Navigation System For Underwater Vehicles Research Articles

An effective LS-SVM/AKF aided SINS/DVL integrated navigation system for underwater vehicles

An effective LS-SVM/AKF aided SINS/DVL integrated navigation system for underwater vehicles

An Autonomous Low-Frequency Broadb and Hydroacoustic Emitting Station with an Electromagnetic Transducer

We describe the design of a hydroacoustic emitting station with an electromagnetic transducer that develops acoustic pressure of up to 2400 Pa (188 dB) reduced to a distance of 1 m from the emitter axis in a frequency range of 420–520 Hz (at the level of –3 dB); it has an operating depth of up to 500 m. The applied technical solutions make it possible to use the station for a wide range of oceanological research, as well as for the construction of navigation systems for underwater vehicles and data transmission via a hydroacoustic channel.

Machine learning post processing of underwater vehicle pressure sensor array for speed measurement

An array of pressure sensors can be used to correct the drift in inertial navigation systems for underwater vehicles (UVs) in absence of other navigation support systems such as acoustic positioning, GPS and Doppler velocity measurements. To date, multiple pressure sensor arrays have been designed, proposed, and tested to prove the concept. However, it has not been researched the inclusion of non-linearities is required in the post-processing. This paper focuses on the use of machine learning as a novel approach to improve the post-processing accuracy, including non-linearities caused by the vehicle acceleration on the estimated speed compared to the linear parametric equation methodology. A series of towing tank experiments have been conducted over an array of pressure sensors located on an UV platform. The results show that pressure measurement array requires the use of non-linear post-processing methodologies as linear methodologies are not able to accurately account for vehicle acceleration effects.

Simultaneous Underwater Navigation and Mapping

The use of underwater autonomous vehicles has been growing, allowing the performance of tasks that cause inherent risks to Human, namely in inspection processes near to structures. With growth in usage of systems with autonomous navigation, visual acquisition methods have also gotten more developed because, they have appealing cost and they also show interesting results when operate at a short distance. It is possible to improve the quality of navigation through visual SLAM techniques which can map and locate simultaneously and its key aspect is the detection of revisited areas. These techniques are not usually applied to underwater scenarios and, therefore, its performance in environment is unknown. The paper presents a more reliable navigation system for underwater vehicles, resorting to some visual SLAM techniques from literature. The results, conducted in a realistic scenario, demonstrated the ability of the system to be applied to underwater environment.

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.

Underwater Inertial Navigation With Long Baseline Transceivers: A Near-Real-Time Approach

Inertial navigation systems for underwater vehicles are often aided by acoustic time-of-flight positioning schemes. One widely implemented long baseline (LBL) approach uses a ping-response protocol resulting in asynchronous measurements that depend on the state of the vehicle at two time instants. Such aiding measurements that depend on the state at multiple time instants do not fit the model format of the standard extended Kalman filter (EKF) framework. This paper proposes a near-real-time (NRT) Bayesian smoothing framework for the LBL-aided inertial navigation system application. Within this NRT framework, the time interval between LBL cycles is divided into two intervals defined by the ping-response cycles of LBL. The time instant when all the LBL responses are received will be referred to as the NRT point . Between the LBL ping and the NRT point, a traditional real-time EKF is implemented using the inertial measurement unit and all aiding measurements. After the NRT point, when all the LBL responses have been received, an optimal Bayesian trajectory estimator executes. This maximum a posteriori (MAP) estimation includes all the measurement information already collected up to the NRT point of the current LBL cycle. The MAP output is a smoothed trajectory estimate for the period of time between the LBL ping and the NRT point. At the conclusion of this smoothing process, the current EKF estimate is corrected to the optimal MAP solution and propagated to the current time, and then the EKF estimation process continues to the NRT point of the next LBL cycle. At all times, the approach maintains a real-time state estimate suitable for use by the control system. This paper presents the theoretical solution, discusses the implementation, and presents implementation results to illustrate the accuracy and reliability of this NRT approach.

