Due to the inherent characteristics of the flight mission of a space launch vehicle (SLV), which is required to fly over very large distances and have very high fault tolerances, in general, SLV tracking systems (TSs) comprise multiple heterogeneous sensors such as radars, GPS, INS, and electrooptical targeting systems installed over widespread areas. To track an SLV without interruption and to hand over the measurement coverage between TSs properly, the mission control system (MCS) transfers slaving data to each TS through mission networks. When serious network delays occur, however, the slaving data from the MCS can lead to the failure of the TS. To address this problem, in this paper, we propose multiple model-based synchronization (MMS) approaches, which take advantage of the multiple motion models of an SLV. Cubic spline extrapolation, prediction through anα-β-γfilter, and a single model Kalman filter are presented as benchmark approaches. We demonstrate the synchronization accuracy and effectiveness of the proposed MMS approaches using the Monte Carlo simulation with the nominal trajectory data of Korea Space Launch Vehicle-I.
In KSLV-I launch mission, real-time data from the tracking stations are acquired, processed and distributed by the Mission Control System to the user group who needed to monitor processed data for safety and flight monitoring purposes. The processed trajectory data by the mission control system is sent to each tracking system for target designation in case of tracking failure. Also, the processed data are used for decision making for flight termination when anomalies occur during flight of the launch vehicle. In this paper, we propose the processing mechanism of slaving data which plays a key role of launch vehicle tracking mission. The best position data is selected by predefined logic and current status after every available position data are acquired and pre-processed. And, the slaving data is distributed to each tracking stations through time delay is compensated by extrapolation. For the accurate processing, operation timing of every procesing modules are triggered by time-tick signal(25ms period) which is driven from UTC(Universial Time Coordinates) time. To evaluate the proposed method, we compared slaving data to the position data which received by tracking radar. The experiments show the average difference value is below 0.01 degree.
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