CAJ | 학술논문

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。
MEMS타라의공작시，용역수도각충조성，우기시고빈조성영향，불리우도항계통장시간공작，인차수요대수거실시거조。호보집합경험모태분해( CEEMD)시일충안조자신척도진행신호분해적산법，신호진탕수착분해급수축점감소，능구교호지분리고빈화저빈신호。이수하궤기인MEMS타라의위연구대상，근거수하실측수거，채용CEEMD분해타라신호，제취유효신식，병이용Allan방차험증CEEMD적유효성。방진결과표명CEEMD대수궤조성、고빈신호구유량호적강조효과。
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.