Abstract
This report presents a fuzzy multi-sensor data fusion process for
combining heading estimates from three separate Kalman filters with
the aim of constructing a fault tolerant navigation system for the
Springer Uninhabited Surface Vehicle (USV). A single gyroscopic unit
and three independent compasses are used to acquire data onboard the
vessel. The inertial data from the gyroscope is combined in turn with
the readings from each compass via a separate Kalman filter (KF). The
three ensuing KF estimates of the heading angle of the vehicle are then
fused via a fuzzy system designed to produce accurate heading
information even in the face of a failure in one of the compasses. A
simulation study demonstrates the effectiveness of the proposed fuzzy
data fusion process.
combining heading estimates from three separate Kalman filters with
the aim of constructing a fault tolerant navigation system for the
Springer Uninhabited Surface Vehicle (USV). A single gyroscopic unit
and three independent compasses are used to acquire data onboard the
vessel. The inertial data from the gyroscope is combined in turn with
the readings from each compass via a separate Kalman filter (KF). The
three ensuing KF estimates of the heading angle of the vehicle are then
fused via a fuzzy system designed to produce accurate heading
information even in the face of a failure in one of the compasses. A
simulation study demonstrates the effectiveness of the proposed fuzzy
data fusion process.
| Original language | English |
|---|---|
| Publisher | MIDAS - Marine and Industrial Dynamic Analysis, Plymouth University |
| Number of pages | 23 |
| Volume | MIDAS.SMSE.2014.TR.010 |
| Publication status | Published - 11 Apr 2014 |