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

Abstract

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

CLC Number:

P228.4

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.

Figures/Tables 5

Fig.1

Fig.2

Table 1

Fig.3

Table 2

References 30

1	HEWITSON S , WANG J . GNSS receiver autonomous integrity monitoring (RAIM) performance analysis[J]. GPS Solutions, 2006, 10 (3): 155- 170. doi: 10.1007/s10291-005-0016-2
2	YANG Y X , XU J Y . GNSS receiver autonomous integrity monito-ring (RAIM) algorithm based on robust estimation[J]. Geodesy and Geodynamics, 2016, 7 (2): 117- 123. doi: 10.1016/j.geog.2016.04.004
3	CHEN J , HUANG Z , RUI L I . Computation of satellite clock-ephemeris augmentation parameters for dual frequency multi constellation satellite-based augmentation system[J]. Journal of Systems Engineering and Electronics, 2018, 29 (6): 1111- 1123. doi: 10.21629/JSEE.2018.06.01
4	TAN T N , KHENCHAF A , COMBLET F , et al. Robust-extended Kalman filter and long short-term memory combination to enhance the quality of single point positioning[J]. Applied Sciences, 2020, 10 (12): 4335. doi: 10.3390/app10124335
5	SUN Y . RAIM-NET: a deep neural network for receiver autonomous integrity monitoring[J]. Remote Sensing, 2020, 12 (9): 1503. doi: 10.3390/rs12091503
6	LUO S L , WANG L , TU R , et al. Satellite selection methods for multi-constellation advanced RAIM[J]. Advances in Space Research, 2020, 65 (5): 1503- 1517. doi: 10.1016/j.asr.2019.12.015
7	SONG J H , JEE G I . Performance enhancement of land vehicle positioning using multiple GPS receivers in an urban area[J]. Sensors, 2016, 16 (10): 1688. doi: 10.3390/s16101688
8	TITOUNI S , ROUABA H . Spectral transformation-based technique for reducing effect of limited pre-correlation bandwidth in the GNSS receiver filter in presence of noise and multipath[J]. Journal of Systems Engineering and Electronics, 2020, 31 (2): 252- 265. doi: 10.23919/JSEE.2020.000003
9	SUN J K , NIU Z , ZHU B F . Fault detection and exclusion method for a deeply integrated BDS/INS system[J]. Sensors, 2020, 20 (7): 1844. doi: 10.3390/s20071844
10	ZHANG K , PAPADIMITRATOS P . Fast multiple fault detection and exclusion (FM-FDE) algorithm for standalone GNSS receivers[J]. IEEE Open Journal of the Communications Society, 2021, 2, 217- 234. doi: 10.1109/OJCOMS.2021.3050333
11	BANG E , MILNER C , MACABIAU C . Cross correlation effect of ARAIM test statistic on false alarm risk[J]. GPS Solutions, 2020, 24 (4): 107- 114. doi: 10.1007/s10291-020-00997-w
12	SU X , ZHAN X M , NIU M R , et al. Receiver autonomous integrity monitoring (RAIM) performances of combined GPS/BeiDou/QZSS in urban canyon[J]. IEEE Trans.on Electrical and Electronic Engineering, 2014, 9 (3): 275- 281. doi: 10.1002/tee.21967
13	LI L , WANG H , JIA C , et al. Integrity and continuity allocation for the RAIM with multiple constellations[J]. GPS Solutions, 2017, 21 (4): 1503- 1513. doi: 10.1007/s10291-017-0627-4
14	GOTTSCHALG G , LEINEN S . Comparison and evaluation of integrity algorithms for vehicle dynamic state estimation in different scenarios for an application in automated driving[J]. Sensors, 2021, 21 (4): 1458. doi: 10.3390/s21041458
15	WANG L Y , LI Z F , WANG N , et al. Real-time GNSS precise point positioning for low-cost smart devices[J]. GPS Solutions, 2021, 25 (2): 69- 84. doi: 10.1007/s10291-021-01106-1
16	李臻, 宋丹, 张鹏飞, 等. 基于抗差扩展卡尔曼滤波和外推积累的RAIM方法[J]. 系统工程与电子技术, 2017, 39 (9): 2094- 2099.
	LI Z , SONG D , ZHANG P F , et al. RAIM method based on robust extended Kalman filter and extrapolation accumulation[J]. Systems Engineering and Electonics, 2017, 39 (9): 2094- 2099.
