Reduce Position and Velocity RMS Error of Non-linear Filters in LEO Satellite Radar Tracking

Abstract

For the detection of and tracking thelow earth orbit Satellites (LEO), there are different methods such as optic, laser and radar tracking, among which radar tracking is the best. Since the common linear tracking filters deployed in available radars are not able to estimate the position of the non-linear dynamic satellites, it is advisable to use non-linear filters. In this paper, firstly, the satellite motion path around the earth as well as radar observations are produced by the STK software. Accordingly, the samples are fed to non-linear Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). Finally, the performance of the aforementioned filters is studied through evaluation of RMS position and estimation errors. Simulation results demonstrate that the Unscented Kalman filter has a better performance in terms of accuracy with respect to the Extended Kalman filter. In addition, using this method, theerror of observations decreases 50% along the range and 70% along the azimuth and elevation.

Keywords

• leo satellites
• radar observation
• extended kalman filter (ekf)
• unscented kalman filter (ukf)
• stk software