With the increasing number of remote sensing (RS) data sources, the joint utilization of multimodal data in Earth observation tasks has become a crucial research topic. As a typical representative of remote sensing data, Hyperspectral images (HSI) provide accurate spectral information, while rich elevation information can be obtained from Light Detection and Ranging (LiDAR) data. However, due to the significant differences in multimodal heterogeneous features, how to efficiently fuse HSI and LiDAR data, which remains one of the challenges faced by existing research. In addition, the edge contour information of images is not fully considered by existing methods, which can easily lead to performance bottlenecks. Thus, a joint classification network of HSI and LiDAR data based on Mamba (HLMamba), is proposed. Specifically, a gradient joint calculation (GJA) is first performed on LiDAR data to obtain the edge contour data of the land distribution. Subsequently, a multimodal feature extraction module (MFEM) was proposed to capture the semantic features of HSI, LiDAR, and edge contour data. Then, to efficiently fuse multimodal features, a novel deep learning (DL) framework called Mamba, was introduced, and a multimodal Mamba fusion module (MMFM) was constructed. By efficiently modeling the long-distance dependencies of multimodal sequences, the MMFM can better explore the internal features of multimodal data and the interrelationships between modalities, thereby enhancing fusion performance. Finally, to validate the effectiveness of HLMamba, a series of experiments were conducted on three common HSI and LiDAR datasets. The results indicate that HLMamba has superior classification performance compared to other the state-of-the-art DL methods. The source code of the proposed method will be available publicly at https://github.com/Dilingliao/HLMamba.

Due to the inherent characteristics of synthetic aperture radar (SAR) imaging, variations in target orientation, and the challenges posed by non-cooperative targets (i.e., targets without cooperative transponders or external markers), limited viewpoint coverage results in a small-sample problem that severely constrains the application of deep learning to SAR image interpretation and target recognition. To address this issue, this paper proposes a multi-target, multi-view SAR image generation method based on conditional information and StyleGAN2, designed to generate high-quality, angle-controllable SAR images of typical targets from limited samples. The proposed framework consists of an angle encoder, a generator, and a discriminator. The angle encoder employs a sinusoidal encoding scheme that combines sine and cosine functions to address the discontinuity inherent in one-hot angle encoding, thereby enabling precise angle control. Moreover, the integration of SimAM and IAAM attention mechanisms enhances image quality, facilitates accurate angle control, and improves the network’s generalization to untrained angles. Experiments conducted on a self-constructed dataset of typical civilian targets and the SAMPLE subset of the MSTAR dataset demonstrate that the proposed method outperforms existing baselines in terms of structural fidelity and feature distribution consistency. The generated images achieve a minimum FID of 6.541 and a maximum MS-SSIM of 0.907, while target recognition accuracy improves by 6.03% and 7.14%, respectively. These results validate the feasibility and effectiveness of the proposed approach for SAR image generation and target recognition tasks.

The traditional two-dimensional multiplication modulation jamming (2D-MMJ) method is widely used in synthetic aperture radar (SAR) deception jamming due to its simple hardware implementation and convenient template preparation. However, it suffers from serious defocusing in high-resolution spaceborne SAR deception jamming. To improve the fidelity of deception jamming, this paper proposes an improved 2D-MMJ method based on defocus correction (2D-MMJDC). First, the defocus correction phase is designed by analyzing the range cell migration (RCM) curves between the real and jamming signals. Next, considering the false point target azimuth deviation caused by the defocus correction, the azimuth deviation correction phase is designed. Finally, two phases are added to the 2D-MMJ method, and a template preparation process based on the two-dimensional Fast Fourier Transform (2D-FFT) is designed. Simulation results show that the 2D-MMJDC method effectively solves the problem of defocusing on the 2D-MMJ method and greatly improves the fidelity of the deception jamming.

The existing single-frequency TomoSAR three-dimensional (3D) reconstruction method suffers from insufficient observation information due to the high cost and long period of acquisition of the required multi-pass data. To effectively alleviate these problems, it is a feasible solution that a sensor carries dual-frequency radars that can work simultaneously and acquire dual-frequency data for 3D imaging. This paper systematically introduces the theory and model of dual-frequency TomoSAR 3D imaging system, and proposes a dual-frequency distributed compressive sensing (DF-DCS) TomoSAR method for reducing flight requirements. Firstly, this method combines the neighborhood pixel information to effectively improve the signal-to-noise ratio. Then, the reflectivity profiles of the dual-frequency data are jointed to increase the unambiguous height and the accuracy of the target position estimation. Finally, the point-target simulation experiments and surface-target experiments based on SARSIM system verify the effectiveness of the proposed method. With the reconstruction accuracy maintained, the DF-DCS method can effectively improve the ability to resolve ambiguity and reduce the required flights.

Atmospheric phase is the main factor affecting the accuracy of ground-based synthetic aperture radar. The atmospheric phase screen (APS) may be very complicated, due to the drastic changes in atmospheric conditions, and the conventional correction methods based on regression models cannot fit and correct it effectively. Partition correction is a feasible path to improve atmospheric phase correction (APC) accuracy for complicated APS, but the overfitting problem cannot be ignored. In this article, we propose a clustering partition method, based on the normal vector of APS, which can partition the complicated APS more reasonably, and then perform APC based on the partition results. APC, and simulation experiments on measurement data, suggests that the proposed method achieves higher accuracy than the conventional model-based methods for complicated APS and avoids severe overfitting, realizing the balance between accuracy and credibility. This article verifies the feasibility and effectiveness of using APS distribution information to guide the partition and conduct APC.

The rise of biomimetic unmanned aerial vehicle clusters has brought new challenges to radar target tracking and recognition. The purpose of flying imitating geese flocks is long-distance transport with low energy consumption. In this paper, the methods of recognition and tracking are proposed for cluster targets with flight pattern of geese. The pattern recognition method, by detecting specific distance pairs according to five constraints including distance, vertical, pedal, orientation, and slope, judges whether there is a ’V’ characteristic in the measurement set and selects the key individuals such as the leading or following geese. Based on the pattern recognition result, the hypothetical pattern measurements are introduced into the filtering process as additional observation information, which aims to solve the problem of tracking accuracy decrease caused by high maneuverability in the formation stage of flying geese. Simulation results show that the proposed methods can not only judge the pattern of the cluster effectively and identify the key individuals but also improve the accuracy compared with the traditional tracking algorithms.