Abstract
In navigation of autonomous underwater vehicles (AUVs), the estimation of position is an important issue, especially, when the sensors such as gyroscopes contain a lot of noise, and the velocity information of Doppler velocity log (DVL) is affected by the motion attitude of the vehicle. In this paper, based on an improved auto regressive (AR) model, a real-time filter is utilized for gyroscope signal de-noising. Meanwhile, according to the characteristics of the AUV, the influence of the vehicle attitude on the DVL velocity measurement error is analyzed and a motion attitude assist (MAA) method based on error model is introduced for enhancing DVL velocity accuracy. In this paper, using the proposed hybrid approach, an inertial navigation system (INS)/DVL integrated navigation system is designed. The proposed approach is evaluated by simulation and experimental test in different acceleration bound, and the existence of the DVL outage for an AUV. The results indicate that the precisions of the velocity and position are improved effectively, especially in complex motion attitude and long sailing conditions.
Highlights
In recent years, autonomous underwater vehicles (AUVs) have played an important role in military missions such as the ocean exploration
The accuracy and convergence of the parameter identification by recursive least squares (RLS) algorithm and modified recursive least squares (MRLS) algorithm are verified through the simulated time series function
In the vehicle test experiment, on the one hand, the effect of the improved auto regressive (AR) model is verified for inertial navigation system (INS)/Doppler velocity log (DVL) integrated navigation system
Summary
Autonomous underwater vehicles (AUVs) have played an important role in military missions such as the ocean exploration. In terms of the INS/DVL integrated navigation fusion algorithm, Reference [6] presents an approach for aiding the INS of an underwater vehicle using velocity measurements provided by an experimentally validated kinetic vehicle model. It is claimed in [7] that the localization of an AUV is performed using two different forms of Kalman filter (KF): extended Kalman filter (EKF) and unscented Kalman filter (UKF), their estimates are compared. According to Reference [18], an adaptive robust Kalman filter (ARKF) based on hybrid-correction grid INS/DVL integrated navigation algorithm is proposed with the unified reference ellipsoid Earth model to improve the navigation accuracy in middle-high latitude regions for marine applications.
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