Accurate Position Estimation of Autonomous Underwater Vehicles by a Kalman Filter Using an Inertial Navigation Sensor

Abstract

Due to the precarious underwater environment, many tasks are impossible to carry out with manned vehicles. It is due to the development of autonomous underwater vehicles (AUVs) that exploration, surveillance, and research have been carried out like never before. AUVs are now extensively used for lengthy missions but face challenges in maintaining an accurate position over a larger operational area due to water currents and oceanic complexities. Hence, more sophisticated navigation systems are required for AUVs. As global positioning systems (GPS) and radio signals are attenuated under water, it becomes difficult to navigate subsea areas. In this research work, we propose the accurate position estimation of an AUV by a Kalman filter using an inertial navigation system (INS). In the MATLAB environment, a sample of the INS accelerometer’s data is simulated to determine position from acceleration using the Kalman filter, and the double integration method of determining position is contrasted. Our experimental results imply that our suggested method produces better outcomes.

Keywords

• inertial navigation system (INS)
• Kalman filter (KF)
• localization
• autonomous underwater vehicle (AUV)
• global positioning systems (GPS)