孙伟,文剑,张远,耿诗涵.MEMS陀螺仪随机误差的辨识与降噪方法研究[J].电子测量与仪器学报,2017,31(1):15-20 |
MEMS陀螺仪随机误差的辨识与降噪方法研究 |
Research on random error identification and denoising method of MEMS gyroscope |
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DOI:10.13382/j.jemi.2017.01.003 |
中文关键词: MEMS陀螺 随机误差 卡尔曼滤波 Allan方差 |
英文关键词:MEMS gyroscope random error Kalman filter Allan variance |
基金项目:国家自然科学基金(41304032)、高等学校博士学科点专项科研基金(新教师类)(20132121120005)、第8批中国博士后科学基金特别项目(2015T80265)、第58批中国博士后科学基金面上项目(2015M581360)、辽宁省高等学校杰出青年学者成长计划(LJQ2015044)、辽宁省自然科学基金(2015020078)、辽宁省“百千万人才工程”培养经费(辽百千万立项【2015】76号)、江西省数字国土重点实验室开放研究基金(DLLJ201501)、对地观测技术国家测绘地理信息局重点实验室开放基金(K201401)、地球空间环境与大地测量教育部重点实验室开放基金(14 01 05)、航空遥感技术国家测绘地理信息局重点实验室经费(2015B11)、精密工程与工业测量国家测绘地理信息局重点实验室开放基金(PF2015 13)、海岛(礁)测绘技术国家测绘地理信息局重点实验室项目(2014B05)资助 |
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Author | Institution |
Sun Wei | School of Geomatics, Liaoning Technical University, Fuxin 123000, China |
Wen Jian | School of Geomatics, Liaoning Technical University, Fuxin 123000, China |
Zhang Yuan | School of Geomatics, Liaoning Technical University, Fuxin 123000, China |
Geng Shihan | School of Geomatics, Liaoning Technical University, Fuxin 123000, China |
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中文摘要: |
针对微机电系统(MEMS)陀螺仪随机误差成为制约其精度和应用范围的主要因素,提出基于回归滑动平均(ARMA)模型的卡尔曼滤波估计方法。首先基于Allan方差分析结果,确定出量化噪声、角度随机游走、零偏不稳定性是MEMS陀螺随机噪声主要组成部分;然后采用时间序列分析法对MEMS陀螺仪随机噪声的平稳性进行检验;最后基于随机漂移ARMA模型建立离散卡尔曼滤波方程对其开展误差估计与补偿。开展车载静、动态环境下的数字降噪与卡尔曼滤波估计补偿对比实验,结果表明基于ARMA模型的卡尔曼滤波估计法在MEMS陀螺随机误差补偿效果上具有更明显优势。 |
英文摘要: |
Aiming at the random error of MEMS gyroscope is the main factor that restricts its precision and application range, the Kalman filter estimation method based on regression moving average (ARMA) model is proposed in this paper. Firstly, based on the results of Allan variance analysis, the quantization noise, angle random walk and zero bias instability are the main parts of the MEMS gyroscope random noise. Then, the stability of MEMS gyroscope random noise is tested by using time series analysis. Finally, based on the random drift of the auto regressive moving average (ARMA) model, a discrete Kalman filter equation is built to actualize its error estimation and compensation. The results of static vehicle and dynamic environment of digital noise reduction and Kalman filtering compensation experiments show that the Kalman filter estimation method based on the ARMA model has more obvious advantages in MEMS Gyroscope random error compensation. |
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