Improving MEMS Gyroscope Performance using Homogenous Sensor Fusion

Totu, Luminita Cristiana ; Konge Koldbæk, Simon

10. term

Elektronik og IT, Kandidatuddannelsen (Spec. Intelligent Autonomous Systems)

2011

2011-05-31

161 pages

The scope of the project is to investigate the possibilities of using noise correlations and Kalman filtering to improve the performance of a sensor array containing multiple MEMS gyroscope. The project is based on the work of Bayard and Ploen whom have showed, trough simulation, that the performance of MEMS gyroscopes can be improved by combining measurements from favorably correlated gyroscopes. In addition, the project also investigates the possibility of identifying noise correlations by using Expectation-Maximization. The project has been proposed and carried out in collaboration with CDL Scotland, which is developer and provider of subsea inertial navigation sensors and solutions. A custom sensor board containing eight medium grade gyroscopes and additional interface hardware has been designed for the project by CDL. Based on Allan Variance analysis and classical signal analysis methods, a simple stochastic model for the random bias component in the gyroscope output signal has been developed and implemented in MATLAB. The model has been validated through comparative analysis of the Root Allan Variance. The Expectation-Maximization algorithm has been implemented in and tested in MATLAB. Several methods of improving the performance of a sensor array containing multiple MEMS gyroscope has been investigated. Using Kalman filter based estimation strategies and benchmarking against a simple signal averaging filter, the group has shown through simulations that performance improvement, especially in the angle drift estimation, is possible assuming that favorable correlations exist between the noise processes of the gyroscope signals. The Kalman filter algorithm is atypically applied to a state space model that is not observable nor detectable, and an interesting analysis of this usage is discussed. The implemented Expectation-Maximization algorithm was not able to identify all the relevant noise correlations with sufficient accuracy during simulations. The main problem is related to the ratio between the measurement noise and the system noise, as the system noise is two orders of magnitude smaller than the measurement noise. As such, the problem of identifying the noise correlations remains open, and the improvement potential of the gyroscope board can not be assessed in a satisfactory manner at the present time.

Kalman filtering noise correlations "Allan Variance" "Expectation Maximization" ALS "Autocovariance Least Squares"

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