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Three Carrier Ambiguity Resolution: Generalised Problems, Models, Methods and Performance Analysis Using Semi-Generated Triple Frequency GPS Data

机译:三个载波歧义分辨率:使用半生成三频GPS数据的广义问题,模型,方法和性能分析

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In this paper, the problems of three carrier phase ambiguity resolution (TCAR) and position estimation (PE) are generalised as real time GNSS data processing problems for a continuously observing network of any large scales. In order to describe these problems, a general linear equation system is presented that combines uniforms various geometry-free, geometry-based and geometry-constrained TCAR models, along with state transition questions between observation times. With this general formation, generalised TCAR solutions are given to cover all the real time GNSS data processing scenarios, which can also be simplified to various integer solutions, such as geometryfree rounding and geometry-based LAMBDA solutions with single and multiple epoch measurements. In fact, various ambiguity resolution (AR) solutions differ in the floating ambiguity estimation and integer ambiguity search processes, although their theoretical equivalence depends on use of the exactly same observational systems models and assumptions. A few key performance benefits achievable with semi-generated triple frequency GPS data from several dual-frequency GPS data sets from the baselines with respective 53km, 78km and 155km in length are demonstrated. These benefits include: (1) with the geometry-based and geometry-free TCAR models, the AR success rates of the two best Extra-widelane (EWL) observables from single-epoch L1, L2 and L5 signals reach almost 100%; (2) using these two ambiguity-fixed EWL signals or other derived WL signals, the ionosphere biases can be determined to the level of centimetres; (3) the ambiguity of the third observable can be fixed to 100% success within the measurements of several minutes; and (4) position estimation using an ambiguity-fixed and ionosphere-free WL observable achieves a 3D RMS accuracy of better than 20 centimetres, where the residual tropospheric errors dominate. With respect to the current dual-frequency based wide area GPS solutions, such as Precise Point Positioning (PPP) solutions, a major benefit of using the additional signals is the reduction of filtering time for the decimetre RMS accuracy from a few tens of minutes to a few minutes.
机译:在本文中,三个载波相位模糊分辨率(TCAR)和位置估计(PE)的问题是实时GNSS数据处理问题,用于任何大尺度的连续观察网络。为了描述这些问题,提出了一般的线性方程系统,其结合了统一的无各种几何,几何形状的和几何约束的TCAR模型,以及在观察时间之间的状态转换问题。利用这种常规形成,给出了广义的TCAR解决方案以覆盖所有实时GNSS数据处理方案,其也可以简化到各种整数解决方案,例如具有单个和多个时期测量的几何图形舍入和基于几何的Lambda解决方案。实际上,各种模糊的分辨率(AR)解决方案在浮动模糊估计和整数模糊的搜索过程中不同,尽管它们的理论等价取决于使用完全相同的观察系统模型和假设。通过从带有相应的53km,78km和155km的基线的多个双频GPS数据组实现的半生成三频GPS数据可实现的一些关键性能益处。这些益处包括:(1)使用基于几何和几何TCAR模型,来自单秒钟L1,L2和L5信号的两个最佳超长的TCAR模型的AR成功率达到近100%; (2)使用这两个模糊的固定EWL信号或其他衍生的WL信号,电离层偏置可以确定为厘米的水平; (3)第三种可观察到的模糊性可以在几分钟的测量中固定到100%的成功; (4)使用模糊 - 固定和离子层的WL可观察到的位置估计可观察到3D RMS精度优于20厘米,其中残余的对流层误差主导。相对于基于当前的双频广域GPS解决方案,如精密单点定位(PPP)的解决方案,利用附加的信号的主要好处是的滤波时间为分米RMS精度从几十分钟到还原几分钟。

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