Hostname: page-component-76fb5796d-2lccl Total loading time: 0 Render date: 2024-04-26T00:29:53.170Z Has data issue: false hasContentIssue false

Particle Filter-Based Inter-System Positioning Model for Non-Overlapping Frequency Code Division Multiple Access Systems

Published online by Cambridge University Press:  18 March 2020

Rui Shang
Affiliation:
(School of Transportation, Southeast University, Nanjing, China) (Nottingham Geospatial Institute, University of Nottingham, Nottingham, United Kingdom)
Xiaolin Meng
Affiliation:
(Nottingham Geospatial Institute, University of Nottingham, Nottingham, United Kingdom)
Chengfa Gao*
Affiliation:
(School of Transportation, Southeast University, Nanjing, China)
Shuguo Pan
Affiliation:
(School of Instrument Science and Engineering, Southeast University, Nanjing, China)
Wang Gao
Affiliation:
(School of Instrument Science and Engineering, Southeast University, Nanjing, China)
Zihan Peng
Affiliation:
(School of Transportation, Southeast University, Nanjing, China)
*

Abstract

In the process of composing a double-differenced positioning model, it is difficult to separate different frequency signals between code division multiple access (CDMA) systems, the single-difference ambiguity of the pivot satellite and phase differential inter-system biases (PDISBs). Hence it is difficult to calibrate in advance the bias between systems in order to build an inter-system model which only needs one pivot satellite. Based on analysis of the stability of PDISB parameters for non-overlapping frequency CDMA systems, this study adopts a particle filter to estimate the fractional part of the PDISBs (F-PDISBs) between the systems and proposes a particle filter-based inter-system positioning model. Results show that the F-PDISBs and code DISBs for the baselines with the same receiver types and some with different receiver types are rather stable over time and for these baselines it is feasible to use a particle filter to estimate the F-PDISB parameters in the initial stage. Having attained the F-PDISBs, the inter-system model can be constructed to improve positioning accuracy in complex operational environments.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2020

