基于奇异谱分解的微机械加速度计振动噪声抑制方法

摘要

Micro-electro mechanical systems (MEMS )-based inertial sensors are low-cost but their performances are also degraded because of large uncertainties in their output and effects caused by vibrations.To estimate the attitude of using the MEMS inertial sensors,a pretreatment method to mitigate the noise of the inertial sensors was proposed based on the singular spectral analysis (SSA).With the so-called lagged covariance matrix in this approach,the trend and periodic components were separated with SSA.As a result,the true attitude signal was contained in the trend component,while the vibration signals were contained in the periodic components.Then,the trend component was extracted by use of the number of zero-crossing.Finally,the true attitude measurements pretreated with SSA were utilized as the measurement input of a fusion filter for accurate attitude estimation.The car tests verified the feasibility and the capacity of the method to improve the accuracy of the attitude estimation.%微机械（MEMS）惯性传感器成本低的同时噪声较大，易受振动信号的干扰。为了利用微机械惯性传感器构成低成本姿态估计系统，提出了一种基于奇异谱分解（SSA）的振动噪声预处理方法。SSA方法的实质是利用延迟扩维矩阵进行主成分分析，其延迟相关的算法能够有效地分离出加速度计测量值中的趋势项与周期项，趋势项中包含有需要的姿态变化信号，周期项即为低频振动噪声，根据过零点检测方法提取出趋势项，将该趋势项作为加速度计的测量值，即可实现对振动噪声信号的抑制，有效地提高姿态估计精度。实际的跑车实验验证了该方法的可行性和有效性。