基于奇异谱分解的微机械加速度计振动噪声抑制方法
De-noising method for MEMS accelerometers based on singular spectrum analysis
Micro-Electro Mechanical Systems (MEMS) based inertial sensors are low-cost but their performances are also degraded because of the large uncertainties in their output and the effects caused by vibrations. To estimate the attitude using the MEMS inertial sensors, a pretreatment method to mitigate the noise of the inertial sensors is proposed based on the singular spectrum analysis (SSA). SSA belongs to the general category of PCA methods. With the so-called lagged covariance matrix of this approach, the trend and periodic components are separated by SSA. As a result, the true attitude signal is contained in the trend component, while the vibrations are contained in the periodic components. Then, the trend component is extracted by the use of the number of zero-crossing. Finally, the true attitude measurements pretreated by the SSA are utilized as the measurement input of the 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 effectively.
奇异谱分析 / 独立分量分析 / 微机械惯性传感器 / 趋势项 / 振动噪声 / 姿态估计 {{custom_keyword}} /
singular spectrum analysis (SSA) / independent component analysis (ICA) / Micro-Electro Mechanical Systems (MEMS) inertial sensors / trend extraction / vibration noise / attitude estimation {{custom_keyword}} /
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