A multi-sensor data fusion navigation system for an unmanned surface vehicle

T. Xu*, R. Sutton, S. Sharma

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

<jats:p> Worldwide there is an increasing interest in the development of unmanned surface vehicles (USVs). In order for such vehicles to undertake missions, they require accurate, robust, and reliable navigation systems. This paper describes the implementation of a fault tolerant autonomous navigation approach for a USV named Springer. An intelligent multi-sensor data fusion navigation algorithm is proposed that is based on a modified form of a federated Kalman filter (FKF) utilizing a fuzzy logic adaptive technique. The fuzzy adaptive technique is used to adjust the measurement noise covariance matrix R to fit the actual statistics of the noise profile present in the incoming sensor measured data using a covariance matching method. Information feedback factors employed in the FKF are tuned on the basis of the accuracy of each sensor. In order to compare the fault-tolerant performance, several fuzzy-logic-based cascaded Kalman filter architectures are also considered. Simulation results demonstrate the algorithm's capability under different types of sensor fault. </jats:p>
Original languageEnglish
Pages (from-to)167-182
Number of pages0
JournalProceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment
Volume221
Issue number4
Early online date30 Nov 2007
DOIs
Publication statusPublished - 1 Dec 2007

Fingerprint

Dive into the research topics of 'A multi-sensor data fusion navigation system for an unmanned surface vehicle'. Together they form a unique fingerprint.

Cite this