Underwater navigation system based on inertial sensor and Doppler velocity log using indirect feedback Kalman filter

Abstract

This paper presents a hybrid underwater navigation system for unmanned underwater vehicles. The navigation system consists of an inertial measurement unit (IMU) and a Doppler velocity log (DVL) along with the magnetic compass and depth sensor. A navigational system error model is derived to include the bias errors of accelerometers and gyros in the IMU, the scale errors of the DVL, and the bias errors of the compass and depth sensor. An extended Kalman filter is used for the error model in indirect feedback, where the measurement equation is composed of the navigational state errors and system parameters. This paper demonstrates the effectiveness of the inertial-Doppler navigation system through a lawn-mowing mode in simulation and circular motion with rotating arm tests in the experiments. The hybrid navigation system equipped with a low-quality IMU sensor improves navigational performance, where the bias error of the accelerometers and the gyros of the IMU are 1.0 mg and 1.0 deg/h, respectively. The tracking error is less than 1.0 m for 10 min of circular motion from the experiments.