基于粒子群优化的三轴磁强计非线性误差校正

三轴磁强计的非线性误差是影响其测量精度的重要因素,而传统三轴磁强计误差模型仅考虑零偏、磁轴非正交和灵敏度误差,无法实现对磁测误差有效剥离和校正.通过对传统误差模型进行扩展,提出了三轴磁强计的非线性误差模型,并利用自适应粒子群优化(APSO)算法对非线性误差模型参数进行反演.实验结果表明:可以有效地补偿三轴磁强计的非线性测量误差,相较于传统校正方法,在误差参数规模较大情况下,APSO算法具有更好的全局搜索能力,可大幅提高误差参数反演精度及算法收敛速度.

Nonlinear error calibration of three-axis magnetometer based on PSO algorithm

The nonlinear error of three-axis magnetometer is an important factor affecting its measurement precision,and the traditional error model for three-axis magnetometer only considering zero bias,axis nonorthogonal and sensitivity error,unable to realize the effective apartment and calibration of magneto measurement errors.By extending traditional error model,propose nonlinear error model for three-axis magnetometer.Adaptive particle swarm optimization (APSO) algorithm is used for nonlinear error model parameters inversion.Experimental results show that the proposed method can effectively compensate the unlinear measuring error of three-axis magnetometer,and under the condition of larger scale of error parameters,APSO has better global search ability compared with the traditional calibration methods,which can significantly improve error parameter inversion precision and the convergence speed of algorithm.