面向城市道路安全的ARAIM保护级优化方法

邓思瑜; 孙蕊; 张立东; 胡浩亮

doi:10.13700/j.bh.1001-5965.2022.1020

面向城市道路安全的ARAIM保护级优化方法

doi: 10.13700/j.bh.1001-5965.2022.1020

1.
南京航空航天大学民航学院，南京 211106
2.
中国航空无线电电子研究所民航空管航空电子技术重点实验室，上海 200241

基金项目: 国家自然科学基金(41974033, 42174025, 42222401)；工信部民用飞机专项科研项目(MJ-2020-S-03)；欧盟地平线2020创新研究项目(875154 GreAT)；江苏省“六大人才高峰”项目(KTHY-014)；江苏省自然科学基金(BK20211569)；中央高校基本科研业务费专项资金(xcxjh20220722)

详细信息

通讯作者:
E-mail：rui.sun@nuaa.edu.cn

中图分类号: V221⁺.3；TB553
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出版历程
- 收稿日期: 2022-12-29
- 录用日期: 2023-03-17
- 网络出版日期: 2023-05-11
- 整期出版日期: 2025-01-31

Protection level optimization method of ARAIM algorithm for urban road safety

1.
College of Civil Aviation，Nanjing University of Aeronautics and Astronautics，Nanjing 211106，China
2.
Key Laboratory of ATM Avionics Technology，China Aeronautical Radio Electronics Research Institute，Shanghai 200241，China

Funds: National Natural Science Foundation of China (41974033, 42174025, 42222401); Civil Aircraft Special Research Project of Ministry of Industry and Information Technology (MJ-2020-S-03); European Union’s Horizon 2020 Research and Innovation Programme (875154 GreAT); Jiangsu Provincial Six Talent Peaks Project (KTHY-014); Natural Science Foundation of Jiangsu Province (BK20211569); Fundamental Research Funds for the Central Universities (xcxjh20220722)

More Information

Corresponding author: E-mail：rui.sun@nuaa.edu.cn

摘要

摘要:
目前，以安全为导向的城市智能交通应用正不断扩展，此类应用不仅对全球卫星导航系统(GNSS)的精度有着一定的要求，同时对GNSS 的完好性也提出了新的挑战。先进接收机自主完好性监测(ARAIM)技术作为一种低成本、自主性强的完好性监测算法，目前已经在空旷的航空领域受到广泛的关注，但是在城市环境的应用尚属空白，且面向航空应用空旷环境下的传统ARAIM算法对完好性风险和连续性风险分配较为简单、导致计算的保护级数值过于保守。针对上述问题，本文提出一种基于教与学(TLBO)算法的保护级优化方法，可以实现城市道路安全的完好性需求下完好性风险和连续性风险的合理分配，从而提高多星座ARAIM的可用性。车载实测数据表明，在全球定位系统(GPS)+伽利略卫星导航系统(GAL)双星座场景下，水平保护级(HPL)和垂直保护级(VPL)的平均优化率为50.58%和44.14%，10 m级告警门限(AL)对应的ARAIM可用性提高了51.29%；GPS+GAL+BDS多星座场景下，HPL和VPL的平均优化率为59.59%和56.33%，10 m级AL对应的ARAIM可用性提高了99.29%。
- 先进接收机自主完好性监测 /
- 垂直保护级 /
- 水平保护级 /
- 风险概率分配 /
- 教与学算法
Abstract:
Safety-critical intelligent transportation systems (ITS) applications are surging in recent years. These kinds of applications not only have the accuracy but also the integrity of global navigation satellite system (GNSS) positioning services. In the field of aviation with an open environment, advanced receiver autonomous integrity monitoring (ARAIM) has been widely concerned as a low-cost, highly autonomous integrity monitoring method. However, there are still gaps in the application of urban environments. Moreover, the probability of integrity risk and continuity risk of traditional ARAIM algorithm which is applied for aviation applications in the open environment has been equally allocated, resulting in the relatively conservative protection level. In order to solve the above problems, this paper proposes a protection-level optimization method based on Teaching-learning-based optimization (TLBO), which can realize the reasonable allocation of integrity risk and continuity risk under the integrity requirements of urban road safety, so as to improve the availability of multi-constellation ARAIM. The on-board measured data shows that under the global position system（GPS） + Galileo satellite navigation system (GAL) dual constellation scenario, the average optimization rates of horizontal protection level (HPL) and vertical protection level (VPL) are 50.58% and 44.14%, and the availability of ARAIM for the 10-meter alert limit (AL) is increased by 51.29%. In the GPS+GAL+BDS multi-constellation scenario, the average optimization rates of HPL and VPL are 59.59% and 56.33%, and the availability of ARAIM for the 10-meter AL is improved by 99.29%.
- ARAIM /
- VPL /
- HPL /
- risk probability allocation /
- TLBO

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图 1 基于TLBO算法的保护级优化方法流程图

Figure 1. Flow chart of TLBO algorithm-based protection level optimization method

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图 2 实验路径图

Figure 2. Experimental trajectory

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图 3 实验设备图

Figure 3. Experimental equipment

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图 4 不同组合的可见卫星数目

Figure 4. Number of visible satellites for different constellation systems

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图 5 GPS+GAL星座组合下水平和垂直方向上的定位误差及优化前后的保护级数值

