摘要
MEMS陀螺仪工作时,容易受到各种噪声,尤其是高频噪声影响,不利于导航系统长时间工作,因此需要对数据实时去噪。互补集合经验模态分解(CEEMD)是一种按照自身尺度进行信号分解的算法,信号震荡随着分解级数逐渐减小,能够较好地分离高频和低频信号。以水下机器人MEMS陀螺仪为研究对象,根据水下实测数据,采用CEEMD分解陀螺信号,提取有效信息,并利用Allan方差验证CEEMD的有效性。仿真结果表明CEEMD对随机噪声、高频信号具有良好的降噪效果。
MEMS-based gyroscope is vulnerable to many noises, especially high-frequency noises when executing underwater tasks,which is detrimental to a long-run system,requiring denoising data in real-time. Complementary Ensemble Empirical Mode Decomposition（ CEEMD） is a novelty signal decomposition algorithm according to scales and sizes,and signal’s vibration gradually reduces with decomposition levels. And it can separate signals among dif-ferent frequencies. Taking MEMS gyroscope of underwater vehicle as a research object,this paper applies CEEMD to decompose gyroscope signals acquired during experiments in order to extract effective information. Meanwhile,Al-lan Variance is utilized to verify the effectiveness of CEEMD. Simulation results demonstrate that CEEMD has a good filtering effect on random noise and high-frequency signals.
出处
《传感技术学报》
CAS
CSCD
北大核心
2014年第12期1622-1626,共5页
Chinese Journal of Sensors and Actuators
基金
江苏省研究生实践创新计划项目(SJLX_0493)
