Navigation technology is vital to determine where Unmanned Underwater Vehicle (UUV) is located. This is essential to complete missions, such as submarine resource development, marine geological survey, marine ecological survey and mine clearance, and make information gathered during the mission more accurate, reliable and valuable. Dead reckoning that commonly uses Inertia Measurement Unit (IMU), Doppler Velocity Logger (DVL) and magnetic compass has position errors due to integrating acceleration and velocity. Moreover, the heading error of magnetic compass based on geodetic north includes declination and sensor noise caused by local magnetic-field effect and characteristics of sensor. This could raise the position error in the North-East-Down (NED) coordinate system in the case of dead reckoning especially using magnetic compass, because it is based on not geodetic north, but magnetic north. This makes it difficult to implement an integrated navigation system or compare the performance of navigation algorithms, such as dead reckoning, satellite navigation using Global Positioning Systems (GPS) and terrain-aided navigation using bathymetry maps.
This thesis introduces a GPS-aided navigation algorithm to reduce errors accumulated while using dead reckoning navigation. This will help better estimate the position of UUVs while using dead reckoning in the NED coordinate system. For sensor fusion and measurement noise rejection, the navigation algorithm was designed to use an Extended Kalman Filter (EKF), which has much fewer calculations than an Unscented Kalman Filter (UKF) and a Particle Filter (PF).
This algorithm defined the heading bias error of a magnetic compass as the difference between the UUV heading angle based on geodetic north and a magnetic compass’ heading measurement. The magnetic compass’ heading bias error was asymptotically estimated by receiving GPS positional data when it surfaced. When the navigation algorithm estimated the magnetic compass’ heading bias error, the UUV’s position was displayed in the NED coordinate system, even when the UUV was submerged.
While using Matlab Simulink, an Autonomous Underwater Vehicle (AUV) dynamic simulation program was built to check the performance of the proposed navigation algorithm. The simulation program consists of a dynamic model, a sensor model, a controller and the navigation algorithm. A Naval Postgraduate School (NPS) AUV called as ARIES was used as the dynamic model because of its detailed dimensions and its precedent research containing large amounts of hydrodynamic coefficients.
Furthermore, the sensor model’s characteristics were decided on according specifications and test results of sensors currently in use. Considering the sensor characteristics, the measured values of GPS, magnetic compass, DVL, gyro and pressure sensor are artificially generated on the basis of the position, attitude and velocity of AUV in the simulation. After receiving the data, the navigation algorithm estimates the compass’ heading bias error and the AUV’s position allowing control of the AUV and the ability to perform way-points and heading control simulation.
The simulation incorporates three different scenarios. Two of them determine and estimate the AUV’s position and heading bias error after receiving(or not) the GPS positional data. The other uses trajectory and heading bias errors similar to those in the field test which allows comparisons of the field test results.
The simulations will show that the navigation algorithm improves the accumulated positional errors of dead reckoning and the magnetic compass’ heading bias errors. In the underwater driving scenario, it was confirmed that the AUV’s position errors were improved. This was accomplished by the navigation algorithm examining the magnetic compass’ heading bias error compared to the conventional dead reckoning method.
The GPS-aided navigation algorithm was applied to navigation system of a hovering-type AUV in order to verify the performance of the algorithm through field test. The applied algorithm estimates the position and attitude of the AUV and the heading bias error of Tilt-compensated Compass Module (TCM) based on geodetic north, by receiving the measurements of GPS, DVL, TCM and Attitude & Heading Reference System (AHRS). The monitoring and control system based on LabVIEW was implemented to provide the operator with the information about the AUV’s operation. Also, the AUV operating system includes the propulsion system to perform the heading control experiment or the way-point control experiment, which can be configured by the operator. Unlike the simulation, the application of GPS positional data and the estimation of TCM heading bias error depend on additional conditions for the efficient application of the navigation algorithm in the field test. In other words, the navigation algorithm utilizes GPS positional data to estimate the position and attitude of the AUV and the TCM heading bias error, so long as the positional information is judged to be efficient. Otherwise, the position and attitude of the AUV are estimated by dead reckoning considering the heading bias error of TCM obtained previously.
As a result, the field test verified the performance of the navigation algorithm, by checking how precisely and accurately the TCM heading bias error was estimated and comparing the position error with the conventional dead reckoning, which was not considering the heading bias error.
This thesis proposes the GPS-aided navigation algorithm for UUV. The algorithm’s performance was verified by the simulation and field test. When there is no positional information provided by acoustic beacon and bathymetry map due to long-term and long-distance voyage, the navigation algorithm can be a crucial part of a UUV’s navigation technology.