Fault-tolerant SINS/HSB/DVL underwater integrated navigation system based on variational Bayesian robust adaptive Kalman filter and adaptive information sharing factor

Shi, Wence; Xu, Jiangning; He, Hongyang; Li, Ding; Tang, Hongqiong; Lin, Enfan

doi:10.1016/j.measurement.2022.111225

Cited by 14 publications

(2 citation statements)

References 29 publications

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“…This improvement is not a little one but a huge step forward, making possible the detailed exploration of physical phenomena with the help of polarization properties. KF's importance lies in various sectors like environmental monitoring [12][13][14][15][16][17][18][19][20][21][22][23][24][25], weather forecast [26][27][28][29][30][31][32][33][34][35][36][37], defense surveillance [38][39][40][41][42][43][44][45][46][47], and autonomous navigation systems . Its capacity to draw clear information even from messy real-world data improving the precision and reliability of measurements has made it a key tool for boosting real-time decision-making abilities [71][72][73][74][75][76][77][78][79][80][81]…”

Section: Introductionmentioning

confidence: 99%

The rise of the polarimetric Kalman filter: a bibliometric study on its growing significance

Obaideen,

AlShabi,

Gadsden

et al. 2024

Polarization: Measurement, Analysis, and Remote Sensing XVI

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This paper presents a comprehensive study on the evolution, applications, and impact of the Polarimetric Kalman Filter (PKF) in the fields of signal processing and remote sensing. By employing a methodological framework that integrates literature collection, screening and selection, bibliometric analysis, and synthesis, we aim to elucidate the PKF's contributions and potential research trajectories within its applications. Our findings highlight the PKF's significant role in enhancing data assimilation, improving predictive accuracy, and refining measurement techniques across various domains, particularly in meteorological research and environmental monitoring. The analysis reveals the interdisciplinary nature of PKF applications and its capability to merge theoretical advancements with practical implementations, underlining the importance of sophisticated signal processing algorithms in interpreting complex environmental data. The study also identifies emerging trends, such as the integration of PKF with machine learning and artificial intelligence, indicating future directions for research that promise to push the boundaries of current methodologies and applications. This paper underscores the PKF's pivotal role in advancing signal processing and remote sensing technologies, offering insights into its continuing development and the expanding scope of its applications.

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Section: Introductionmentioning

confidence: 99%

The rise of the polarimetric Kalman filter: a bibliometric study on its growing significance

Obaideen,

AlShabi,

Gadsden

et al. 2024

Polarization: Measurement, Analysis, and Remote Sensing XVI

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show abstract

“…Owing to the complexity of the marine environment, DVL measurements are easily affected by seawater properties. Many studies have been devoted to solving the problems posed by the time-varying noise, non-Gaussian noise, and outliers of the 3D velocity in LC systems [14][15][16][17]. Research on LC systems has been fruitful, and it has been applied to practical systems.…”

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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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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An innovation gain-adaptive Kalman filter for unmanned vibratory roller positioning

et al. 2022

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Fault-tolerant SINS/HSB/DVL underwater integrated navigation system based on variational Bayesian robust adaptive Kalman filter and adaptive information sharing factor

Cited by 14 publications

References 29 publications

The rise of the polarimetric Kalman filter: a bibliometric study on its growing significance

The rise of the polarimetric Kalman filter: a bibliometric study on its growing significance

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

An innovation gain-adaptive Kalman filter for unmanned vibratory roller positioning

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