IMPROVING POSITIONING ACCURACY DURING KINEMATIC DGPS OUTAGE PERIODS USING SINS/DGPS INTEGRATION AND SINS DATA DE-NOISING

Abstract

In the standard integration of a Differential Global Positioning System (DGPS) and a Strapdown Inertial Navigation System (SINS), the DGPS provides position information while the SINS provides attitude information. In addition, the DGPS measurements are used to estimate the inertial sensors systematic errors and the SINS is used to detect and correct GPS cycle slips. In case of GPS signal blockages, the SINS is used instead for positioning as a stand-alone system until the GPS signals are available again. To obtain accurate positions during DGPS outages, near real-time (or post-mission) techniques should be applied, where these techniques are known as bridging algorithms. In such algorithms, new and improved positions of the outage periods are estimated. In this paper, two different bridging methods are used namely: backward smoothing and parametric modeling. An SINS/DGPS data collected with a van has been used in the analysis. The results show that both bridging algorithms reduce the SINS positional errors for DGPS outages of 75 to 100 seconds with an average of 1.35 m to an RMSE of 19 cm in case of backward smoothing and 10 cm in case of parametric modeling. To separate between the actual motion dynamics and other disturbing vibrations, a de-noising of the SINS raw data is required. Therefore, a de-noising of the van SINS data has been applied using a wavelet decomposition technique to eliminate or minimize the effect of sensor noise and other high frequency disturbances (such as engine vibrations). An analysis of the SINS sensor kinematic raw data in the frequency domain shows clearly that the majority of the van motion dynamics are contained in the low frequency portion of the spectrum (below 3.0 Hz). Consequently, several levels of wavelet decomposition can be performed without losing any motion information. The application of both bridging methods after the SINS data de-noising reduces the positional RMSE to 11 cm and 7.7 cm using backward smoothing and parametric modeling, respectively.