The long-term prediction of hypersonic glide vehicle (HGV) trajectory based on radar tracking data still faces challenges. Existing approaches are hard to provide satisfactory performances in long-term HGV trajectory prediction tasks due to insufficient long-range dependency extraction ability and cumulative errors. To address the above issues, a trajectory prediction model Physics-informed Transformer (PIT) is proposed in this work. The Top $\tau$ -Mean attention mechanism and the generative decoder are designed in this model to extract the trajectories' long-range dependency and reduce the cumulative errors. Furthermore, the physical knowledge is applied at both the input and output ends of the model, which further improves the model's prediction accuracy even in the case of missing a small amount of radar tracking data. A simulated HGV trajectory dataset is established based on the dynamic equations for the PIT trajectory prediction model. The experimental results show that the PIT performs better than other state-of-the-art deep neural network models. The additional experiments on a small-scale dataset and datasets with missing tracking data further verify the excellent performance and robustness of the PIT in these cases.

The applications of unmanned swarms have become increasingly widespread, gradually transforming production processes and daily life. Task allocation, the top-level design for unmanned swarm missions, is pivotal to maximizing the efficiency of the entire swarm. However, traditional optimization methods and intelligent algorithms, including reinforcement learning (RL), often struggle to adapt to the complex and unpredictable situations in these tasks. To address this challenge, we propose a novel multiagent proximal policy optimization (MAPPO) algorithm combined with the population-based learning and Gaussian mixture model (GMM)-based adjustment mechanisms (PG-MAPPO). In PG-MAPPO, the population-based learning mechanism is integrated to enable agents with diverse exploration preferences to uncover optimal collaboration patterns among unmanned aerial vehicles (UAVs), thereby enhancing cooperative efficiency. The GMM-based adjustment mechanism dynamically adjusts UAV formations for each agent, significantly improving the swarm’s flexibility and adaptability in rapidly changing environments. To demonstrate the effectiveness of PG-MAPPO, a maritime rescue simulation containing multiple complex and dynamic scenarios is conducted. Experimental results show that our algorithm achieves higher rescue success rate with faster convergence and greater stability than state-of-the-art multiagent reinforcement learning (MARL) methods in all scenarios. Notably, the PG-MAPPO algorithm improves the rescue success rate by 31.6% compared to the best-performing baseline under challenging conditions.

Recent advanced trackers, composed of discriminative classification and dedicated bounding box estimation, have achieved remarkable advancements in performance of visual object tracking. However, existing methods cannot satisfy the demands of tracking tasks in complex scenes, such as occlusion, scale variations, and etc. To this end, we propose a novel online multi-scale classification and global feature modulation for robust visual tracking, which is developed over accurate tracking by overlap maximization, named ATOM+. First, coordinate attention (CA) is applied to enhance the target features in the channel dimension and spatial dimension, which can effectively optimize the feature representation ability of the backbone network. Second, an online multi-scale classification (OMC) module is designed. During the online tracking phase, more reliable matching responses are comprehensively generated by aggregating information from different scales related to the target. This new operation enables stable perception of the target by the tracker, particularly when severe changes in the appearance and posture of the target are encountered. Third, a global feature modulation (GFM) mechanism is constructed, which requires only a small amount of computational resources, to fuse the spatial contextual information of the template image into the search region. This integration refines the bounding box to obtain an accurate estimate of the target state. Finally, comprehensive experiments on conventional tracking benchmarks of OTB100, LaSOT, and VOT2018 show that our tracker can sufficiently address different challenging scenarios, and achieves state-of-the-art performance. For the average running speed, our tracker can achieve 37 FPS in real time.