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

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.

[1]  Simo Särkkä,et al.  Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations , 2009, IEEE Transactions on Automatic Control.

[2]  Fabio Dovis,et al.  Autonomous orbit determination for future GEO and HEO missions , 2010, 2010 5th ESA Workshop on Satellite Navigation Technologies and European Workshop on GNSS Signals and Signal Processing (NAVITEC).

[3]  Vassilis Gikas,et al.  Dynamic modeling for land mobile navigation using low-cost inertial sensors and least squares support vector machine learning , 2010 .

[4]  Bo J. Naasz,et al.  NAVIGATION PERFORMANCE IN HIGH EARTH ORBITS USING NAVIGATOR GPS RECEIVER , 2006 .

[5]  Penina Axelrad,et al.  GEO Satellite Positioning Using GPS Collective Detection , 2010 .

[6]  Emanuela Falletti,et al.  Low Complexity Carrier-to-Noise Ratio Estimators for GNSS Digital Receivers , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Jian Wang,et al.  GPS/INS/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications , 2014, Journal of Navigation.

[8]  Simo Särkkä,et al.  Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter , 2013, Comput. Stat. Data Anal..

[9]  Prabir Bhattacharya,et al.  A novel hybrid fusion algorithm to bridge the period of GPS outages using low-cost INS , 2014, Expert Syst. Appl..

[10]  Cyril Botteron,et al.  GNSS PERFORMANCES FOR MEO, GEO AND HEO , 2013 .

[11]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[12]  Jari Nurmi,et al.  Vector tracking loop design for degraded signal environment , 2010, 2010 Ubiquitous Positioning Indoor Navigation and Location Based Service.

[13]  Erkan Besdok,et al.  A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor Based Terrain Classification , 2009, Sensors.

[14]  James L. Garrison,et al.  GPS Receiver Architecture and Expected Performance for Autonomous Navigation in High Earth Orbits , 2000 .

[15]  G. W. Davis,et al.  Results from the GPS Flight Experiment on the High Earth Orbit AMSAT OSCAR-40 Spacecraft , 2002 .

[17]  Matthew J. Beal.  Variational algorithms for approximate Bayesian inference , 2003 .

[18]  Xiyuan Chen,et al.  Performance Enhancement for a GPS Vector-Tracking Loop Utilizing an Adaptive Iterated Extended Kalman Filter , 2014, Sensors.

[19]  B. Schaffrin,et al.  A Bayes filter in Friedland form for INS/GPS vector gravimetry , 2002 .

[20]  Dah-Jing Jwo,et al.  Vector tracking loop assisted by the neural network for GPS signal blockage , 2015 .

[21]  Fredrik Gustafsson,et al.  Some Relations Between Extended and Unscented Kalman Filters , 2012, IEEE Transactions on Signal Processing.

[22]  Simo Särkkä,et al.  Bayesian Filtering and Smoothing , 2013, Institute of Mathematical Statistics textbooks.

[23]  Vincent Calmettes,et al.  Design of an Adaptive Vector-tracking Loop for Reliable Positioning in Harsh Environment , 2013 .

[24]  Nobuaki Kubo,et al.  Multipath mitigation and NLOS detection using vector tracking in urban environments , 2015, GPS Solutions.

[25]  T. Hausotte,et al.  The Parallel Bayesian Toolbox for High-performance Bayesian Filtering in Metrology , 2013 .

[26]  Gyu-In Jee,et al.  Adaptive vector-tracking loop for low-quality GPS signals , 2011 .

[27]  Masa-aki Sato,et al.  Online Model Selection Based on the Variational Bayes , 2001, Neural Computation.

[28]  John Y. Hung,et al.  Performance Analysis of Vector Tracking Algorithms for Weak GPS Signals in High Dynamics , 2009, IEEE Journal of Selected Topics in Signal Processing.

[29]  Na Shen,et al.  The Design for High Dynamic GPS Receiver in a Combinated Method of FLL and PLL , 2013 .

[30]  Guangcai Li,et al.  Double differencing within GNSS constellations , 2017, GPS Solutions.