Tightly coupled ultrashort baseline and inertial navigation system for underwater vehicles: An experimental validation

A Robust INS/USBL/DVL Integrated Navigation Algorithm Using Graph Optimization

The Autonomous Underwater Vehicle (AUV) is usually equipped with multiple sensors, such as an inertial navigation system (INS), ultra-short baseline system (USBL), and Doppler velocity log (DVL), to achieve autonomous navigation. Multi-source information fusion is the key to realizing high-precision underwater navigation and positioning. To solve the problem, a fusion scheme based on factor graph optimization (FGO) is proposed. Due to multiple iterations and joint optimization of historical data, FGO could usually show a better performance than the traditional Kalman filter. In addition, considering that USBL and DVL are usually heavily influenced by the environment, outliers are often present. A robust integrated navigation algorithm based on a maximum correntropy criterion and FGO scheme is proposed. The proposed algorithm solves the problem of multi-sensor fusion and non-Gaussian noise. Numerical simulations and field tests demonstrate that the proposed FGO scheme shows a better performance and robustness than the traditional Kalman filter. Compared with the traditional Kalman filtering, the positioning accuracy is improved by 5.3%, 9.1%, and 5.1% in the east, north, and height directions. It can realize a more accurate navigation and positioning of underwater multi-sensors.

Position Correction and Trajectory Optimization of Underwater Long-Distance Navigation Inspired by Sea Turtle Migration

Accumulating evidence suggests that migrating animals store navigational “maps” in their brains, decoding location information from geomagnetic information based on their perception of the magnetic field. Inspired by this phenomenon, a novel geomagnetic inversion navigation framework was proposed to address the error constraint of a long-distance inertial navigation system. In the first part of the framework, the current paper proposed a geomagnetic bi-coordinate inversion localization approach which enables an autonomous underwater vehicle (AUV) to estimate its current position from geomagnetic information like migrating animals. This paper suggests that the combination of geomagnetic total intensity (F) and geomagnetic inclination (I) can determine a unique geographical location, and that there is a non-unique mapping relationship between the geomagnetic parameters and the geographical coordination (longitude and latitude). Then the cumulative error of the inertial navigation system is corrected, according to the roughly estimated position information. In the second part of the framework, a cantilever beam model is proposed to realize the optimal correction of the INS historical trajectory. Finally, the correctness of the geomagnetic bi-coordinate inversion localization model we proposed was verified by outdoor physical experiments. In addition, we also completed a geomagnetic/inertial navigation integrated long-distance semi-physical test based on the real navigation information of the AUV. The results show that the geomagnetic inversion navigation framework proposed in this paper can constrain long-distance inertial navigation errors and improve the navigation accuracy by 73.28% compared with the pure inertial navigation mode. This implies that the geomagnetic inversion localization will play a key role in long-distance AUV navigation correction.

Maximum correntropy delay Kalman filter for SINS/USBL integrated navigation

Communication delay and non-Gaussian noise are challenging issues for underwater navigation and positioning. This study proposes a filtering algorithm for strapdown inertial navigation system/ultra-short baseline (SINS/USBL) integrated navigation to deal with time-varying delay in underwater acoustic communication and cope with non-Gaussian noise induced by outliers and measurement noises. Considering the influence of platform error angle, the measurement equation of SINS/USBL is derived. According to the distance-related time delay characteristics of USBL acoustic communication, the delay system model is obtained based on state inversion. A linear recursive model based on a delay system model is constructed to update the posterior estimation and covariance matrix by combining it with the maximum correntropy criterion. The algorithm solves the problems of communication delay and non-Gaussian noise and greatly reduces the computational complexity due to its adaptive adjustment function. Simulation and experimental results verify the filter's improved accuracy and robustness.

Induced Magnetic Field-Based Indoor Positioning System for Underwater Environments

Autonomous underwater vehicles (AUV) are seen as an emerging technology for maritime exploration but are still restricted by the availability of short range, accurate positioning methods necessary, e.g., when docking remote assets. Typical techniques used for high-accuracy positioning in indoor use case scenarios, such as systems using ultra-wide band radio signals (UWB), cannot be applied for underwater positioning because of the quick absorption of the positioning medium caused by the water. Acoustic and optic solutions for underwater positioning also face known problems, such as the multi-path effects, high propagation delay (acoustics), and environmental dependency. This paper presents an oscillating magnetic field-based indoor and underwater positioning system. Unlike those radio wave-based positioning modalities, the magnetic approach generates a bubble-formed magnetic field that will not be deformed by the environmental variation because of the very similar permeability of water and air. The proposed system achieves an underwater positioning mean accuracy of 13.3 cm in 2D and 19.0 cm in 3D with the multi-lateration positioning method and concludes the potential of the magnetic field-based positioning technique for underwater applications. A similar accuracy was also achieved for various indoor environments that were used to test the influence of cluttered environment and of cross environment. The low cost and power consumption system is scalable for extensive coverage area and could plug-and-play without pre-calibration.

