Application of Improved Robust Kalman Filter in Data Fusion for PPP/INS Tightly Coupled Positioning System

• Autorzy: Li Z. , Yao Y. , Wang J. , Gao J.

Identyfikatory

DOI

10.1515/mms-2017-0031

• Języki publikacji: EN

Abstrakty

EN

A robust Kalman filter improved with IGG (Institute of Geodesy and Geophysics) scheme is proposed and used to resist the harmful effect of gross error from GPS observation in PPP/INS (precise point positioning/inertial navigation system) tightly coupled positioning. A new robust filter factor is constructed as a three-section function to increase the computational efficiency based on the IGG principle. The results of simulation analysis show that the robust Kalman filter with IGG scheme is able to reduce the filter iteration number and increase efficiency. The effectiveness of new robust filter is demonstrated by a real experiment. The results support our conclusion that the improved robust Kalman filter with IGG scheme used in PPP/INS tightly coupled positioning is able to remove the ill effect of gross error in GPS pseudorange observation. It clearly illustrates that the improved robust Kalman filter is very effective, and all simulated gross errors added to GPS pseudorange observation are successfully detected and modified.

Słowa kluczowe

EN

PPP/INS tightly coupled positioning; robust filter; IGG scheme; Mahalanobis distance

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2017
• Tom: Vol. 24, nr 2
• Strony: 289--301
• Opis fizyczny: Bibliogr. 18 poz., rys., tab., wykr., wzory

Twórcy

autor

Li Z.

• China University of Mining and Technology, School of Environment and Spatial Informatics, Xuzhou, China

autor

Yao Y.

• China University of Mining and Technology, School of Environment and Spatial Informatics, Xuzhou, China

autor

Wang J.

• China University of Mining and Technology, School of Environment and Spatial Informatics, Xuzhou, China

autor

Gao J.

• China University of Mining and Technology, School of Environment and Spatial Informatics, Xuzhou, China

Bibliografia

• [1] Chu, H.J., Tsai, G.J., Chiang, K.W., Duong, T.T. (2013). GPS/MEMS INS data fusion and map matching in urban areas. Sensors, 13(9), 11280-11288.
• [2] Nassar, S. (2003). Improving the Inertial Navigation System (INS) Error Model for INS and INS/DGPS Applications. Ph.D. Thesis. The University of Calgary.
• [3] Kouba, J., Héroux, P. (2001). Precise point positioning using IGS orbit and clock products. GPS Solut., 5(2), 12-28.
• [4] Du, S., Gao, Y. (2012). Inertial aided cycle slip detection and identification for integrated PPP GPS and INS. Sensors, 12(11), 14344-14362.
• [5] Ali, J., Ushaq, M. (2009). A consistent and robust Kalman filter design for in-motion alignment of inertial navigation system. Measurement, 42(4), 577-582.
• [6] Gao, S., Zhong, Y., Li, W. (2011). Robust adaptive filtering method for SINS/SAR integrated navigation system. Aerosp. Sci. Technol., 15(6), 425-430.
• [7] Huang, G., Zhang, Q. (2012). Real-time estimation of satellite clock offset using adaptively robust Kalman filter with classified adaptive factors. GPS Solut., 16(4), 531-539.
• [8] Guo, F., Zhang, X. (2014). Adaptive robust Kalman filtering for precise point positioning. Meas. Sci. Technol., 25(10), 1-8.
• [9] Chang, G. (2014). Robust Kalman filtering based on Mahalanobis distance as outlier judging criterion. J. Geod., 88(4), 391-401.
• [10] Du, S. (2010). Integration of precise point positioning and low cost MEMS IMU. Ph.D. Thesis. The University of Calgary.
• [11] Abdel-salam, M.A. (2005). Precise point positioning using un-differenced code and carrier phase observations. Ph.D. Thesis. The University of Calgary.
• [12] Titterton, D. (2004). Strapdown inertial navigation technology. 2nd ed. MIT Lincoln Laboratory.
• [13] Han, S., Wang, J. (2012). Integrated GPS/INS navigation system with dual-rate Kalman Filter. GPS Solut., 16(3), 389-404.
• [14] Li, Z., Wang, J., Li, B., Gao, J., Tan, X. (2014). GPS/INS/Odometer integrated system using fuzzy neural network for land vehicle navigation applications. J. Navigation, 67(6), 967-983.
• [15] Zhang, Y., Gao, Y. (2008). Integration of INS and un-differenced GPS measurements for precise position and attitude determination. J. Navigation, 61(1), 87-97.
• [16] Nassar, S., El-Sheimy, N. (2006). A combined algorithm of improving INS error modeling and sensor measurements for accurate INS/GPS navigation. GPS Solut., 10(1), 29-39.
• [17] Chang, G. (2014). Kalman filter with both adaptivity and robustness. J. Process Contr., 24(3), 81-87.
• [18] Yang, Y. (1994). Robust estimation for dependent observations. Manuscripta Geodaetica, 19(1), 10-17.

Uwagi

EN

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).

PL

The work was partially sponsored by China’s Post-doctoral Science Fund (grant number: 2015M580490) and partially sponsored by Natural Science Foundation of Jiangsu Province (grant number: BK20160247). The authors would like to thank Dr. Xiaolin Meng and all experienced members in the University of Nottingham for their help in collecting and processing the field test data.