Scientific paper ID 2051 : 2020/3

Because of low inherent cost, small size, low power consumption, and solid reliability, often MEMS based sensors are used by inertial systems for navigation or object’s dynamic parameters measurement. It is well known that Inertial Navigation Systems can provide high accuracy information on the position, velocity, and attitude over a short period. However, their accuracy degrades rapidly with time. The presence of unwanted noise in the output signal of the sensor additionally makes the results of the measurements worst ant therefore it is very important the signal must properly treat, and the unwanted noise removed. The necessity of accurate leverage of the information s applying reliable and proven methods for removing the noise in the output sensor’s signal. The article resumes different kinds of adaptive filters and algorithms, their core principles and usual application on MEMS sensors. Following studied parameters and characteristics the RLS algorithm for adaptive filtration was chosen, because of the easy program implementation and the ability to fine tune the “forgetting factor”. Experimental data from MEMS was gathered and the selected adaptive filter was applied. Results of the denoising analysis are shown.