A Novel GPS Based Real Time Orbit Determination Using Adaptive Extended Kalman Filter

Regular Section

Shanghai Engineering Center for Microsatellites, Chinese Academy of Sciences
University of Chinese Academy of Sciences">Yang XIAO, Limin LI, Shanghai Engineering Center for Microsatellites, Chinese Academy of Sciences
University of Chinese Academy of Sciences">Jiachao CHANG, Shanghai Engineering Center for Microsatellites, Chinese Academy of Sciences">Kang WU, Shanghai Engineering Center for Microsatellites, Chinese Academy of Sciences">Guang LIANG, Shanghai Engineering Center for Microsatellites, Chinese Academy of Sciences">Jinpei YU

Author information

Yang XIAO
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences
Shanghai Engineering Center for Microsatellites, Chinese Academy of Sciences
University of Chinese Academy of Sciences
Limin LI
College of Physics and Electronic Information Engineering, Wenzhou University
Jiachao CHANG
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences
Shanghai Engineering Center for Microsatellites, Chinese Academy of Sciences
University of Chinese Academy of Sciences
Kang WU
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences
Shanghai Engineering Center for Microsatellites, Chinese Academy of Sciences
Guang LIANG
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences
Shanghai Engineering Center for Microsatellites, Chinese Academy of Sciences
Jinpei YU
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences
Shanghai Engineering Center for Microsatellites, Chinese Academy of Sciences

Keywords: reduced dynamic orbit determination (RDOD), measurement noise, carrier to noise ratio (C/N₀), adaptive extended Kalman filter (AEKF)

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2018 Volume E101.A Issue 1 Pages 287-292

DOI https://doi.org/10.1587/transfun.E101.A.287

Details

Abstract

The combination of GPS measurements with the dynamic model via a Kalman filter or an extended Kalman filter, also known as GPS based reduced dynamic orbit determination (RDOD) techniques, have been widely used for accurate and real time navigation of satellites in low earth orbit (LEO). In previous studies, the GPS measurement noise variance is empirically taken as a constant, which is not reasonable because of insufficient prior information or dynamic environment. An improper estimate of the measurement noise may lead to poor performance or even divergence of the filter. In this letter, an adaptive extended Kalman filter (AEKF)-based approach using GPS dual-frequency pseudo-range measurements is presented, where the GPS pseudo-range measurement noise variance is adaptively estimated by the Carrier to Noise Ratio (C/N₀) from the tracking loop of GPS receiver. The simulation results show that the proposed AEKF approach can achieve apparent improvements of the position accuracy and almost brings no extra computational burdens for satellite borne processor.

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