基于子集包含减少ARAIM子集数量的方法

刘金鑫; 滕继涛; 李锐; 王君君

doi:10.13700/j.bh.1001-5965.2019.0517

基于子集包含减少ARAIM子集数量的方法

doi: 10.13700/j.bh.1001-5965.2019.0517

1.
北京航空航天大学电子信息工程学院, 北京 100083
2.
中国人民解放军 95899部队, 北京 100085

详细信息

作者简介:
刘金鑫男, 硕士研究生。主要研究方向:先进接收机自主完好性监测

李锐男, 博士, 高级工程师。主要研究方向:卫星导航完好性

通讯作者:
李锐. E-mail:lee_ruin@263.net

中图分类号: V249.31
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- 被引次数: 0
出版历程
- 收稿日期: 2019-09-20
- 录用日期: 2019-11-08
- 网络出版日期: 2020-08-20

Method for reducing number of ARAIM subsets based on subset inclusion

1.
School of Electronic and Information Engineering, Beihang University, Beijing 100083, China
2.
Unit 95899 of PLA, Beijing 100085, China

More Information

Corresponding author: LI Rui. E-mail:lee_ruin@263.net

摘要

摘要:
先进接收机自主完好性监测（ARAIM）技术可用于多星座组合定位时的完好性监测，ARAIM技术子组推荐的多假设分离解（MHSS）标准算法，存在子集数量过多带来大量计算负载的问题。针对此问题，在双星座组合定位情景下，通过分析子集之间的包含关系及空间位置精度因子（PDOP）的变化，提出了使用一个子集代替多个子集减少子集数量的方法。所提方法可以明显地减少子集数量，不同参数下的仿真结果表明，优化后的算法效率至少提高2倍以上，并且优化前后的ARAIM可用性变化最大不超过3%。
- 先进接收机自主完好性监测(ARAIM) /
- 多假设分离解(MHSS) /
- 双星座 /
- 子集 /
- 空间位置精度因子(PDOP)
Abstract:
Advanced Receiver Autonomous Integrity Monitoring (ARAIM) technology can be used for integrity monitoring in multi-constellation combination positioning. The Multi-Hypothesis Separation Solution (MHSS) standard algorithm, recommended by the ARAIM technology subgroup, may produce lots of subsets to bring heavy computational load. Aimed at this problem, in the dual-constellation combined positioning scenario, by analyzing the inclusion relationship between subsets and the change of Position Dilution of Precision (PDOP), a method of reducing the number of subsets by using one subset to replace multiple subsets is proposed. This method can significantly reduce the number of subsets. The simulation results under different parameters show that the efficiency of the optimized algorithm is at least tripled, and the maximum ARAIM availability difference before and after optimization is no more than 3%.
- Advanced Receiver Autonomous Integrity Monitoring(ARAIM) /
- Multi-Hypothesis Separation Solution(MHSS) /
- dual-constellation /
- subsets /
- Position Dilution of Precision(PDOP)

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图 1 MHSS算法流程图

Figure 1. MHSS algorithm flowchart

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图 2 双星座下故障模式代替规则

①为星座1故障+星座2单星故障或星座1单星故障+星座2故障；②为星座1单星故障+星座2故障；③为星座1故障+星座2单星故障；④为星座1故障+星座2卫星故障或星座1卫星故障+星座2故障；“—”表示星座故障和星座故障组合时不进行代替。

Figure 2. Failure mode substitution rule under dual-constellation

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图 3 第1组参数下的全球可用性

Figure 3. Global availability under the first set of parameters

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图 4 第2组参数下的全球可用性

Figure 4. Global availability under the second set of parameters

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图 5 第3组参数下的全球可用性

Figure 5. Global availability under the third set of parameters

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图 6 第4组参数下的全球可用性

Figure 6. Global availability under the fourth set of parameters

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图 7 第5组参数下的全球可用性

Figure 7. Global availability under the fifth set of parameters

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图 8 第6组参数下的全球可用性

Figure 8. Global availability under the sixth set of parameters

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表 1 LPV-200服务在完好性上的要求^[12]

Table 1. Integrity requirements of LPV-200^[12]

LPV-200服务告警值	数值
水平保护级告警值/m	40
垂直保护级告警值/m	35
垂向有效监测阈值/m	15
完好性风险限值	2×10^-7(进近)

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表 2 标准算法在相关参数下产生的子集数量

Table 2. Number of subsets produced by standard algorithm with related parameters

各星座可见星数	子集数
各星座可见星数	P_{sat, 1}=10^-4, P_{sat, 2}=10^-3, P_{const, 1}=10^-4, P_{const, 2}=10^-4	P_{sat, 1}=10^-4, P_{sat, 2}=10^-4, P_{const, 1}=10^-4, P_{const, 2}=10^-4	P_{sat, 1}=10^-4, P_{sat, 2}=10^-5, P_{const, 1}=10^-4, P_{const, 2}=10^-4
6/6	106	106	106
6/8	697	137	137
6/10	988	172	172
8/6	137	137	137
8/8	988	172	172
8/10	1 351	211	211
10/6	172	172	172
10/8	1 351	211	211
10/10	1 794	254	254

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表 3 优化后算法产生的子集数量

Table 3. Number of subsets produced by optimized algorithm

各星座可见星数	子集数
各星座可见星数	P_{sat, 1}=10^-4, P_{sat, 2}=10^-3, P_{const, 1}=10^-4, P_{const, 2}=10^-4	P_{sat, 1}=10^-4, P_{sat, 2}=10^-4, P_{const, 1}=10^-4, P_{const, 2}=10^-4	P_{sat, 1}=10^-4, P_{sat, 2}=10^-5, P_{const, 1}=10^-4, P_{const, 2}=10^-4
6/6	26	26	26
6/8	30	30	30
6/10	34	34	34
8/6	30	30	30
8/8	34	34	34
8/10	38	38	38
10/6	34	34	34
10/8	38	38	38
10/10	42	42	42

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表 4 相关参数设定

Table 4. Related parameter setting

组别	N_sat	P_sat	P_const	b_nom	σ_URA	σ_URE
1	27	10^-4	10^-4	0.5	1	0.66
1	23	10^-4	10^-4	0.5	1	0.66
2	24	10^-5	10^-4	0.5	1	0.66
2	24	10^-3	10^-4	0.5	1	0.66
3	23	10^-5	10^-4	0.5	1	0.66
3	23	10^-4	10^-4	0.5	2	1.32
4	27	10^-5	10^-4	0.5	1	0.66
4	24	10^-4	10^-4	0.5	1	0.66
5	24	10^-5	10^-4	0.5	1	0.66
5	24	10^-4	10^-4	0.5	1	0.66
6	24	10^-4	10^-4	0.5	1	0.66
6	24	10^-4	10^-4	0.5	1	0.66

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表 5 优化前后子集数量范围及仿真用时对比

Table 5. Comparison of subset number range and simulation time before and after optimization

组别	子集数量		仿真用时/min		用时比
组别	优化前	优化后	优化前	优化后	用时比
1	92~352	26~52	82	24	3.4
2	92~2 325	26~49	603	34	17.7
3	79~301	24~48	78	21	3.7
4	106~352	28~53	87	23	3.8
5	92~301	26~49	80	22	3.6
6	92~301	26~49	77	20	3.9

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表 6 优化前后的算法可用性比较

组别	算法可用性/%
组别	优化前	优化后
1	63.4	60.5
2	96.9	96.6
3	8.18	8.49
4	100	100
5	97.7	98.2
6	97.5	97.5

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