Sensor Error Correction Based on Least Squares

Abstract

Triaxial fluxgate sensors are widely used in military and civilian fields, but their measurement accuracy is affected by inherent errors such as triaxial non-orthogonality, bias, and inconsistent scale factors. To improve accuracy, this study first analyzes the error mechanism and establishes an error model. The least squares method is utilized to accurately estimate the error parameters, thereby enabling the correction of magnetic measurement data. Simulation results show that this algorithm can effectively estimate error parameters and significantly improve the measurement accuracy of both magnetic field components and the total field magnitude. Further experiments with actual magnetic field measurements validate the algorithm's effectiveness: after applying the algorithm to estimate sensor error parameters and correct the measured data, measurement errors are effectively suppressed, leading to a significant enhancement in the sensor's measurement accuracy.