A Wave Peak Frequency Tracking Method Based on Two-Stage Recursive Extended Least Squares Identification Algorithm

Abstract

This paper proposes the wave peak frequency tracking methods based on the least squares identification algorithm. The wave disturbance model is transformed into an autoregressive moving average (ARMA) model and a recursive extended least squares (RELS) algorithm is derived to identify the model parameters by using the auxiliary model identification idea. Furthermore, a two-stage recursive extended least squares (2S-RELS) algorithm is presented to improve the convergence speed by using the hierarchical identification principle. A ship heading control system with the wave peak frequency tracker is built to evaluate the effectiveness of the proposed algorithms. Finally, simulation results show that the proposed algorithms can estimate the wave peak frequency accurately and the 2S-RELS algorithm can improve the convergence speed effectively.

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