Quantum mechanics

Nonlinear filter

Algorithm

Gaussian

Control (management)

Nonlinear system identification

Biology

Identification (biology)

Statistics

Extended Kalman filter

Gaussian filter

Convergence (economics)

Economics

Estimation theory

Computer science

Botany

Filter (signal processing)

Covariance

Physics

Nonlinear system

Database

Mathematics

Image (mathematics)

System identification

Artificial intelligence

Kalman filter

Model parameter

Economic growth

Control theory (sociology)

Filter design

Parameter identification problem

Data modeling

Computer vision

Ocean Engineering

In order to solve the problem of parameter identification of nonlinear ship motion model in ship autonomous navigation control, a real-time parameter identification method based on nonlinear Gaussian filtering algorithm and nonlinear ship response model is proposed. It is proved theoretically that the influence of parameter drift on parameter identification can be reduced by increasing the number of observers and filters, and the system identification accuracy can be improved. The validity of the proposed method is verified by parameter identification experiments based on Zig-zag motion simulation data of Mariner standard ship model. Simulation results show that compared with EKF, the nonlinear Gaussian filter algorithm can effectively improve the parameter identification accuracy and reduce the computational complexity. The application of parallel structure is helpful to improve the identification accuracy and convergence rate of nonlinear Gaussian filter algorithm.

Real-time parameter identification of ship maneuvering response model based on nonlinear Gaussian Filter