Two-Step Adaptive Augmented Unscented Kalman Filter for Roll Angles of Spinning Missiles Based on Magnetometer Measurements

Abstract

It is critical to measure the roll angle of a spinning missile quickly and accurately. Magnetometers are commonly used to implement these measurements. At present, the estimation of roll angle parameters is usually performed with the unscented Kalman filter algorithm. In this paper, the two-step adaptive augmented unscented Kalman filter algorithm is proposed to calibrate the biaxial magnetometer and circuit measurements quickly, which allows accurate estimates of the missile roll angle. Unlike the existing algorithms, the state vector of the algorithm is based on the missile roll angle parameters and the error factors caused by the magnetometer and the measurement circuit errors. Next, a two-step fast fitting algorithm is used to fit the initial value. After satisfying the stop rule, the state vector of the filter is configured to estimate the roll angle parameters and the calibration parameters. This method is evaluated by running numerous simulations. In the experiment, the algorithm completes the calibration of the magnetometer and the measurement circuit 1 s after the missile launches, with a sampling rate of 1 ms and an output roll attitude angle with a 0.0015 rad precision. The conventional unscented Kalman filter algorithm requires more time to achieve such a high accuracy. The simulation results demonstrate that the proposed two-step augmented unscented Kalman filter outperforms the conventional unscented Kalman filter in its estimation accuracy and convergence characteristics.