17	PARKINSON B W , AXELRAD P . Autonomous GPS integrity monitoring using the pseudorange residual[J]. Navigation, 1988, 35 (2): 255- 274. doi: 10.1002/j.2161-4296.1988.tb00955.x
18	KALAFUS R M , CHIN G Y . Performance measures of receiver-autonomous GPS integrity monitoring[J]. Institute of Navigation, 1988, 32 (2): 223- 229.
19	王煜东, 许承东, 郑学恩. 基于斜率加权的最小二乘RAIM算法研究[J]. 电光与控制, 2020, 27 (3): 13- 16, 64. doi: 10.3969/j.issn.1671-637X.2020.03.003
	WANG Y D , XU C D , ZHENG X E . An RAIM algorithm based on slope-weighted least square method[J]. Electronics Optics and Control, 2020, 27 (3): 13- 16, 64. doi: 10.3969/j.issn.1671-637X.2020.03.003
20	SONG D S , SHI C , WANG Z D , et al. Correlation-weighted least squares residual algorithm for RAIM[J]. Chinese Journal of Aeronautics, 2020, 33 (5): 1505- 1516. doi: 10.1016/j.cja.2019.12.012
21	STURZA , MARK A . Navigation system integrity monitoring using redundant measurements[J]. Navigation, 1988, 35 (4): 483- 501. doi: 10.1002/j.2161-4296.1988.tb00975.x
22	MA X , YU K , MONTILLET J P , et al. Equivalence proof and performance analysis of weighted least squares residual method and weighted parity vector method in RAIM[J]. IEEE Access, 2019, 7, 97803- 97814. doi: 10.1109/ACCESS.2019.2929073
23	ANGRISANO A , GAGLIONE S , CROCETTO N , et al. PANG-NAV: a tool for processing GNSS measurements in SPP, including RAIM functionality[J]. GPS Solutions, 2020, 24 (1): 19- 35. doi: 10.1007/s10291-019-0935-y
24	张倩倩, 归庆明, 宫轶松. 卫星多故障探测和识别的独立分量分析法[J]. 测绘学报, 2017, 46 (6): 698- 705.
	ZHANG Q Q , GUI Q M , GONG Y S . Multiple satellite faults detection and identification based on the independent component analysis[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46 (6): 698- 705.
25	王文博, 徐颖. 基于稳健MM估计的REKF RAIM算法[J]. 系统工程与电子技术, 2021, 43 (1): 216- 222.
	WANG W B , XU Y . REKF RAIM algorithm based on MM-estimation[J]. Systems Engineering and Electonics, 2021, 43 (1): 216- 222.
26	GU S , BEI J M , SHI C S , et al. RAIM algorithm based on fuzzy clustering analysis[J]. Computer Modeling in Engineering & Sciences, 2019, 119 (2): 281- 293.
27	HUI Z Q , JIN S F , XIA Y , et al. A mean shift segmentation morphological filter for airborne LiDAR DTM extraction under forest canopy[J]. Optics & Laser Technology, 2021, 136, 106728.
28	GUO P S , LIU Z , WANG J B . Radar group target recognition based on HRRPs and weighted mean shift clustering[J]. Journal of Systems Engineering and Electronics, 2020, 31 (6): 1152- 1159. doi: 10.23919/JSEE.2020.000087
29	陶本藻, 姚宜斌, 施闯. 基于相关分析的粗差可区分性[J]. 武汉大学学报: 信息科学版, 2004, 29 (10): 881- 884.
	TAO B Z , YAO Y B , SHI C . Distinguishability of outlier based on correlative analysis[J]. Geomatics and Information Science of Wuhan University, 2004, 29 (10): 881- 884.
30	LI Z , LI M , SHI C , et al. A new fuzzy-cluster-based cycle-slip detection method for GPS single-frequency observation[J]. Remote Sensing, 2019, 11 (24): 2896. doi: 10.3390/rs11242896

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

[1]	Wenbo WANG, Ying XU. REKF RAIM algorithm based on robust MM-estimation [J]. Systems Engineering and Electronics, 2021, 43(1): 216-222.
[2]	LI Zhen, SONG Dan, ZHANG Pengfei, XU Chengdong. RAIM method based on robust extended Kalman filter and extrapolation-accumulation [J]. Systems Engineering and Electronics, 2017, 39(9): 2094-2099.

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

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 30

Related Articles 2

Recommended Articles

Metrics

Comments