AUV (autonomous underwater vehicle) integrated navigation method integrating Kalman filtering and particle filtering

Abstract

The invention discloses an AUV (autonomous underwater vehicle) integrated navigation method integrating Kalman filtering and particle filtering. The AUV integrated navigation method comprises the following steps of: 1) data collection: using a global positioning system to obtain initial position information of an AUV when the AUV is on the water surface, and collecting the information of speed, attitude angle and the like of the AUV by utilizing navigation sensors such as a Doppler log, an electronic compass and the like; and 2) filter positioning: fusing navigation information collected by the sensor by utilizing a filtering algorithm based on the combination of the Kalman filtering and the particle filtering, estimating to obtain the position and attitude change information of the AUV at every moment, and realizing overall positioning of the AUV. The invention provides the AUV integrated navigation method integrating the Kalman filtering and the particle filtering, which can improve the precision.

Description

technical field [0001] The invention relates to the field of marine engineering, in particular to an integrated navigation method for an autonomous underwater vehicle (Autonomous Underwater Vehicle, AUV). Background technique [0002] Autonomous underwater vehicles are currently a hotspot in the development of marine engineering technology, and have been widely used in military fields such as underwater environmental monitoring, offshore petroleum engineering operations, underwater search and mapping, mine countermeasures, and real-time theater alerts. Navigation technology is the key to realizing autonomous navigation of AUVs. Due to the characteristics of AUVs such as long working hours, complex environments, few information sources, and high requirements for concealment, this brings great difficulties and challenges to stable and accurate AUV autonomous navigation. [0003] In recent years, the international research on AUV navigation technology has been very active. Und...

AI Technical Summary

Problems solved by technology

[0004] Existing AUVs usually use the attitude angle and acceleration information collected by the inertial navigation system (Inertial Navigation System, INS), the attitude angle information collected by the three-dimensional electronic compass (TCM), and the Doppler Velocity Log (DVL) to collect The commonly used data fusion methods include Kalman Filter (KF), Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (Particle Filter, PF) and Ensemble Kalman Filter (Ensemble Kalman Filter, EnKF), etc., the common problem of extended Kalman filter and tasteless Kalman filter is that the convergence drops sharply or even diverges when the nonlinear / non-Gaussian is strong

The particle filter uses the sequential Monte Carlo method, which has better performance for nonlinear / non-Gaussian problems, but in the particle filter, if the latest observation information is located at the tail of the prior probability distribution or the likelihood function is compared with the prior The probability is peaked, which will lead to the blindness of particle selection and reduce the estimation accuracy

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