基于选星预处理的改进随机抽样一致性RAIM方法

多星座导航能够增加可视卫星数量,改善卫星几何构型，已成为卫星导航定位领域发展的重要方向之一。多星座导航接收机自主完好性监测（RAIM）技术对提高导航系统的完好性具有重要作用。面向多星座导航的完好性监测需求，分析了传统随机抽样一致性（RANSAC）故障检测方法的不足，提出了一种基于最小样本集选星预处理的改进RANSAC RAIM算法。该算法基于最大四面体积法和GDOP值贡献度的选星方法选取具有较好构型的卫星构成卫星子集，取代了传统RANSAC RAIM方法通过遍历构成卫星子集，可有效避免卫星子集中存在较差卫星几何构型的情况，减少子集数量，提升故障检测的准确率。静态和动态仿真实验表明，改进的RANSAC RAIM算法在检测效率和检测准确率等方面明显优于传统方法。

Improved random sampling consensus RAIM algorithm based on satellite selection preprocessing

Multi-constellation navigation, which can increase the number of visible satellites and improve satellite geometry, has become one of the important directions for the development of satellite navigation and positioning. The receiver autonomous integrity monitoring (RAIM) technology for multi-constellation navigation receivers plays an important role in improving the integrity of navigation systems. The paper focuses on the integrity monitoring requirements of multi-constellation navigation, analyzes the shortcomings of traditional random sample consensus (RANSAC) fault detection methods, and proposes an improved RANSAC RAIM algorithm based on minimum sample set satellite selection preprocessing. Based on the maximum tetrahedral volume method and the satellite selection method of GDOP value contribution, this algorithm selects four satellites with good satellite configurations to form a satellite subset, replacing the traditional RANSAC RAIM method by traversing the combination of four satellites to form a satellite subset. It can effectively avoid the situation of poor satellite geometry in the satellite subset, reduce the number of subsets, and improve the precision of fault detection. Static and dynamic simulation experiments have shown that the improved RANSAC RAIM algorithm is significantly superior to traditional methods in terms of detection efficiency and precision.