Correlation method and Kalman filter-based adaptive angle rate estimation for time-varying periodic signals of the attitude and heading reference system

Abstract

An angle differential estimation method is needed for the attitude and heading reference system (AHRS), which is unavailable to measure the angle rate directly. Differential estimation accuracy is seriously affected by the model error of existing differential filters for time-varying periodic angles provided by the AHRSs. To address this challenge, an adaptive angle rate estimation method based on the Kalman filter and correlation method is proposed. The Kalman filter based on the jerk model is selected to estimate the angle rate. The process noise covariance model of the Kalman filter is established considering amplitude and period fluctuations of the periodic signals. The correlation method is then combined with the Kalman filter to estimate the amplitude and period of the periodic signals, which are utilized to optimize the process noise covariance matrix of the Kalman filter. Compared with traditional differential estimation methods, the proposed method improves the angle rate estimation accuracy by adjusting the parameter of the differential filter according to the model error of the time–varying periodical angle. A simulation is conducted to verify the performance of the proposed method by continuously changing the amplitude and period of the signals. Simulation results show that the proposed method is least affected by model error and achieves the best performance compared with seven other traditional rate estimation methods. Turntable and sea tests also demonstrate the correctness and effectiveness of the proposed angle rate estimation method.