Zhipeng Wang

Prediction and analysis of GBAS integrity monitoring availability at LinZhi airport

Prediction and analysis of GBAS integrity monitoring is important, especially at the airports where a GBAS station it to be installed. Based on existing standard documents and published research, we present a software tool for GBAS availability prediction. Simulations have been conducted that include single point, single approach path, and multiple repetitions of a same approach path in order to analyze the availability of GNSS signal integrity monitoring with a GBAS at LinZhi airport. The results show that the long-term 24-h service availability figure at two typical single points along the approach path for GBAS Approach Service Type C (GAST C) is above 99.999xa0% for each point, and for GBAS Approach Service Type D (GAST D) at three typical single points, it is lower than 99.8xa0% for each point. The unavailability percentage over a 24-h period is 0.76 and 2.40xa0% for GAST C and GAST D, respectively. The results of sensitivity tests show that the impact of the mask angle and the latitude on the GBAS availability at LinZhi airport are more important than that of the constellation. Our conclusions could also be of interest for the implementation of GBAS stations at other plateau airports.

Reduced ARAIM monitoring subset method based on satellites in different orbital planes

With the development of the Global Navigation Satellite System, the increased number of satellites has resulted in more fault hypothesis situations and subset solutions. This situation represents a new challenge for advanced receiver autonomous integrity monitoring (ARAIM) in terms of the computational load. To efficiently detect faults and reduce the computational load, a method based on the association between satellite features in the same orbital plane is proposed. This approach first tests subsets that exclude entire constellations to narrow the search range for faults. Next, we evaluate multiple-fault cases directly by utilizing the subsets that exclude entire orbit satellites. Compared with the baseline Multiple Hypothesis Solution Separation (MHSS) method, our method can clearly reduce the number of subsets and the computational time under a typical multi-constellation situation while satisfying the localizer precision vertical 200 performance requirement, i.e., the guidance supports approach operations down to 200-foot altitudes. Furthermore, the experimental results illustrate that the number of subsets is reduced at most by two orders of magnitude, from 1330 to 87, and the computational time is decreased by 66.6%. The effective monitoring threshold and the fault-free 10−7 error bound on the accuracy of our method are much closer to those of the baseline MHSS method, and the usability coverage of both methods reaches 100%. This study verifies that the monitoring subsets and the calculation time for ARAIM are dramatically reduced by the new method.

Upper bound estimation of positioning error for ground-based augmentation system

Ground-based augmentation systems (GBAS) calculate protection levels (PL), which are bounds of the GBAS positioning errors associated with given integrity levels. In practice, PLs calculated using a Gaussian overbound method tend to be overestimated when the actual (non-Gaussian-distributed) GBAS ranging errors exhibit heavy tails. We propose a stable distribution-based method to overcome this problem. The heavy-tailed stable distribution gives a more appropriate representation of the GBAS ranging error. Based on a symmetric stable distribution, the overbound of the GBAS ranging error is estimated using numerical computations. The stable overbound can tightly bound both the core and the tails of the GBAS ranging error. The PL calculated using this stable overbound is less conservative than that calculated using the Gaussian overbound, although both methods have similar computational complexity. A performance evaluation based on simulated measurements collected from a GBAS prototype shows that the proposed approach increases the availability of GBAS.

Availability prediction method for EGNOS

Following the recent development of wide-area differential technology, satellite-based augmentation systems (SBASs) have been applied in many fields. However, the capability of monitoring stations used for generating error correction might degenerate with the aging of ground equipment over time, and the poor geometry between ranging and integrity monitoring stations (RIMS) and satellites could affect the reliability of navigation systems in supplying safety of life service. Therefore, it is necessary to predict SBAS availability so that users can choose a safe and efficient navigation system. Predictions of user difference range error indicator (UDREI) and grid ionospheric vertical error indicator (GIVEI) are the two difficulties in predicting SBAS availability. Considering the effect of geometry on UDREI, satellite geometric dilution of precision is defined to distinguish different geometries such that the relationship between the number of visible RIMS and UDREI in different geometries can be obtained. With regard to the effect of geometry on GIVEI, a weighted number of visible ionospheric pierce points (IPPs) is defined to describe the geometric IPP distribution such that the relationship between the number of visible IPPs and GIVEI in different geometries can be achieved. Finally, experiments are performed to evaluate the effectiveness of our proposed method. With the prediction algorithm, the prediction is consistent with actual performance over 75.17% of the entire European region. In particular, when focusing on central Europe, where the distribution of RIMS is uniform, the level of consistency can reach 95–100%. It can be concluded that the prediction performance of the algorithm is encouraging and that this model may be considered a good contender for predicting SBAS availability.