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作 者:杜江波 张晓明 薛晓华 DU Jiangbo;ZHANG Xiaoming;XUE Xiaohua(National Key Laboratory for Electronic Measurement Technology,North University of China,Taiyuan 030051,China;School of Instrument and Electronics,North University of China,Taiyuan 030051,China;Key Laboratory of Instrumentation Science&Dynamic Measurement of Ministry of Education,North University of China,Taiyuan 030051,China)
机构地区:[1]中北大学电子测试技术国家重点实验室,山西太原030051 [2]中北大学仪器与电子学院,山西太原030051 [3]中北大学仪器科学与动态测试教育部重点实验室,山西太原030051
出 处:《传感器与微系统》2025年第1期29-33,共5页Transducer and Microsystem Technologies
基 金:国防科技基础加强计划项目(2021-JCJQ-JJ-0947);航空科学基金项目(202000010U0004)。
摘 要:在三轴磁传感器采样过程中,不可避免地出现人为干扰或环境磁场干扰,导致模值约束的椭球校准精度受到影响,因此提出了一种基于稳健估计的椭球校准算法。在椭球模型约束的最小二乘法基础下,加入稳健估计的思想,对采样数据进行弹性赋权处理,多次迭代逐步减小粗差对拟合精度的影响,进而提高地磁测量的精度。仿真试验表明,稳健估计椭球拟合在有限异常值干扰下得到的校准参数,其精度相比于传统椭球拟合算法提升了1~2个数量级,且校正后的数据模值均方差改善比提高了90%以上;实测试验中,所提出的方法对磁场模值均值和均方差的改善比相较于原始算法各提高了9%和23.2%。During sampling process of three-axis magnetic sensors,human interference or environmental magnetic field interference inevitably occurs,resulting in the ellipsoid calibration precision of the mode-value constraints is affected,so an ellipsoid calibration algorithm based on robust estimation is proposed.Based on the ellipsoid modelconstrained least-squares method,the idea of robust estimation is added,the sampled data are flexibly empowered,and the effect of roughness on the fitting precision is gradually reduced by several iterations to improve the precision of geomagnetic measurement.The simulation tests show that the precision of the calibration parameters obtained by robust estimation ellipsoid fitting under the interference of finite outliers is improved by 1~2 orders of magnitude compared with the traditional ellipsoid fitting algorithm,and the improvement ratio of the mean squared error of the modular values of the corrected data is improved by more than 90%;in the measurement tests,the improvement ratios of the mean and mean squared error of the modular values of the magnetic field are increased by 9%and 23.2%respectively by the proposed approach compared with the original algorithm.
分 类 号:TP212.9[自动化与计算机技术—检测技术与自动化装置]
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