A Compensation Algorithm with Motion Constraint in DVL/SINS Tightly Coupled Positioning

Jin, Kaidi; Chai, Hongzhou; Su, Chuhan; Xiang, Ming

doi:10.1080/01490419.2022.2040662

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…where Ẋ is the derivative of X, F t and G t are the state transition matrix and state noise drive matrix, respectively, and W b is the state noise. The specific form of the matrices can be found elsewhere [23].…”

Section: Dvl/sins Tc Modelmentioning

confidence: 99%

“…In a previous study, a dual adaptive factor was used to avoid beam outliers and inaccurate filter models in TC systems [22]. Subsequently, a motion compensation algorithm for a TC method was developed [23]. Unfortunately, as the number of available beams decreases, less auxiliary information of DVL is provided to the SINS.…”

Section: Introductionmentioning

confidence: 99%

“…However, because the motion of UUV is six degrees of freedom in practice, it is unrealistic to assume that the lateral or skyward velocity of the UUV is zero. In [23], the NHC-based virtual beam construction method has been shown to be equivalent to the motion constraint assisted method. In [26], The SINS velocities in xy directions are considered error-free to construct the virtual beam, which is equivalent to not correcting the corresponding SINS velocities by Kalman filtering.…”

Section: Introductionmentioning

confidence: 99%

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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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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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Section: Dvl/sins Tc Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Its free, real-time, high-precise, satellitebased enhancement and other characteristics are very suitable for marine navigation. Integrated navigation with strapdown inertial navigation system (SINS) can obtain high-precise navigation information such as attitude, velocity, and position (Jin et al 2022). At present, there are 26 precise correction messages of the BeiDou-3 satellite, which provides more possibilities for PPP-B2b PPP based on BDS-3.…”

Section: Introductionmentioning

confidence: 99%

Real-time marine PPP-B2b/SINS integrated navigation based on BDS-3

Chai

2023

Meas. Sci. Technol.

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The BeiDou-3 global navigation satellite system (BDS-3) was operated successfully in July 2020. It broadcasts PPP-B2b signals through geosynchronous earth orbit (GEO) satellites and provides real-time precise point positioning (PPP) services free of charge, which greatly meets the navigation needs of real-time users, especially warship navigation. This paper introduces the real-time positioning strategy of PPP-B2b in detail, and the performance of PPP-B2b positioning and PPP-B2b/strapdown inertial navigation system (SINS) integrated navigation is studied through marine experiments. The experimental results show the real-time dynamic positioning accuracy of PPP-B2b can reach the decimeter level, and the positioning accuracy is not significantly improved when integrated with SISN. Furthermore, dynamic alignment experiments with different yaw misalignment angles are conducted. The experimental results show after adding TDCP and Doppler velocimetry information, the yaw misalignment angle is improved by 60.4%, and 63.0% respectively when the yaw misalignment angle is 30°, 34.7%, and 38.2% respectively when the yaw misalignment angle is 90°, 60.2% and 61.4% respectively when the yaw misalignment angle is 120° compared with PPP-B2b only. The accuracy of the extended Kalman filter (EKF) algorithm is 16.7% higher than that of the factor graph optimization (FGO) algorithm after the convergence of the yaw angle error.

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“…To handle the measurement noise variance of each DVL beam individually, Jin et al [ 23 ] proposed a tightly coupled method in which an adaptive Kalman filter was utilized to dynamically estimate the observation noise. Xu et al [ 24 ] applied the statistical similarity measure (SSM) to quantify the similarity between two random vectors of DVL.…”

Section: Introductionmentioning

confidence: 99%

A Tightly Integrated Navigation Method of SINS, DVL, and PS Based on RIMM in the Complex Underwater Environment

Yang

Gao

Huang

et al. 2022

Sensors

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Navigation and positioning of autonomous underwater vehicles (AUVs) in the complex and changeable marine environment are crucial and challenging. For the positioning of AUVs, the integrated navigation of the strap-down inertial navigation system (SINS), Doppler velocity log (DVL), and pressure sensor (PS) has a common application. Nevertheless, in the complex underwater environment, the DVL performance is affected by the current and complex terrain environments. The outliers in sensor observations also have a substantial adverse effect on the AUV positioning accuracy. To address these issues, in this paper, a novel tightly integrated navigation model of the SINS, DVL, and PS is established. In contrast to the traditional SINS, DVL, and PS tightly integrated navigation methods, the proposed method in this paper is based on the velocity variation of the DVL beam by applying the DVL bottom-track and water-track models. Furthermore, a new robust interacting multiple models (RIMM) information fusion algorithm is proposed. In this algorithm, DVL beam anomaly is detected, and the Markov transfer probability matrix is accordingly updated to enable quick model matching. By simulating the motion of the AUV in a complex underwater environment, we also compare the performance of the traditional loosely integrated navigation (TLIN) model, the tightly integrated navigation (TTIN) model, and the IMM algorithm. The simulation results show that because of the PS, the velocity and height in the up-change amplitude of the four algorithms are small. Compared with the TLIN algorithm in terms of maximum deviation of latitude and longitude, the RIMM algorithm also improves the accuracy by 39.1243 m and 26.4364 m, respectively. Furthermore, compared with the TTIN algorithm, the RIMM algorithm improves latitude and longitude accuracy by 1.8913 m and 11.8274 m, respectively. A comparison with IMM also shows that RIMM improves the accuracy of latitude and longitude by 1.1506 m and 7.2301 m, respectively. The results confirm that the proposed algorithm suppresses the observed noise and outliers of DVL and further achieves quick conversion between different DVL models while making full use of the effective information of the DVL beams. The proposed method also improves the navigation accuracy of AUVs in complex underwater environments.

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A Compensation Algorithm with Motion Constraint in DVL/SINS Tightly Coupled Positioning

Cited by 8 publications

References 17 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

Real-time marine PPP-B2b/SINS integrated navigation based on BDS-3

A Tightly Integrated Navigation Method of SINS, DVL, and PS Based on RIMM in the Complex Underwater Environment

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