Error Analysis and Compensation of Gyrocompass Alignment for SINS on Moving Base

As the exploration of marine resources continues to deepen, the utilization of Autonomous Underwater Vehicles (AUVs) for conducting marine resource surveys and underwater environmental mapping has become a common practice. In order to successfully accomplish exploration missions, AUVs require high-precision underwater navigation information as support. A Strapdown Inertial Navigation System (SINS) can provide AUVs with accurate attitude and heading information, while a Doppler Velocity Log (DVL) is capable of measuring the velocity vector of the AUVs. Therefore, the integrated SINS/DVL navigation system can furnish the necessary navigational information required by an AUV. In response to the issue of DVL being susceptible to external environmental interference, leading to reduced measurement accuracy, this paper proposes an end-to-end deep-learning approach to enhance the accuracy of DVL velocity vector measurements. The utilization of the raw measurement data from an Inertial Measurement Unit (IMU), which includes gyroscopes and accelerometers, to assist the DVL in velocity vector estimation and to refine it towards the Global Positioning System (GPS) velocity vector, compensates for the external environmental interference affecting the DVL, therefore enhancing the navigation accuracy. To evaluate the proposed method, we conducted lake experiments using SINS and DVL equipment, from which the collected data were organized into a dataset for training and assessing the model. The research results show that the DVL vector predicted by our model can achieve a maximum improvement of 69.26% in terms of root mean square error and a maximum improvement of 78.62% in terms of relative trajectory error.

In order to solve the problem that the traditional navigation algorithm is unavailable for Unmanned Underwater Vehicles (UUVs) navigation in the polar region, a transversal navigation algorithm for polar UUV navigation is proposed in this paper. Traditional latitude and longitude is redefined in the transversal navigation algorithm to overcome the problems caused by the rapid convergence of the meridians in high latitude areas. Based on the transversal latitude and longitude coordinate system, the relevant frames are also redefined. According to the frames, the error equations of polar UUV navigation are established. Then, the states and observation equations are proposed. Considering the characters of UUV, OCTANS and Doppler velocity log (DVL) are chosen to assist the polar UUV navigation. Transversal Strapdown Inertial Navigation System (SINS), OCTANS and DVL form the integrated navigation algorithm. An adaptive Kalman filter (AKF) is introduced to realize the data fusion. Compared with the traditional SINS, the performance of the transversal navigation algorithm proposed in this paper is verified. The simulation results demonstrate that the proposed transversal navigation algorithm is suitable for polar UUV navigation.

Due to the influence of the complex underwater environment, the initial alignment method for Doppler velocity log (DVL)-aided strap-down inertial navigation systems (SINS) often suffer from performance degradation, especially when DVL measurements are contaminated by outliers. In this paper, an outlier-resistant Initial Alignment method with interference suppression for SINS/DVL integrated navigation system is proposed, by which, by constructing an improved Mahalanobis distance anomalous detection criterion, the anomaly of the residual vector composed of observation vectors is judged, and an adaptive weighting factor is introduced into the observation matrix to suppress the abnormal interference in the alignment process. Simulation and experimental results show that, compared with existing initial alignment methods, the proposed method achieves higher alignment accuracy in the presence of outliers, which is more suitable for the SINS/DVL integrated navigation system.

In the strap-down inertial navigation system (SINS), the initial attitude matrix is acquired through alignment. Though there were multiple valid methods, alignment time and accuracy are still core issues, especially regarding the condition of the motion carrier. Inspired by the idea of constructing nonlinear vectors by velocity in a different coordinate frame, this paper proposes an innovative alignment method for a vehicle-mounted SINS in motion. In this method, the core issue of acquiring the attitude matrix is to calculate the matrix between the inertial frame and the initial body frame, which can be constructed through the nonlinear velocity vectors’ information from the GPS and the odometer at different moments, which denominate the multi-vector attitude determination. The possibility of collinearity can easily be avoided by a turning movement. The characteristic of propagation of error is analyzed in detail, based on which an improved method is put forward to depress the effect of random noise. Compared with the existing alignment methods, this method does not use the measurement information of accelerometers. In order to demonstrate its performance, the method is compared with the two-position alignment method and the traditional two-stage alignment method. Simulation and vehicle-based experiment results show that the proposed alignment method can establish an attitude reference in 100 s with an azimuth error of less than 0.06°, and that the accuracy does not have a strong correlation with the accelerometer.

