Health Supervision Based on Low Rank Analysis for Aerospace Tracking

In view of the big noises and performance degradation on tracking process with a set of ground system of TTC (Tracking, Telemetering, and Command), it is difficult to diagnose and identify the abnormal conditions problems. A method for establishing a low rank analysis model is present. Through the tracking of historical data, a mathematical model of low rank decomposition is established. Furthermore, the anomaly monitoring and identification of tracking process can be carried out more accurately through the establishment of maximum variance statistic control line. According to the projection of statistics, the influence variables of abnormal occurrence are separated and achieve abnormal separation and alarm. The multi-loop tracking data for a satellite by actual tracking can be analyzed to show that his method can effectively eliminate the influence of measurement noise in tracking process, effectively identify abnormal land realize abnormal separation and alarm.