Optimization of Protection Level of GBAS with Gaussian Mixture Model

The Gaussian mixture model (GMM) is commonly used to the heavy tail of ground-based augmentation system (GBAS) range error distribution. In practice, over-bounding based on a GMM over-bound ranging errors, but GBAS protection levels (PLs) tend be overestimated. Based idea solution separation and overcoming shortcoming its direct reference GBAS, this paper analyses constraint conditions objective functions optimal level under distribution, proposes that multi-hypothesis set classification can effectively reduce computational complexity. At same time, least squares optimization dynamic allocation integrity risk are further level. This verifies validity parameters actual airport data, performs simulation verification typical scenarios CAT I II/IIIa global Beidou 3 constellation, performance improvement effect different traversal depths. results show when depths 4 6, vertical component ground reduced by 14% total 10%.