Spaceborne Radar Altimeter Autonomous Navigation Technology

Autonomous navigation is the process that the satellite runs by itself and is dependent on ground support guidance system a little while. With the autonomous navigation technology, the expenditure of satellite can be reduced, at the same time, survival ability increased. Satellite autonomous, position determination founds on the measurements of distances between the satellite and the surface of the earth with radar altimeters. According to the geometry knowledge, three or more different distances can calculate the radius vector of satellite relatively to the earth's core. Then the orbit of satellite can be determined. The main characteristics of satellite autonomous navigation system (based on radar altimeters) can be summarized as follows: â…  The navigation system can determine the orbit of satellite independently, â…¡ The accuracy of the navigation system is influenced by many factors, such as uncertain errors of earth surface model; â…¢.Because of weight and power consume, the navigation system is unfit for satellite higher than 1000km or so.This thesis mainly dwells on two kinds of autonomous position determination schemes with radar altimeter measurement. One is based on standard sphere earth model. The other is Clairaut ellipsoid model. At first, satellite orbit dynamic model and sea surface model are introduced. Secondly, using least squares and Kalman filter algorithms, autonomous position determination schemes are illustrated aim at two kinds of model. Especially for Kalman filter algorithm, two kinds of observation are considered. Finally, the positioning error of different earth models and different schemes are compared and analyzed. Analysis and simulation results both show that: when considering standard sphere earth model, the fluctuant sea surface affects the positioning accuracy seriously; but the modificatory measurement can enhance the accuracy remarkably; Clairaut ellipsoid model can depress the influence of fluctuant sea surface effectively; and the Kalman filter cannot eliminate the influence of the fluctuantion.