New optimal smoothing scheme for improving relative and absolute accuracy of tightly coupled GNSS/SINS integration
- 802 Downloads
For mobile surveying and mapping applications, tightly coupled integration of global navigation satellite system (GNSS) and Strap down Inertial Navigation System is usually recommended for direct georeferencing since it can provide position, velocity, and attitude information at higher accuracy and better reliability in a self-contained manner. A post-mission smoothing method is applied to optimally use observation information of both systems and to overcome the shortcomings of Kalman filter in GNSS degraded environments. We propose the revised Rauch–Tung–Streibel Smoother (RTSS) and Forward–Backward combination (FBC) smoothing algorithms for tightly coupled integration. From the analysis and field test, it is found that RTSS smoothing mainly improves the relative accuracy, while FBC mainly contributes to the absolute accuracy. With the complementary characteristics of both smoothing algorithms, an optimal new smoothing scheme combining RTSS with FBC is built. The performance of these three smoothing algorithms is evaluated through a real vehicular test. Compared with RTSS and FBC smoothing algorithms, the new smoothing scheme improves the mean 3D position RMS and the mean 3D attitude RMS by 65.7 and 70%, respectively. It provides better accuracy and smoothness for the position, velocity, and attitude at the same time.
KeywordsTightly coupled architecture GNSS/SINS integration Rauch–Tung–Streibel Smoother Forward–Backward combination
The authors gratefully acknowledge two anonymous reviewers for their valuable comments and improvements to this manuscript. This study was supported by the National Key Research and Development Program of China (Grant No. 2016YFB0501803), Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University (Grant No. 15-02-010) and Special Scientific Research Fund for Public Welfare Profession of China (Grant No. 201512002).
- Crassidis JL, Junkins JL (2011) Optimal estimation of dynamics systems. CRC Press, Boca Raton, pp 334–337Google Scholar
- Godha S, Cannon ME (2005) Integration of DGPS with a low cost MEMS—based Inertial Measurement Unit (IMU) for land vehicle navigation application. In: Proceedings of the ION GNSS 2005, Institute of Navigation, Long Beach, CA, USA, September 13–16, pp. 333–345Google Scholar
- Groves PD (2013) Principles of GNSS, inertial, and multisensor integrated navigation systems. Artech House, Boston, pp 90–92Google Scholar
- Hide C, Moore T (2005) GPS and low cost INS integration for positioning in the urban environment. In: Proceedings of the ION GNSS 2005, Institute of Navigation, Long Beach, CA, USA, September 13–16, pp. 1007–1015Google Scholar
- Nassar S (2002) Different algorithms for bridging kinematic DGPS outages using SINS/DGPS integration. In: Proceedings of the ION GNSS 2002, Institute of Navigation, Portland, OR, USA, September 24–27, pp. 1474–1482Google Scholar