With the development of deep learning (DL), several fields have ushered in leapfrog development, such as image classification and natural language processing. Combing the powerful feature extraction tool, DL-based Automatic Modulation Classification (AMC) emerges. However, most DL-based AMC methods require massive labeled samples for training, which is difficult for non-cooperative scenarios. In this paper, we propose a novel self-supervised AMC method called GAF-MAE. Gramian Angular Field (GAF) is applied for domain transformation from time series to images first, then a Masked Autoencoder (MAE) is built for self-supervised reconstruction tasks through unlabeled samples. After self-supervised pretrain, a small number of labeled samples are used for downstream classification finetuning. We conduct a comprehensive evaluation of a public dataset RadioML.2016.10a. The simulation results show that GAF-MAE can achieve a relatively high average accuracy of 54.85% even if the label proportion is 5% and 61.38% when the label proportion reaches 100%, which outperforms other well-known models.

Traditional synthetic aperture radar (SAR) target detection methods use matched filtered (MF) SAR images as input, and the detection performance is restricted due to the high side lobes and speckle noise of these images. Sparse SAR imaging methods developed in recent years provide the advantages of reducing side lobes, noise and clutter. The imaging results obtained with these methods could help improve the SAR target detection performance. In this paper, to improve the target detection performance using sparse SAR images as input, we proposed a convolutional sparse feature enhancement method to meet the needs of Bayesian saliency detection. The proposed Bayesian saliency joint target detection method comprised the following three steps: first, to obtain sparse SAR images with continuous contours and fewer holes in the target area, we proposed a convolutional L1 sparse regularization method. Second, a regularization parameter optimization method was derived to quickly obtain optimal regularization parameters for saliency detection. Finally, target detection results were obtained through a superpixel-based Bayesian saliency joint detector. Extensive experiments verified that the proposed method could improve the SAR target detection accuracy in complex backgrounds.

Imaging jamming of inverse synthetic aperture radar (ISAR) has been studied extensively in recent decades. Among them, the periodic interrupted sampling repeater jamming (P-ISRJ) technology can generate a series of false targets through matching filtering. However, with the rapid development of anti-jamming technology, it is necessary to improve the existing periodic sampling method to achieve better jamming effects. The latest research proposes that the two-dimensional square jamming strip can be generated based on the nonperiodic interrupted sampling repeater jamming (NP-ISRJ) to cover the target. In this article, the influence of NP-ISRJ on ISAR is studied with the model of escort free-flight. A linear weighted optimization method is proposed to improve the uneven energy distribution. The experimental results show that a square jamming strip with better blanket effects can be generated under the traditional range Doppler imaging method. While for the compressed sensing imaging method, the imaging fails because the sparse condition is no longer satisfied. The validity of the proposed blanket jamming method in different ISAR imaging reconstruction methods is verified.

Partial-band noise jamming is an important countermeasure against frequency-hopping spread spectrum technology in wireless sensor networks, and the jamming resource allocation (JRA) problem involved is a high-dimensional combinatorial optimization and also an NP-hard problem. Moreover, the users can dynamically alter their communication status, such as changing the channel spectrum distributions of hopping sets, posing additional difficulties for JRA. This paper develops two methods to address above challenges. First, a deep reinforcement learning (DRL)-based method is proposed for efficient JRA optimization, with the jamming scheme of each jamming node decided sequentially by the policy neural network, and its parameters are updated through trust region policy optimization (TRPO) within a trust region to ensure stable and fast convergence. Second, we propose a meta-TRPO based method to improve the generalization capability of the policy network. After the meta-training process, it can update the meta policy network with just a few fine-tuning steps and quickly obtain a task-specific policy for the fresh task. Extensive simulation results show the proposed DRL-based method converges faster than other DRL methods. Additionally, the proposed meta-TRPO based method can rapidly adapt to unseen jamming tasks with only a small quantity of training trajectories.

The advancement of deep learning (DL) techniques has led to significant progress in Automatic Modulation Classification (AMC). However, most existing DL-based AMC methods require massive training samples, which are difficult to obtain in non-cooperative scenarios. The identification of modulation types under small sample conditions has become an increasingly urgent problem. In this paper, we present a novel few-shot AMC model named the Spatial Temporal Transductive Modulation Classifier (STTMC), which comprises two modules: a feature extraction module and a graph network module. The former is responsible for extracting diverse features through a spatiotemporal parallel network, while the latter facilitates transductive decision-making through a graph network that uses a closed-form solution. Notably, STTMC classifies a group of test signals simultaneously to increase stability of few-shot model with an episode training strategy. Experimental results on the RadioML.2018.01A and RadioML.2016.10A datasets demonstrate that the proposed method perform well in 3way-Kshot, 5way-Kshot and 10way-Kshot configurations. In particular, STTMC outperforms other existing AMC methods by a large margin.

Radar networks can provide spatial diversity by observing targets from different perspectives with adequately separated receiving stations. This paper proposes a mainlobe interference suppression approach for radar networks based on covariance matrix reconstruction. First, we formulate the mainlobe interference suppression under multi-radar configuration as a multichannel signal enhancement problem and perform range and Doppler equalization to improve interferences’ spatial correlation. Then, we estimate the signal and interference covariance matrix from the sampling covariance matrix by solving a robust principal component analysis (RPCA) model. After that, we devise a time domain-constrained (TDC) linear estimator dependent on the reconstructed covariance matrices to enhance target echo. To remove those duplicate false targets caused by signal enhancement and estimate the state of the physical targets, we establish and solve a generalized multiple measurement vectors (GMMV) model based on the fact that targets have unique locations in the state space. Finally, we confirm the effectiveness of the proposed approach with numerical experiments and demonstrate better performance in comparison with the subspace projection method.