Autonomous navigation of Unmanned Aerial Vehicles (UAVs) based on deep reinforcement learning (DRL) has made great progress. However, most studies assume relatively simple task scenarios and do not consider the impact of complex task scenarios on UAV flight performance. This paper proposes a DRL-based autonomous navigation algorithm for UAVs, which enables autonomous path planning for UAVs in high-density and highly dynamic environments. This algorithm proposes a state space representation method that contains position information and angle information by analyzing the impact of UAV position changes and angle changes on navigation performance in complex environments. In addition, a dynamic reward function is constructed based on a non-sparse reward function to balance the agent’s conservative behavior and exploratory behavior during the model training process. The results of multiple comparative experiments show that the proposed algorithm not only has the best autonomous navigation performance but also has the optimal flight efficiency in complex environments.

Intelligent health maintenance of bearings usually consists of two stages: constructing effective health assessment indicators and accurate remaining useful life prediction models. However, many prediction models are available only when many constraints are met, and the health assessment indicators may not be able to accurately track the performance degradation process of bearings. This study proposes a bearing performance degradation assessment and remaining life prediction method. First, the health evaluation index family (referred to as the generalized high-order moment coefficient) was constructed based on the generalized power mean and high-order origin moments for health assessment. Subsequently, an improved Paris–Erdogan model is proposed, which uses the optimal health evaluation index as input to predict the remaining life of the bearing after the initial failure. The experimental results show that the proposed method has a higher performance degradation tracking accuracy and a smaller prediction error than the combination of traditional statistical indicators and prediction models.

As artificial intelligence technology advances, the application of object detection technology in the field of SAR (synthetic aperture radar) imagery is becoming increasingly widespread. However, it also faces challenges such as resource limitations in spaceborne environments and significant uncertainty in the intensity of interference in application scenarios. These factors make the performance evaluation of object detection key to ensuring the smooth execution of tasks. In the face of such complex and harsh application scenarios, methods that rely on single-dimensional evaluation to assess models have had their limitations highlighted. Therefore, this paper proposes a multi-dimensional evaluation method for deep learning models used in SAR image object detection. This method evaluates models in a multi-dimensional manner, covering the training, testing, and application stages of the model, and constructs a multi-dimensional evaluation index system. The training stage includes assessing training efficiency and the impact of training samples; the testing stage includes model performance evaluation, application-based evaluation, and task-based evaluation; and the application stage includes model operation evaluation and model deployment evaluation. The evaluations of these three stages constitute the key links in the performance evaluation of deep learning models. Furthermore, this paper proposes a multi-indicator comprehensive evaluation method based on entropy weight correlation scaling, which calculates the weights of each evaluation indicator through test data, thereby providing a balanced and comprehensive evaluation mechanism for model performance. In the experiments, we designed specific interferences for SAR images in the testing stage and tested three models from the YOLO series. Finally, we constructed a multi-dimensional performance profile diagram for deep learning object detection models, providing a new visualization method to comprehensively characterize model performance in complex application scenarios. This can provide more accurate and comprehensive model performance evaluation for remote sensing data processing, thereby guiding model selection and optimization. The evaluation method proposed in this study adopts a multi-dimensional perspective, comprehensively assessing the three core stages of a model’s lifecycle: training, testing, and application. This framework demonstrates significant versatility and adaptability, enabling it to transcend the boundaries of remote sensing technology and provide support for a wide range of model evaluation and optimization tasks.

Unmanned aerial vehicles (UAVs) autonomous navigation based on reinforcement learning usually requires training agents in simulation scenarios and then transferring the trained agents to application scenarios. However, due to serious distribution mismatch between the idealized simulation scenario and the application environment and the inevitable uncertainty perception problem of airborne sensors in complex scenarios, the navigation performance of UAV under migration applications is not ideal. This work fully analyzes the factors that affect UAV navigation performance, including algorithm performance, training strategy, and state awareness. Based on the analysis results, this article proposes a framework to improve the autonomous navigation performance of UAVs in the migration process from training to application, which consists of three parts: ‘scenario-perception-algorithm’. In addition, this paper proposes improvement strategies for each part from the perspectives of spatial features, temporal features, and perceptual denoising. We combine the proposed framework with navigation algorithms to improve the navigation decision-making performance of UAVs in migration applications under uncertainty perception. Many simulation experiments demonstrate the effectiveness of the proposed framework and its robustness to uncertainty perception.

In complex electromagnetic environments, radar systems face increasing challenges from advanced jamming techniques. These challenges mainly stem from the diversity of jamming patterns, the complexity of compound jamming signals, and the difficulty of recognition under low jamming-to-noise ratio conditions. Accurate recognition of such signals is critical for enhancing radar anti-jamming capabilities. However, traditional methods often struggle with diverse and evolving jamming patterns. To address this issue, we propose a novel deep learning-based approach for accurate and robust recognition of complex radar jamming signals. Specifically, the proposed network adopts a dual-branch architecture that concurrently processes time-domain and time–frequency-domain features of jamming signals. It further incorporates a multi-branch convolutional structure to strengthen feature extraction and applies an effective feature fusion strategy to capture subtle patterns. Simulation results demonstrate that the proposed method outperforms six representative baseline approaches in recognition accuracy and noise robustness, particularly under low jamming-to-noise ratio conditions.