Multi-Type GNSS User Classification Using RANSAC-K-means Clustering

LI Kun; CHEN Shuailong; ZHENG Shuaiyong; WANG Xuanwen; LIU Jixi; YANG Peng; GAO Mengzhi; JIN Xiaoqin

doi:10.23919/cje.2024.00.041

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Article Navigation > Chinese Journal of Electronics > 2024 > Accepted Manuscript

Kun LI, Shuailong CHEN, Shuaiyong ZHENG, et al., “Multi-Type GNSS User Classification Using RANSAC-K-means Clustering,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–9, xxxx doi: 10.23919/cje.2024.00.041

Citation:

Kun LI, Shuailong CHEN, Shuaiyong ZHENG, et al., “Multi-Type GNSS User Classification Using RANSAC-K-means Clustering,” Chinese Journal of Electronics, vol. x, no. x, pp. 1–9, xxxx doi: 10.23919/cje.2024.00.041

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Multi-Type GNSS User Classification Using RANSAC-K-means Clustering

doi: 10.23919/cje.2024.00.041

LI Kun^1
,,
CHEN Shuailong^1
,,
ZHENG Shuaiyong^{1, 2
,
,},
WANG Xuanwen^1
,,
LIU Jixi^1
,,
YANG Peng^1
,,
GAO Mengzhi^1
,,
JIN Xiaoqin^3
,

1.
Tianjin University of Technology, No. 391 Bin Shui Xi Dao Road, Xiqing District, Tianjin 300384, China
2.
Aerospace Information Research Institute, Chinse Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094
3.
Nankai University, No. 94, Weijin Road, Nankai District, Tianjin 300350, China

More Information

Author Bio:
Kun LI received the PH.D from Tianjin University. He is currently a associate professor of Tianjin University of Thchnology. His research interests are satellite navigation and signal processing. (Email: likun_tjut@163.com)

Shuailong CHEN is currently pursuing the Master degree in the school of Tianjin University of Technology. His research interests include low earth orbit satellite enhancement in satellite navigation and GNSS user clustering analysis in PNT. (Email: Shuailongchen2000@163.com)

Shuaiyong ZHENG received the PH.D from Beihang University and is currently pursuing a postdoctoral degree at the Chinese Academy of Sciences. He is currently a Lecturer of Tianjin University of Thchnology. His research interests are satellite navigation and integrated navigation. (Email: syzheng21@email.tjut.edu.cn)

Xuanwen WANG is currently pursuing the Master degree in the school of Tianjin University of Technology. His research interests include inertial navigation and integrated navigation. (Email: 16622900154@163.com)

Jixi LIU is currently pursuing the Master degree in the school of Tianjin University of Technology. His research interests include low earth orbit satellite enhancement in satellite navigation and inertial navigation. (Email: liujixi_tjut@163.com)

Peng YANG received the PH.D from Naikai University. He is currently a associate professor of Tianjin University of Thchnology. His research interests are satellite attack and defense and clustering analysis. (Email: 29139475@qq.com)

Mengzhi GAO received the Master from Tianjin University of Technology. His research interests include satellite navigation. (Email:gmengzvip@163.com)

Xiaoqin JIN Xiaoqin Jin is currently pursuing the PH.D degree in Nankai University. Her research interests include satellite navigation and clustering analysis in PNT. (Email: jxqsusan@126.com)
Corresponding author: Email: syzheng21@email.tjut.edu.cn
Available Online: 2024-07-23

Abstract

Abstract

The BeiDou Navigation Satellite system (BDS-3) has provided Positioning, Navigation and Timing (PNT) services to global users across land, maritime, and aviation. However, how to classify these three users with complex movement patterns poses great challenges to the work of monitoring and evaluating of PNT system. To accurately classify multi-type Global Navigation Satellite System (GNSS) users, this paper proposes a method that combines Random Sample Consensus (RANSAC) and K-means clustering to track the movements of massive users and classify them based on their dynamic characteristics in different areas, which is noted as RANSAC-K-means. The simulated massive user data show that the recognition rate of the proposed algorithm exceeds 83.22%, compared with the conventional method, the proposed RANSAC-K-means method improved the recognition rate by 11.16%. The RANSAC-K-means method can provide more accurate clustering results under the situations where multi-type users present dynamic characteristics with significant differences, showing significant stability and robustness. The proposed method is more suitable for monitoring and evaluating the service performance of satellite navigation systems.
- PNT,
- Multi-type user,
- User classification,
- RANSAC-K-means

