Particle filter-based estimation of inter-frequency phase bias for real-time GLONASS integer ambiguity resolution

GLONASS could hardly reach the positioning performance of GPS, especially for fast and real-time precise positioning. One of the reasons is the phase inter-frequency bias (IFB) at the receiver end prevents its integer ambiguity resolution. A number of studies were carried out to achieve the integer ambiguity resolution for GLONASS. Based on some of the revealed IFB characteristics, for instance IFB is a linear function of the received carrier frequency and L1 and L2 have the same IFB in unit of length, most of recent methods recommend estimating the IFB rate together with ambiguities. However, since the two sets of parameters are highly correlated, as demonstrated in previous studies, observations over several hours up to 1 day are needed even with simultaneous GPS observations to obtain a reasonable solution. Obviously, these approaches cannot be applied for real-time positioning. Actually, it can be demonstrated that GLONASS ambiguity resolution should also be available even for a single epoch if the IFB rate is precisely known. In addition, the closer the IFB rate value is to its true value, the larger the fixing RATIO will be. Based on this fact, in this paper, a new approach is developed to estimate the IFB rate by means of particle filtering with the likelihood function derived from RATIO. This approach is evaluated with several sets of experimental data. For both static and kinematic cases, the results show that IFB rates could be estimated precisely just with GLONASS data of a few epochs depending on the baseline length. The time cost with a normal PC can be controlled around 1 s and can be further reduced. With the estimated IFB rate, integer ambiguity resolution is available immediately and as a consequence, the positioning accuracy is improved significantly to the level of GPS fixed solution. Thus the new approach enables real-time precise applications of GLONASS.

[1]  Binghao Li,et al.  Network-based RTK Positioning Using Integrated GPS and GLONASS Observations , 2011 .

[2]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[3]  A. E. Zinoviev,et al.  Renovated GLONASS: Improved Performances of GNSS Receivers , 2009 .

[4]  Anton J. Haug Bayesian Estimation and Tracking: A Practical Guide , 2012 .

[5]  Jinling Wang,et al.  An approach to GLONASS ambiguity resolution , 2000 .

[6]  Lambert Wanninger,et al.  Carrier-phase inter-frequency biases of GLONASS receivers , 2012, Journal of Geodesy.

[7]  G. Kitagawa Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[8]  Chris Rizos,et al.  A Comparative Study of Mathematical Modelling for GPS/GLONASS Real-Time Kinematic (RTK) , 2012 .

[9]  Gerd Gendt,et al.  Improving carrier-phase ambiguity resolution in global GPS network solutions , 2005 .

[10]  G. Blewitt Carrier Phase Ambiguity Resolution for the Global Positioning System Applied to Geodetic Baselines up to 2000 km , 1989 .

[11]  Tomoji Takasu,et al.  Development of the low-cost RTK-GPS receiver with an open source program package RTKLIB , 2009 .

[12]  Peter Teunissen,et al.  Penalized GNSS Ambiguity Resolution , 2004 .

[13]  Christian Rocken,et al.  Obtaining single path phase delays from GPS double differences , 2000 .

[14]  Chris Rizos,et al.  A New Data Processing Strategy for Combined GPS/GLONASS Carrier Phase-Based Positioning , 1999 .

[15]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[16]  Kristine L. Bell,et al.  A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking , 2007 .

[17]  Paul Collins,et al.  GLONASS ambiguity resolution of mixed receiver types without external calibration , 2013, GPS Solutions.

[18]  M. Pratt,et al.  Single-Epoch Integer Ambiguity Resolution with GPS-GLONASS L1 Data , 1997 .

[19]  Y. Bock,et al.  Global Positioning System Network analysis with phase ambiguity resolution applied to crustal deformation studies in California , 1989 .

[20]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[21]  Andrew Simsky,et al.  Origin and Compensation of GLONASS Inter-frequency Carrier Phase Biases in GNSS Receivers , 2012 .

[22]  F Gustafsson,et al.  Particle filter theory and practice with positioning applications , 2010, IEEE Aerospace and Electronic Systems Magazine.

[23]  Alfred Leick,et al.  GLONASS Satellite Surveying , 1998 .

[24]  Sean McKee,et al.  Monte Carlo Methods for Applied Scientists , 2005 .

[25]  Fredrik Gustafsson,et al.  Particle filters for positioning, navigation, and tracking , 2002, IEEE Trans. Signal Process..

[26]  Lambert Wanninger,et al.  Combined Processing of GPS, GLONASS, and SBAS Code Phase and Carrier Phase Measurements , 2007 .

[27]  Tao Li,et al.  Comparing the mathematical models for GPS&GLONASS integration , 2011 .

[28]  C. Rizos,et al.  An enhanced calibration method of GLONASS inter-channel bias for GNSS RTK , 2013, GPS Solutions.

[29]  P. Teunissen The least-squares ambiguity decorrelation adjustment: a method for fast GPS integer ambiguity estimation , 1995 .

[30]  Chris Rizos,et al.  GPS and GLONASS Integration: Modeling and Ambiguity Resolution Issues , 2001, GPS Solutions.

[31]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .