基于Mean Shift模型的多粗差探测RAIM算法

doi:10.12305/j.issn.1001-506X.2022.02.35

系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (2): 644-650.doi: 10.12305/j.issn.1001-506X.2022.02.35

基于Mean Shift模型的多粗差探测RAIM算法

刘一^1,^2,³, 周威^1,^3,*, 金际航⁴, 边少锋¹, 谷守周²

1. 海军工程大学电气工程学院, 湖北武汉 430033
2. 中国测绘科学研究院, 北京 100830
3. 广西空间信息与测绘重点实验室, 广西桂林 541004
4. 海军海洋测绘研究所, 天津 300061

收稿日期:2021-02-09 出版日期:2022-02-18 发布日期:2022-02-24
通讯作者: 周威
作者简介:刘一(1992—), 男, 博士研究生, 主要研究方向为GNSS数据处理|周威(1992—), 男, 博士研究生, 主要研究方向为导航、制导与控制|金际航(1978—), 男, 高级工程师, 博士, 主要研究方向为惯性导航和卫星导航技术|边少锋(1961—), 男, 教授, 博士研究生导师, 博士, 主要研究方向为大地测量|谷守周(1984—), 男, 副研究员, 博士, 主要研究方向为GNSS实时钟差估计与高精度定位
基金资助:
国家自然科学基金(41631072);国家自然科学基金(41971416);国家自然科学基金(41876222);国家重点研发计划(2016YFB0501801);全球连续监测评估系统(GFZX0301040308-06);湖北省杰出青年科学基金(2019CFA086);广西空间信息与测绘重点实验室(19-050-11-02)

RAIM algorithm for multiple gross errors detection based on Mean Shift model

Yi LIU^1,^2,³, Wei ZHOU^1,^3,*, Jihang JIN⁴, Shaofeng BIAN¹, Shouzhou GU²

1. College of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China
2. Chinese Academic of Surveying and Mapping, Beijing 100830, China
3. Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China
4. Naval Institute of Hydrographic Surveying and Charting, Tianjin 300061, China

Received:2021-02-09 Online:2022-02-18 Published:2022-02-24
Contact: Wei ZHOU

摘要/Abstract

摘要：

针对当前接收机自主完备性监测(receiver autonomous integrity monitoring, RAIM)算法中存在多粗差探测识别能力较弱、计算效率不足等问题, 提出一种基于Mean Shift (MS)模型的多粗差探测RAIM算法。首先利用QR奇偶检校法构建样本数据集和QR检验向量; 其次基于MS模型估计样本密度中心, 并以此作为MS检校向量, 使用观测向量与MS检校向量的距离来评价观测值可靠程度, 从而确定异常观测卫星; 最后联合观测向量、MS和QR检校向量构建基于距离关系的权系数函数, 对多个异常观测进行处理。实验结果表明, 基于MS检校向量的粗差判别方法在多粗差存在的情况下，具有更灵敏的粗差识别能力; 相比最小二乘残差法, 新型RAIM算法改善了多粗差探测识别能力和计算效率, 可有效提高多系统融合单点定位的可靠性。

关键词: 接收机自主完好性监测, 多粗差, 均值漂移, 多全球导航卫星系统, 单点定位

Abstract:

There is no a good balance between the performance of the detection and the recognition and the calculation efficiency for multiple gross errors in the current receiver autonomous integrity monitoring (RAIM) algorithm. In this paper, the Mean Shift (MS) model is introduced to resolve these problems of the RAIM algorithm. Firstly, the QR parity check method is used to build a sample dataset and the QR calibration vector. Then, the density center is estimated by using the MS model, which is regarded as the MS calibration vector. The distance between the observation vector and the MS calibration vector can be applied for evaluating the reliability of global navigation satellite system (GNSS) observations, and determining the abnormal satellites. Finally, we use the weight coefficient function with a qualitative distance which is derived from the combination of the observation vector, the MS calibration vector and the QR calibration vector to select the abnormal observations and to furtherly promote the performance of detection and recognition of the calculation efficiency of multiple gross errors. The experimental results demonstrate that the gross error discrimination method based on the MS calibration vector has a more sensitive recognition ability in the presence of multiple gross errors. In addition, the new RAIM algorithm can not only obtain the better performance of detection and recognition and the calculation efficiency of multiple gross errors, but also can effectively improve the reliability of single point positioning with multi-system fusion, compared with the least square residual method.

Key words: receiver autonomous integrity monitoring (RAIM), multiple gross errors, Mean Shift (MS), multi-GNSS, single point positioning

中图分类号:

P228.4

刘一, 周威, 金际航, 边少锋, 谷守周. 基于Mean Shift模型的多粗差探测RAIM算法[J]. 系统工程与电子技术, 2022, 44(2): 644-650.

Yi LIU, Wei ZHOU, Jihang JIN, Shaofeng BIAN, Shouzhou GU. RAIM algorithm for multiple gross errors detection based on Mean Shift model[J]. Systems Engineering and Electronics, 2022, 44(2): 644-650.

图/表 5

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表1

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表2

参考文献 30

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方案	MS检校向量/m				QR检校向量/m				t-d	权系数
方案	G12	G13	G17	G25	G12	G13	G17	G25	t-d	G12	G13	G17	G25
0 m	0.14	0.18	0.29	0.17	0.55	0.42	0.62	0.50	0.41	0.88	0.84	0.81	0.85
2 m	1.74	0.20	0.26	0.16	0.55	1.57	1.62	1.44	1.57	0.64	0.94	0.93	0.95
3 m	2.59	0.20	0.26	0.17	0.55	2.40	2.43	2.27	2.40	0.63	0.96	0.95	0.97
4 m	3.44	0.20	0.26	0.16	0.55	3.25	3.26	3.11	3.24	0.62	0.97	0.96	0.98
3 m、2 m	2.59	1.29	0.23	0.17	1.50	2.40	2.70	2.45	2.59	0.68	0.82	0.96	0.97
3 m、3 m	2.59	1.82	0.23	0.17	2.02	2.40	2.97	2.69	2.84	0.71	0.77	0.96	0.97
3 m、3 m、3 m	2.59	1.78	2.63	0.15	1.98	3.26	2.97	2.45	2.54	0.70	0.79	0.72	0.97
3 m、2 m、4 m	2.58	1.25	3.42	0.15	3.17	2.65	3.83	3.04	3.10	0.75	0.84	0.72	0.96
3 m、2 m、30 m	2.58	1.26	23.93	0.14	22.88	23.63	2.70	22.72	22.69	0.95	0.97	0.62	1.00

站点	LSR RAIM			MS RAIM			定位精度提升百分比/%	有效历元提升百分比/%	效率提升百分比/%
站点	有效历元数	定位精度/m	耗时/s	有效历元数	定位精度/m	耗时/s	定位精度提升百分比/%	有效历元提升百分比/%	效率提升百分比/%
GAMG	2 880	6.967	46.221	2 880	7.221	8.578	-3.65	0	81.44
COCO	2 818	8.071	61.049	2 880	7.807	9.543	3.27	2.15	84.37
NRMG	2 795	11.338	45.663	2 858	11.248	9.741	0.79	2.19	78.67
SGOC	2 833	7.871	70.277	2 880	7.123	11.013	9.50	1.63	84.33
WUH2	2 880	9.990	46.091	2 880	7.378	9.774	26.15	0	78.79
ULAB	2 826	7.378	51.727	2 880	6.784	9.432	8.05	1.88	81.77
CUSV	2 874	7.124	75.216	2 880	6.480	11.437	9.04	0.21	84.79

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基于Mean Shift模型的多粗差探测RAIM算法

RAIM algorithm for multiple gross errors detection based on Mean Shift model

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