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.

Bionic Research and it’s Application of Artificial Lateral Line System

Nowadays, lots of efforts have been made to develop new technology for navigation system of underwater vehicle with the increasingly improved requirements of precise positioning, obstacles-avoidance, energy-saving in locomotion and stabilization in response to turbulent currents. In nature, fish have a great ability to localize prey, school, navigate, rheotaxis, etc. using the lateral-line organ. The biological lateral line system has inspired the researchers’ efforts to artificial lateral lines for applications in underwater vehicles and robots. In this paper, the working mechanism of the fish lateral line system has been described. Then the research status both at biology and at mechanics on the artificial lateral line system at home and abroad has been reviewed. To make a comparison with acoustic navigation system USBL,The key technologies and developing tendency as well as potential applications are described. At last, an insight on the future research directions and route of artificial lateral-line system have been given.

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

AbstractThis paper presents a new ultrashort baseline (USBL) tightly coupled integration technique to enhance error estimation in low‐cost strapdown inertial navigation systems (INSs), with application to underwater vehicles. In the proposed strategy, the acoustic array spatial information is directly exploited in an extended Kalman filter (EKF) implemented in a direct feedback structure. Instead of using the USBL position fixes or computed range and elevation/bearing angles to correct the INS error drifts, as in classical loosely coupled strategies, the novel tightly coupled strategy directly embeds in the EKF the round‐trip‐time and time‐difference‐of‐arrival of the acoustic signals arriving at the onboard receivers. The enhanced performance of the proposed filtering technique is evidenced both through extensive numerical simulations and with experimental data obtained in field tests at sea. The tightly coupled filter is also shown to be able to operate closer to theoretical performance lower bounds, such as the posterior Cramér‐Rao lower bound, using Monte‐Carlo simulations. This paper details the design and description of an USBL/INS prototype to be used as a low‐cost navigation system, including the acoustic processing and positioning system, fully developed in‐house. The developed system validates the usage of the proposed technique with real data in real world operation scenarios, and its enhanced performance compared to classical strategies is evaluated experimentally (median improvement level of 15% in typical operating conditions). Improved and faster convergence to nominal trajectories from multiple initial conditions, as well as enhanced accelerometer and rate gyros estimation capabilities, are also demonstrated experimentally for the new tightly coupled filter.

Design and Evaluation of a Micro-Electromechanical Systems Inertial Navigation System for Underwater Vehicle

Design and Evaluation of a Micro-Electromechanical Systems Inertial Navigation System for Underwater Vehicle

Advances in single-beacon one-way-travel-time acoustic navigation for underwater vehicles

This paper reports the formulation and evaluation of a centralized extended Kalman filter designed for a novel navigation system for underwater vehicles. The navigation system employs Doppler sonar, depth sensors, synchronous clocks, and acoustic modems to achieve simultaneous acoustic communication and navigation. The use of a single moving reference beacon eliminates the requirement for the underwater vehicle to remain in a bounded navigable area; the use of underwater modems and synchronous clocks enables range measurements based on one-way time-of-flight information from acoustic data-packet broadcasts. The acoustic data packets are broadcast from a single, moving reference beacon and can be received simultaneously by multiple vehicles within acoustic range. We report results from a simulated deep-water survey and real field data collected from an autonomous underwater vehicle survey in 4000 m of water on the southern Mid-Atlantic Ridge with an independent long-baseline navigation system for ground truth.

Three-Dimensional Ocean Wave Simulation Based on Directional Spectrum

Simulating real virtual ocean environment is necessary for the research of interaction simulation of underwater gravity aided inertial navigation system. One of the key techniques of realizing virtual ocean environment is modeling and simulating three-dimensional ocean waves. Numerical simulation of three-dimensional ocean waves in the case of different wind speeds is realized using MATLAB based on directional spectrum composed of Pierson-Moscowitz frequency spectrum and directional spreading function. Experiments show that the simulation speed is fast and the simulation results are vivid if suitable simulation frequency band, interval of wave frequency and interval of direction angle are selected. The simulation can provide some technological supports for interaction simulation of gravity aided navigation system for underwater vehicles.

