A comparison of TCAR, CIR and LAMBDA GNSS ambiguity resolution
A comparison of TCAR, CIR and LAMBDA GNSS ambiguity resolution
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发表时间：
2003
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通讯作者：
P. Teunissen;P. Joosten;C. Tiberius
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作者：
P. Teunissen;P. Joosten;C. Tiberius
随着三载波GNSS（现代化的GPS、Galileo）的设想引入，已经开发了模糊度解算的新方法。在这篇文章中，我们将比较两个重要的候选方法，三载波模糊度解决方案与现有的LAMBDA（最小二乘模糊度解相关调整）方法：TCAR（三载波模糊度解决方案）方法，和CIR（级联解相关）方法。它将表明，他们的估计原则，TCAR和CIR依赖于整数自举，而LAMBDA是基于整数最小二乘，其中最优性已被证明，即成功的概率最高。在TCAR和CIR中，使用预定义的模糊度变换，而LAMBDA利用全模糊度方差-协方差矩阵的信息内容，其中统计去相关是构造模糊度变换的目标。在解模糊方面，设计了TCAR和CIR算法，用于无几何模型。LAMBDA本身可以处理任何具有整周模糊度的GNSS模型，从而利用卫星几何结构来使其在基于几何结构的模型中受益。
With the envisioned introduction of three-carrier GNSS’s (modernized GPS, Galileo), new methods of ambiguity resolution have been developed. In this contribution we will compare two important candidate methods for triplefrequency ambiguity resolution with the already existing LAMBDA (Least-squares Ambiguity Decorrelation Adjustment) method: the TCAR (Three-Carrier Ambiguity Resolution) method, and the CIR (Cascading Integer Resolution) method. It will be shown that for their estimation principle, both TCAR and CIR rely on integer bootstrapping, whereas LAMBDA is based on integer least-squares, of which optimality has been proven, that is, highest probability of success. In TCAR and CIR pre-defined ambiguity transformation are used, whereas LAMBDA exploits the information content of the full ambiguity variance-covariance matrix, with statistical decorrelation the objective in constructing the ambiguity transformation. For the aspect of resolving the ambiguities, TCAR and CIR are designed for use with the geometry-free model. LAMBDA can intrinsically handle any GNSS model with integer ambiguities and thereby utilize satellite geometry to its benefit in geometry-based models.