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References(30)

References

[1]	R. H. Liu and Y. T. Tian, “Analysis of the differences in the messages broadcast by different GEOs of WAAS,” Journal of Physics: Conference Series, vol. 2290, article no. 012113, 2022. doi: 10.1088/1742-6596/2290/1/012113
[2]	S. Y. Zheng, M. Z. Gao, Z. G. Huang, et al., “Satellite integrity monitoring for satellite‑based augmentation system: An improved covariance‑based method,” Satellite Navigation, vol. 3, no. 1, article no. 9, 2022. doi: 10.1186/s43020-022-00070-6
[3]	S. Y. Zheng, R. Li, Z. G. Huang, et al., “Determination of fast corrections for satellite-based augmentation system,” IEEE Access, vol. 7, pp. 178662–178674, 2019. doi: 10.1109/ACCESS.2019.2953096
[4]	R. Li, S. Y. Zheng, Z. P. Wang, et al., “Evaluation of GNSS signal-in-space continuity: A weibull-distribution-based method,” Chinese Journal of Electronics, vol. 27, no. 3, pp. 634–640, 2018. doi: 10.1049/cje.2018.02.014
[5]	S. Y. Zheng, M. Z. Gao, K. Li, et al. , “Potential performance of satellite-based augmentation system based on CMONOC in China,” in China Satellite Navigation Conference (CSNC 2022) Proceedings, C. F. Yang and J. Xie, Eds. Springer, Singapore, pp. 231–243, 2022.
[6]	X. Q. Jin, R. S. Ma, J. C. Wang, et al. , “Analysis on positioning performance of BDS-3 system in polar region,” in Advances in Guidance, Navigation and Control: Proceedings of 2022 International Conference on Guidance, Navigation and Control, L. Yan, H. B. Duan, and Y. M. Deng, Eds. Springer, Singapore, pp. 572–583, 2023.
[7]	X. Q. Jin, X. Y. Zhang, S. P. Li, et al., “GNSS anti-spoofing: A sliding composite delta metric using maximum likelihood estimation,” IEEE Sensors Journal, vol. 23, no. 20, pp. 24885–24894, 2023. doi: 10.1109/JSEN.2023.3310494
[8]	S. Y. Zheng, M. Z. Gao, K. Li, et al. , “Potential performance of satellite-based augmentation system based on CMONOC in China,” in China Satellite Navigation Conference (CSNC 2022) Proceedings, C. F. Yang and J. Xie, Eds. Springer, Singapore, pp. 231–243, 2022. (查阅网上资料,本条文献与第5条文献重复,请确认) .
[9]	D. Song, S. Q. Geng, Z. G. Huang, et al. , “Design of tracking loop for single antenna satellite navigation receivers under the situation of rotation,” in China Satellite Navigation Conference (CSNC) 2018 Proceedings, J. D. Sun, C. F. Yang, and S. R. Guo, Eds. Springer, Singapore, pp. 435–444, 2018.
[10]	T. K. Biswas, K. Giri, and S. Roy, “ECKM: An improved K-means clustering based on computational geometry,” Expert Systems with Applications, vol. 212, article no. 118862, 2023. doi: 10.1016/j.eswa.2022.118862
[11]	Z. R. Zhang, J. Y. Lan, and Z. R. Zhang, “K-means clustering algorithm based on bee colony strategy,” Journal of Physics: Conference Series, vol. 2031, article no. 012058, 2021. doi: 10.1088/1742-6596/2031/1/012058
[12]	L. J. Zhu, L. Chuang, J. H. McClellan, et al., “A multi-channel approach for automatic microseismic event association using RANSAC-based Arrival Time Event Clustering (RATEC),” Earthquake Research Advances, vol. 1, no. 3, article no. 100008, 2021. doi: 10.1016/j.eqrea.2021.100008
[13]	L. H. Chen, Y. Luo, X. D. Liu, et al., “Improved collaborative filtering recommendation algorithm based on user attributes and K-means clustering algorithm,” Journal of Physics: Conference Series, vol. 1903, article no. 012036, 2021. doi: 10.1088/1742-6596/1903/1/012036
[14]	Y. X. Cheng, Z. Q. Huang, S. W. Quan, et al., “Sampling locally, hypothesis globally: Accurate 3D point cloud registration with a RANSAC variant,” Visual Intelligence, vol. 1, no. 1, article no. 20, 2023. doi: 10.1007/S44267-023-00022-X