A Novel Underwater Simultaneous Localization and Mapping Online Algorithm Based on Neural Network

The navigation and localization of autonomous underwater vehicles (AUVs) in seawater are of the utmost importance for scientific research, petroleum engineering, search and rescue, and military missions concerning the special environment of seawater. However, there is still no general method for AUVs navigation and localization, especially in the featureless seabed. The reported approaches to solving AUVs navigation and localization problems employ an expensive inertial navigation system (INS), with cumulative errors and dead reckoning, and a high-cost long baseline (LBL) in a featureless subsea. In this study, a simultaneous localization and mapping (AMB-SLAM) online algorithm, based on acoustic and magnetic beacons, was proposed. The AMB-SLAM online algorithm is based on multiple randomly distributed beacons of low-frequency magnetic fields and a single fixed acoustic beacon for location and mapping. The experimental results show that the performance of the AMB-SLAM online algorithm has a high robustness. The proposed approach (the AMB-SLAM online algorithm) provides a low-complexity, low-cost, and high-precision online solution to the AUVs navigation and localization problem in featureless seawater environments. The AMB-SLAM online solution could enable AUVs to autonomously explore or autonomously intervene in featureless seawater environments, which would enable AUVs to accomplish fully autonomous survey missions.

Robust data cleaning methodology using online support vector regression for ultra-short baseline positioning system

Ultrashort baseline (USBL) acoustic positioning system is a significant navigation means for human occupied vehicle due its simple structure, convenient operation, and large-scale-maneuver capacity. In order to improve the quality of USBL raw data effectively and efficiently, a robust data cleaning methodology using Online Support Vector Regression (OSVR) is proposed to deal with measurement outliers and missing values. In this study, we applied sliding-window samples to train the OSVR model for online time series prediction and then utilized the obtained one-step ahead prediction to detect and replace outliers or supplement missing values. The experimental results of the online test show that the proposed methodology can satisfy the requirement of real-time navigation and acquire consecutive and consistent positioning data for USBL. In comparison with the raw data, the root mean square error results in longitude and latitude are reduced by 91.75% and 85.53%, respectively. In addition, such methodology outperforms other data cleaning algorithms based on Least Square (LS) and kernel recursive LS.

An Ultra-Short Baseline Positioning Model Based on Rotating Array & Reusing Elements and Its Error Analysis

The USBL (Ultra-Short Base Line) positioning system is widely used in underwater acoustic positioning systems due to its small size and ease of use. The traditional USBL positioning system is based on ‘slant range and azimuth’. The positioning error is an increasing function with the increase in distance and the positioning accuracy depends on the ranging accuracy of the underwater target. This method is not suitable for long-distance underwater positioning operations. This paper proposes a USBL positioning calculation model based on depth information for ‘rotating array and reusing elements’. This method does not need to measure the distance between the USBL acoustic array and target, so it can completely eliminate the influence of long-distance ranging errors in USBL positioning. The theoretical analysis and simulation experiments show that the new USBL positioning model based on ‘rotating array and reusing elements’ can completely eliminate the influence of the wavelength error and spacing error of underwater acoustic signals on the positioning accuracy of USBL. The positioning accuracy can be improved by approximately 90%, and the horizontal positioning error within a positioning distance of 1000 m is less than 1.2 m. The positioning method has high precision performance in the long distance, and provides a new idea for the engineering design of a USBL underwater positioning system.

A misalignment angle error calibration method of underwater acoustic array in strapdown inertial navigation system/ultrashort baseline integrated navigation system based on single transponder mode

The angle misalignment error of a USBL (Ultrashort Baseline) acoustic array is one of the major error sources of the strapdown inertial navigation system/USBL positioning system, which will directly affect the positioning accuracy of the USBL positioning system. For the traditional calibration method cannot accurately estimate the angle misalignment error due to its strict trajectory requirements in the field experiment and the high-precision layout of the transceiver array elements, a new method for estimating the angle misalignment error of a USBL acoustic array based on single transponder and dual-vector reconstruction is studied in this paper. The precondition of USBL misalignment calibration is to locate the underwater transponder accurately. In this paper, the single transponder segmentation iterative long baseline method is used to locate the underwater target transponder. The dual-vector reconstruction method is studied to control the estimation accuracy of USBL misalignment error calibration based on the traditional single transponder method, which provides a theoretical basis for the determination of the iteration times to the USBL angle misalignment error estimation module. The underwater experiment results show that the positioning error could be reduced to less than 1 m after the angle misalignment error compensation. The underwater transponder positioning and the angle misalignment error estimation of USBL could be accomplished in a circle sailing. It is a new method with good performance of high estimation accuracy, simple operation, and easy realization.

