Filters navigation and positioning based on mining vehicle motion model

Abstract

The navigational positioning accuracy of a seabed mining vehicle not only directly affects the efficiency of aggregation, but also affects the reliability and stability of mining operations. In order to determine the noise reduction filtering algorithm, Which is most suitable for state estimation and position estimation models in mining vehicle sea trials, an Adaptive Kalman Filter (AKF) based on linear self-navigation position estimation is first proposed under an ideal Gaussian noise model and compared With conventional Kalman filter (KF) and Innovation Kalman filter (IKF) algorithms. Secondly, based on the underwater projection observatory, a nonlinear position estimation model based on distance and angle is proposed, introducing Gaussian noise. Particle Filter (PF) and improved particle filtering algorithms such as the Unscented Kalman Particle Filter(UPF), the Extended kalman Filter(EPF) are used for state estimation. The simulation results show that under the nonlinear position estimation model, UPF not only solves the problem of conventional Particle Filter (PF) divergence, but also significantly improves the accuracy of position estimation compared to self-navigation position estimation. The UPF algorithm based on an underwater projection observatory is best suited for navigational positioning of polymetallic nodule seabed mining vehicles.