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.

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.

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.

Field calibration is an important method to guarantee the accuracy of a strapdown inertial navigation system. Zero velocity update based on the zero-velocity constraint when the carrier is without translational motion is a typical system-level calibration method. In zero velocity update, there is a coupling between biases and horizontal misalignment angles. The accuracy of horizontal misalignment angles is determined by the equivalent accelerometer biases in horizontal directions, which means that improving the accuracy of horizontal angles needs accurate calibration of accelerometer biases. Meanwhile, alignment with gravitational apparent motion is widely used taking advantages of its alignment ability in a swinging condition. But it is an analytical method and cannot calibrate sensor biases and is always dealt as a coarse alignment method. In order to calibrate accelerometer biases and utilize advantages of the alignment method with gravitational motion, a method to estimate accelerometer biases based on an iterative optimization method and gravitational apparent motion is presented in this paper. First, accelerometer biases are introduced to calculate apparent acceleration and an objective function is constructed. Then, Newton’s iteration is applied to iteratively optimize the parameters describing gravitational apparent motion and accelerometer biases. As revealed by the theoretical analysis and experimental results, different patterns of gravity and accelerometer biases will be generated when the carrier exhibits a swinging motion; thus, the convergence of the proposed algorithm will be ensured. After accelerometer biases are removed, initial alignment performed with the gravitational apparent motion reconstructed by the estimated parameters gives nearly zero horizontal misalignment angles.

The initial alignment method, including the identification of inertial device error parameters, has always been a key issue in an inertial navigation system (INS). This study focuses on the error caused by the random noise of inertial devices that can be compensated by the reconstruction of gravitational apparent motion in an inertial frame under the condition of swinging motion. Attitude angles and accelerometer bias can also be estimated. However, the analysis and simulation results indicate that the existing methods cannot estimate the gyroscope bias. The accelerometer and the gyroscope bias will change over a long time, which will lead to long-term parameter identification accuracy decline or even failure. In this paper, a parameter identification algorithm based on Newton iterative optimization combined with a window loop calculation is designed to solve these problems. Simulation and turntable tests indicate that the proposed new algorithm can fulfill the initial alignment of strapdown INS under the swinging condition and estimate accelerometer bias effectively. Moreover, the new algorithm improves data utilization, which also has better time sensitivity, and the calculated alignment errors can nearly approach zero.