The attitude stabilization problem of rigid bodies with external disturbance is studied. A novel disturbance observer-based control scheme is presented. Comparing with the existing observers for external disturbance, the proposed observer releases the assumption that the external disturbance should be constant or with the minor rate of change. The closed-loop attitude stabilization system is governed by the controller to be exponentially stable with disturbance rejected. The observer error of disturbance, the attitude, and the angular velocity are exponentially stabilized to be zero or within a small set with an arbitrary radius. The key feature of this scheme is that it ensures the control performance to be more robust to any external disturbance. The controller has a simple structure and does not involve complicated computation. The effectiveness of the presented solution is validated by a rigid satellite example.

This article studies the reliable and coordinated control problem of formation flying spacecraft without velocity measurement. External disturbances and directed communication topology, which explicitly has bounded communication delays, are considered. A novel observer is preliminarily designed to ensure a precise and fast estimation of immeasurable velocity. A virtual velocity input is then synthesized for the kinematics. In accordance, a finite-time and velocity-free controller is finally developed for the dynamics with the virtual velocity precisely followed after a finite time. The velocity-free control strategy can ensure the formation spacecraft to follow their desired relative trajectories in a coordinated manner. Numerical simulation is also conducted to validate the performance of the designed scheme.

Hyperspectral anomaly detection represents a crucial application of intelligent sensing, focusing on the identification and localization of anomalous targets. However, the complicated background distribution of hyperspectral imagery (HSI) and the lack of exploration of the intrinsic structure raise enormous challenges for efficient anomaly detection. To address these issues, we introduce the t ensor m ulti-subspace learning strategy with n onconvex l ow-rank r egularization (TMNLR) for anomaly detection in HSI. The HSI is considered as a third-order tensor and is decomposed to background and anomaly, where the tensor subspace and the coefficient tensor are obtained from the background via the tensor multi-subspace learning strategy. To improve detection accuracy, the nonconvex low-rank regularization is introduced for suppressing the background, where the optimization process is designed to extract the background coefficient tensor. And the nonisotropic total variation (TV) regularization is jointly implemented to maintain the local spatial similarity of HSI and promote spatial smoothness. Results demonstrate that the proposed framework could achieve an average detection accuracy rate of 97.98% on four real-scene datasets. Extensive experiments validate the effectiveness and robustness of the TMNLR over the comparative methods.

Single UAVs have limited capabilities for complex missions, so suitable solutions are needed to improve the mission success rate, as well as the UAVs’ survivability. A cooperative multi-UAV formation offers great advantages in this regard; however, for large and complex systems, the traditional control methods will be invalid when faced with unstable and changing environments. To deal with the poor self-adaptability and high requirements for the environmental state information of traditional control methods for a multi-UAV cluster, this paper proposes a consistent round-up strategy based on PPO path optimization to track targets. In this strategy, the leader is trained using PPO for obstacle avoidance and target tracking, while the followers are expected to establish a communication network with the leader to obtain environmental information. In this way, the tracking control law can be designed, based on the consistency protocol and the Apollonian circle, to realize the round-up of the target and obstacle avoidance. The experimental results show that the proposed strategy can achieve the round-up of the target UAV and guide the pursuing multi-UAV group to avoid obstacles in the absence of the initial detection of the target. In multiple simulated scenarios, the success rates of the pursuit multi-UAV cluster for rounding up the target are maintained above 80%.

The accurate detection of small, densely packed and arbitrarily oriented aircraft in high-resolution remote sensing imagery remains highly challenging due to significant variations in object scale, orientation and background complexity. Existing detection frameworks often struggle with insufficient representation of small objects, instability of rotated bounding box regression and inability to adapt to complex background. To address these limitations, we propose SPOD-YOLO, a novel detection framework specifically designed for small aircraft in remote sensing images. This method is based on YOLOv11, combined with the feature attention mechanism of swintransformer, through targeted improvements on cross-scale feature modelling, dynamic convolutional adaptation, and rotational geometry optimization to achieve effective detection. Additionally, we have constructed a new dataset based on satellite remote sensing images, which has high density of small aircraft with rotated bounding box annotations to provide more realistic and challenging evaluation settings. Extensive experiments on MAR20, UCAS-AOD and the constructed dataset demonstrate that our method achieves consistent performance gains over state-of-the-art approaches. SPOD-YOLO achieves an 4.54% increase in mAP50 and a 11.78% gain in mAP50:95 with only 3.77 million parameters on the constructed dataset. These results validate the effectiveness and robustness of our approach in complex remote sensing scenarios, offering a practical advancement for the detection of small objects in aerospace imagery.