Underwater Integrated Navigation System Research Articles

A Low-Cost and High-Precision Underwater Integrated Navigation System

The traditional underwater integrated navigation system is based on an optical fiber gyroscope and Doppler Velocity Log, which is high-precision but also expensive, heavy, bulky and difficult to adapt to the development requirements of AUV swarm, intelligence and miniaturization. This paper proposes a low-cost, light-weight, small-volume and low-computation underwater integrated navigation system based on MEMS IMU/DVL/USBL. First, according to the motion formula of AUV, a five-dimensional state equation of the system was established, whose dimension was far less than that of the traditional. Second, the main source of error was considered. As the velocity observation value of the system, the velocity measured by DVL eliminated the scale error and lever arm error. As the position observation value of the system, the position measured by USBL eliminated the lever arm error. Third, to solve the issue of inconsistent observation frequencies between DVL and USBL, a sequential filter was proposed to update the extended Kalman filter. Finally, through selecting the sensor equipment and conducting two lake experiments with total voyages of 5.02 km and 3.2 km, respectively, the correctness and practicality of the system were confirmed by the results. By comparing the output of the integrated navigation system and the data of RTK GPS, the average position error was 4.12 m, the maximum position error was 8.53 m, the average velocity error was 0.027 m/s and the average yaw error was 1.41°, whose precision is as high as that of an optical fiber gyroscope and Doppler Velocity Log integrated navigation system, but the price is less than half of that. The experimental results show that the proposed underwater integrated navigation system could realize the high-precision and long-term navigation of AUV in the designated area, which had great potential for both military and civilian applications.

GSCV-XGBoost based information reconstruction and fusion method for SINS/DVL integrated navigation system

An underwater navigation system has specific requirements for reliability. In the frequently used strapdown inertial navigation system/Doppler velocity log (SINS/DVL) integrated navigation system, the process of the DVL measuring velocity is always disturbed in a complex underwater environment. Thus the velocity measurements on acoustic beam directions are prone to get lost, which seriously affects system reliability. This paper proposes a novel information reconstruction and integration algorithm to solve this problem, based on the tree boosting model. The algorithm is mainly divided into two consecutive stages: training and reconstruction. When the DVL velocity in all beam directions is available, the GSCV-XGBoost (extreme gradient boosting based on the grid-search and cross-validation theory) method is used to train the mapping model between DVL velocity and SINS/DVL integrated navigation system velocity. When the DVL velocity is incompletely available, the mapping model, that has been trained in advance, is used to reconstruct the missing DVL velocity measurements and resume normal SINS/DVL integration conditions. Simulation and underwater vehicle-mounted experiments are applied to verify that the proposed algorithm reconstructs more accurate velocity information than traditional XGBoost, which shows great prospects for application and reliability in the SINS/DVL underwater integrated navigation system.

A Hybrid Method for Dealing With DVL Faults of SINS/DVL Integrated Navigation System

Aiming at the situation that the underwater integrated navigation system is easy to be disturbed and the output is unavailable during the task of underwater vehicle, a RVBAKF/ LSTM hybrid method is proposed in this paper. Considering the actual underwater working environment of autonomous underwater vehicles (AUV), the measured value of Doppler velocity log (DVL) is prone to outliers or even interruption, so it is very important to improve the fault tolerance of integrated navigation system. For the processing of outliers, a robust adaptive filter based on VB theory is proposed. Its advantage is to realize the adaptation of time-varying noise and reduce the interference of outliers to the integrated navigation system. Firstly, selecting the SINS output related to AUV velocity, such as velocity, attitude, angular velocity and specific force as the training input when the DVL output is normal. When DVL output is interrupted, the long short-term memory (LSTM) model is used to provide pseudo measurement values for integrated navigation system. The effectiveness of the method is verified by the on-board experimental data. When the DVL output is interrupted, the method can effectively improve the accuracy of the integrated navigation system. The method in this paper is compared with four different hybrid methods, and the results are obviously better than the four methods.

Fault-tolerant SINS/HSB/DVL underwater integrated navigation system based on variational Bayesian robust adaptive Kalman filter and adaptive information sharing factor

To solve the problem that position information obtained by SINS/DVL integrated navigation system is divergent in the underwater navigation applications, this paper introduces hydroacoustic single beacon (HSB) into the underwater integrated system and proposes a novel SINS/HSB/DVL fault-tolerant federated variational Bayesian robust adaptive Kalman filter (FVBRAKF). In the FVBRAKF, the traditional KF is replaced by VBRAKF, which not only suppress the adverse influence of outliers based on Mahalanobis distance (MD) effectively, but also estimate the unknown measurement noise covariance based on variational Bayesian (VB) approximation adaptively. Meanwhile, a novel adaptive information sharing factor (ISF) method is proposed during the information fusion process to form an improved FVBRAKF（IFVBRAKF）, which can adjust the fusion weights in real-time according to the accuracy of local filter. The semi-physical simulation experiments for SINS/HSB/DVL underwater integrated navigation system based on the test data are carried out to verify the adaptive ability of the ISF and the robust adaptive ability of the method, respectively. Experimental results demonstrate that the proposed algorithm can not only improve the estimation accuracy, but also enhance the fault-tolerant performance.

