To achieve global network coverage and the need for high-speed communication, the idea of providing Internet access from space has made a strong comeback in recent years. The low earth orbit (LEO) communication satellite constellation is once again on the stage of the world with its unique features and new technology. In order to provide faster and more affordable communication resources, low-orbit satellites need be customized to design satellites. The beam coverage design is essential to the user-customized design. This paper combines the user traffic demand model and the low-orbit satellite beam coverage model to analyze the impact of beam coverage characteristics on the performance of low-orbit satellite systems. The user traffic model bases on the user simulative distribution (uniform, normal) and the user geographic distribution (according to the AIS and ADS-B historical data acquired by STU-2B and STU-2C which are the LEO satellites launched in Sep, 2015, Jiuquan, China). The beam coverage model compares the OneWeb system to the SpaceX system. The beam coverage model takes the variability in performance induced by atmospheric conditions for the user links into account. Follow that this paper proposes a system method to simulate the two satellite system which described by the throughput, delay, access probability. Finally, the sensitivity of beam coverage to user diversification is summarized and discussed.

Timely and effective detection of potential incipient faults in satellites plays an important role in improving their availability and extending their service life. In this paper, the problem of detecting incipient faults using projection vector (PV) and Kullback-Leibler (KL) divergence is studied in the context of detecting incipient faults in satellites. Under the assumption that the variables obey a multidimensional Gaussian distribution and using KL divergence to detect incipient faults, this paper models the optimum PV for detecting incipient faults as an optimization problem. It proves that the PVs obtained by principal component analysis (PCA) are not necessarily the optimum PV for detecting incipient faults. It then compares the on-line probability density function (PDF) with the reference PDF for detecting incipient faults on the local optimum PV. A numerical example and a real satellite fault case were used to assess the validity and superiority of the method proposed in this paper over conventional methods. Since the method takes into account the characteristics of the actual incipient faults, it is more adaptable to various possible incipient faults. Fault detection rates of three simulated faults and the real satellite fault are 98%, 84%, 93% and 92%, respectively.

Detection of faults at the incipient stage is critical to improving the availability and continuity of satellite services. The application of a local optimum projection vector and the Kullback–Leibler (KL) divergence can improve the detection rate of incipient faults. However, this suffers from the problem of high time complexity. We propose decomposing the KL divergence in the original optimization model and applying the property of the generalized Rayleigh quotient to reduce time complexity. Additionally, we establish two distribution models for subfunctions F1(w) and F3(w) to detect the slight anomalous behavior of the mean and covariance. The effectiveness of the proposed method was verified through a numerical simulation case and a real satellite fault case. The results demonstrate the advantages of low computational complexity and high sensitivity to incipient faults.