Kalman filtering for aided inertial navigation system

Abstract

This thesis develops a Kalman filter which integrates the inertial navigation system of the Vorticity Control Unmanned Undersea Vehicle (VCUUV) with redundant navigation sensor measurements. The model for the Kalman filter uses redundant measurements in a feedback loop to better estimate navigation variables. Using outputs from the Inertial Measurement Unit (IMU) and from a depth sensor, a velocity sensor and a magnetometer, a Kalman filter is developed. Actual test runs on the VCUUV prove the new system superior to the previously used open-loop navigation system.