A Support Vector Regression-Based Integrated Navigation Method for Underwater Vehicles

When Doppler velocity log (DVL) works in a complex underwater environment, it has the possibility of malfunction at any time, which will affect the positioning accuracy of underwater integrated navigation system (INS). In this work, the INS/DVL integrated navigation system model is established to deal with DVL malfunctions, and the support vector regression (SVR) algorithm is used to establish the velocity regression prediction model of DVL. An optimized grid search-genetic algorithm is used to select the best parameters of SVR. Simulations are designed to compare the results of SVR prediction model and isolating DVL during DVL failure. The semi-physical experiment is carried out to verify the validity and applicability of DVL velocity prediction model. The experimental results show that the INS/DVL integrated navigation system with the proposed model based on SVR performs better than the original integrated navigation system during DVL malfunction.

A USBL Position-assisted Information Calibration Method for DVL Measurement Error in Deep Water Environment

The primary problem with the SINS/DVL underwater integrated navigation system is to calibrate the installation relationship between SINS and DVL. The traditionally used method is GNSS velocity-assisted calibration. However, when it comes to a deep water environment, there is no GNSS signal, thus there is no effective external speed reference information underwater. Aiming at solving the problem, this paper proposes a USBL position-assisted DVL calibration method, taking advantage of position reference information provided by USBL underwater. The new method was deduced from GNSS velocity-assisted calibration method by integral operation. A sea trial experiment data was processed to validate the feasibility of the method. The results showed that the positioning error of SINS/DVL underwater integrated navigation after calibration by this method is reduced by nearly 50%. It can be concluded that this DVL calibration method can significantly improve the navigation and positioning accuracy of SINS/DVL underwater integrated navigation system in deep water.

A Low Complexity Integrated Navigation System for Underwater Vehicles

This paper proposes a simplified algorithm for reducing the computational load of the conventional underwater integrated navigation system. The system usually comprises a three-dimensional accelerometer, a three-dimensional gyroscope, a three-dimensional Doppler Velocity Log (DVL) and a data fusion algorithm, such as a Kalman Filter (KF). Since the expected variations of roll, pitch and depth are small, these quantities are assumed to be constant, and the proposed system is designed in a two-dimensional form. Due to the low speed of the vehicle, the nonlinear dynamic equation of the velocity can be simplified in a linear form. We also simplify the conventional KF in order to avoid matrix multiplications and matrix inversions. The performance of the designed system is evaluated in a sea trial by an Autonomous Underwater Vehicle (AUV). The results show that the proposed system can significantly reduce the computational load of the conventional integrated navigation system without a significant reduction in position and velocity accuracy.

An Asynchronous Adaptive Direct Kalman Filter Algorithm to Improve Underwater Navigation System Performance

In the conventional integrated navigation systems, such as direct Kalman filter, the statistical information of the process and measurement noises is considered constant. In real applications, due to the variation of vehicle dynamics, the environmental conditions and imperfect knowledge of the filter statistical information, the process and measurement covariance matrices are unknown and time dependent. To improve performance of the direct Kalman filter algorithm, this paper presents an asynchronous adaptive direct Kalman filter (AADKF) algorithm for underwater integrated navigation system. The designed navigation system is composed of a high-rate strapdown inertial navigation system along with low-rate auxiliary sensors with different sampling rates. The auxiliary sensors consist of a global positioning system (GPS), a Doppler velocity log (DVL), a depthmeter, and an inclinometer. Performance of the proposed algorithm is investigated using real measurements. The experimental results indicate the proposed AADKF algorithm outperforms asynchronous direct Kalman filter (ADKF) algorithm, i.e., the relative root mean square error (RMSE) of the estimated position is reduced by 61% on average.

Improved Underwater Integrated Navigation System using Unscented Filtering Approach

In underwater navigation, the conventional Error State Kalman Filter (ESKF) is used for combining navigation data where due to first order linearization of the nonlinear equations of the dynamics and measurements, considerable error is induced in estimated error state and covariance matrices. This paper presents an underwater integrated inertial navigation system using the unscented filter as an improved nonlinear version of the Kalman filter family. The designed system consists of a strap-down inertial navigation system accompanying Doppler velocity log and depth meter. In the proposed approach, to use the nonlinear capabilities of the unscented filtering approach the integrated navigation system is implemented in a direct approach where the nonlinear total state dynamic and and measurement models are utilised without any linearization. To our knowledge, no results have been reported in the literature on the experimental evaluation of the unscented-based integrated navigation system for underwater vehicles. The performance of the designed system is studied using real measurements. The results of the lake test show that the proposed system estimates the vehicle's position more accurately compared with the conventional ESKF structure.

