Continuous underwater bearing-only target tracking with limited sensor detection sectors presents a significant challenge for Autonomous Underwater Vehicles (AUVs). This challenge arises from the tight coupling between target state estimation and AUV trajectory optimization. This paper proposes the Model Predictive Path Integral with Dynamic Weight Integration Tracking (MPPI-DWIT) framework to solve the problem of bearing-only target tracking under limited sensor detection sectors, by integrating an observability metric based on Fisher Information Matrix (FIM) into the MPPI path planner. The observability metric directly improves the accuracy of target state estimation during trajectory sampling. The framework uses a hard-boundary strategy to strictly enforce sensor detection sectors limits and AUV maneuverability constraints. An innovative dynamic weight integration mechanism is proposed, which automatically balances the need to maximize observability with the need to maintain a safe tracking distance. Extensive Monte Carlo simulations show that MPPI-DWIT outperforms the state-of-the-art methods, in terms of estimation accuracy, tracking times, and failure rates. Real-world single AUV tracking trials further confirm the effectiveness and robustness of MPPI-DWIT.

UAVs are now platforms that can carry out activities on their own thanks to technological advancements. This research develops a hierarchical reinforcement learning-based decision-making framework to tackle the decision-making difficulties encountered by UAVs in air combat. An approach based on the LSTM-DQN model for UAV air combat decision-making is suggested. To overcome challenges like high-dimensional space, prolonged exploration, and scarce rewards in traditional reinforcement learning, the approach splits the air combat process into a decision layer and a control layer. The behavior tree governs how action sequences are carried out in the control layer, while the enhanced DQN model is in charge of task generation in the decision layer. This framework increases training efficiency, decreases the dimensionality of the state space, and shortens exploration time. Win-rate analysis in a simulated environment is used to verify the algorithm’s efficacy and the model’s viability.

Infrared small target detection (IRSTD) is challenging due to low target-background contrast and small target size, leading to missed detections and false alarms (Fas). To address these problems, a novel gradient vector self-learning network (GVSLNet) is proposed. First, the self-learning gradient vector (SLGV) module is designed based on the unique high correlation of infrared small target gradient. Traditional gradient vector field operators cannot update and learn the deep features. SLGV overcomes these limitations by using CNN to adaptively learn and calculate the gradient vector of infrared images, improving the ability to distinguish between targets and backgrounds under complex environments. Then, edge features are encoded into the global-local attention fusion (GLAF) module, which is based on Transformer and dilated convolution. Infrared small target images often display nonlocal self-similarity, where background signals tend to share similar structures. The GLAF module leverages this characteristic to further enhance target intensity while effectively suppressing background noise. The proposed GVSLNet can dynamically calculate gradients based on scene information, improving the detection ability of small targets in complex environments. Experimental results prove that the proposed GVSLNet outperforms state-of-the-art methods on public datasets while maintaining high inference speed.

The task of underwater image enhancement aims to improve the quality of the images and promote the visualization effect. Current methods still face some challenges, which include the difficulty to model the real ocean environment and the lack of simultaneous exploitation and modeling of both global and local information in images. Particularly, in resource-constrained underwater scenarios, models need to enhance images on embedded devices in real-time, which usually requires them to strike a balance between the inference speed and accuracy. In this paper, we put forward a real-time underwater image enhancement network (RUE-Net). Compared to previous advanced models using single branch structure or traditional U-shaped networks, it consists of parallel Receptive Field Enhancement (RFE) Module and Fine Grain Detail (FGD) Module, which perform parallel modeling and fusion of image information at global and local scales. Furthermore, dense connections are established in each stage throughout the whole model. Compared with current models, RUE-Net shows the best performance in the image enhancement task in LSUI, UFO-120, and EUVP datasets. Furthermore, based on the RUE-Net, we introduce a simultaneous enhancement and super-resolution (SESR) multi-task model. The experiments prove RUE-Net has an excellent capability for image restoration and super-resolution reconstruction in UFO-120 and USR-248 datasets. RUE-Net is also deployed on the Jetson Orin Nano, which achieves an impressive inference speed of up to 51 frames per second (FPS), demonstrating its value in underwater robot with limited computing capabilities. The code is available at https://github.com/GuocunWang/RUE-Net.

Infrared small target detection under complex backgrounds, especially in dense cloud and changeable clutter scenes, has always been a challenging research task. In order to improve the detection ability of small targets under complex backgrounds, an infrared small target detection method based on gradient correlation filtering and gradient contrast measurement (GCF-CM) is proposed in this article. The infrared gradient vector field (IGVF) of the original image is first constructed through the facet model. Then, considering the unique gradient characteristics of small targets, a gradient correlation filtering (GCF) method is proposed to filter small targets and background clutters. Meanwhile, a gradient contrast measurement (GCM) method is designed to further enhance the intensity of the small target. Finally, after fusing the two response maps, an adaptive threshold is adopted to extract small targets. Experimental results demonstrate that the proposed method can improve the intensity of the small target and suppress clutter sufficiently. In comparison with other excellent methods, the proposed method exhibits a robust detection performance.

To overcome the interference of complex background and improve the detection ability of infrared small target under low signal-to-clutter ratio (SCR) scenes, a novel detection method based on gradient correlation measure (GCM) is proposed in this letter. Initially, the infrared gradient vector field (IGVF) of the original image is constructed based on the facet model. Then, a gradient correlation template is designed to distinguish the difference of local gradient between small targets and background. Finally, an adaptive threshold is adopted to extract small targets from background clutter. The proposed GCM method can identify the unique gradient characteristics of small targets. Experimental evaluations prove that the proposed method can achieve higher SCR scores in complex backgrounds. Especially in the scene where the gray contrast of small targets is low, the proposed GCM method shows a more robust detection performance.