To improve the navigation accuracy of AUV (autonomous underwater vehicle), SINS (strapdown inertial navigation system), GPS (global positioning system), DVL (Doppler velocity log) and TAN (terrain aided navigation) are adopted in the AUV integrated navigation system. The mathematic model of the AUV integrated navigation system and the observation model of the chosen navigation sensors are built according to the system simulation experiments data. An improved filter based on RBF neural network for adjusting the information sharing factors is designed and implemented in the AUV integrated navigation system. Simulation experiments are carried out according to the mathematic model. It can be concluded from the simulation experiments that the navigation accuracy is improved substantially with the multiple sensors and federated filter in case that colored noise is engaged. The novel integrated navigation system is effective in prohibiting the divergence of the filter and improving fault tolerance ability and it greatly raises the precision of the navigation accuracy for the AUV integrated navigation system.

In order to improve the positioning accuracy of two leader autonomous underwater vehicles (AUVs) in underwater cooperative navigation formation, a decentralized cooperative localization algorithm based on adaptive cubature Kalman filter (ACKF-DCL) for strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated navigation system is proposed. Firstly, the kinematic equations of the SINS are used to establish the direct filtering approach model for integrated navigation. Secondly, the Sage-Husa adaptive filtering algorithm and the sequential Kalman filtering (SKF) method are introduced into the nonlinear cubature Kalman filter (CKF) to adjust adaptively the measurement noise and maintain the positivity of the covariance matrix. Finally, the cross-correlation covariance between two AUVs is derived according to the characteristics of a decentralized data structure, so as to track the inter-AUV dependencies. And the acoustic ranging measurement is introduced to achieve relative measurement update. The lake trial demonstrates the proposed algorithm’s effectiveness and superiority, and the results show that the algorithm can effectively track the measurement noise covariance matrix and improve the positioning accuracy of both AUVs by position correction.

To improve the performance of strapdown inertial navigation system (SINS)/ Doppler velocity logs (DVL) integrated navigation system, a new Interacting Multiple Model Feedback Particle Filter (IMMFPF) approach is proposed under the condition of complex and changeable underwater environment. In this scheme, the Interacting Multiple Model uses to deal with the model switch, while FPF approach takes each particle as a controllable stochastic system. By finding the solution about the Euler-Lagrange boundary value, the optimal feedback control law of Kullback-Leibler distance is obtained, and then the state of the particle itself is corrected and updated. Both simulations and field test illustrate that proposed IMMFPF approach can achieve a more accurate solution and make a more correct and effective response for SINS/DVL integrated navigation system.

A correcting digital filter for a mechanically dithered single-axis ring laser gyro (RLG) is proposed. The filter is intended to determine the angular position of a strapdown inertial navigation system (SINS) in the RLG sensitivity axis using the known value of the RLG angular position with respect to inertial space, taking into account the models of the suspension elastic forces nonlinearity, the hysteresis model of vibration energy dissipation, and the model of the suspension piezoceramic vibratory drive. Parameterized models of nonlinearity, dissipation, and the output characteristic of the suspension piezoceramic drive have been developed. A method for finding nonlinearity parameters of elastic forces using a set of experimental amplitudefrequency characteristics (AFC) is proposed. Numerical simulations of the AFCs of an elliptic eliminator and a correcting filter under sinusoidal rotation of the SINS with the amplitude of 1 arcmin in the frequency band of 80–3600 Hz have been performed. The efficiency of the proposed filter has been verified experimentally under pulse action on the SINS; the test results are discussed. Comparison of the experimental and calculated data shows the adequacy of the proposed models and satisfactory operation of the correcting digital filter.

