Weidong (John) Ding

Optimal Integration of GPS with Inertial Sensors: Modelling and Implementation

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University
University of NSW
Supervisor (Academic)
Dr Jinling Wang, University of NSW
Supervisor (Industry)
Mr Doug Kinlyside, Dept of Lands Bathurst
Projects
mysite
Employment
Technical Specialist, Sydney Trains
Thesis Abstract

Integration of GPS with Inertial Navigation Systems (INS) can provide reliable and complete positioning and geo-referencing parameters including position, velocity, and attitude of dynamic platforms for a variety of applications. This research focuses on four modelling and implementation issues for a GPS/INS integrated platform in order to optimise the overall integration performance:

a) Time synchronization
Having recognised that having a precise time synchronisation of measurements is fundamental in constructing a multi-sensor integration platform and is critical  or achieving high data fusion performance, various time synchronisation scenarios and solutions have been investigated. A criterion for evaluating  synchronisation accuracy and error impacts has been derived; an innovative time synchronisation solution has been proposed; an applicable data logging system has been implemented with off-the shelf components and tested.

b) Noise suppression of INS raw measurements
Low cost INS sensors, especially MEMS INS, would normally exhibit much larger measurement noise than conventional INS sensors. A novel method of using vehicle dynamic information for de-noising raw INS sensor measurements has been proposed in this research. Since the vehicle dynamic model has the characteristic of a low pass filter, passing the raw INS sensor measurements through it effectively reduces the high frequency noise component.

c) Adaptive Kalman filtering
The present data fusion algorithms, which are mostly based on the Kalman filter, have the stringent requirement on precise a priori knowledge of the system model and noise properties. This research has investigated the utilization issues of online stochastic modelling algorithm, and then proposed a new adaptive process noise scaling algorithm which has shown remarkable capability in autonomously tuning the process noise covariance estimates to the optimal magnitude.

d) Integration of a low cost INS sensor with a standalone GPS receiver
To improve the performance where a standalone GPS receiver integrated with a MEMS INS, additional velocity aiding and a new integration structure has been adopted in this research. Field test shows that velocity determination accuracy could reach the centimetre level, and the errors of MEMS INS have been limited to such a level that it can generate stable attitude and heading references under low dynamic conditions.