Multiple payloads are increasingly employed for satellites, but the coupling among payloads degrades their performance obviously. In order to comprehensively manage multiple payloads while ensuring the performance of each payload, this paper proposes an integrated payload-centric control approach. First, the satellite control system is divided into a platform computer and a payload computer. The platform computer is employed for high-reliability fundamental control of the satellite, and the payload computer is employed for high-performance precise control of multiple payloads. Then, an integrated dynamic model is established, and simulation studies are carried out by parallel operation of multi-payload control loops. Finally, an integrated control program is designed and the proposed approach is verified by a ground experiment; experimental results indicate that high-precision and high-stability control of a multi-payload satellite can be achieved.

With the growing efficiency of the use of unlicensed spectrum, the challenge of ensuring spectrum security has become increasingly daunting. Spectrum managers aim to accurately and efficiently detect and recognize anomaly behaviors in the spectrum. In this study, we propose a novel framework for spectrum anomaly detection and localization by spectrum interpolation recovery. Spectrum interpolation recovery refers to the recovery of the rest of the spectrum distribution based on a part of the spectrum distribution, which is achieved through a masked autoencoder (MAE) model with a core of multi-head self-attention (MHSA) mechanism. The spectrum interpolation recovery method restores the region where the masked abnormal signals are present, yielding anomaly-free results, with the difference between the restored and the masked representing the anomaly signals. The proposed method has been demonstrated to effectively reduce model-induced over-recovery of anomalous signals and dilute large-scale generation errors caused by anomalies, thereby improving the detection and localization performance of anomaly signals, and improving the area under the receiver operating characteristic curve (AUC) and the area under the precision–recall curve (AUPRC) by 0.0382 (3.68%) and 0.1992 (68.90%), respectively. On a designed dataset containing 3 variables of interference-to-signal ratio (ISR), signal-to-noise ratio (SNR), and anomaly type, the total recall of anomaly detection and localization at a 5% false alarm rate reached 0.8799 and 0.5536, respectively. Furthermore, a comparative study among different methods demonstrates the effectiveness and rationality of the proposed method.