[1]BAO Zhengkai,ZHU Qidan,LIU Yongchao.Ship heading model identification based on full rank decomposition least square method[J].CAAI Transactions on Intelligent Systems,2022,17(1):137-143.[doi:10.11992/tis.202104020]
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Ship heading model identification based on full rank decomposition least square method

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 1 Page number: 137-143 Column: 学术论文—人工智能基础 Public date: 2022-01-05
Title:
Ship heading model identification based on full rank decomposition least square method
Author(s):
BAO Zhengkai;  ZHU Qidan;  LIU Yongchao
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
forgetting factor least square algorithm; data under excitation; ship heading model; full rank decomposition; parameter identification; ocean environment disturbance; parameter identification convergence; ship navigation data
CLC:
TP18;U661.3
DOI:
10.11992/tis.202104020
Abstract:
In order to solve the problem of parameter drift and divergence in the on-line identification of the ship heading model by the forgetting factor least squares method, considering the marine environment disturbance and data under-excitation in actual navigation, we propose a forgetting factor recursive least square algorithm based on full rank decomposition, which uses the ship navigation data to identify the ship heading model parameters, and compare the identification results with the identification results of the standard forgetting factor least squares algorithm, multi-innovation least square algorithm, least square support vector algorithm. The comparison result shows that the full rank decomposition method can effectively reduce the parameter drift caused by the disturbance in the online identification process and successfully suppress divergence of the parameters, improve the accuracy of the forgetting factor least square algorithm and reduce the dependence of the least square method on continuous data excitation.
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