Simplified Orbit Determination Algorithm for Low Earth Orbit Satellites Using Spaceborne GPS Navigation Sensor

Abstract

In this paper, the main work is focused on designing and simplifying the orbit determination algorithm which will be used for Low Earth Orbit (LEO) navigation. The various data processing algorithms, state estimation algorithms and modeling forces were studied in detail, and simplified algorithm is selected to reduce hardware burden and computational cost. This is done by using raw navigation solution provided by GPS Navigation sensor. A fixed step-size Runge-Kutta 4th order numerical integration method is selected for orbit propagation. Both, the least square and Extended Kalman Filter (EKF) orbit estimation algorithms are developed and the results of the same are compared with each other. EKF algorithm converges faster than least square algorithm. EKF algorithm satisfies the criterions of low computation burden which is required for autonomous orbit determination. Simple static force models also feasible to reduce the hardware burden and computational cost.

Extended Kalman filter based statistical orbit determination for geostationary and geosynchronous satellite orbits in BeiDou constellation

“…P nm is the associated Legendre polynomial of degree n and order m. C nm and S nm are the tesseral harmonics coefficients. The explicit equations for solving geopotential acceleration are given in Chobotov (2002;chapter 9, p. 202) and Aghav and Gangal (2014).…”

“…Hwang et al (2013) have reported geostationary orbit determination using a single station data with km level accuracy. Aghav and Gangal (2014) have made an attempt to compare Least-Squares and Kalman filter for orbit determination of a satellite in low-Earth orbit. This study reported prediction up to 60 seconds with an accuracy of km level.…”

Velocity Estimation for Space Infrared Dim Targets Based on Multi-Satellite Observation and Robust Locally Weighted Regression

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