GLONASS phase inter-frequency bias (IFB) is linearly correlated to ambiguity, so it is difficult to separate phase IFB and ambiguity quickly. To solve this problem, a real-time estimate method for GLONASS phase IFB is proposed. By analyzing the relationship between the phase IFB parameter and the RATIO value, the phase IFB estimation problem comes down to solve the optimization problem. The particle swarm optimization (PSO) algorithm is one of the optimization methods, which is used to estimate the phase IFB parameters. This method can search the IFB rate parameter in an effective and reliable way without increasing the number of estimated parameters and prior information, and GLONASS ambiguities can be real-time fixed. The experimental results show that the average number of searching per epoch is 32 for single-epoch solution, which is far below what particle filter-based estimation of phase IFB needs, the number of searching per epoch is always 200 by using particle filter-based estimation. The average number of searching per epoch is only 9 by using PSO for filtering solution. The ambiguity-fixing success rate is above 96.2% whether for single-epoch solution or filtering solution, and maximal position differences of fixed solution are all below 4 cm.
SUI Xin
,
XU Aigong
,
HAO Yushi
,
WANG Changqiang
. Real-time Estimation Method for GLONASS Phase Inter-frequency Bias Based on Particle Swarm Optimization[J]. Acta Geodaetica et Cartographica Sinica, 2018
, 47(5)
: 584
-591
.
DOI: 10.11947/j.AGCS.2018.20170244
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