GBAS integrity performance evaluation method based on pseudo-range error distribution model

Abstract

The invention provides a GBAS integrity performance evaluation method based on a pseudo-range error distribution model. The method is characterized in that an airborne receiving end collects pseudo-range error sample data for GBAS integrity performance evaluation, and comprises the following steps: a) grouping the pseudo-range error sample data; b) for each group of pseudo-range error samples, establishing a Gaussian kernel quadratic Gaussian polynomial tail distribution model; c) carrying out weighted sum on each group of pseudo-range error distribution model to obtain an overall pseudo-rangeerror distribution model; d) projecting the pseudo-range domain error to positioning domain error; e) calculating the probability that the positioning domain error is larger than an alarm limit to obtain an integrity risk probability value; and f) evaluating the integrity performance of a GBAS system. The GBAS integrity performance evaluation method based on the pseudo-range error distribution model improves calculation accuracy of the integrity risk probability value and improves effectiveness of integrity assessment.

Description

technical field [0001] The invention relates to the technical field of satellite navigation, in particular to a GBAS integrity performance evaluation method based on a pseudorange error distribution model. Background technique [0002] The Ground Based Augmentation System (Ground Based Augmentation, GBAS) realizes the enhancement of the positioning accuracy and the real-time monitoring of the integrity of the navigation signal by establishing the ground reference station with known position and performing differential processing on the ranging value. To evaluate whether the navigation service provided by the installed GBAS system in the local airspace (within the 50KM range centered on the GBAS ground station) meets the corresponding flight performance requirements needs to be investigated from four aspects: accuracy, continuity, integrity and availability. Among them, integrity is very important to ensure the safety of users, and it is the most concerned performance require...

AI Technical Summary

Problems solved by technology

[0004] However, there is a problem of strong sample correlation in the evaluation of integrity performance in the prior art. In the case of a small number of samples, the accuracy of the integrity risk probability value will be reduced, and the effectiveness of the evaluation is poor.

Images