Model identification of a Marine robot in presence of IMU-DVL misalignment using TUKF

Farhad, Ghanipoor,; Alasty, Aria; Salarieh, Hassan; Hashemi, Mojtaba; Shahbazi, Mohammad

doi:10.1016/j.oceaneng.2020.107344

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…, n, and n is the dimension of the state vector; λ is the gain parameter depending on the dimension and distribution of the state vector. 11 To guarantee the positive definiteness of the covariance matrix, the square root of the state covariance matrix is calculated using the singular value decomposition (SVD) method. 22 Next, based on the state equation, the a priori estimate of each sigma point is determined using Equation (4) during the time update step.…”

Section: The Unscented Kalman Filtermentioning

confidence: 99%

“…Subsequently, the measurement prediction value for each sigma point is calculated using Equation ( 8), based on the measurement equation. The corresponding mean, innovation covariance, and cross covariance are then determined using Equations ( 9)- (11), respectively.…”

Section: The Unscented Kalman Filtermentioning

confidence: 99%

“…During the time update step, the first step involves generating (2 n + 1) sigma points using the UT process, as illustrated in Equation ().

\begin{equation}{{{ \hat{\bm\chi }}}_{k - 1}} = \left[{ \hat{\bm X}}_{k{\mathrm{ - 1}}}^ + ,{ \hat{\bm X}}_{k{\mathrm{ - 1}}}^ + + \sqrt {n + \lambda } {{\left( {\sqrt {{\bf P}_{k{\bf - 1}}^ + } } \right)}_j},{ \hat{\bm X}}_{k{\mathrm{ - 1}}}^ + - \sqrt {n + \lambda } {{\left( {\sqrt {{\bf P}_{k{\bf - 1}}^ + } } \right)}_j}\right]\end{equation}

where j = 1,2, …, n , and n is the dimension of the state vector; λ is the gain parameter depending on the dimension and distribution of the state vector 11 …”

Section: Algorithms Illustrationmentioning

confidence: 99%

“…As a result, it achieves second-order accuracy in estimating the mean and even attains third-order accuracy for Gaussian distributions. 9 Currently, the UKF has found widespread applications in various fields, including navigation, 10 state estimation of Autonomous Underwater Vehicles, 11,12 aircraft state estimation, 13 State of Energy estimation, 14 fatigue crack prediction, 15 lifetime prediction, 16,17 model updating, 18 and external load estimation. 19,20 It is worth noting that the mathematical model parameters of these applications are assumed to be time-invariant.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive unscented Kalman filter methods for identifying time‐variant parameters via state covariance re‐updating

Zhang,

Ding,

et al. 2024

Earthq Engng Struct Dyn

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The conventional parameter identification process generally assumes that parameters remain constant. However, under extreme loading conditions, structures may exhibit nonlinear behavior, and parameters could demonstrate time‐variant characteristics. The unscented Kalman filter (UKF), as an efficient online recursive estimator, is widely used for identifying parameters of nonlinear systems. Nevertheless, it exhibits limitations when attempting to identify time‐variant parameters. To address this issue, this paper proposes a covariance matching technique that produces an array of adaptive UKF algorithms. Firstly, the sensitivity parameter η is defined to identify the instant when the parameter change occurs, and its threshold is calculated based on the sensitivity parameter time history curve. Secondly, an adaptive forgetting factor is introduced to simultaneously update the innovation, cross, and state covariance matrices when the kth‐step sensitive parameter surpasses the threshold. Finally, a secondary correction forgetting factor (SCFF) is employed to further re‐update the state covariance values at the identified damage locations. This creative step enhances the adaptive capability and optimizes the identification accuracy of the proposed algorithms. Both the numerical simulations and shaking table test demonstrate that the proposed adaptive algorithms can efficiently identify the time‐variant stiffness‐type parameters, and accurately capture their time‐variant characteristics.

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Section: The Unscented Kalman Filtermentioning

confidence: 99%

Section: The Unscented Kalman Filtermentioning

confidence: 99%

“…During the time update step, the first step involves generating (2 n + 1) sigma points using the UT process, as illustrated in Equation ().

\begin{equation}{{{ \hat{\bm\chi }}}_{k - 1}} = \left[{ \hat{\bm X}}_{k{\mathrm{ - 1}}}^ + ,{ \hat{\bm X}}_{k{\mathrm{ - 1}}}^ + + \sqrt {n + \lambda } {{\left( {\sqrt {{\bf P}_{k{\bf - 1}}^ + } } \right)}_j},{ \hat{\bm X}}_{k{\mathrm{ - 1}}}^ + - \sqrt {n + \lambda } {{\left( {\sqrt {{\bf P}_{k{\bf - 1}}^ + } } \right)}_j}\right]\end{equation}

where j = 1,2, …, n , and n is the dimension of the state vector; λ is the gain parameter depending on the dimension and distribution of the state vector 11 …”

Section: Algorithms Illustrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive unscented Kalman filter methods for identifying time‐variant parameters via state covariance re‐updating

Zhang,

Ding,

et al. 2024

Earthq Engng Struct Dyn

View full text Add to dashboard Cite

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“…Underwater vision has broad application prospects in the tasks of marine science and engineering, such as in underwater monitoring, 1 , 2 underwater detection, 3 , 4 underwater robots, 5 , 6 and underwater navigation 7 , 8 . Clear underwater images or videos are significant for underwater tasks 9 .…”

Section: Introductionmentioning

confidence: 99%

Underwater image restoration based on second-order statistical characteristics

et al. 2023

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Underwater image restoration is of great significance to underwater vision operation. We propose an underwater image restoration method based on the independent color channel of a single image. In this method, the relationship between the transmittance parameters and the second-order statistics of the scattered light underwater is established through a multi-layer continuous model of the water. First, the region with approximate depth is obtained by super-pixel segmentation, then the sub-color channel statistical calculation is carried out for each super-pixel independently, and finally the transmittance of each color channel is estimated combined with the relational expression. Experimental results show that this method has better effect on color fidelity of underwater image restoration.

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