Three-dimensional (3D) morphology of Sansha Yongle Blue Hole in the South China Sea revealed by underwater remotely operated vehicle

The Sansha Yongle Blue Hole (SYBH) is the deepest blue hole found anywhere to date. Study of the SYBH can provide insight into the interactions between hole wall morphology and many geological/hydrological mechanisms. A comprehensive investigation of the SYBH was carried out for the first time in 2017 using a professional-grade underwater remotely operated vehicle (ROV) to obtain accurate depth and three-dimensional (3D) topographic data. The SYBH resembles a ballet dancer’s shoe and has a volume of ~499609 m³. The observed deepest portion of the SYBH is at 301.19 m below the local 10-year mean sea level. The cave bottom laterally deviates from its entrance by 118 m at an azimuth of 219 degrees. The cave entrance is shaped like a comma and has an average width of 130 m; the widest part is 162.3 m wide, while the narrowest part is 26.2 m wide and is at 279 mbsl (meters below sea level). The 3D topography of the SYBH and underwater photography revealed two large transitions at ~76 to 78 mbsl and at 158 mbsl, indicating that the initiation of the blue hole was likely a step wise process and that the hole wall morphology was subsequently remolded through a paleo-sea level stillstand (at or near Younger Dryas). The topographic data also indicated that the blue hole is situated within an isolated environment with no water or material exchange with the outside open sea.

A Direct and Non-Singular UKF Approach Using Euler Angle Kinematics for Integrated Navigation Systems

This paper presents a direct and non-singular approach based on an unscented Kalman filter (UKF) for the integration of strapdown inertial navigation systems (SINSs) with the aid of velocity. The state vector includes velocity and Euler angles, and the system model contains Euler angle kinematics equations. The measured velocity in the body frame is used as the filter measurement. The quaternion nonlinear equality constraint is eliminated, and the cross-noise problem is overcome. The filter model is simple and easy to apply without linearization. Data fusion is performed by an UKF, which directly estimates and outputs the navigation information. There is no need to process navigation computation and error correction separately because the navigation computation is completed synchronously during the filter time updating. In addition, the singularities are avoided with the help of the dual-Euler method. The performance of the proposed approach is verified by road test data from a land vehicle equipped with an odometer aided SINS, and a singularity turntable test is conducted using three-axis turntable test data. The results show that the proposed approach can achieve higher navigation accuracy than the commonly-used indirect approach, and the singularities can be efficiently removed as the result of dual-Euler method.

AUV Positioning Method Based on Tightly Coupled SINS/LBL for Underwater Acoustic Multipath Propagation

This paper researches an AUV (Autonomous Underwater Vehicle) positioning method based on SINS (Strapdown Inertial Navigation System)/LBL (Long Base Line) tightly coupled algorithm. This algorithm mainly includes SINS-assisted searching method of optimum slant-range of underwater acoustic propagation multipath, SINS/LBL tightly coupled model and multi-sensor information fusion algorithm. Fuzzy correlation peak problem of underwater LBL acoustic propagation multipath could be solved based on SINS positional information, thus improving LBL positional accuracy. Moreover, introduction of SINS-centered LBL locating information could compensate accumulative AUV position error effectively and regularly. Compared to loosely coupled algorithm, this tightly coupled algorithm can still provide accurate location information when there are fewer than four available hydrophones (or within the signal receiving range). Therefore, effective positional calibration area of tightly coupled system based on LBL array is wider and has higher reliability and fault tolerance than loosely coupled. It is more applicable to AUV positioning based on SINS/LBL.

Observability analysis of DVL/PS aided INS for a maneuvering AUV

Recently, ocean exploration has increased considerably through the use of autonomous underwater vehicles (AUV). A key enabling technology is the precision of the AUV navigation capability. In this paper, we focus on understanding the limitation of the AUV navigation system. That is, what are the observable error-states for different maneuvering types of the AUV? Since analyzing the performance of an underwater navigation system is highly complex, to answer the above question, current approaches use simulations. This, of course, limits the conclusions to the emulated type of vehicle used and to the simulation setup. For this reason, we take a different approach and analyze the system observability for different types of vehicle dynamics by finding the set of observable and unobservable states. To that end, we apply the observability Gramian approach, previously used only for terrestrial applications. We demonstrate our analysis for an underwater inertial navigation system aided by a Doppler velocity logger or by a pressure sensor. The result is a first prediction of the performance of an AUV standing, rotating at a position and turning at a constant speed. Our conclusions of the observable and unobservable navigation error states for different dynamics are supported by extensive numerical simulation.