Application of Modified Self-Adaptive Kalman Filter in Integrated Navigation System of Autonomous Underwater Vehicle

Owing to the complex operating environment of underwater vehicles, many uncertainties of sensors data, big noises of sensors , low precision and high rate of wild points of underwater acoustic sensors, data processing of motion sensors data for underwater vehicle navigation system becomes extremely important. The integrated navigation system of autonomous underwater vehicle based on dead-reckoning is introduced. An modified adaptive Kalman filter is adopted for underwater vehicle sensors information data processing. Experimental results show that the modified self-adaptive Kalman filter(SAKF) is effective, and can meet the underwater robots perform a variety of tasks in the navigation and positioning accuracy..

Small Autonomous Underwater Vehicle Navigation System Based on Adaptive UKF Algorithm

Ocean current disturbance and attitude,heading errors can cause uncertain navigation system model error.To solve the above problem,an adaptive unscented Kalman filter (AUKF) with model error is designed for a small autonomous underwater vehicle s (SAUV) dead reckoning (DR) navigation system.Firstly,three-dimensional motion of SAUV continuous time model is designed.Then,the proposed AUKF algorithm is deduced according to maximum a posterior (MAP) theory.Finally,simulation results show that the algorithm can overcome the model error caused by disturbance currents and attitude,and heading errors.Compared with the conventional UKF algorithm,the filter precision of the SAUV s DR navigation system in complex sea state is improved a lot by adopting the proposed algorithm.

MMAE terrain reference navigation for underwater vehicles using PCA

The integration of a feature based positioning sensor, rooted on principal component analysis, with a multi-model adaptive estimator is proposed and discussed in detail as the solution to terrain reference navigation systems for underwater vehicles. The adequacy on the use of the proposed feature based non-linear positioning sensor is studied, the error sources are enumerated, a stochastic characterization is performed, and the attainable performance is discussed, based on the results from a series of experiments for a large set of synthesized terrains. Resorting to a non-linear Lyapunov transformation, the synthesis and analysis of a multiple-model multirate adaptive estimator (MMAE) in the stochastic setting is also presented, with overall guaranteed stability and optimal performance over equilibrium trajectories. Finally, results from Monte Carlo simulations to assess the performance of the overall system are included.

Simulation of an Inertial Acoustic Navigation System With Range Aiding for an Autonomous Underwater Vehicle

This paper presents an integrated navigation system for underwater vehicles to improve the performance of a conventional inertial acoustic navigation system by introducing range measurement. The integrated navigation system is based on a strapdown inertial navigation system (SDINS) accompanying range sensor, Doppler velocity log (DVL), magnetic compass, and depth sensor. Two measurement models of the range sensor are derived and augmented to the inertial acoustic navigation system, respectively. A multirate extended Kalman filter (EKF) is adopted to propagate the error covariance with the inertial sensors, where the filter updates the measurement errors and the error covariance and corrects the system states when the external measurements are available. This paper demonstrates the improvement on the robustness and convergence of the integrated navigation system with range aiding (RA). This paper used experimental data obtained from a rotating arm test with a fish model to simulate the navigational performance. Strong points of the navigation system are the elimination of initial position errors and the robustness on the dropout of acoustic signals. The convergence speed and conditions of the initial error removal are examined with Monte Carlo simulation. In addition, numerical simulations are conducted with the six-degrees-of-freedom (6-DOF) equations of motion of an autonomous underwater vehicle (AUV) in a boustrophedon survey mode to illustrate the effectiveness of the integrated navigation system.

