A Pretreatment Method for the Velocity of DVL Based on the Motion Constraint for the Integrated SINS/DVL

Zhao, Liye; Liu, Xianjun; Wang, Lei; Zhu, Yulian; Liu, Xixiang

doi:10.3390/app6030079

Cited by 27 publications

(11 citation statements)

References 17 publications

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“…Unfortunately, the observability of heading is low for SINS/DVL integrated navigation system, therefore a MCP is adopted to provide heading information. In addition, a PS is used to provide height (or depth) information due to the divergence of the height from the SINS and the low accuracy of the vertical velocity from the DVL [4,22]. Accordingly, SINS/DVL/MCP/PS integrated navigation system is established to perform independent underwater navigation.…”

Section: System Structurementioning

confidence: 99%

“…This kind of approaches is cost saving, but there is the potential risk of cross contamination. There are also other researchers working to find a replacement for the KF [22][23][24][25], and various models have been built to predict the errors of the reference system. Semeniuk and Noureldin [23] proposed an artificial-intelligence-based segmented forward predictor to overcome situations of GPS satellite signals blockage.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems

Zhu

Cheng

et al. 2017

Applied Sciences

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Abstract:As a common device for underwater integrated navigation systems, Doppler velocity log (DVL) has the risk of malfunction. To improve the reliability of navigation systems, a hybrid approach is presented to forecast the measurements of the DVL while it malfunctions. The approach employs partial least squares regression (PLSR) coupled with support vector regression (SVR) to build a hybrid predictor. As the current and past calculating velocities of strapdown inertial navigation system (SINS) are taken as the predictor's inputs, PLSR is applied to cope with the situation where there exists intense relativity among independent variables. Since PLSR is a linear regression, SVR is used to predict the residual components of the PLSR prediction to improve the accuracy. When the DVL works well, the hybrid predictor is trained online as a backup, whereas during malfunctions, the predictor offers the estimation of the DVL measurements for information fusion. The performance of the proposed approach is verified with simulations based on SINS/DVL/MCP/pressure sensor (PS) integrated navigation system. The comparison results indicate that the PLSR-SVR hybrid predictor can correctly provide the estimated DVL measurements and effectively extend the tolerance time on DVL malfunctions, thereby improving the navigation accuracy and reliability.

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Section: System Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems

Zhu

Cheng

et al. 2017

Applied Sciences

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“…A loosely coupled approach has a design advantage due to the simplicity of its measurements, but its accuracy in measuring body frame velocities is greatly impaired if any of the DVL transducer signals cannot be received. To overcome this drawback, various filter designs have also been proposed that use sensors other than DVL or apply a set of constraints [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Performance Enhancement for Conventional Tightly Coupled INS/DVL Navigation System Using Regeneration of Partial DVL Measurements

Yoo¹,

Kim²,

Yoon

et al. 2020

Journal of Sensors

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Inertial navigation systems/Doppler velocity log (INS/DVL) integrated navigation systems are widely used in underwater environments where GPS is unavailable. An INS/DVL integrated navigation system is generally loosely coupled; however, this does not work if any of the DVL transducers do not work. If a system is tightly coupled, velocity error can be estimated with fair accuracy even if some of the transducers fail. However, despite the robustness of a tightly coupled system compared to a loosely coupled one, velocity error estimation accuracy of the former decreases as the number of faulty transducers increases. Therefore, this paper proposes an INS/DVL/revolutions per minute (RPM) integrated navigation filter designed to improve the performance of conventional tightly coupled integrated systems by estimating data from faulty transducers using RPM data. Two salient features of the proposed filter are (1) estimating RPM data accounting for error from the effect of tidal currents and (2) continuous estimation of error in RPM data by selectively converting only the measurements of faulty transducers. The performance of the proposed filter was first verified using Monte Carlo numerical simulations with the analysis range set to 1 standard deviation (1σ, 68%) and then with real sea test measurement data.

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“…The SINS system error model is nonlinear, and a nonlinear filtering algorithm is generally used for state estimation. The earliest nonlinear filtering method used in engineering was the Extend Kalman Filter (EKF) algorithm [ 10 , 11 ]. Its core idea is to approximate a linear expansion of the current nonlinear state equation (namely a Taylor series expansion, truncating high-order terms, retaining first-order terms) to apply the rules of EKF.…”

Section: Introductionmentioning

confidence: 99%

Square-Root Unscented Information Filter and Its Application in SINS/DVL Integrated Navigation

Guo

Tang

et al. 2018

Sensors

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To address the problem of low accuracy for the regular filter algorithm in SINS/DVL integrated navigation, a square-root unscented information filter (SR-UIF) is presented in this paper. The proposed method: (1) adopts the state probability approximation instead of the Taylor model linearization in EKF algorithm to improve the accuracy of filtering estimation; (2) selects the most suitable parameter form at each filtering stage to simply the calculation complexity; (3) transforms the square root to ensure the symmetry and positive definiteness of the covariance matrix or information matrix, and then to enhance the stability of the filter. The simulation results indicate that the estimation accuracy of SR-UIF is higher than that of EKF, and similar to UKF; meanwhile the computational complexity of SR-UIF is lower than that of UKF.

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A Pretreatment Method for the Velocity of DVL Based on the Motion Constraint for the Integrated SINS/DVL

Cited by 27 publications

References 17 publications

A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems

A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems

Performance Enhancement for Conventional Tightly Coupled INS/DVL Navigation System Using Regeneration of Partial DVL Measurements

Square-Root Unscented Information Filter and Its Application in SINS/DVL Integrated Navigation

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