多星座多频观测量和地面监测站的使用，提高了先进接收机自主完好性监测（ARAIM）技术的定位精度，但引入多种可能的误差和故障源，增大了完好性风险产生概率，严重影响民航安全。为此，本项目从ARAIM完好性风险建模、风险检测和风险包络三个层面开展研究。首先,建立基于模糊支持向量机分类的风险数据时间序列，揭示风险数据相关性对完好性的影响机制，提出考虑时空梯度动态变化的中国区域泛Kriging插值电离层异常风险模型；然后,提出基于变点检测理论的双阈值慢增长故障完好性风险检测方法、基于奇偶空间向量的带约束广义似然比双故障完好性风险检测、和故障模式矩阵驱动下的极大似然多故障完好性风险检测方法；最后,以获取最大监测冗余度为目标，提出极值理论与异方差模型融合的完好性风险安全包络方法、并基于非线性回归分析理论判定和推演风险包络性能。所取成果可为提升北斗空管导航能力提供理论储备和技术支撑。

With multi-constellation and multi-frequency measurements，and ground monitor stations, the position accuracy of Advanced Receiver Autonomous Integrity Monitoring (ARAIM) is improved. However, a number of possible sources of error and failure are introduced, and the probability of the risk integrity is increased, so the civil aviation safety is impact seriously. Therefore, this subject conducts research from three levels-- ARAIM integrity risk modeling, risk detection and risk envelope. Firstly, using fuzzy support vector machine (FSVM), we carry out classified statistics of constructed time series of risk data, reveal the mechanism of the risk data correlation effects on the integrity, and build model on China regional ionospheric anomalies risk based on extensive Kriging Pan-interpolation in consider of dynamic changes in spatiotemporal gradient. As follows, we propose method of integrity risk detection on slow-growth faults using variable point detection theory assisted by leverage, on double faults with constraints based on parity space vector and generalized likelihood ratio, and on multiple faults driven by matrix of fault mode using maximum-likelihood method. Finally, with the objective of obtaining maximum monitoring redundancy, we propose a method of integrity risk envelope combining extreme value theory with Heteroscedastic Mode, then estimate and infer the performance of risk envelope based on the theory of nonlinear regression analysis. Results achieved can provide theoretical reserves and technical support for improving the capacity of BDS air traffic management navigation.