Abstract

In Global Positioning System (GPS), Precise Point Positioning (PPP) achieves the highest accuracy in point positioning. It approaches centimetre-level accuracy in static mode and sub-decimetre accuracy in kinematic mode. PPP is an alternative approach to carrier-phase-based Differential GPS (DGPS) and offers advantages over DGPS. PPP uses GPS observations from a single receiver for position estimation, which is simpler than using more than one GPS receiver. However, PPP needs rigorous modelling for all errors and biases, which are otherwise cancelled out or mitigated when using DGPS. PPP’s popularity is on the rise, as it is ideal for land-vehicle positioning and navigation. However, in challenging environments, PPP suffers from a signal loss that prevent continuous navigation or a reduction in the number of visible satellites that causes accuracy degradation. This research integrates PPP with a Reduced Inertial Sensors System (RISS) — a low-cost system that uses data from reduced MEMS-based inertial sensors and vehicle odometry — to provide accurate and inexpensive land-vehicle navigation systems. The system is integrated in a tightly coupled mode through the use of an Extended Kalman Filter (EKF), which employs an improved error model for the RISS data. The system was tested using data from real driving routes with single-frequency code-based PPP/RISS (SF-code-PPP/RISS), dual-frequency code-based PPP (DF-code-PPP/RISS), smoothed dual-frequency code-based PPP (S-DF-code-PPP/RISS), and code- and carrier-phase-based PPP (code-carrier-PPP/RISS). The performance of the developed PPP/RISS was evaluated using position RMS and maximum errors during continuous GPS availability as well as during signal outages. The developed integrated algorithms were assessed using three real road tests that capture different navigational conditions. The results show that when five or more satellites are available, code-carrier-PPP/RISS solution is superior to that of SF- and DF-code-PP/RISS. For latitude, code-carrier-PPP/RISS solution was 47% and 20% more precise than the SF- and DF-code- PP/RISS counterparts, respectively. For longitude, code-carrier-PPP/RISS solution was 65% and 31% more precise than the SF- and DF-Code-PP/RISS counterparts, respectively. Similarly, the altitude solution was improved by 46% and 25%, respectively. During GPS signal outages of 60 seconds, code-carrier-PPP/RISS’s algorithms outperformed that of SF- and DF-code-PPP/RISS by about 35% when the satellite availability level was set to three satellites. For other satellite availability levels, the algorithms performed almost identically.

Highlights

  • The integration of Global Positioning System (GPS) and Inertial Navigation System (INS) often refers to the data fusion of measurements obtained from a GPS receiver and inertial sensors

  • The performance of the developed tightly coupled Precise Point Positioning (PPP)/Reduced Inertial Sensors System (RISS) algorithms was tested on real data collected through various road trajectories conducted under different navigational conditions in a van-type land vehicle

  • The state vector of the developed integrated system includes position, velocity, attitude, sensor biases, and receiver’s clock bias and drift; the results presentation is focused on the positioning results

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Summary

Introduction

The integration of Global Positioning System (GPS) and Inertial Navigation System (INS) often refers to the data fusion of measurements obtained from a GPS receiver and inertial sensors (accelerometers and gyroscopes). GPS/INS integration is formulated as a state-space estimation problem where the Kalman Filter (KF), or a modified version of it, is applied to obtain suboptimal solutions. GPS provides positioning information with consistent and acceptable accuracy when a GPS receiver gets signals from four or more GPS satellites (Grewal et al, 2007). GPS suffers from problems such as signal outage and multipath, which degrades its accuracy or even makes the system useless. INS is an autonomous system that is immune to external interference; its accuracy decreases in the long term because of the sensor’s bias error drift, scale factor instability, and misalignment (Gleason and Gebre-Egziabher, 2009)

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