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USV Motion State Estimation by Using Credibility Theory in Improved GS-SRCKF
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DOI：10.1109/TII.2024.3413365.png)
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Abstract
En 中文
A credibility-based and improved Gaussian sum square root cubature Kalman filter (IGS-SRCKF) estimation method for nonlinear non-Gaussian systems is proposed to improve the accuracy of unmanned surface vehicles (USVs) motion state estimation in complex environments. This method elevates the linear Gaussian credibility theory to the nonlinear non-Gaussian credibility theory by utilizing the improved Gaussian sum algorithm and constructing the square root cubature Klaman filter pseudomatrix, solving the problem of defining trust factors in nonlinear non-Gaussian systems, and measuring the credibility of the filter. At the same time, the conditions for designing high-performance filters are obtained using this credibility theory. Then, a new USV motion state estimation method based on credibility and the IGS-SRCKF framework is proposed by combining the hunter-prey optimizer and dingo optimization algorithm based on the obtained conditions for designing high-performance filters. Finally, the progressiveness and superiority of this algorithm are verified by simulation and actual data.
Keywords:
Kalman filters
Accuracy
Matrix decomposition
Atmospheric modeling
Signal processing algorithms
Oceans
Covariance matrices
Credibility theory
dingo optimization algorithm (DOA)
Gauss sum
hunter-prey optimizer (HPO)
nonlinear non-Gaussian
unmanned surface vehicle (USV)
Journal
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