基于多星最差故障搜索的RAIM完好性风险估计方法

doi:10.7527/S1000-6893.2023.28684

Abstract

Abstract:

As the core function of Receiver Autonomous Integrity Monitoring （RAIM）， strict integrity risk estimation is the key to accurately measure navigation and positioning integrity. The interoperation of global navigation satellite systems increases the probability of multi-satellite faults dramatically， which reduces the rigor of the traditional integrity risk estimation based on the worst fault bias of a single satellite. In this paper， an integrity risk estimation method considering multi-satellite faults is proposed. As the rigorous estimation of multi-satellite fault integrity risk is limited by the accurate determination of the multi-satellite worst fault bias， a two-step method for determining the worst fault bias is established by determining the worst-case fault unit vector and search interval of the worst-case fault magnitude. The conservative estimation of the integrity risk of navigation and positioning is realized. Constellation simulation software is used to simulate satellite fault scenarios to test the rigor of the integrity risk estimation method. Experimental results show that compared with the traditional integrity risk estimation method， the proposed method can significantly reduce the rate of missed detection in multi-constellation scenarios. In addition， when compared to advanced RAIM method that consider multi-constellation and multi-satellite faults， the proposed method can improve the proportion meeting a global availability of 99.5% by 9.42% at most.

Key words: Receiver Autonomous Integrity Monitoring (RAIM), integrity, Global Navigation Satellite System, multi-satellite fault, availability

CLC Number:

V249.3

Ruijie LI, Liang LI, Jiachang JIANG, Li CHENG, Liuqi WANG. RAIM integrity risk estimation method based on worst multi⁃satellite faults searching[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(2): 328684-328684.

Figures/Tables 11

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References 25

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参数	取值
星历和钟差误差标准差σ_URA/m	2.4^［22］
对流层延迟误差标准差σ_trop/m	0.12×1.001/（0.002 001+sin²（（πθ）/180））^1/2［5］
多径标准差σ_MP/m	0.13+0.53exp（-θ/10）^［5］
接收机噪声标准差σ_Noise/m	0.15+0.43exp（-θ/6.9）^［5］
完好性风险指标 I_REQ	1×10^-7
连续性风险指标 C_REQ	4×10^-6
卫星故障先验概率 P_sat	GPS： 10^-5； Galileo： 3×10^-5； BDS： 10^-5［14］
星座级故障先验概率 P_const	GPS： 10^-8； Galileo： 2×10^-4； BDS： 6×10^-5［14］
P_NM的阈值	6×10^-8

测站点	方法	性能/%	场景1	场景2		场景3
测站点	方法	性能/%	GPS	GPS/Galileo	GPS/BDS	GPS/Galileo/BDS
北京（40°N， 116°E）	传统方法	单故障漏检比率	0	0	0	0
		多故障漏检比率	0	3.29	3.97	3.47×10^-2
		可用性	99.16	98.26	98.90	99.98
	ARAIM	漏检比率	0	0	0	0
	ARAIM	可用性	99.16	59.65	63.03	99.31
	所提方法	漏检比率	0	0	0	0
	所提方法	可用性	99.16	63.34	69.85	99.49
新德里（28°N， 77°E）	传统方法	单故障漏检比率	0	0	0	0
		多故障漏检比率	0.000	2.23	2.98	0
		可用性	99.83	99.09	99.19	99.99
	ARAIM	漏检比率	0	0	0	0
	ARAIM	可用性	99.83	64.90	69.41	99.51
	所提方法	漏检比率	0	0	0	0
	所提方法	可用性	99.83	67.69	72.48	99.70
纽约（40°N， 74°W）	传统方法	单故障漏检比率	0	0	0	0
		多故障漏检比率	0	2.16	1.47	0
		可用性	99.39	98.89	99.57	99.99
	ARAIM	漏检比率	0	0	0	0
	ARAIM	可用性	99.37	65.35	69.99	99.97
	所提方法	漏检比率	0	0	0	0
	所提方法	可用性	99.36	67.68	74.32	99.97
开普敦（33°S， 18°E）	传统方法	单故障漏检比率	0	0	0	0
		多故障漏检比率	0	1.42	1.54	0
		可用性	99.58	99.29	99.57	99.99
	ARAIM	漏检比率	0	0	0	0
	ARAIM	可用性	99.58	64.42	70.93	99.66
	所提方法	漏检比率	0	0	0	0
	所提方法	可用性	99.58	68.31	77.11	99.67
伦敦（51°N， 0.1°E）	传统方法	单故障漏检比率	0	0	0	0
		多故障漏检比率	0	2.13	2.40	0
		可用性	100	99.06	99.35	99.98
	ARAIM	漏检比率	0	0	0	0
	ARAIM	可用性	99.99	66.46	72.05	99.72
	所提方法	漏检比率	0	0	0	0
	所提方法	可用性	100	69.53	78.08	99.97

星座	平均可用性/%				满足99.5%指标的覆盖范围/%
星座	L_BC	L_WE	L_ARAIM	L_IR	L_BC	L_WE	L_ARAIM	L_IR
GPS/BDS	25.95	68.11	70.37	75.50	0	0.31	0.31	0.62
GPS/BDS/GAL	53.94	67.14	90.06	95.55	0	0	21.91	31.33

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RAIM integrity risk estimation method based on worst multi⁃satellite faults searching

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