Improving Accuracy of Low-Cost Vehicle Attitude Estimation by Denoising MIMU Based on SSA and ICA

In this article, we develop a novel method to improve the accuracy of the low-cost vehicle attitude estimation using singular spectrum analysis (SSA) and independent component analysis (ICA). The microelectromechanical system (MEMS) accelerometers and the MEMS gyros are fused by the Kalman filtering algorithm to provide a low-cost attitude estimation. The low-frequency vibration noise and the maneuvering acceleration disturbance in the measurements of the MEMS accelerometers introduce large errors to the attitude estimation. To mitigate the low-frequency vibration noise, we propose a method based on SSA to separate the quasi-periodic components in accelerometer measurements and keep the trend remained as the true attitude measurement. SSA is effective in decomposing original signal into trend, periodic or quasi-periodic components, and thus, the low-frequency vibration noise that behaves as a quasi-periodic signal can be well separated. However, the acceleration disturbance still exists in the trend. Thus, a method based on ICA is then proposed to remove the acceleration disturbance by the aid of the turning rate measurement. The proposed method is verified with driving tests under city road and off-road conditions, showing a significant improvement of the attitude accuracy.