A Novel Geometry-free and Geometry-based Combined TCAR Method: Preliminary Performance Analysis for BeiDou System

Chun Jia, Liang Li, Lin Zhao, Zeyu Xin

Peer Reviewed

Abstract:	With the GNSS modernization, much research has been done to improve the efficiency and the reliability of ambiguity resolution (AR) by using triple carrier ambiguity resolution (TCAR) methods. The popular TCAR methods could be categorized into the geometry-free (GF) model and geometry-based (GB) model. Although it has been proven that the LAMBDA method, particularly used for the GB model, can achieve a better success rate than the integer rounding estimator, the whole AR may fail due to the presence of one biased ambiguity in the case of large observation error, e.g. atmospheric error. In contrast, the integer rounding estimator neglects the correlation among the ambiguities, the correctness of the other AR can be immune to one biased ambiguity. Regarding to the GF-based TCAR, the integer rounding estimator is always used, which can be quite computation efficient and reliable when the ratio of the noise to the wavelength is sufficiently small, e.g. extra-wide lane (EWL) ambiguities. But, the requirements cannot be always satisfied in the wide lane (WL) and narrow lane (NL) ambiguities. Therefore, it is difficult to satisfy the requirement of high precision and high reliability real-time positioning using the GF model and GB model independently. In this paper, a new GF and GB combined TCAR method is proposed by fully utilizing advantage of the two models. Firstly, Owing to the EWL ambiguities with the very small ratio of noise to wavelength, two constructed EWL ambiguities can be reliable resolved in GF model by using integer rounding estimator if the biased ambiguity is detected and excluded. Secondly, construct an ambiguity-corrected WL combination following the minimum of observation noise and bias by using correctly resolved EWL ambiguities. Finally, the narrow lane (NL) ambiguity is resolved using the GB model with aiding of the WL combination and atmospheric residuals estimator, which can reduce the risk of ambiguity solution biases, compared with absence of atmospheric residuals and aiding of the low precision observation. Then, the LAMBDA method can be safely used to improve the performance of AR. The proposed method was testified using three static and one kinematic real-world data from Beidou system, compared with the traditional GF-based TCAR and GBbased TCAR. Three static tests indicate that the proposed method can obtain the best accuracy and reliability of positioning for short baseline, while it has the limitation for medium and long baseline. Moreover, the kinematic test shows that the proposed method improves the positioning accuracy by 10cm and enhances the continuity by 96.9% in single epoch positioning mode. Therefore, the proposed method has a satisfied performance in the real-time high precision and high reliability positioning applications.
Published in:	Proceedings of the 2017 International Technical Meeting of The Institute of Navigation January 30 - 2, 2017 Hyatt Regency Monterey Monterey, California
Pages:	773 - 781
Cite this article:	Jia, Chun, Li, Liang, Zhao, Lin, Xin, Zeyu, "A Novel Geometry-free and Geometry-based Combined TCAR Method: Preliminary Performance Analysis for BeiDou System," Proceedings of the 2017 International Technical Meeting of The Institute of Navigation, Monterey, California, January 2017, pp. 773-781. https://doi.org/10.33012/2017.14917
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