By analyzing the motion characteristics and the radar observation model of triaxial stabilized space targets, a new 3-D geometry reconstruction method is proposed based on the energy accumulation of inverse synthetic aperture radar (ISAR) image sequence. According to the radar line of sight (LOS), we first construct the projection vectors of the 3-D geometry of a space target on the imaging planes. Then, by projecting the 3-D scatterer candidates on each imaging plane, we can accumulate the scattering energy of the corresponding 2-D projection position in each image. The 3-D scatterer candidates occupying the larger accumulated energy will be reserved as the real scatterers. To improve the efficiency, the real 3-D scatterers will be searched by using the particle swarm optimization (PSO) algorithm one by one. Compared with traditional 3-D geometry reconstruction methods, the proposed one never needs the 2-D scatterer extraction and trajectory association, which remains the challenges in ISAR image processing. Experimental results based on the simulated point target and electromagnetic data are presented to validate the effectiveness and robustness of the proposed method.

Aiming at the problem of the difficulty of high-resolution synthetic aperture radar (SAR) image acquisition and poor feature characterization ability of low-resolution SAR image, this paper proposes a method of an automatic target recognition method for SAR images based on a super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN). First, the threshold segmentation is utilized to eliminate the SAR image background clutter and speckle noise and accurately extract target area of interest. Second, the low-resolution SAR image is enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in the SAR image. Third, the automatic classification and recognition for SAR image is realized by using DCNN with good generalization performance. Finally, the open data set, moving and stationary target acquisition and recognition, is utilized and good recognition results are obtained under standard operating condition and extended operating conditions, which verify the effectiveness, robustness, and good generalization performance of the proposed method.

The recognition of synthetic aperture radar (SAR) jamming has gained significant attention due to its critical role in enhancing radar system performance in complex electromagnetic environments. However, existing research typically addresses a limited number of jamming categories, hindering practical applications that require the simultaneous identification of multiple jamming sources. Moreover, the challenges in feature distribution optimization lead to overlaps in inter-class features and inconsistencies within intra-class features, leading to a decrease in the accuracy of SAR jamming recognition. To tackle these issues, we propose a time-frequency aware hierarchical feature optimization method for SAR jamming recognition. Specifically, we propose a timefrequency aware dual attention mechanism to focus on crucial features in the time-frequency map of SAR jamming signal through parallel attention and unified attention. In addition, we propose a hierarchical jamming feature distribution optimization method that further improves the compactness and discrimination of SAR jamming feature representation. We construct an SAR jamming signal dataset with 50 jamming types to validate the proposed recognition method. The detailed results of ablative experiments and comparative experiments demonstrate the superior performance across different JNR conditions achieved by our method, establishing its efficacy for robust SAR jamming recognition.

Semi-supervised segmentation presents a promising approach for large-scale medical image analysis, effectively reducing annotation burdens while achieving comparable performance. This methodology holds substantial potential for streamlining the segmentation process and enhancing its feasibility within clinical settings for translational investigations. While cross-supervised training, based on distinct co-training sub-networks, has become a prevalent paradigm for this task, addressing critical issues such as predication disagreement and label-noise suppression requires further attention and progress in cross-supervised training. In this paper, we introduce an Evidential Tri-Branch Consistency learning framework (ETC-Net) for semi-supervised medical image segmentation. ETC-Net employs three branches: an evidential conservative branch, an evidential progressive branch, and an evidential fusion branch. The first two branches exhibit complementary characteristics, allowing them to address prediction diversity and enhance training stability. We also integrate uncertainty estimation from the evidential learning into cross-supervised training, mitigating the negative impact of erroneous supervision signals. Additionally, the evidential fusion branch capitalizes on the complementary attributes of the first two branches and leverages an evidence-based Dempster-Shafer fusion strategy, supervised by more reliable and accurate pseudo-labels of unlabeled data. Extensive experiments conducted on LA, Pancreas-CT, and ACDC datasets demonstrate that ETC-Net surpasses other state-of-the-art methods for semi-supervised segmentation. The code will be made available in the near future at https://github.com/Medsemiseg.

The classification and recognition of human activities plays an important role in the fields of security, crime prevention, medical surveillance, etc. The swings of the limbs during the movement of the human body introduce rich micro-Doppler information. Different human activities correspond to different micro-Doppler distributions. Therefore, the discrepancies in micro-Doppler distributions can be utilized to classify different human activities. At the same time, the deep learning method based on image domain has been widely utilized in the classification and recognition of radar targets. To improve human activities classification performance, this paper proposes a human activities classification method based on complex-value convolutional neural network (CV-CNN). The motion capture data are utilized to obtain the motion trajectories of various parts of the human, and then the radarechoes of human can be got. After that, the time-frequency (TF) analysis is utilized to obtain the TF representations, and the activities sample database. A CV-CNN structure is built to train micro-Doppler characteristics are analyzed to establish a human the complex-value TF data of different human activities. The experiment results which the classification accuracy reaches 99.81% verify the effectiveness of the proposed method. The results under different training sample proportions and SNRs demonstrate the robustness of the proposed method, which is 14.22% higher than that of CNN when the ratio of training set is 10%, and 11.34% higher than that of CNN when the SNR is -5dB.

Automatic modulation classification (AMC), as a key technology of cognitive radio, has become a focal point of research. However, most deep learning-based AMC methods require an extensive number of labeled signals to acquire a comprehensive understanding of modulation types, placing substantial pressure on signal acquisition and labeling. To solve this issue, we propose a few-shot AMC (FSAMC) method to facilitate rapid generalization and recognition with limited data, namely multi-scale feature fusion and distribution similarity network (MS2F-DS). Firstly, we design a multi-scale feature fusion (MS2F) model, which aims to extract features with varying fields of view and boost feature fusion, enabling the derivation of contextual information from the signal. Furthermore, we introduce a distribution similarity (DS) classifier to address the insufficient measurement of current similarity measurement functions by considering both micro and macro perspectives of vectors, further increasing intra-class compactness and inter-class separability. Finally, extensive experiments were conducted on 3-way 1, 3, and 5-shot FSAMC tasks using public datasets RML2016.10a and RML2016.10b, and the results demonstrated the effectiveness of our method.