Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-22T18:23:55.900Z Has data issue: false hasContentIssue false

A Fast Rotating Partition Satellite Selection Algorithm Based on Equal Distribution of Sky

Published online by Cambridge University Press:  21 February 2019

Fangchao Li
Affiliation:
(NASG Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou, China)
Zengke Li*
Affiliation:
(NASG Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou, China)
Jingxiang Gao
Affiliation:
(NASG Key Laboratory of Land Environment and Disaster Monitoring, China University of Mining and Technology, Xuzhou, China)
Yifei Yao
Affiliation:
(College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling, China)
*

Abstract

To achieve fast satellite selection for a multi-Global Navigation Satellite System (GNSS), thereby reducing the burden on a receiver's processing element and the cost of hardware, and improving the utilisation ratio of receiver signal channels, the relationship between the number of satellites and Geometric Dilution Of Precision (GDOP), the number of satellites selected and the computation time is analysed. A fast rotating partition algorithm for satellite selection based on equal distribution of the sky is proposed. The algorithm divides the satellite selection process into two parts: rough selection and detailed selection. Unhealthy satellites, according to a health identifier, and low elevation angle satellites with a large troposphere delay are eliminated during the rough selection process. During the detailed satellite selection process, the satellite sky is divided and rotated to match satellites based on the average angle distance between the satellite and central partition line. Static data from the International GNSS Service (IGS) station and dynamic data collected at China University of Mining and Technology were used to verify the algorithm, and the results demonstrated that an inverse matrix could be avoided to reduce computation complexity. Additionally, the new satellite selection algorithm has the merit that there is little effect on the computation when the selected satellites and number of satellites in the field increased. A single system of the Global Positioning System (GPS) and double system of GPS/Globalnaya Navigazionnaya Sputnikovaya Sistema (GLONASS) both passed the hypothesis test for each epoch. By including BeiDou Navigation Satellite System (BDS) data, data utilisation increased to more than 95% using the rotating partition algorithm. Also, the GDOP and positioning performance of a rotating partition algorithm and an optimal Dilution Of Precision (DOP) algorithm are compared in this paper, and the analysis result shows that both of the algorithms have only a small difference of GDOP and have comparable positioning performance.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Azami, H., Mosavi, M. R. and Sanei, S. (2013). Classification of GPS satellites using improved back propagation training algorithms. Wireless Personal Communications, 71(2), 789803.10.1007/s11277-012-0844-7Google Scholar
Belabbas, B., Hornbostel, A., Sadeque, M.Z. and Denks, H. (2005). Accuracy study of a single frequency receiver using a combined GPS/GALILEO constellation, Proceedings of the 18th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2005), Long Beach, CA, September, 10221033.Google Scholar
Blanco-Delgado, N. and Nunes, F. D. (2010). Satellite selection method for multi-constellation GNSS using convex geometry. IEEE Transactions on Vehicular Technology, 59(9), 42894297.10.1109/TVT.2010.2072939Google Scholar
Bo, X. and Shao, B. (2009) Satellite selection algorithm for combined GPS-Galileo navigation receiver. 4th International Conference on Autonomous Robots and Agents, Wellington, New Zealand 149–154.Google Scholar
Cong, L., Abidat, A.I. and Tan, Z.Z. (2006) Analysis and simulation of the GDOP of satellite navigation, Acta Electronica Sinica, 34(12), 22042208.Google Scholar
Doong, S. H. (2009) A closed-form formula for GPS GDOP computation, GPS Solutions, 13(3), 183190.10.1007/s10291-008-0111-2Google Scholar
Holland, J. H. (1992). Adaptation in natural and artificial systems. Cambridge, MIT Press.10.7551/mitpress/1090.001.0001Google Scholar
Januszewski, J (2005) Geometry and visibility of satellite navigation systems in restricted area. Proceedings of the 2005 National Technical Meeting of The Institute of Navigation, San Diego, CA, January, 827839.Google Scholar
Kihara, M. and Okada, T. (1984) A satellite selection method and accuracy for the global positioning system, Navigation, 31(1), 820.10.1002/j.2161-4296.1984.tb00856.xGoogle Scholar
Li, P., Zhang, X. and Guo, F. (2016) Ambiguity resolved precise point positioning with GPS and BeiDou, Journal of Geodesy, 91(1), 2540.Google Scholar
Li, Z., Gao, J., Wang, J. and Yao, Y. (2017) PPP/INS tightly coupled navigation using adaptive federated filter, GPS Solutions, 21(1), 13714810.1007/s10291-015-0511-zGoogle Scholar
Mok, E. and Cross, P.A. (1994). A fast satellite selection algorithm for combined GPS and GLONASS receivers The Journal of Navigation, 47(03), 383389.10.1017/S0373463300012327Google Scholar
Mosavi, M.R. and Divband, M. (2010) Calculation of geometric dilution of precision using adaptive filtering technique based on evolutionary algorithms. International Conference on Electrical and Control Engineering, Wuhan 2010, 48424845.10.1109/iCECE.2010.1171Google Scholar
Phatak, M.S. (2001) Recursive method for optimum GPS satellite selection IEEE Transactions on Aerospace and Electronic Systems, 37(2), 751754.10.1109/7.937488Google Scholar
Teng, Y and Wang, J. (2016). Some remarks on PDOP and TDOP for Multi-GNSS constellations. Journal of Navigation, 69(01), 145155.10.1017/S0373463315000508Google Scholar
Yong, Y. and Miao, L. (2004) GDOP results in all-in-view positioning and in four optimum satellites positioning with GPS PRN codes ranging. PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556) Monterey, CA, USA, 723727.10.1109/PLANS.2004.1309065Google Scholar
Yu, X., Sun, Y., Liu, J. and Miao, J. (2009) Fast algorithm of selecting satellites for multiple satellite integrated navigation system. 2009 WRI World Congress on Computer Science and Information Engineering, Los Angeles, CA, 2009, 121125.Google Scholar
Zhang, M. and Zhang, J. (2009) A fast satellite selection algorithm: beyond four satellites IEEE Journal of Selected Topics in Signal Processing, 3(5), 740747.10.1109/JSTSP.2009.2028381Google Scholar
Zhang, M., Zhang, J. and Yong, Q. (2008) Satellite selection for multi-constellation. 2008 IEEE/ION Position, Location and Navigation Symposium, Monterey, CA, 2008. 10531059.10.1109/PLANS.2008.4570112Google Scholar