Zixuan Wang

The strap-down inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation system are widely used for autonomous underwater vehicles (AUVs). Whereas DVL works in the water tracking mode, the velocity provided by DVL is relative to the current layer and cannot be directly used to suppress the divergence of SINS errors. Therefore, the estimation and compensation of the ocean current velocity play an essential role in improving navigation positioning accuracy. In recent works, ocean currents are considered constant over a short term in small areas. In the common KF algorithm with the ocean current as a state vector, the current velocity cannot be estimated because the current velocity and the SINS velocity error are coupled. In this paper, two virtual metrology filter (VMF) methods are proposed for estimating the velocity of ocean currents based on the properties that the currents remain unchanged at the adjacent moments. New measurement equations are constructed to decouple the current velocity and the SINS velocity error, respectively. Simulations and lake tests show that both proposed methods are effective in estimating the current velocity, and each has its advantages in estimating the ocean current velocity or the misalignment angle.

The integration of the strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) has become a basic navigation solution for Autonomous Underwater Vehicles (AUVs). However, DVL cannot obtain the velocity relative to the ground when the distance between the AUV and seabed is over the operating range, which occurs often when AUVs are sailing in the middle layer of the ocean. When the DVL velocity relative to the current is used for an integrated filter, the unknown current velocity is coupled with the measured velocity error, which decreases the positioning accuracy. To address this problem, the effect of unknown coupled current velocity is analyzed from the perspective of filter observability, and an integrated SINS/DVL/virtual velocity navigation method is proposed. The virtual velocity based on the velocity variation extracted from the inertial measurement unit and DVL is constructed and used as an aided measurement for the Kalman filter. With the help of virtual velocity, the current velocity can be easily decoupled from measured SINS velocity error. The results of simulation and experiments demonstrated that the proposed method can effectively improve both the convergence speed and accuracy of velocity error compared with the classical method with SINS/DVL integration and, thus, significantly improve the positioning accuracy.

The Doppler velocity log-aided strap-down inertial (SINS/DVL) integrated navigation system has been widely used in autonomous underwater vehicles (AUV), and the installation angle error is the major factor that affects the accuracy of the SINS/DVL integrated navigation system. A novel two-step in-motion calibration of the installation angle error and navigation is proposed in this paper. Firstly, an optimization-based alignment (OBA) method is developed to derive a rough estimation of the installation angle error and the initial attitude. During the OBA process, the required velocity and position are all approximated using their initial values. Secondly, using the results of the former rough estimation as the initial value for the Kalman-filter (KF) method. Simulations and vehicle tests have shown that the OBA method can efficiently estimate the installation error between SINS/DVL, which as an initial value contributes to a higher navigation performance of the KF method.

Transfer alignment is widely used for Strapdown Inertial Navigation System (SINS) on ship but the static flexural deformation between Slave SINS and Master SINS, also installing errors of Slave SINS will introduce alignment errors. Meanwhile calibrating those deformations and installing errors needs spcial maneuvers along different axes, that is difficult to fulfill for ships’ poor maneuverability. Aiming to solve this problem, a novel transfer alignment method based on combined double-time observations with velocity and attitude is proposed, current observation subtract last observation are used as measurements for Kalman filter (KF). For the advantages of subtracting, system error in measurement introduced by deformation and installing can be effectively removed and alignment accuracy improved. Simulation and turntable tests verify that the proposed method can improve alignment accuracy and shorten alignment process and not require any active maneuvers or external equipment.

Estimating inertial measurement unit error without any external reference information has always been a difficult problem. The method based on the reconstruction of the gravitational apparent motion in the inertial frame can estimate the accelerometer bias, but a long identification time is needed to get high alignment accuracy. In order to reduce the convergence time and improve the accuracy of estimation, a fast estimation method based on repetitive navigation under the excitation of swing motion is proposed. Two data processing methods, the forward–forward loop and the backward–forward loop, are studied and compared in this paper. In addition, the influence of data length on the experiment result was analyzed. The simulation and experimental results show that the forward–forward loop calculation cannot solve the contradiction between speed and accuracy, while the backward–forward loop calculation can effectively estimate the accelerometer bias and realize self-alignment, with a short alignment time and high alignment accuracy.