An Improved Adaptive Kalman Filter for Underwater SINS/DVL System

Wang, Di; Xu, Xiaosu; Hou, Lanhua

doi:10.1155/2020/5456961

Cited by 16 publications

(10 citation statements)

References 24 publications

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“…According to the left-invariant error definition in (26), the initial covariance for The Kalman filter is employed to estimate and update the error states, as defined in (24,27,31,36,40). In addition, the corrected error states are feedback to the SINS solution (navigation parameters) to inhibit its error divergence, i.e., error feedback correction.…”

Section: B Parameters Initialization and Feedback Correctionmentioning

confidence: 99%

“…To address the parameter mismatch, adaptive method includes correction, covariance matching, maximum likelihood, and Bayesian inference can be used to Kalman filter [33]- [40]. The [36] introduces the improved Sage-Husa adaptive Kalman filter (SHAKF) into a SINS/DVL tightly integrated model, where four beams' measurements are used rather the 3D velocity. Based on the maximum likelihood (ML) principle, a new adaptive UKF with process noise covariance estimation is proposed to enhance the UKF robustness against process noise uncertainty [37].…”

Section: Introductionmentioning

confidence: 99%

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Invariant Error-Based Integrated Solution for SINS/DVL in Earth Frame: Extension and Comparison

Tang

Chang

et al. 2023

IEEE Trans. Instrum. Meas.

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The error propagation of traditional strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) is not autonomous because its error state model is trajectorydependence. Recently, the invariant error defined on Lie group has raised much attention due to its trajectory-independent and autonomous error propagation. In this paper, the invariant errorbased Kalman filter for SINS/DVL integration solution is investigated with main focus on its extension and comparison. The contributions of this study are threefold. First, the invariant error defined on matrix Lie group (group of double direct isometrics) are extended to model the non-group-affine traditional SINS mechanism and the group-affine transformed SINS mechanism, both of them are derived in Earth frame and augmented with the drift and bias of the inertial measurement units (IMUs). Then, the observation equations for different invariant error-based state models are derived for SINS/DVL application, theoretical analysis is performed and comprehensive evaluation are conducted under different maneuvering condition by lake field trial. Finally, the variational Bayesian approach is introduced into invariant errorbased Kalman filter to inferring the inaccurate process noise covariance matrix (PNCM) and time-varying measurement noise covariant matrix (MNCM) from a practical perspective, experimental results demonstrate that it can improve the navigation accuracy significantly. This study is expected to facilitate the selection of appropriate invariant error to SINS/DVL application.

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Section: B Parameters Initialization and Feedback Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Invariant Error-Based Integrated Solution for SINS/DVL in Earth Frame: Extension and Comparison

Tang

Chang

et al. 2023

IEEE Trans. Instrum. Meas.

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“…The improved Sage-Husa adaptive Kalman filter was claimed to enhance the underwater navigation accuracy of a tightly coupled, strapped down inertial navigation system (SINS) and Doppler velocity log (DVL)-based system [20]. The method employed the forgetting factor for memory optimization and variable sliding window for decreasing computational time.…”

Section: Review Of Previous Workmentioning

confidence: 99%

Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion

Shaukat

Moinuddin

Otero

2021

Sensors

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The ability of the underwater vehicle to determine its precise position is vital to completing a mission successfully. Multi-sensor fusion methods for underwater vehicle positioning are commonly based on Kalman filtering, which requires the knowledge of process and measurement noise covariance. As the underwater conditions are continuously changing, incorrect process and measurement noise covariance affect the accuracy of position estimation and sometimes cause divergence. Furthermore, the underwater multi-path effect and nonlinearity cause outliers that have a significant impact on positional accuracy. These non-Gaussian outliers are difficult to handle with conventional Kalman-based methods and their fuzzy variants. To address these issues, this paper presents a new and improved adaptive multi-sensor fusion method by using information-theoretic, learning-based fuzzy rules for Kalman filter covariance adaptation in the presence of outliers. Two novel metrics are proposed by utilizing correntropy Gaussian and Versoria kernels for matching theoretical and actual covariance. Using correntropy-based metrics and fuzzy logic together makes the algorithm robust against outliers in nonlinear dynamic underwater conditions. The performance of the proposed sensor fusion technique is compared and evaluated using Monte-Carlo simulations, and substantial improvements in underwater position estimation are obtained.

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“…To solve the problem of time-varying noise and outliers, scholars have proposed some new Kalman filters. Sage–Husa AKF (SHAKF) is a covariance matching method that recursively estimates noise statistics, but cannot guarantee convergence to an accurate noise matrix, causing the filter to diverge [ 25 , 26 ]. In addition, adaptive filters (AKF) can also estimate measurement noise, such as multi-model AKF (MMAKF).…”

Section: Introductionmentioning

confidence: 99%

A New Robust Adaptive Filter Aided by Machine Learning Method for SINS/DVL Integrated Navigation System

Zhu

Qin

et al. 2022

Sensors

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As an important means of underwater navigation and positioning, the accuracy of SINS/DVL integrated navigation system greatly affects the efficiency of underwater work. Considering the complexity and change of the underwater environment, it is necessary to enhance the robustness and adaptability of the SINS/DVL integrated navigation system. Therefore, this paper proposes a new adaptive filter based on support vector regression. The method abandons the elimination of outliers generated by Doppler Velocity Logger (DVL) in the measurement process from the inside of the filter in the form of probability density function modeling. Instead, outliers are eliminated from the perspective of external sensors, which effectively improves the robustness of the filter. At the same time, a new Variational Bayesian (VB) strategy is adopted to reduce the influence of inaccurate process noise and measurement noise, and improve the adaptiveness of the filter. Their advantages complement each other, effectively improve the stability of filter. Simulation and ship-borne tests are carried out. The test results show that the method proposed in this paper has higher navigation accuracy.

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An Improved Adaptive Kalman Filter for Underwater SINS/DVL System

Cited by 16 publications

References 24 publications

Invariant Error-Based Integrated Solution for SINS/DVL in Earth Frame: Extension and Comparison

Invariant Error-Based Integrated Solution for SINS/DVL in Earth Frame: Extension and Comparison

Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion

A New Robust Adaptive Filter Aided by Machine Learning Method for SINS/DVL Integrated Navigation System

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