A windowing-recursive approach for GPS real-time kinematic positioning
- 287 Downloads
All conventional Kalman filtering methods depend to a great extent on dynamic models for describing the motion state of vehicle. However, low-cost GPS navigation systems do not provide velocity and acceleration measurements to construct dynamic models. Therefore, it is rather difficult to establish reasonable dynamic models. A windowing-recursive approach (WRA) which employs previous positions to predict the current position is proposed, and the transition matrix is modeled for transforming the previous positions to the current one. Two typical transition matrices are constructed by numerical polynomial fitting and extrapolation. A real vehicular GPS experiment is carried out to demonstrate the WRA performances in two relative positioning scenarios. The data are processed by the least squares approach and by WRA using the two developed transition matrices. The results show that the WRA performed excellently in a high sampling rate data. In case of a lower sampling rate, higher-order polynomial fitting and extrapolation models work better than lower-order models for a given window. In addition, the extrapolation models can alleviate the computation burdens significantly relative to the polynomial fitting models.
KeywordsGPS Transition matrix Windowing-recursive approach Kalman filter
The work is partially sponsored by Natural Science Foundations of China (grant no. 40674003, 40874016), and partially supported by the fund from the Key Laboratory of Advanced Surveying Engineering of SBSM (grant no. TJES0809). The authors are very grateful to the anonymous reviewers for their constructive comments and suggestions.
- Chen W, Cross PA (1990) Integration of GPS and an inertial system for precise surveying applications. Surv Rev 30(238):375–395Google Scholar
- El-Diasty M, El-Rabbany A, Pagiatakis S (2006) New developments in state estimation for INS/GPS integrated systems. In: ION GNSS 19th international technical meeting of the satellite division, Fort Worth, TXGoogle Scholar
- Hatch R (1982) The synergism of GPS code and carrier measurements. In: Proceedings of the third international geodetic symposium on satellite Doppler positioning, Las Cruces, NM, vol 2, pp 1213–1231Google Scholar
- Mao XC, Wada M, Hashimoto H (2002) Nonlinear filtering algorithms for GPS using pseudorange and Doppler shift measurements. In: The IEEE 5th international conference on intelligent transportation systems, SingaporeGoogle Scholar
- Mohammed AQ, Robert BN, Washington YO (2006) A high accuracy fuzzy logic based map matching algorithm for road transport. J Intell Transp Syst 10(3):103–115Google Scholar
- Schwarz KP, Cannon ME, Wong RVC (1989) A comparison of GPS kinematic models for determination of position and velocity along a trajectory. Manuscripta Geod 14:345–353Google Scholar
- Wang J (2000) Stochastic modelling for RTK GPS/Glonass positioning, Navigation. J US Inst Navig 46(4):297–305Google Scholar