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作 者:胡佳[1] 蔡成林[1,2] HU JIA;CAI Chenglin(Guangxi Key Laboratory of Precision Navigation Technology and Application,Guilin University of Electronic Technology,Guilin 541000,China;Center for Intelligent Navigation and Remote Sensing,Xiangtan University,Xiangtan 411100,China)
机构地区:[1]桂林电子科技大学广西精密导航技术与应用重点实验室,广西桂林541000 [2]湘潭大学智能导航与遥感研究中心,湖南湘潭411100
出 处:《传感器与微系统》2021年第9期132-134,共3页Transducer and Microsystem Technologies
基 金:国家自然科学基金资助项目(61771150);湖南省科技创新计划资助项目(2018GK2014,2018RS3089);广西科技计划资助项目(桂科AB17129028);桂林电子科技大学研究生教育创新计划项目(2019YCXS033)。
摘 要:为了减小微机电系统(MEMS)陀螺输出信号中的噪声分量,提出一种改进最小均方(LMS)算法结合移动平均滤波(MAF)的降噪方法。首先通过改进LMS算法对陀螺数据进行预处理,然后量化分析MAF不同滑动窗口长度以及滤波器收敛因子对陀螺仪噪声抑制的效果;最后,采用MEMS陀螺仪惯性测量单元搭建测试环境,将所提出算法应用于实际降噪研究中,并分别与小波降噪以及传统LMS进行对比。采用Allan方差(AV)对MEMS陀螺仪随机误差参数进行辨识,结果表明:经过LMS-MAF算法滤波处理后的陀螺各项误差系数明显减小,角随机游走、量化噪声、零偏不稳定性、速度斜坡、速度随机游走均降低90%以上,大大降低了陀螺仪噪声,有效提高了陀螺仪精度。In order to reduce the noise component in output signal of micro-electro-mechanical system(MEMS)gyroscope,a denoising method combines improved least mean square(LMS)algorithm with moving average filtering(MAF)is proposed.Firstly,preprocess the gyroscope data by improving the LMS algorithm,and then analyzes quantitatively the effect of different sliding window length of MAF and filter convergence factor on the noise suppression of gyroscope.Finally,a certain type of MEMS gyroscope inertial measurement unit is used to build the test environment.The proposed algorithm is applied to the actual noise reduction research,and the algorithm is compared with wavelet noise reduction and traditional LMS.Allan variance(AV)is used to identify the random error parameters of MEMS gyroscope.The results show that the error coefficients of gyroscope after filtering by LMS-MAF algorithm are significantly reduced,and the random walk of angle,quantization noise,zero deviation instability,speed slope and speed are all reduced by more than 90%,which greatly reduces the noise of gyroscope and effectively improves the accuracy of gyroscope.
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