A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System

Xu, Xiaosu; Li, Peijuan; Liu, Jianjuan

doi:10.1155/2015/581909

Cited by 13 publications

(8 citation statements)

References 24 publications

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“…As a small sample prediction method, grey prediction method has been widely used in terms of small sample prediction, and the prediction results have been universally accepted in time series data (Zhang et al, 2019). Scholars improved the grey prediction method like Xu et al (2015) optimized GM (1,1) model according to the formula of background value. In the application of fault diagnosis of AUV, Jia et al (2014) used grey correlation analysis to compare fault pattern vector and testing vector, and directly determined whether there was a fault in the sensor of AUV.…”

Section: Methodsmentioning

confidence: 99%

An improved particle filter propeller fault prediction method based on grey prediction for underwater vehicles

Cao

et al. 2020

Transactions of the Institute of Measurement and Control

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The overall architectural complexity of autonomous underwater vehicles continuous to increase, enlarging the probability of fault occurrence in subsystems. Estimating the thrust loss by particle filter provided a useful method to detect the fault in propeller subsystem. In order to detect the fault in propellers as early as possible, the particle filter direct prediction method could amplify the fault trend and detect the fault earlier, but at the same time increase the possibility of false diagnosis. Therefore, a more accurate fault diagnosis method was required to discover the fault early and decrease the occurrence of false diagnosis. In this paper, an improved particle filter prediction method was proposed, combining the advantage of grey prediction to forecast the motion state, reducing the uncertainty in particle filter direct prediction process. Besides, the Gaussian kernel function was applied to judge the credibility of the prediction result, decreasing the possibility of the false diagnosis. In the experiments with simulated working conditions data and a section of actual sea trial data with propeller fault, the proposed method detected the fault earlier compared with the original particle filter method, and reduced the false diagnosis rate compared with the particle filter direct prediction method. The results show that the proposed method is effective in detecting the fault early with low false diagnosis.

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Section: Methodsmentioning

confidence: 99%

An improved particle filter propeller fault prediction method based on grey prediction for underwater vehicles

Cao

et al. 2020

Transactions of the Institute of Measurement and Control

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“…where I (n+1)×(n+1) represents an identity matrix. Substituting Equations (17) and (18) into Equation (15), the recursive estimation formula of the parameters can be described as:…”

Section: B Modified Rls For Model Parameters Estimationmentioning

confidence: 99%

“…Liu et al [16] suggest a fast gradual fault detection method based on the improved residual χ 2 detection algorithm for underwater integrated navigation systems. In Reference [17] a fault-tolerant adaptive Kalman filter (FTAKF) algorithm is designed for INS composed of an INS, a DVL, and a magnetic compass (MCP). The evolutionary artificial neural networks (EANN) is used in self-learning and training of the intelligent data fusion algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach Based on Improved AR Model and MAA for INS/DVL Integrated Navigation Systems

Wang

Yao

et al. 2019

IEEE Access

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In navigation of autonomous underwater vehicles (AUVs), the estimation of position is an important issue, especially, when the sensors such as gyroscopes contain a lot of noise, and the velocity information of Doppler velocity log (DVL) is affected by the motion attitude of the vehicle. In this paper, based on an improved auto regressive (AR) model, a real-time filter is utilized for gyroscope signal denoising. Meanwhile, according to the characteristics of the AUV, the influence of the vehicle attitude on the DVL velocity measurement error is analyzed and a motion attitude assist (MAA) method based on error model is introduced for enhancing DVL velocity accuracy. In this paper, using the proposed hybrid approach, an inertial navigation system (INS)/DVL integrated navigation system is designed. The proposed approach is evaluated by simulation and experimental test in different acceleration bound, and the existence of the DVL outage for an AUV. The results indicate that the precisions of the velocity and position are improved effectively, especially in complex motion attitude and long sailing conditions. INDEX TERMS Autonomous underwater vehicle (AUV), auto regressive (AR) model, motion attitude assist (MAA), INS/DVL integrated navigation.

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“…Fault diagnosis with χ 2 rules is a common method to detect sudden changes in engineering [16]. In this method, the measurement at time k´1 is assumed without a sudden change, and the prediction X k,k´1 with state vector at k´1 is without error.…”

Section: Velocity Tracing Algorithm For Dvl Based On Auv Motion Constmentioning

confidence: 99%

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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A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System

Cited by 13 publications

References 24 publications

An improved particle filter propeller fault prediction method based on grey prediction for underwater vehicles

An improved particle filter propeller fault prediction method based on grey prediction for underwater vehicles

A Hybrid Approach Based on Improved AR Model and MAA for INS/DVL Integrated Navigation Systems

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

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