Figure 5. Positioning error and value of protection level before and after optimization in horizontal and vertical direction for GPS+GAL constellation systems

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图 6 GPS+GAL+BDS星座组合下垂直方向上的定位误差及优化前后的保护级数值

Figure 6. Positioning error and value of protection level before and after optimization in horizontal and vertical direction for GPS+GAL+BDS constellation systems

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图 7 实验道路转角部分

Figure 7. Corner section of experimental road

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图 8 有遮挡场景下的不同星座组合水平方向上的定位误差及优化前后的保护级数值

Figure 8. Positioning error and the value of protection level before and after optimization in horizontal direction for different constellation systems with obscured scenes

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图 9 GPS+GAL双星座场景下水平保护级优化前后结果图

Figure 9. Horizontal protection level results before and after optimization in GPS+GAL dual-constellation scenario

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图 10 GPS+GAL+BDS多星座场景下水平保护级优化前后结果图

Figure 10. Horizontal protection level results before and after optimization in GPS+GAL+BDS multi-constellation scenario

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表 1 ISM参数定义

Table 1. Definitions of ISM parameter

参数	定义
${P_{{\text{sat}},i}}$	一定时间内卫星 $i$ 发生故障的先验概率
${P_{{\text{const}},j}}$	一定时间内星座 $j$ 发生故障的先验概率
${\sigma _{{\text{URA}},i}}$	用户测距精度，用于评估完好性的高斯分布标准差
${\sigma _{{\text{URE}},i}}$	用户测距误差，用于评估精度和连续性的高斯分布标准差
${b_{{\text{nom}},i}}$	用于评估完好性的正常状态下最大伪距偏差

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表 2 公路用户的导航性能需求

Table 2. Navigation requirements of highway user

需求	完好性/m	连续性	精度/m	可用性/%
导航和路线引导	2～20	待定	1～20	95
自动车辆监控	0.2～30		0.1～20	95
自动车辆识别	3		1	99.7
公共安全	0.2～30		0.1～30	95～99.7
资源管理	0.2～1		0.005～30	99.7
避撞	0.2		0.1	99.9
智能交通	0.2		0.1	99.9

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表 3 货运用户的导航性能需求

Table 3. Navigation requirements of rail user

需求	完好性/m	连续性	精度/m	可用性/%
停车场	50	待定	2～20	95
地理围栏	10		10～20	99
危险品运输	10		10～20	99
拖车跟踪	50		20	95
沿海运输违规检查	10		10～20	99
车队管理	50		20	95
驾考	10		5～20	99

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表 4 ISM参数设置^[28-30]

Table 4. ISM parameter setting^[28-30]

星座	${P_{{\text{sat}}}}$	${P_{{\text{const}}}}$	${\sigma _{{\text{URE}}}}$	${\sigma _{{\text{URA}}}}$	${b_{{\text{nom}}}}$
GPS	$1 \times {10^{ - 5}}$	$1 \times {10^{ - 8}}$	1.0	1.5	0.75
GAL	$3 \times {10^{ - 5}}$	$2 \times {10^{ - 4}}$	1.0	1.5	0.75
BDS	$1 \times {10^{ - 5}}$	$6 \times {10^{ - 5}}$	1.0	1.5	0.75

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表 5 双星座和多星座组合下的平均保护级优化率

Table 5. Average protection level optimization ratios for dual and multi-constellation systems

星座	${\bar H_{{\text{PL}}}}$		${\bar H_{{\text{PL}}}}$ 优化率/%	${\bar V_{{\text{PL}}}}$		${\bar V_{{\text{PL}}}}$ 优化率/%
星座	优化前	优化后	${\bar H_{{\text{PL}}}}$ 优化率/%	优化前	优化后	${\bar V_{{\text{PL}}}}$ 优化率/%
GPS+GAL	21.49	10.62	50.58	11.78	6.58	44.14
GPS+GAL+BDS	17.67	7.14	59.59	8.06	3.52	56.33

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表 6 不同应用需求门限下的ARAIM可用性提高率

Table 6. ARAIM availability improvement rates under different application requirements thresholds

星座	需求门限/m	优化前可用性/%	优化后可用性/%	提高率/%
GPS+GAL	10	0.00	51.29	51.29
	20	47.86	99.14	51.29
	30	96.43	99.71	3.29
	40	99.57	100.00	0.43
	50	99.71	100.00	0.29
GPS+GAL+BDS	10	0.00	99.29	99.29
	20	88.71	100.00	11.29
	30	98.71	100.00	0.00
	40	100.00	100.00	0.00
	50	100.00	100.00	0.00

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表 7 有遮挡场景下双星座和多星座组合的平均保护级优化率

Table 7. Average protection level optimization ratios for dual and multi-constellation systems with obscured scenes

星座	${\bar H_{{\text{PL}}}}$		优化率/%
星座	优化前	优化后	优化率/%
GPS+GAL	21.48	9.76	54.56
GPS+GAL+BDS	18.45	7.16	61.19

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