The integrated navigation system of an autonomous underwater vehicle and the experience from its application in high arctic latitudes

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..

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Design of Inertial/Acoustic Integrated Navigation System of Underwater Vehicle

2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP) ◽

10.1109/icsidp47821.2019.9172920 ◽

2019 ◽

Author(s):

Lin Guo ◽

Yang Shi ◽

Shuxiang Wang

Keyword(s):

Navigation System ◽

Underwater Vehicle ◽

Integrated Navigation ◽

Integrated Navigation System

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Modified Sage-Husa Adaptive Kalman Filter-Based SINS/DVL Integrated Navigation System for AUV

Journal of Sensors ◽

10.1155/2021/9992041 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Ruixin Liu ◽

Fucheng Liu ◽

Chunning Liu ◽

Pengchao Zhang

Keyword(s):

Kalman Filter ◽

Navigation System ◽

Autonomous Underwater Vehicle ◽

Positive Definiteness ◽

Integrated Navigation ◽

Adaptive Kalman Filter ◽

Integrated Navigation System ◽

The Stability ◽

Navigation Accuracy ◽

Navigation Errors

This paper presents a modified Sage-Husa adaptive Kalman filter-based SINS/DVL integrated navigation system for the autonomous underwater vehicle (AUV), where DVL is employed to correct the navigation errors of SINS that accumulate over time. When negative definite items are large enough, different from the positive definiteness of noise matrices which cannot be guaranteed for the conventional Sage-Husa adaptive Kalman filter, the proposed modified Sage-Husa adaptive Kalman filter deletes the negative definite items of adaptive update laws of the noise matrix to ensure the convergence of the Sage-Husa adaptive Kalman filter. In other words, this method sacrifices some filtering precision to ensure the stability of the filter. The simulation tests are implemented to verify that expected navigation accuracy for AUV can be obtained using the proposed modified Sage-Husa adaptive Kalman filter.

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A Low Complexity Integrated Navigation System for Underwater Vehicles

Journal of Navigation ◽

10.1017/s0373463318000140 ◽

2018 ◽

Vol 71 (5) ◽

pp. 1161-1177 ◽

Cited By ~ 6

Author(s):

Mehdi Emami ◽

Mohammad Reza Taban

Keyword(s):

Navigation System ◽

Autonomous Underwater Vehicle ◽

Three Dimensional ◽

Low Complexity ◽

Doppler Velocity ◽

Integrated Navigation ◽

Fusion Algorithm ◽

Sea Trial ◽

Integrated Navigation System ◽

Computational Load

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.

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