A fast mutant fault detection method of underwater integrated navigation

The concepts of the mean of residual and the sum of absolute residuals are introduced in this paper. Both of them along with the fault detection function are used to detect mutant fault of the assisted navigation devices in underwater integrated navigation system. (1) The traditional residual χ2 detection method is improved according to the structural characteristics of the underwater navigation system. (2) The mean of residual and the sum of absolute residuals are introduced as the characteristics of mutant faults. (3) The improved residual χ2 detection method is used to make sure of the happening of the mutant fault firstly. Then the residual mean and the sum of residual absolute values are used to distinguish the noise mutant system fault and the information mutant system fault. The results of simulations and the vehicle tests show that: (1) the mutant fault of the sub-system can be detected and the faulty sub-system can be isolated immediately when the fault appears with our novel method; (2) The noise mutant fault and the information mutant fault can be distinguished efficiently according to characteristics of the mean of residuals and the sum of the absolute residuals.

Asynchronous direct Kalman filtering approach for underwater integrated navigation system

This paper presents an asynchronous direct Kalman filter (ADKF) approach for underwater integrated navigation system to improve the performance of the prevalent indirect Kalman filter structure. The designed navigation system is composed of a strapdown inertial navigation system (SDINS) along with Doppler Velocity Log, inclinometer, and depthmeter. In the proposed approach, prediction procedure is placed in the SDINS loop and the correction procedure operates asynchronously out of the SDINS loop. In contrast to the indirect Kalman filter, in the ADKF, the total state such as position, velocity, and orientation are estimated directly within the filter, and further calculations are not performed outside of the filter. This reduces the running time of the computations. To the best of our knowledge, no results have been reported in the literature on the experimental evaluation of a direct Kalman filtering for underwater vehicle navigation. The performance of the designed system is studied using real measurements. The results of the lake test show that the running time of the proposed approach can be improved approximately 7.5 % and also the ADKF exhibits an average improvement of almost 20 % in position estimate with respect to the prevalent indirect Kalman filter.

DVL/RPM Based Velocity Filter Aiding in the Underwater Vehicle Integrated Inertial Navigation System

The purpose of this paper is to design a DVL-RPM based VKF (Velocity Kalman Filter) design for a performance improvement underwater integrated navigation system. The integrated navigation sensor using DVL (Doppler Velocity Log) is widely used to improve the underwater navigation performance. However, the DVL’s range of measuring varied depending on the characteristics of sensor. So, if the sea gets too deep suddenly, it cannot measure the velocity. To complement such a weak point, the VKF was additionally designed, which was made of DVL, RPM (Revolve Per Minutes) of motor, and ES (Echo Sounder). The proposed approach relies on a VKF, augmented by an altitude from ES based switching architecture to yield robust performance, even when DVL exceeds the measurement range and the measured value is unable to be valid. The proposed approach relies on two parts: 1) indirect feedback navigation Kalman filter design, 2) VKF design. To evaluate the proposed method, we compare the VKF aided navigation system with PINS (Pure Inertial Navigation System) and conventional INS-DVL navigation system through simulation results. Simulations illustrate the effectiveness of the underwater navigation system assisted by the additional DVL-RPM based VKF in underwater environment.

수중운동체 복합항법 성능 향상을 위한 DVL/RPM 기반의 속도 필터 설계

The purpose of this paper is to design a DVL-RPM based VKF (Velocity Kalman Filter) design for a performance improvement underwater integrated navigation system. The proposed approach relies on a VKF, augmented by a altitude from Echo-sounder based switching architecture to yield robust performance, even when DVL (Doppler Velocity Log) exceeds the measurement range and the measured value is unable to be valid. The proposed approach relies on two parts: 1) Indirect feedback navigation Kalman filter design, 2) VKF design. To evaluate proposed method, we compare the results of the VKF aided navigation system with simulation result from a PINS (Pure Inertial Navigation System) and conventional INS-DVL method. Simulations illustrate the effectiveness of the underwater navigation system assisted by the additional DVL-RPM based VKF in underwater environment.

Kalman filter applied in underwater integrated navigation system

For the underwater integrated navigation system, information fusion is an important technology. This paper introduces the Kalman filter as the most useful information fusion technology, and then gives a summary of the Kalman filter applied in underwater integrated navigation system at present, and points out the further research directions in this field.