An Algorithm for Determining the Navigation Parameters of AUVs Based on the Combination of Measuring Devices

An extended nonlinear Kalman filter (EKF) for a real-time estimation of the navigation parameters of autonomous underwater vehicles (AUVs) based on the combination of angular rate sensors, magnetometers, accelerometers and speedometers, pressure sensors or GPS is recently developed. Due to the combination of the measuring devices using the EKF, the accuracy of the navigation parameters is improved because the drifts of angular rate sensors, accelerometers and noise of the measuring devices are ignored. Moreover, this combination helps us to reduce the capacity of the computation in comparison with the inertial navigation methods. We combine the inertial navigation equipments with the speed measurement devices based on an extended Kalman filter in order to improve the speed accuracy of an underwater equipment. The obtained results of the test on the turntable with 3 degrees of freedom Aerosmith and of the firm test on underwaterr rescue robots for big fire have proved the correctness of the algorithm.