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An Adaptive Vector Tracking Scheme for High-Orbit Degraded GNSS Signal

Published online by Cambridge University Press:  17 November 2020

Chenyang Jiao
Affiliation:
(School of Astronautics, Beihang University, China)
Xinlong Wang*
Affiliation:
(School of Astronautics, Beihang University, China)
Dun Wang
Affiliation:
(State Key Laboratory of Space-Ground Information Technology, Beijing, China)
Qunsheng Li
Affiliation:
(School of Instrumentation Science and Opto-electronics Engineering, Beihang University, China)
Jinpeng Zhang
Affiliation:
(Luoyang Optoelectro Technology Development Center, Luoyang, China)
Yuanwen Cai
Affiliation:
(Department of Graduate School, Space Engineering University, Beijing, China)
*
(E-mail: xlwon@163.com)

Abstract

Global navigation satellite system (GNSS) receivers meet numerous challenges in a high-orbit environment, including weak and discontinuous signal, and time-varying strength. To resolve these issues and enhance reliability, an innovative adaptive vector tracking loop (VTL) scheme is proposed. Non-linear models of the VTL filter are established to calculate code phase and carrier frequency errors accurately. Based on this, a deep analysis has been developed on the measurement noise. To reduce the impact of the interdependent noises among channels in VTL, an adaptive VTL algorithm assisted by the variational Bayesian (VB) learning network is proposed to estimate the measurement noise and maintain the error convergence in the time-varying noise or signal outage conditions. Further, the implementation steps of the adaptive algorithm have been designed in detail. In particular, the carrier-to-noise power ratio (C/N0) estimation method is further employed to update the a prior probability density in case of change of tracking satellite. The simulation results indicate that the proposed VTL scheme with VB algorithm is a promising method to improve the accuracy and reliability of GNSS receivers significantly under a high-orbit degraded signal environment.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2020

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