|Abstract:||The auto-correlation characteristics of pseudo-random noise (PRN) codes modulated in GNSS signals is very important in signal detection and estimation. The ideal auto-correlation curve is triangular and traditional code discriminator is based on this hypothesis. However, the limited RF front-end bandwidth will deform the shape of PRN code signals, thus deforming the shape of auto-correlation curve. In this case, the code phase error outputted from traditional code discriminator is not accurate. Besides, the noise in GNSS signals is hardly to be filtered completely by radio frequency (RF) filter and correlation operation, which will make code phase estimation more difficult. The pseudo-range measurements accuracy and position accuracy will decrease subsequently. This paper demonstrates two techniques to improve accuracy of code phase measurement. The first one is coherent integration Kalman filter (CI-KF), which can be used remove noise in coherent integration results. The filtered coherent integration results are then used as input of normalized E-L envelope code discriminator. The second one is code phase error compensation technique. The concept of common bias and uncommon bias are introduced to compensate the code phase error estimation in order to get more accurate code phase measurements. The common bias compensation is based on the analysis of PRN code that is filtered by RF front-end, while the non-common bias compensation is based on the auto-correlation curve fitting technique. The simulations and experiments demonstrate these two methods can improve the accuracy of pseudo-range measurements and position fixes.|
Proceedings of the 2017 International Technical Meeting of The Institute of Navigation
January 30 - 2, 2017
Hyatt Regency Monterey
|Pages:||365 - 387|
|Cite this article:||
Luo, Zhibin, Ding, Jicheng, Zhao, Lin, Wu, Mouyan, "The Code Phase Error Compensation Technique in GNSS Receivers," Proceedings of the 2017 International Technical Meeting of The Institute of Navigation, Monterey, California, January 2017, pp. 365-387.
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