Satellite anomaly is a process of evolution. Detecting this evolution and the underlying feature changes is critical to satellite health prediction, fault early warning, and response. Analyzing the correlation between telemetry parameters is more convincing than detecting single-point anomalies. In this article, principal component analysis method was adopted to downscale the multivariate probability model, T 2 {T}^{2} statistic was checked to determine the data anomaly, without the trouble of threshold setting. After an anomaly was detected, time-domain visualization and dimension reduction methods were introduced to visualize the satellite anomaly evolution, where the dimensions of telemetry or features were reduced and presented in two- or three-dimensional coordinates. Engineering practice shows that this method facilitates the early detection of satellite anomalies, and helps ground operators to respond in the early stages of an anomaly.

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Data loss is ubiquitous in practical engineering applications due to communication delay or congestion. Data loss rate is a key metric to evaluate the reliability of state estimation. To jointly estimate system state and data loss rate, we propose a class of Gaussian-Beta filters for linear and moderate nonlinear Gaussian state-space models with unknown probability of measurement loss. In the filters, the arrival of the measurement at each time is formulated as a binary random variable, which is determined by the classical threshold technology. In addition, the hidden state and the unknown probability of measurement loss are modeled as a product of Gaussian and Beta distributions, and the form remains unchanged through recursive operations. Simulation results verify the effectiveness of the proposed Gaussian-Beta filters compared with the existing filtering algorithms.INDEX TERMS State-space model, measurement loss, threshold technology, Gaussian-Beta filter.