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

Gao, W., Gao, C., Pan, S., Meng, X. and Xia, Y. (2017a). Inter-system differencing between GPS and BDS for medium-baseline RTK positioning. Remote Sensing, 9(9), 948.CrossRefGoogle Scholar
Gao, W., Gao, C. and Pan, S. (2017b). A method of GPS/BDS/GLONASS combined RTK positioning for middle-long baseline with partial ambiguity resolution. Survey Review, 49(354), 212220.CrossRefGoogle Scholar
Gao, W., Meng, X., Gao, C., Pan, S. and Wang, D. (2018). Combined GPS and BDS for single-frequency continuous RTK positioning through real-time estimation of differential inter-system biases. GPS Solutions, 22(1), 20.CrossRefGoogle Scholar
Julien, O., Alves, P., Cannon, M. E. and Zhang, W. (2003). A tightly coupled GPS/GALILEO combination for improved ambiguity resolution. In: Proceedings of the European Navigation Conference (ENC-GNSS'03), Graz, Austria, 114.Google Scholar
Li, G., Wu, J., Zhao, C. and Tian, Y. (2017). Double differencing within GNSS constellations. GPS Solutions, 21(3), 11611177.CrossRefGoogle Scholar
Nadarajah, N., Khodabandeh, A. and Teunissen, P. J. G. (2015). Assessing the IRNSS L5-signal in combination with GPS, Galileo, and QZSS L5/E5a-signals for positioning and navigation. GPS Solutions, 20(2), 289297.CrossRefGoogle Scholar
Odijk, D. and Teunissen, P. J. (2013a). Characterization of between-receiver GPS-Galileo inter-system biases and their effect on mixed ambiguity resolution. GPS Solutions, 17(4), 521533.CrossRefGoogle Scholar
Odijk, D. and Teunissen, P. J. G. (2013b). Estimation of Differential Intersystem Biases between the Overlapping Frequencies of GPS, Galileo, BeiDou and QZSS. Proc. 4th International Colloquium: Scientific and Fundamental Aspects of the Galileo Program, Prague, Czech Republic, December 4–6, 1–8.Google Scholar
Odijk, D., Nadarajah, N., Zaminpardaz, S. and Teunissen, P. J. (2017). GPS, Galileo, QZSS and IRNSS differential ISBs: Estimation and application. GPS Solutions, 21(2), 439450.CrossRefGoogle Scholar
Odolinski, R. and Teunissen, P. J. (2016). Single-frequency, dual-GNSS versus dual-frequency, single-GNSS: A low-cost and high-grade receivers GPS-BDS RTK analysis. Journal of Geodesy, 90(11), 12551278.CrossRefGoogle Scholar
Odolinski, R., Teunissen, P. J. G. and Odijk, D. (2014). Combined GPS + BDS + Galileo + QZSS for Long Baseline RTK Positioning. Proc. ION GNSS 2014, Institute of Navigation, Tampa, Florida, USA, September 8–12, 2326–2340.Google Scholar
Odolinski, R., Teunissen, P. J. and Odijk, D. (2015). Combined BDS, galileo, QZSS and GPS single-frequency RTK. GPS Solutions, 19(1), 151163.CrossRefGoogle Scholar
Paziewski, J. and Wielgosz, P. (2015). Accounting for Galileo–GPS inter-system biases in precise satellite positioning. Journal of Geodesy, 89(1), 8193.CrossRefGoogle Scholar
Paziewski, J. and Wielgosz, P. (2017). Investigation of some selected strategies for multi-GNSS instantaneous RTK positioning. Advances in Space Research, 59(1), 1223.CrossRefGoogle Scholar
Quasi-Zenith Satellite System (QZSS) (2019). https://qzss.go.jp/en/technical/satellites/index.html. Accessed 14 October 2019.Google Scholar
Shang, R., Gao, C., Pan, S., Meng, X. and Gao, W. (2019). Tightly combined GPS + GLONASS positioning with consideration of inter-system code bias and GLONASS inter-frequency code bias. The Journal of Navigation, 116.Google Scholar
Teunissen, P. J. G. (2004). Penalized GNSS ambiguity resolution. Journal of Geodesy, 78(4–5), 235244.CrossRefGoogle Scholar
Teunissen, P. J. G. and Kleusberg, A. (1998). GPS observation equations and positioning concepts. In: Teunissen, P. J. G. and Kleusberg, A. (eds) GPS for Geodesy. Berlin, Heidelberg: Springer, 187229.CrossRefGoogle Scholar
Tian, Y., Ge, M. and Neitzel, F. (2015). Particle filter-based estimation of inter-frequency phase bias for real-time GLONASS integer ambiguity resolution. Journal of Geodesy, 89(11), 11451158.CrossRefGoogle Scholar
Tian, Y., Ge, M., Neitzel, F. and Zhu, J. (2017). Particle filter-based estimation of inter-system phase bias for real-time integer ambiguity resolution. GPS Solutions, 21(3), 949961.CrossRefGoogle Scholar
Wu, M., Zhang, X., Liu, W., Ni, S. and Yu, S. (2017). Tightly Combined BeiDou B2 and Galileo E5b Signals for Precise Relative Positioning. The Journal of Navigation, 70(6), 12531266.CrossRefGoogle Scholar
Yang, Y., Gao, W., Guo, S., Mao, Y. and Yang, Y. (2019). Introduction to BeiDou-3 navigation satellite system. Navigation, 66(1), 718.CrossRefGoogle Scholar
Zhang, B., Teunissen, P. J. and Yuan, Y. (2017). On the short-term temporal variations of GNSS receiver differential phase biases. Journal of Geodesy, 91(5), 563572.CrossRefGoogle Scholar
Zhang, B., Chen, Y. and Yuan, Y. (2019). PPP-RTK based on undifferenced and uncombined observations: theoretical and practical aspects. Journal of Geodesy, 93(7), 10111024.CrossRefGoogle Scholar