MHSS ARAIM Algorithm Combined with Gross Error Detection

doi:10.11947/j.JGGS.2020.0104

Abstract

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.

Key words: gross error detection; ARAIM; fault detection and identification; MHSS ARAIM

Yabin ZHANG,Li WANG,Lihong FAN,Xuanyu QU. MHSS ARAIM Algorithm Combined with Gross Error Detection[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(1): 36-44.

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Gross Errors m	Number of Faults	Number of Identification	Identification Rate %	Number of Exclusion	Exclusion Rate %
20	288	286	99.3	286	100
30	288	286	99.3	286	100
40	288	288	100	288	100
50	288	288	100	288	100

Parameters	Value
b_max	0.75m
b_norm	0.1m
PHMI	10^-7/approach
Pfa	4×10^-6/approach
VAL	35m
EMT	15m
URA	0.5m
URE	0.25m
Single-fault Psat	10^-5
Double-fault Psat	10^-4

Station	MHSS ARAIM			EMT<15		Combined with Gross Error Detection MHSS ARAIM				EMT<15
Station	Maximum	Minimum	Mean		VPL<35		Maximum	Minimum	Mean		VPL<35
URUM
EMT	8.77	0.12	1.51	100%		5.40		0.10	0.95	100%
VPL	25.70	5.50	9.45	100%		17.90		3.84	6.38	100%
SHAO
EMT	5.63	0.13	1.16	100%		4.92		0.12	0.96	100%
VPL	20.95	4.81	8.16	100%		16.82		2.86	5.53	100%
CHAN
EMT	4.72	0.11	1.30	100%		4.16		0.11	1.13	100%
VPL	24.32	5.89	10.43	100%		13.08		4.64	7.08	100%
KUN1
EMT	7.53	0.08	1.20	100%		7.20		0.08	1.10	100%
VPL	23.39	4.75	8.43	100%		21.06		4.66	7.28	100%
BJF1
EMT	4.37	0.10	1.37	100%		4.37		0.10	1.22	100%
VPL	20.19	6.16	9.98	100%		20.19		4.54	8.75	100%

Station		MHSS ARAIM							Combined with Gross Error Detection MHSS ARAIM				EMT<15
Station		Maximum		Minimum		Mean		VPL<35		Maximum	Minimum	Mean		VPL<35
URUM
EMT	15.86		2.56		4.81		99.79%		5.40		0.09	0.96	100%
VPL	42.78		6.08		13.51		99.72%		19.14		4.16	6.67	100%
SHAO
EMT	14.94		1.41		8.05		100%		8.80		0.10	1.07	100%
VPL	39.94		5.00		11.32		99.65%		21.67		3.03	5.93	100%
CHAN
EMT	60.75		2.26		5.37		99.10%		4.16		0.11	1.30	100%
VPL	160.53		6.53		15.09		98.23%		13.89		4.54	7.45	100%
KUN1
EMT	18.56		2.20		5.40		99.62%		15.20		1.78	3.60	99.98%
VPL	43.21		6.11		12.57		99.54%		36.10		5.21	10.12	99.91%
BJF1
EMT	19.45		3.08		8.02		99.53%		15.12		1.78	5.89	99.96%
VPL	52.42		7.81		19.42		99.39%		35.78		6.67	16.12	99.92%