MEMS Gyroscope Raw Data Noise Reduction Using Fading Memory Filter

Abstract

Nowadays, MEMS sensors are widely used in systems such as autonomous vehicles, but they still suffer from high stochastic errors such as Angle random walk (ARW) noise, which causes failure in real-signals and produces an error in the position and attitude of mobile systems. So far, many filters are developed to reduce the amount of noise in the output of the MEMS sensors. The computational overhead, the rate of noise reduction, and the phase-delay of the filter are the most important characteristics of choosing a suitable filter. In this paper, a low pass filter based on the alpha-beta filter with a very low computational overhead is proposed to reduce the amount of noise. In order to find the optimal filter gain, the improvement in the positioning is selected as a criterion, which is a tradeoff between the amount of noise reduction and the phase delay of the filtered signal. In this work, the KITTI database is used to evaluate the proposed filter. The results show that the proposed filter reduces the sensor’s noise and improves the positioning of the moving car, significantly.