INS-DVL Navigation Improvement Using Rotational Motion Dynamic Model of AUV

With the aid of Doppler velocity logger (DVL), strapdown inertial navigation systems (SINS) can provide continuous and accurate navigation parameters for unmanned underwater vehicles (UUVs). However, owing to the complex underwater environment, partial DVL beams may fail to be reflected by the seafloor, resulting in DVL measurement outage. In this study, a novel data-driven approach enhancing DVL/SINS integrated navigation system is proposed to improve the robustness and accuracy of UUV navigation with limited DVL beams. First, to make full use of the available beam, the velocities of DVL beams are used to assist the SINS navigation instead of the 3D velocity of DVL. Subsequently, a virtual beam predictor based on multi-output least-squares support vector regression (MLS-SVR) is built to run in parallel with the DVL/SINS integrated system. Specifically, when the DVL operates normally, all four beams can be applied for integrated navigation and stored as the training dataset. Once partial beams are missing, the available beams and the outputs of SINS and pressure sensor can train the MLS-SVR model along with the corresponding missing beam in the training set. Subsequently, the missing beams can be predicted by the trained model for integrated navigation together with the available beams. UUV sea trial indicate the proposed system can accurately predict the unavailable beams and improve the positioning accuracy of the DVL/SINS integrated navigation system.

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“…where k is the epoch of beam interruption, a is the set of available beams of the DVL with a ∈ [1,2,3,4]. v D a is the available DVL beams.…”

Section: Training Dataset Construction For Mls-svr Methodsmentioning

confidence: 99%

“…widely applied in military and civilian fields owing to their high degree of automation and good maneuverability [1,2]. To complete a mission smoothly and safely, the UUV must be able to navigate accurately [3,4].…”

Section: Introductionmentioning

confidence: 99%

A performance-enhanced DVL/SINS integrated navigation system based on data-driven approach

Jin

Chai

et al. 2023

Meas. Sci. Technol.

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“…To overcome this problem, an aided navigation system must be introduced to assist the SINS in achieving high-precision navigation. Considering navigation accuracy and the concealment requirement, the Doppler velocity log (DVL) is ideal equipment for assisting an AUV’s SINS [ 9 , 10 ]. The DVL transmits ultrasonic waves from the hull to the seabed or water layer and receives the reflected signal, with which it can calculate the speed of the AUV in real-time according to the Doppler effect.…”

Section: Introductionmentioning

confidence: 99%

A Polar Robust Kalman Filter Algorithm for DVL-Aided SINSs Based on the Ellipsoidal Earth Model

Tian

Liang

Liao

et al. 2022

Sensors

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Autonomous underwater vehicles (AUVs) play an increasingly essential role in the field of polar ocean exploration, and the Doppler velocity log (DVL)-aided strapdown inertial navigation system (SINS) is widely used for it. Due to the rapid convergence of the meridians, traditional inertial navigation mechanisms fail in the polar region. To tackle this problem, a transverse inertial navigation mechanism based on the earth ellipsoidal model is designed in this paper. Influenced by the harsh environment of the polar regions, unknown and time-varying outlier noise appears in the output of DVL, which makes the performance of the standard Kalman filter degrade. To address this issue, a robust Kalman filter algorithm based on Mahalanobis distance is used to adaptively estimate measurement noise covariance; thus, the Kalman filter gain can be modified to weight the measurement. A trial ship experiment and semi-physical simulation experiment were carried out to verify the effectiveness of the proposed algorithm. The results demonstrate that the proposed algorithm can effectively resist the influence of DVL outliers and improve positioning accuracy.

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“…To eliminate the influence of complex measurement noise and the measurement information randomly lost, several Bayesian-theory-based filtering algorithms have been proposed and applied, such as the extended Kalman filter (EKF) [8,9], unscented Kalman filter (UKF) [10], Cubature Kalman filter (CKF) [11], and so on. The performance of the filtering algorithms is crucial in judging whether the AUV can properly work and safely return [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Switched Variational Estimation Algorithm for Continuous Discrete Measurement Information Loss in Underwater Navigation: Linear versus Nonlinear

2022

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In various applications of automatic underwater vehicles (AUVs), it is necessary to acquire the real-time location and speed information of the AUV. However, the complicated and fluctuating marine environment leads to measurement information loss. The accuracy of noise measurement is vital for accurate state estimation, but it is difficult for traditional algorithms to acquire time-varying noise measurements. Due to inaccurate process models and measurement noise, the filtering performance becomes poor or even diverges. To address the problems above, a switched variational estimation filtering (SVEF) algorithm, which combines the advantages of both Gaussian filtering and the variational estimation (VE) method, is proposed. In SVEF, VE is embedded into a linear or nonlinear filtering algorithm. Owing to the continuous discrete underwater navigation model, the state vector is estimated by the SVEF in the case of measurement information loss, and the accurate position and velocity of the AUV are determined. The experimental results prove that the SVEF achieves better positioning precision and is more robust than other conventional algorithms for AUV applications.

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INS-DVL Navigation Improvement Using Rotational Motion Dynamic Model of AUV

Cited by 36 publications

References 22 publications

A performance-enhanced DVL/SINS integrated navigation system based on data-driven approach

A performance-enhanced DVL/SINS integrated navigation system based on data-driven approach

A Polar Robust Kalman Filter Algorithm for DVL-Aided SINSs Based on the Ellipsoidal Earth Model

A Switched Variational Estimation Algorithm for Continuous Discrete Measurement Information Loss in Underwater Navigation: Linear versus Nonlinear

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