Sensor Fusion for Navigation of Autonomous Underwater Vehicle Using Kalman Filtering

Abstract

An Autonomous Underwater Vehicle (AUV) is a robot that can travel underwater without requiring any intervention from the operator. As opposed to AUV, Remotely Operated Vehicle (ROV) is an underwater robot which requires manual control through a tethered wire connected to a base ship or a station. AUV finds tremendous applications in the field of defense, underwater mine detection, study of ocean floor, repair of undersea cables and is also pursued as a hobby. For an automated vehicle to travel from point A to point B, requires three interrelated technologies: Navigation, Guidance and Control. This thesis mainly focuses on the Navigation aspect of the AUV.
Inertial Navigation System (INS) use accelerometers and gyroscopes to measure acceleration and attitude (orientation) rates respectively to estimate position, velocity and attitude in three orthogonal directions. Global Navigation Satellite System (GNSS) uses a cluster of satellites to estimate the position of GNSS receiver close to the surface of the earth. INS gives accurate short term navigation solutions yet its accuracy diminishes overthe long run because of accumulation of errors. The precision of GNSS navigation solution is not so good when contrasted with INS but they don't corrupt over the long run. When these two navigation systems are fused or integrated using a Kalman filter, the subsequent system performs better than either of the individualsystems even when sensors of lower cost and lower performance are used. One disadvantage of using GNSS is that the GNSS signals are lost whenever the AUV dives inside the water. But by using an integrated GNSS/INS system, an INS is allowed to navigate with improved initial error even when GNSS signals are lost, thus achieving the desired standalone performance. Moreover, whenever the GNSS signals are available, the system utilizes the INS data to decrease the signal reacquisition time for GNSS. Thus each system supports the other system to achieve the desired performance. The thesis focuses on the design and implementation of Kalman filters for these applications. First of all, dynamic model and sensor error model for strapdown INS has been developed. The effectiveness of the model was studied using Schuler oscillation test, bias error test and stationary INS test. Next, an error model for GNSS has been developed. Subsequently, various vi types of vehicle dynamic model for GNSS receivers has been developed and its error characteristics were compared using a simulated Figure‐8 track (track in the shape of 8). Finally, performance analysis of INS, GNSS and integrated GNSS/INS is studied on a Figure‐8 simulated track. Effect of loss of GNSS signals on the performance is also studied.