【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。更多还原

【Abstract】 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 different 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,Allan 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.更多还原