Abstract
The multi-model adaptive hull deformation estimation algorithm is proposed for the uncertainty of system parameters and the statistical characteristics of measurement noise. Based on the multi-model idea, a variable parameter multi-model algorithm is designed. By pre-estimating the main frequency, the problem of the main frequency uncertainty in the actual measurement is solved. Based on the Sage-Husa adaptive filtering idea, the adaptive volume Kalman filter algorithm is designed to track and estimate the measurement noise covariance matrix in real time, which effectively solves the problem of statistical uncertainty of measurement noise. The effectiveness of the multi-model adaptive hull deformation estimation algorithm is verified by simulation. When the main frequency and the statistical characteristics of the measurement noise are unknown, the estimation accuracy of the hull deformation angle is better than 0.1 angular.
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