Kalman Filter for Cross-Noise in the Integration of SINS and DVL

Liu, Xixiang; Xu, Xiaosu; Liu, Yiting; Wang, Lihui

doi:10.1155/2014/260209

Cited by 16 publications

(24 citation statements)

References 11 publications

(48 reference statements)

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“…To shorten the length of this paper, the characteristics of the MCP and PS measurements are not given in detail but can be found in [5,6,12].…”

Section: Error Propagation Models Of Mcp and Psmentioning

confidence: 99%

“…From the projection process V n DVL " C n b V b DVL , it can be observed that there is cross-noise between C n b and V b DVL . In this paper, the enlarged variance of V is used to include the crossed noise, and more details to address this noise can be found in [12]. In the following sections, this noise is not considered, and SINS and DVL are assumed to be independent.…”

Section: System and Measurement Equations Of Sins/dvlmentioning

confidence: 99%

“…Several self-adaptive filters have been presented from the aspects of error parameter modeling. Optimal estimation can be obtained only when the model and statistical parameters are consistent with actual ones [3][4][5][6]12,14]. However, how to restrain random noise with mathematical methods has received little attention.…”

Section: Analysis Of the Relationship Between The Dvl Error And Sins mentioning

confidence: 99%

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

et al. 2016

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It is difficult for autonomous underwater vehicles (AUVs) to obtain accurate aided position information in many locations because of underwater conditions. The velocity accuracy from the Doppler velocity log (DVL) is a key element in deciding the AUV position accuracy when the integration system of Strapdown Inertial Navigation System/DVL/Magnetic Compass/Press Sensor (SINS/DVL/MCP/PS) is adopted. However, random noise and sudden noise in DVL caused by sound scattering, fishing populations, and seafloor gullies introduce level attitude errors and accumulate as position error. To restrain random noise, a velocity tracing method is designed based on the constant velocity model and the assumption of slow motion of AUV. To address sudden noise, a fault diagnosis method based on the χ 2 rule is introduced to judge sudden changes from innovation. When a sudden change occurs, the time update of the velocity from the tracing model is used for data fusion instead of the velocity from DVL. Simulation test results indicate that with this velocity tracing algorithm, random noise in the DVL can be effectively restrained. The level attitude accuracy and the level position accuracy are also improved with the time update of the velocity when the sudden change occurs.

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“…To shorten the length of this paper, the characteristics of the MCP and PS measurements are not given in detail but can be found in [5,6,12].…”

Section: Error Propagation Models Of Mcp and Psmentioning

confidence: 99%

Section: System and Measurement Equations Of Sins/dvlmentioning

confidence: 99%

Section: Analysis Of the Relationship Between The Dvl Error And Sins mentioning

confidence: 99%

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

et al. 2016

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“…In general, the velocity correction algorithm of a DVLaided inertial navigation system (INS) employs a loosely coupled approach that uses the body frame velocities calculated by the DVL as the input to the Kalman filter. There has been more research on loosely coupled approaches than tightly coupled ones, and these have focused on designing and verifying navigation filters based mostly on the extended Kalman filter [3][4][5][6]. Recent design and verification studies have also used other types of filters such as the unscented Kalman filter and cubature Kalman filter [7][8][9].…”

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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“…Now the integrated navigation system which consists of multiple navigation sensors is widely adopted on underwater vehicles [6][7][8]. As a high-precision velocity measuring instrument, the Doppler velocity log (DVL) has become increasingly popular as the fundamental component of underwater integrated navigation systems [9][10][11][12][13].…”

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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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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Kalman Filter for Cross-Noise in the Integration of SINS and DVL

Cited by 16 publications

References 11 publications

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

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

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

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

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