A fuzzy Kalman filter optimized using a multi-objective genetic algorithm for enhanced autonomous underwater vehicle navigation

In an autonomous underwater vehicle integrated navigation system, short-term temporal accuracy is provided by an inertial navigation subsystem (INS) and long-term accuracy by a global positioning system (GPS). The Kalman filter has been a popular method for integrating the data produced by the two systems to provide optimal estimates of autonomous underwater vehicle position and attitude. In this paper, a sequential use of a linear Kalman filter and extended Kalman filter is proposed. The former is used to fuse the data from a variety of INS sensors whose output is used as an input to the latter where integration with GPS data takes place. The use of a fuzzy-rule-based adaptation scheme to cope with the divergence problem caused by the insufficiently known a priori filter statistics is also explored. The choice of fuzzy membership functions for an adaptation scheme is first carried out using a heuristic approach. Multiobjective genetic algorithm techniques are then used to optimize the parameters of the membership functions with respect to a certain performance criteria in order to improve the overall accuracy of the integrated navigation system. Simulation results are presented that show that the proposed algorithms can provide a significant improvement in the overall navigation performance of an autonomous underwater vehicle navigation system.

A fuzzy Kalman filter optimized using a multi-objective genetic algorithm for enhanced autonomous underwater vehicle navigation

In an autonomous underwater vehicle integrated navigation system, short-term temporal accuracy is provided by an inertial navigation subsystem (INS) and long-term accuracy by a global positioning system (GPS). The Kalman filter has been a popular method for integrating the data produced by the two systems to provide optimal estimates of autonomous underwater vehicle position and attitude. In this paper, a sequential use of a linear Kalman filter and extended Kalman filter is proposed. The former is used to fuse the data from a variety of INS sensors whose output is used as an input to the latter where integration with GPS data takes place. The use of a fuzzy-rule-based adaptation scheme to cope with the divergence problem caused by the insufficiently known a priori filter statistics is also explored. The choice of fuzzy membership functions for an adaptation scheme is first carried out using a heuristic approach. Multiobjective genetic algorithm techniques are then used to optimize the parameters of the membership functions with respect to a certain performance criteria in order to improve the overall accuracy of the integrated navigation system. Simulation results are presented that show that the proposed algorithms can provide a significant improvement in the overall navigation performance of an autonomous underwater vehicle navigation system.

Testing and evaluation of an integrated GPS/INS system for small AUV navigation

A Small Autonomous Underwater Vehicle Navigation System (SANS) is being developed at the Naval Postgraduate School. The SANS is an integrated Global Positioning System/Inertial Navigation System (GPS/INS) navigation system composed of low-cost and small-size components. It is designed to demonstrate the feasibility of using a low-cost strap-down inertial measurement unit (IMU) to navigate between intermittent GPS fixes. The present hardware consists of a GPS/DGPS receiver, IMU, compass, water speed sensor, water depth sensor, and a data processing computer. The software is based on a 12-state complementary filter that combines measurement data from all sensors to derive a vehicle position/orientation estimate. This paper describes hardware and software design and testing results of the SANS. It is shown that results from tilt table testing and bench testing provide an effective means for tuning filter gains. Ground vehicle testing verifies the overall functioning of the SANS and exhibits an encouraging degree of accuracy.

Geometrical aspects of a sonar-based navigation system for underwater vehicles

A novel self-contained navigation system has been devised for underwater vehicles operating in and around offshore installations. This system matches data from a sector-scanning sonar device to a computer model of the installation. The paper begins by highlighting the existing approaches to subsea navigation before outlining the main features of the proposed system. It then concentrates on a key component of this system which is a method for calculating the position and heading of an underwater vehicle navigating in the vincinity of tubular steel structures. An iterative solution method is presented which incorporates six degree of freedom vehicle motions and this is verified in a series of laboratory experiments with various arrangements of structural members and using a commercial sonar device. The key features, applications and performance of this method are discussed. The main conclusion is that the proposed method for calculating the position and heading of an underwater vehicle contributes towards achieving an accurate and reliable subsea navigation capability.