The rapid advancement in anti-jamming technologies presents significant challenges to the communication of jamming UAVs. Although deep reinforcement learning (DRL) offers a promising solution, existing methods still rely to some extent on prior knowledge, such as the communication channel states, and they lack robustness against dynamic communication policies. To respond, this paper develops an intelligent jamming decision-making scheme against unknown communication policies. Firstly, we formulate the communication countermeasure as a partially observable random game (POSG), where the jammer operates without the communication channel state information. Then, we design a proximal policy optimization with policy feedback (PPO-PF) algorithm to derive the best response jamming strategy (BRJS). In contrast to conventional algorithms based on actor-critic (AC), PPO-PF directly uses policy value to update the value function, enabling joint optimization of policy and value function. An ensemble evaluation network is introduced to enhance value estimation accuracy in the case of limited training data. To enhance the robustness of the algorithm for dynamic communication policies, we propose a teacher-student framework (TSF) that leverages imperfect demonstrations interventions to enhance the learning efficiency. The results demonstrate our approach’s superior adaptability to diverse communication policies, achieving significant performance improvements over baselines. Notably, the TSF exhibits two key advantages: (1) effective utilization of imperfect teacher demonstrations to accelerate learning, and (2) guaranteed student policy performance independent of teacher policy quality. In the testing faced with communication strategy μDQN, our approach achieves performance improvements of 48.83%, 14.79%, and 17.85% in relation to various levels of PPO teacher policies.

This letter investigates the jammer path planning and jamming power allocation problem during airborne deterrence operation (ADO) in highly dynamic environments. In response to airborne threats posed by enemy aircraft formations, jammers must rely on perceptual information to plan trajectories and emit jamming signals to paralyze the ground-to-air (G2A) communication networks. Unlike traditional static scenarios, the high mobility of both sides presents significant challenges. Most works only study jamming solutions for static ground or single airborne targets, failing to address multiple airborne targets. We propose a joint path planning and jamming power allocation approach based on deep reinforcement learning (JPPJPA-DRL). This approach considers the impact of flight paths on receiving antenna gain, models the ADO as a Markov Decision Process (MDP), and uses the proximal policy optimization (PPO) algorithm to generate optimized path points and jamming power allocation schemes. In addition, a scientific reward function is designed to guide the learning process, and a visual communication countermeasure simulation platform is developed. The results show that the proposed approach can efficiently paralyze G2A communication networks, outperforming the baseline.

Traditional synthetic aperture radar (SAR) target detection methods use matched filtered SAR images as input, and the detection performance is restricted due to the high sidelobes and speckle noise of these images. Sparse SAR imaging methods developed in recent years provide the advantages of reducing sidelobes, noise, and clutter. The imaging results obtained with these methods could help improve the SAR target detection performance. In this article, 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.

To study the maneuvering target imaging problem of seaborne inverse synthetic aperture radar, an ISAR autofocusing algorithm based on the minimization criterion of image entropy is proposed in this paper. Firstly, in the range-frequency azimuth-time domain, the filter function related to the equivalent motion parameters of platform and the target is constructed, and the focus result is modeled as a function of the equivalent motion factors. The image entropy is minimized as a criterion. Finally, the defocused data is compensated by the convergent filter function to obtain a focused well image. The simulation results verify the effectiveness of the proposed algorithm.