[15]	L. Y. Li, Y. Y. Zhang, B. Li, et al., “Distributed storage and computing method of massive GNSS data,” Journal of Navigation and Positioning, vol. 3, no. 4, pp. 62–68, 2015. doi: 10.16547/j.cnki.10-1096.20150412
[16]	A. Martín, R. M. Capilla, and A. B. Anquela, “Big data architecture and data mining analysis for market segment applications of differential global navigation satellite system (GNSS) services: Case study of the analysis of the demand for navigation and agriculture,” The Journal of Navigation, vol. 75, no. 2, pp. 421–436, 2022. doi: 10.1017/S0373463322000030
[17]	W. J. Chen, F. Zhu, F. Guo, et al., “GNSS signal characteristics analysis in different water layers and navigation context clustering,” Geomatics and Information Science of Wuhan University, vol. 49, no. 1, pp. 139–145, 2024. doi: 10.13203/j.whugis20220048
[18]	Z. H. Wang, L. L. Tang, X. Yang, et al., “The U-Turn information collecting method using vehicle GNSS trajectory data,” Acta Geodaetica et Cartographica Sinica, vol. 52, no. 8, pp. 1330–1341, 2023. doi: 10.11947/j.AGCS.2023.20220063
[19]	C. C. Peng, “K-means based RANSAC algorithm for ICP registration of 3D point cloud with dense outliers,” in 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), Penghu, China, pp. 1–2, 2021.
[20]	M. Saval-Calvo, J. Azorin-Lopez, A. Fuster-Guillo, et al., “Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC,” Applied Soft Computing, vol. 34, pp. 572–586, 2015. doi: 10.1016/j.asoc.2015.05.007
[21]	P. Bide and R. Shedge, “Improved document clustering using k-means algorithm,” in IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, pp. 1–5, 2015.
[22]	J. J. R. Critchley-Marrows, X. F. Wu, and I. H. Cairns, “An architecture for a visual-based PNT alternative,” Acta Astronautica, vol. 210, pp. 601–609, 2023. doi: 10.1016/J.ACTAASTRO.2023.05.022
[23]	Q. Hu, J. Jia, Y. Zhu, et al., “A multisource PNT fusion algorithm based on a variance genetic model,” International Journal of Control, Automation and Systems, vol. 20, no. 4, pp. 1294–1304, 2022. doi: 10.1007/s12555-020-0758-1
[24]	S. Caizzone, M. Schönfeldt, W. Elmarissi, et al., “Antennas as precise sensors for GNSS reference stations and high-performance PNT applications on earth and in space,” Sensors, vol. 21, no. 12, article no. 4192, 2021. doi: 10.3390/S21124192
[25]	Y. Bar-Shalom, T. E. Fortmann, and P. G. Cable, “Tracking and data association,” The Journal of the Acoustical Society of America, vol. 87, no. 2, pp. 918–919, 1990. doi: 10.1121/1.398863
[26]	K. Saho and T. Shibata, “Closed-form steady-state bias error of tracking filter using coordinated turn model for constant velocity target,” IEICE Communications Express, vol. 11, no. 5, pp. 245–250, 2022. doi: 10.1587/comex.2022XBL0022
[27]	D. Barath, L. Cavalli, and M. Pollefeys, “Learning to find good models in RANSAC,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, pp. 15723–15732, 2022.
[28]	W. J. Li, Z. H. Zhang, Z. S. Jiang, et al., “A RANSAC based phase noise filtering method for the camera-projector calibration system,” Optoelectronics Letters, vol. 18, no. 10, pp. 618–622, 2022. doi: 10.1007/s11801-022-2045-2
[29]	L. Stanković, M. Brajović, I. Stanković, et al., “RANSAC-based signal denoising using compressive sensing,” Circuits, Systems, and Signal Processing, vol. 40, no. 8, pp. 3907–3928, 2021. doi: 10.1007/s00034-021-01654-4
[30]	M. Ahmed, R. Seraj, and S. M. S. Islam, “The k-means algorithm: A comprehensive survey and performance evaluation,” Electronics, vol. 9, no. 8, article no. 1295, 2020. doi: 10.3390/electronics9081295