoA) is key to those applications, but has been hindered by a spatial-wideband effect in wideband systems. This paper proposes to exploit (rather than circumventing) the spatial-wideband effect to develop a fast and accurate AoA estimation approach for LAAs. Specifically, we unveil new spatial-frequency patterns based on the spatial-wideband effect, and establish one-to-one mappings between the patterns and the strongest discrete Fourier transform (DFT) beam containing the AoA. With the strongest DFT beam identified, we propose a method to estimate the AoA uniquely and accurately, using only a few training symbols. This is achieved by deriving a new one-to-one mapping between the AoA and the set of DFT beams judiciously selected based on the strongest. In the case that an impinging path is uniformly distributed in [0,2π], simulations show that the proposed algorithm is able to reduce the mean squared error of the AoA estimation by as much as 82.1% while reducing the number of required symbols by 93.2%, as compared to existing techniques. The algorithm can also increase the spectral efficiency by 89% when the average SNR is -20 dB at each antenna of the receiver.

The blind speed side lobe (BSSL) induced by the ambiguous velocity is a main challenge in the long-time coherent integration based high-speed target detection. In this paper, considering the support area difference between the BSSL and the real target, we propose an improved minimized windowed Radon-Fourier transform (IMWRFT)-based radar high-speed target detection method to suppress the BSSL and realize target detection. Firstly, two groups of window functions are designed to force the BSSL support areas to split, and then, minimum operations are adopted to suppress the BSSL. The effect of minimum operation on target detection is theoretically analyzed for the first time. The relationship between detection threshold and false alarm rate is deduced and an iterative numerical method is designed to determine the threshold. Numerical simulations of the detection performance and BSSL suppression ability are done for the radon-Fourier transform (RFT), PRF changing (PRFC) method, complementary window function method (CWFM) and the proposed method, which demonstrates the superiority of the proposed detection method. Finally, the validity of the proposed algorithm is verified by the measured data. Compared with RFT and CWFM, this algorithm can suppress BSSL more significantly.

Antenna design and angle-of-arrival (AoA) estimation are critical to the efficiency of wireless information and power transfer (WIPT). The AoA estimation is challenging for energy-efficient lens antenna arrays (LAAs), due to discrete sets of fixed discrete Fourier Transform (DFT) beams. This paper presents a novel fast and accurate approach for the AoA estimation of LAAs. The key idea is that we prove the two differential outputs of three adjacent lens beams, referred to as “DFT beam differences (DBDs)”, are the strongest at the two sides of an AoA. They are easy to identify and robust to noises, and their powers are proved to provide an accurate estimate of the AoA. Another important aspect is a new beam synthesis technique which produces different beam widths based on DFT beams and practical 1-bit phase shifts in real time. As a result, the angular region containing the AoA can exponentially narrow down, and the two strongest DBDs can be quickly identified. The proposed approach can operate in coupling with successive interference cancellation to estimate the AoAs of multiple paths. Simulations show that the proposed approach is able to outperform the state of the art by orders of magnitude in term of accuracy. The power transfer efficiency can be dramatically improved.