分步数据融合的多频多系统网络RTK模糊度快速解算方法研究

Global navigation satellite system (GNSS) has entered into an era of four global positioning systems with multi frequencies, which has introduced new challenges for network-based real-time kinematic (RTK) positioning. The difference between different GNSS systems increases the difficulty of data integration and decreases the efficiency of data processing, the independent ambiguity resolution between different systems for reference stations reduces the reliability of the integrated system, and the inconsistence of different GNSS systems and signals for different users at rover stations reduces the applicability of present ambiguity resolution algorithms. In this program, we carry out a research on ambiguity resolution using integrated GNSS observations from the aspects of reference stations and rover stations, and the following issues are addressed: (1) propose a stepwise data integration mode with product integration for reference stations and observations integration for rover stations to achieve the integration optimization of GNSS observations; (2) propose a both intra- and inter-system differencing ambiguity resolution algorithm for mixed-frequency signals to realize the joint data processing of the 2nd and the 3rd generation BDS; (3) establish atmospheric error models using multi-frequency BDS and Galileo observations to assist in realizing fast AR for reference stations using integrated GNSS observations; (4) establish a unified data processing model with single-differenced zero-combination observations and utilize the dimension-reduced float ambiguities by differencing and combination transformation to achieve fast ambiguity resolution for rover stations without limitations of various system and frequency integration. The research of this program can further promote the efficiency and reliability of network RTK based on integrated multi-frequency and multi-constellation GNSS observations, and have academic reference significance and application value.

全球导航卫星系统已进入四系统多频发展时代，给网络RTK带来新的挑战，如不同系统的差异性增大了数据有效集成的难度、降低了联合数据处理的效率，基准站各系统独立的模糊度解算降低了集成系统的可靠性，流动站用户系统及信号的不一致降低了目前模糊度解算算法的适用性。本项目从基准站及流动站两方面入手开展多频多系统集成的模糊度解算方法研究，拟采用基准站产品数据融合+流动站观测数据融合的分步数据融合模式实现多频多系统数据的集成优化，采用系统内/间分步差分的混合频率模糊度解算算法实现新旧北斗三频数据的联合处理，采用多频北斗及Galileo观测数据构建大气误差模型辅助实现基准站多频多系统的快速模糊度解算，采用单差非频率组合的统一数据处理模型及差分/组合浮点模糊度变换的降维搜索实现流动站不限系统及频率的快速模糊度解算。项目的研究将进一步提高多频多系统网络RTK模糊度解算的效率和可靠性，具有重要理论意义和应用价值。

本项目针对多频多系统网络RTK定位中存在的诸如不同系统的差异导致数据有效集成难度加大、基准站各系统独立模糊度解算导致集成系统可靠性降低、流动站用户中系统及信号不一致导致模糊度解算算法难以统一等难题，开展多频北斗/GPS/GLONASS/Galileo信号观测值在网络RTK技术上的集成方式及基于以上集成数据的网络RTK基准站间和流动站端模糊度快速解算算法研究。研究发现：(1) 网络RTK技术在服务端可通过大气改正产品实现不同系统间的数据集成，而用户端则可采用单差非频率组合模式实现不限系统不限频率的流动站定位；(2) 北斗二号和北斗三号在不同类型接收机的同频率伪距间存在系统间偏差，该偏差可达1米，但相位不存在系统间偏差；通过系统间差分融合北斗二号三频数据和北斗三号四频数据，相比系统内差分可提高15%的模糊度固定率，缩短37%的模糊度初始化时间；(3) 基于无电离层模型的参考站间三频模糊度解算方法在多系统中的联合解算可提高25.7%的模糊度精度，缩短47.1%的收敛时间，同时天顶对流层延迟误差更适合拆分成平面相关项和高程相关性分开建模；(4) 在信号遮挡环境下流动站端四系统联合紧组合定位相比松组合而言能提升30%的模糊度固定率。