Wavelet DE-noising and Kalman Filtering of MEMS Sensors for Autonomous Latitude Determination

Abstract

Background: There is a task of the position latitude autonomous determination of unmoved vehicles. Also, there is another task of the initial value latitude determination as a prepared operation of gimbaled and strap-down inertial navigation systems. For both cases, it is necessary to have an inertial measurement unit (IMU) with triad gyroscopes and triad accelerometers. Using the IMU by micro-machined electromechanical systems (MEMS) technology, the output signals of micromechanical gyroscope and accelerometers have significant noise compo-nents. Objective: Normally to filter output signals of MEMS sensors averaging and filtering are used. However, for Kalman filtering, it is necessary to know the exact mathematical model of the sensors and a lot of their initial random charac-teristics. The study of the possibility of the wavelet transform usage to filter the output signals MEMS accelerometers and gyroscopes for the latitude autono-mous determination was considered in the paper. Method: The wavelet transform method for the filtering of MEMS accelerometers and gyroscopes output signals for accuracy increasing of the position latitude autonomous determination was conducted. The autonomous latitude de-termination efficiency of IMU-based on MEMS gyroscope and accelerometers has been experimentally confirmed. The projections of the Earth’s angular rate and gravitational acceleration were obtained from the MEMS IMU. After that, the signals of the IMU gyroscopes and accelerometers were filtered, using the wavelet ‘Daubechies’ in decomposition and averaged. These signals were used in a computational algorithm to determine the latitude. Results: The results showed that unlike the well-known Kalman filter wavelet de-noising reduced calculation error by almost twice. Conclusion: Wavelet de-noising could be used for output signals filtering of micromechanical gyroscope and accelerometers for the position latitude auton-omous determination.

Calibration of the Inertial Measurement Unit in Critical Conditions

“…Існує кілька способів компенсації шумових складових вихідних сигналів датчиків. Найбільш поширеним є використання фільтра Калмана [11,12] та іноді -Вейвлет перетворення [13]. Відомі випадки використання алгоритмів штучних нейронних мереж [14,15].…”

Permutation fuzzy entropy based ICEEMDAN de-noising for inertial sensors

“…Among them, the WD method has good multi-scale time-frequency analysis ability, and the signals can be decomposed into sub-signals with different frequencies, so multiscale analysis and signal de-noising can be realized. Noureldin et al (2003), Nassar and El-Sheimy (2005), Hasan et al (2010), Dong et al (2019), Lin et al (2019, Avrutov et al (2022), Sadek et al (2022a), Sadek et al (2022b applied this method for IMU data de-noising, and the results showed that most random noises in IMU data could be eliminated by the WD method, the accuracy of INS positioning could be improved. Nevertheless, due to the limitation of Heisenberg's uncertainty principle, the WD de-noising method will lose some useful information in the process of non-stationary decomposition (Zhou et al 2015).…”