The evolution of satellite surveillance technology, coupled with advanced on-board intelligent systems and improved attitude maneuver capabilities, has positioned mission scheduling and execution as a prominent and active research area in recent years. With the urgent need for mission scheduling and execution to evolve from the static ground target to the time-sensitive moving target, the existing satellite often yields unsatisfactory results when managed through conventional manual methods for continuously tracking a time-sensitive moving target. In response to this challenge, the paper introduces a reinforcement learning driven method enabling the satellite to schedule and execute the tracking of time-sensitive moving target autonomously. In the paper, we conduct a meticulous analysis of the target tracking process, which we decompose into three distinct sub-phases and address it utilizing attitude control, elucidating the integration of our model with satellite attitude dynamics and kinematics. Besides, we propose Flight Trajectory Prediction Module to predict the future trajectory position and minimize the position error due to the communication latency. With the dual aim of expeditiously tracking the target and concurrently enhancing payload stability, a well-designed reward function, which is applied to various reinforcement learning (RL) algorithms, effectively governs satellite attitude control to achieve accurate and stable tracking of moving target. In addition, through the eight distinct tracking scenarios simulated in our proposed Satellite Target Tracking Environment (STTE), our extensive experiments and ablation studies consistently underscore the advantages and efficacy of our proposed method. This work serves as a foundational cornerstone for future research endeavors focused on tracking multiple time-sensitive moving targets within extensive and complex intelligent satellite constellation.

Effective detection and tracking of dim and small targets with low signal-to-clutter ratio (SCR) has become a research hotspot due to its wide range of applications. However, most of the previously proposed methods seldom utilize the abundant temporal features formed by target motion, resulting in poor detection and tracking performance for low SCR targets. In this article, we analyze the difficulty based on spatial features and the feasibility based on temporal features of realizing effective detection. According to this analysis, we use a multi-frame as a detection unit and propose a detection method based on temporal energy selective scaling (TESS). Specifically, we investigated the composition of intensity temporal profiles (ITPs) formed by pixels on a multi-frame detection unit. For the target-present pixel, the target passing through the pixel will bring a weak transient disturbance on the ITP and introduce a change in the statistical properties of ITP. We use a well-designed function to amplify the transient disturbance, suppress the background and noise components, and output the trajectory of the target. Subsequently, to solve the contradiction between the detection rate and the false alarm rate brought by the traditional threshold segmentation, we associate the temporal and spatial features of the trajectory and propose a trajectory extraction method based on the 3D Hough transform. Finally, we propose a trajectory segments-based multi-target tracking method. Compared with the various state-of-the-art detection and tracking methods, experiments in multiple scenarios prove the superiority of our proposed methods for dim and small targets in low SCR.

As a core technology of space situational awareness, dim and small space target detection plays a critical role in satellite surveillance and space defense. However, it remains highly challenging due to low signal-to-noise ratios of targets, stellar clutter interference, dynamic backgrounds, and imaging noise. Space-based imagery often lacks texture and contextual information of both targets and backgrounds. Most existing detection methods are developed for uncrewed aerial vehicle (UAV) detection in ground-based scenes with abundant spatial details. These methods often treat spatial and temporal features independently, neglecting the importance of spatiotemporal collaboration. Consequently, their effectiveness diminishes in space-based imagery, where spatial features are insufficiently reliable for accurate detection. To address it, we propose a temporal evolution-aware network with spatial guidance (TESNet). Rather than depending on conventional spatial backbone networks, TESNet employs a lightweight spatiotemporal backbone to capture spatial and temporal representations jointly from early stages. A dual-branch architecture is then designed to further enhance spatial and temporal features, where the temporal branch models cross-frame temporal evolution, and the spatial branch refines target spatial representations. In addition, a novel gated fusion module based on dual attention is introduced to effectively integrate spatial and temporal features, thereby strengthening deep spatiotemporal correlations. To train and evaluate our approach, we generate a simulated dataset, SIMDataset, specifically designed for dim and small target detection in space-based scenarios. Through a series of experiments and comparisons with current methods, we demonstrate the effectiveness of TESNet in addressing the challenges of dim and small space target detection under complex deep-space backgrounds. Our code and simulated dataset is publicly accessible online.

Wind is an important renewable energy source. The energy and economic return from building wind farms justify the expensive investments in doing so. However, without an effective monitoring system, underperforming or faulty turbines will cause a huge loss in revenue. Early detection of such failures help prevent these undesired working conditions. We develop three tests on power curve, rotor speed curve, pitch angle curve of individual turbine. In each test, multiple states are defined to distinguish different working conditions, including complete shut-downs, under-performing states, abnormally frequent default states, as well as normal working states. These three tests are combined to reach a final conclusion, which is more effective than any single test. Through extensive data mining of historical data and verification from farm operators, some state combinations are discovered to be strong indicators of spindle failures, lightning strikes, anemometer faults, etc, for fault detection. In each individual test, and in the score fusion of these tests, we apply multidimensional scaling (MDS) to reduce the high dimensional feature space into a 3-dimensional visualization, from which it is easier to discover turbine working information. This approach gains a qualitative understanding of turbine performance status to detect faults, and also provides explanations on what has happened for detailed diagnostics. The state-of-the-art SCADA (Supervisory Control And Data Acquisition) system in industry can only answer the question whether there are abnormal working states, and our evaluation of multiple states in multiple tests is also promising for diagnostics. In the future, these tests can be readily incorporated in a Bayesian network for intelligent analysis and decision support.