Abstract
MEMS gyroscopes are advantageous devices that promote a wide range of applications, however, they suffer from various stochastic errors some of which accumulate over time (angle random walk, bias random walk). To be able to use such devices, one should apply mathematical models of stochastic processes and hardware-software tools for investigation into noise, figure out the noise characteristics and develop an appropriate method of adaptive signal filtering. The Allan deviation plot is considered the most common tool for studying noise spectral characteristics. However, when distorted by unexpected noise components, the Allan deviation plot becomes difficult to interpret. The aim of this work is to present an algorithm for generating noise typical of real MEMS gyroscopes and its implementation as a part of a complex hardware-software tool for investigation into inertial measurement units, being developed by the authors. With such a tool for simulating noise with specific spectral characteristics, the researcher will be able to understand and explain the behavior of a MEMS gyroscope and thus fit a reasonable filtering method for it.
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Fedasyuk, D., Marusenkova, T. (2020). MEMS Gyroscopes’ Noise Simulation Algorithm. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing IV. CSIT 2019. Advances in Intelligent Systems and Computing, vol 1080. Springer, Cham. https://doi.org/10.1007/978-3-030-33695-0_62
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