This study investigates the scheduling problem of return communication tasks for unmanned aerial vehicle (UAV) swarms, where disaster relief environmental global positioning is hampered. To characterize the utility of these tasks and optimize scheduling decisions, we developed a time window-constrained scheduling model that operates under constraints, including communication base station time windows, battery levels, and task uniqueness. To solve the above model, we propose an enhanced algorithm through integrating Dueling Deep Q-Network (Dueling DQN) into adaptive large neighborhood search (ALNS), referred to as Dueling DQN-ALNS. The Dueling DQN component develops a method to update strategy weights, while the action space defines the destruction and selection strategies for the ALNS scheduling solution across different time windows. Meanwhile, we design a two-stage algorithm framework consisting of centralized offline training and decentralized online scheduling. Compared to traditionally optimized search algorithms, the proposed algorithm could continuously and dynamically interact with the environment to acquire state information about the scheduling solution. The solution ability of Dueling DQN is 3.75% higher than that of the Ant Colony Optimization (ACO) algorithm, 5.9% higher than that of the basic ALNS algorithm, and 9.37% higher than that of the differential evolution algorithm (DE). This verified its efficiency and advantages in the scheduling problem of return communication tasks for UAVs.

A multi-target robust observation method for satellite constellations based on hypergraph algebraic connectivity and observation precision theory is proposed to address the challenges posed by the surge in space targets and system failures. First, a precision metric framework is constructed based on nonlinear batch least squares estimation theory, deriving the theoretical precision covariance through cumulative observation matrices to provide a theoretical foundation for tracking accuracy evaluation. Second, multi-satellite collaborative observation is modeled as an edge-dependent vertex-weighted hypergraph, enhancing system robustness by maximizing algebraic connectivity. A constrained simulated annealing (CSA) algorithm is designed, employing a precision-guided perturbation strategy to efficiently solve the optimization problem. Simulation experiments are conducted using 24 Walker constellation satellites tracking 50 targets, comparing the proposed method with greedy algorithm, CBBA, and CSA-bipartite Graph methods across three scenarios: baseline, maneuvering, and failure. Results demonstrate that the CSA-hypergraph method achieves 0.089 km steady-state precision in the baseline scenario, representing a 41.4% improvement over traditional methods; in maneuvering scenarios, detection delay is reduced by 34.3% and re-achievement time is decreased by 47.4%; with a 30% satellite failure rate, performance degradation is only 9.8%, significantly outperforming other methods.

Non-Gaussian dynamic processes are ubiquitous due to the presence of non-Gaussian distributed variables. Therefore, fault detection of non-Gaussian dynamic processes plays a vital role to maintain the safe operation of systems and symmetry of data distribution. In this paper, a dynamic generalized likelihood ratio (DGLR)-based fault detection method is proposed for non-Gaussian dynamic processes. Different from the conventional principal component analysis (PCA)-based, dynamic PCA-based, and PCA-based GLR fault detection methods, the novelty of the proposed method is that the GLR is extended to non-Gaussian dynamic processes, and the randomized algorithm is integrated for threshold setting to attenuate the influence of non-Gaussian. The application scope of these methods is also discussed. The proposed method is compared with four existing fault detection methods on a numerical simulation and the continuous stirred-tank reactor (CSTR) process. The achieved results show that the proposed method is able to significantly improve the detection performance in terms of fault detection rate and prompt response to faults.

Greedy schedulers are widely used for low-Earth-orbit satellite task scheduling for their computational efficiency and determinism. However, they select candidate arcs by primary criteria alone, so near-equivalent alternatives are separated arbitrarily, potentially reducing scheduling flexibility for later tasks under contention. We present FAMAS-G, a lightweight conflict-aware extension of greedy scheduling rather than a new optimization framework. Its threshold-gated conflict-aware arc selection activates only when primary scores cannot clearly distinguish among candidates, applying a bounded conflict adjustment that favors lower-conflict arcs without displacing clearly superior ones. It relies only on pre-computed arc-level conflict degrees, adding no message passing, backtracking, or iterative search. We evaluate FAMAS-G against Greedy-Central, Base-CNP, and the original FAMAS in Starlink mega-constellation simulations at 100, 300, and 500 tasks per 6 h period across four TLE epochs. Hierarchical bootstrap analysis shows statistically supported improvements over Greedy-Central at all scales ($\Delta \mathrm{WFR}=+0.0111$, $+0.0051$, and $+0.0024$), with the advantage attenuating as contention rises. Ablation isolates the conflict-aware tiebreak as the primary positive contributor; urgent priority shows no measurable effect, whereas the risk tiebreak shows a small but significant negative effect. Lightweight conflict awareness thus improves greedy scheduling under contention while retaining single-pass, sub-second execution. Significance at the largest scale is sensitive to epoch inclusion; results are specific to the evaluated Starlink scenarios and require further validation.

In recent years, the number of satellites in space has experienced explosive growth, and the number of non-cooperative satellites requiring close attention and precise tracking has also increased rapidly. Despite this, the world’s satellite precision tracking equipment is constrained by factors such as a slower growth in numbers and a scarcity of available deployment sites. To rapidly and efficiently identify satellites with potential new anomalies among the large number of cataloged non-cooperative satellites currently transiting, we have constructed a Bi-Directional Minimal GRU deep learning network model incorporating an attention mechanism based on Minimal GRU. This model is termed the Attention-based Bi-Directional Minimal GRU model (ABMGRU). This model utilizes tracking data from relatively inexpensive satellite observation equipment such as phased array radars, along with catalog information for non-cooperative satellites. It rapidly detects anomalies in target satellites during the initial phase of their passes, providing decision support for the subsequent deployment, scheduling, and allocation of precision satellite tracking equipment. The satellite tracking observation data used to support model training is predicted through Satellite Tool Kit simulation based on existing catalog information of non-cooperative satellites, encompassing both anomaly free data and various types of data containing anomalies. Due to limitations imposed by relatively inexpensive observation equipment, satellite tracking data is restricted to the following categories: time, azimuth, elevation, distance, and Doppler shift, while incorporating realistic noise levels. Since subsequent precision tracking requires utilizing more satellite pass time, the duration of tracking data collected during this phase should not be excessively long. The tracking observation time in this study is limited to 1000 s. To enhance the efficiency and effectiveness of satellite anomaly detection, we have developed an Attention-based Bi-Directional Minimal GRU deep learning network model. Experimental results demonstrate that the proposed method can detect non-cooperative anomalous satellites more effectively and efficiently than existing lightweight intelligent algorithms, outperforming them in both completion efficiency and detection performance. It exhibits superiority across various non-cooperative satellite anomaly detection scenarios.