On the error state selection for stationary SINS alignment and calibration Kalman filters – part I: Estimation algorithms

Alignment accuracy and alignment time are two important indexes of the initial alignment procedure of a strapdown inertial navigation system (SINS). However, large misalignment angles will bring nonlinear problems of SINS error models and prolong the convergence time of the initial alignment procedure. In this paper, an innovative control scheme is proposed for the initial alignment of SINS on stationary base, which is implemented by active disturbance rejection control (ADRC). Compared with the conventional compass method, the proposed method has a lower requirement on high accuracy of the coarse alignment. Therefore, the total alignment time is decreased, meanwhile the interferences caused by the system errors are decreased and the stability of the closed-loop system is enhanced. Simulations and experiments show that the alignment procedure has been greatly hastened and the accuracy has been improved.

The strapdown inertial navigation system can provide the navigation information for the boom-type roadheader in the unmanned roadway tunneling working face of the coal mine. However, the complex vibration caused by the cutting process of the boom-type roadheader may result in significant errors of its attitude and position measured by the strapdown inertial navigation system. Thus, an error compensation method based on the vibration characteristics of the roadheader is proposed in this paper. In order to further analyze the angular and linear vibration of the fuselage, as the main vibration sources of the roadheader, the dynamic model of the roadheader is formulated based on the cutting load. Following that, multiple sub-samples compensation algorithms for the coning and sculling errors are constructed. Simulation experiments were carried out under different subsample compensation algorithms, different coal and rock characteristics, and different types of roadheader. The experimental results show that the proposed error compensation algorithm can eliminate the effect of the angular and linear vibration on the measurement accuracy. The coning and sculling error of the strapdown inertial navigation system can reduce at least 52.21% and 42.89%, respectively. Finally, a strapdown inertial navigation error compensation accuracy experiment system is built, and the validity and superiority of the method proposed in this paper are verified through calculation and analysis of the data collected on the actual tunneling work face.

Dynamic alignment is a challenging issue for the strapdown inertial navigation system (SINS), especially in the case of no Global Position System assistance. This study proposes a novel coarse alignment algorithm for b‐frame velocity‐aided SINS. The attitude is aligned with the optimisation‐based alignment method, and the alignment process is treated in the inertial frame, so the Coriolis effect can be partly excluded. Meanwhile, the position of SINS is updated in real time by rigorous dead reckoning method. Simulation and experimental results prove that the proposed method has a good performance in the dynamic coarse alignment of SINS.

Rotation modulation (RM) technology can effectively improve the navigation accuracy of strapdown inertial navigation system (SINS) by the periodic rotation of the rotation mechanism. But the original error characteristics of SINS will be changed after introducing the rotation modulation technology. Aiming at the practical micro-electro-mechanical system RMSINS, the error characteristics are comprehensively analyzed, and then a complete error model is established. At the same time, a reasonable parameter calibration scheme and the compensation method are designed to compensate the error in this paper. On this basis, static and dynamic experiments are carried out. The position accuracies of the rotation modulation state in the static and dynamic experiments are about ten times and about three times than that of the non-rotation state. The experimental results show that the proposed error analysis and compensation method are feasible which have been applied to the practical engineering.

Initial alignment is of vital importance to inertial navigation systems (INS). The two main methods of self-alignment, the traditional gyro-compassing alignment method and the Kalman filter method, are discussed. We apply and compare the two methods via simulation and test in use of a strapdown inertial navigation system (SINS) based fiber optic gyroscope (FOG), respectively. The simulation and test show that Kalman filter method is better in convergent time and accuracy of attitude and azimuth, however, gyro-compassing alignment method is better in robustness. This provides a useful reference for designing alignment algorithm for FOG SINS.

In order to solve the strapdown inertial navigation system (INS) alignment problem under the marine mooring condition, the rapid strapdown INS fine alignment method is proposed. This method uses the gravity in the inertial frame to deal with the lineal and angular disturbances. Also the forward and backward processes for strapdown INS calculation and filter estimation are designed. Making use of the forward and backward processes to repeatedly process the saved inertial measurement unit (IMU) data sequence could quickly obtain the initial strapdown attitude matrix. Simulation and trial test validate the performance of the proposed rapid fine strapdown INS alignment.