Abstract
Abstract A real-time calibration method for magnetometers based on the recursive least squares (RLS) algorithm is proposed to address the issues of large data processing and lack of real-time performance in the calibration of magnetometers in complex magnetic environments using the traditional least squares (LS) algorithm. Firstly, an error model for the magnetometer is established. Then, using this error model as the measurement equation, the current measurement value is iterated from the previous measurement value through least squares iteration to update the state parameters. Finally, the effectiveness of the algorithm was verified by shaking the magnetometer in a “figure-eight” pattern. Experiments have shown that compared with the traditional least squares algorithm, the recursive least squares algorithm requires less data processing and offers faster calculation speed. Compared to LS calibration, the standard deviation of the magnetic vector modulus obtained from RLS calibration decreased by 24.4%, 17.4%, and 23.2% in three different environments, respectively. Additionally, by performing real-time detection and analysis of the state covariance matrix, it was determined that the calibration process is essentially complete after 3500 iterations.
Published Version
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