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.