MHSS ARAIM Algorithm Combined with Gross Error Detection

Abstract

Due to some shortcomings in the current multiple hypothesis solution separation advanced receiver autonomous integrity monitoring (MHSS ARAIM) algorithm, such as the weaker robustness, a number of computational subsets with the larger computational load, a method combining MHSS ARAIM with gross error detection is proposed in this paper. The gross error detection method is used to identify and eliminate the gross data in the original data first, then the MHSS ARAIM algorithm is used to deal with the data after the gross error detection. Therefore, this makes up for the weakness of the MHSS ARAIM algorithm. With the data processing and analysis from several international GNSS service (IGS) and international GNSS monitoring and assessment system (iGMAS) stations, the results show that this new algorithm is superior to MHSS ARAIM in the localizer performance with vertical guidance down to 200 feet service (LPV-200) when using GPS and BDS measure data. Under the assumption of a single-faulty satellite, the effective monitoring threshold (EMT) is improved about 22.47% and 9.63%, and the vertical protection level (VPL) is improved about 32.28% and 12.98% for GPS and BDS observations, respectively. Moreover, under the assumption of double-faulty satellites, the EMT is improved about 80.85% and 29.88%, and the VPL is improved about 49.66% and 18.24% for GPS and BDS observations, respectively.

Keywords

• |gross error detection|araim|fault detection and identification|mhss araim