## 5 Conclusions  
Due to PCIM-01-inertial measurement unit module [42] construction, the tested MEMS accelerometers enable practical measurement of vibrations with frequencies ranging from tenths of a hertz to even 1 kHz. MEMS gyroscopes can be used to measure angular velocities and, indirectly, angular oscillations in similar frequency ranges [42; 52]. MEMS inertial sensors, although unfortunately burdened with serious stochastic errors, are widely used not only in commonly used devices but also in such safety-critical solutions as AHRS. The aim of this article was to present the stochastic properties of sensors used in the flight parameter recording system of a light aircraft. The article discusses the issue of the influence of the stability of the sensors' operating temperature on the obtained time characteristics and on the power spectral density of the output signals. The frequency characteristics pointed to the areas related to the temperature drift and to the electro-mechanical noise of the sensors. The stochastic properties of MEMS accelerometers and gyroscopes under stable operating temperature conditions were additionally assessed using power spectral density, Allan variance plots, and the generalized method of wavelet moments. The stochastic properties of MEMS sensors determined by the PSD, AV, and GMWM are convergent. However, only the results achieved with the GMWM method enable direct verification of the models obtained. The white noise N parameters determined with different methods have similar values, and the maximum difference for the accelerometer in the \(z\)-axis, during the warming-up process (Table 2), was 21% of the relative standard deviation (RSD). In the majority of remaining cases, the variance was not higher than 10%. [PERSON]'s method of variance is the easiest way to determine the drift coefficient, as the drift is determined on the basis of the lowest point on the characteristic curve. In turn, the PSD method, for which it is necessary to adjust a line with a \(-\)1 dB/dec deviation to irregular characteristics, seems to be the most problematic. The results obtained with the AV and GMWM methods are often identical. However, when all three methods for drift determination are compared, the greatest difference, i.e., 109% of RSD, was observed for the accelerometer in the \(z\)-axis under stable operating temperature conditions (Table 2). In some cases, it was impossible to determine the Brown noise because it occurred in negligible values. When the noise was higher, the AV and GMWM methods were highly effective, and the results obtained were very similar or identical (e.g., with accelerometers at stabilized operating temperature, Table 2)  
The analysis of the stochastic parameters of gyroscopes and accelerometers is helpful in the selection of adequate sensors and in the process of AHRS algorithm synthesis. The results obtained with the PSD, AV, and GMWM methods are complementary, as well as being mutually verifiable, especially in the case of AV and GMWM methods. With precise information on the stochastic parameters of sensors, it is possible to determine types of phenomena that can be analyzed with the help of the results obtained. In the case of flight parameter recorders, it is also possible to determine incidents that can be detected with a certain sensor unit. Conceptualization, P.R., G.K. and P.S. ([PERSON]); methodology, P.R. and G.K.; software, P.R. ; validation, P.R., P.S. ([PERSON]) and G.K.; formal analysis, P.R., P.S. ([PERSON]), G.K. and P.S. ([PERSON]); investigation, P.R. and G.K.; resources, P.R. ; data curation, P.R. and G.K.; writing--original draft preparation, P.R., P.S. ([PERSON]), G.K. and P.S. ([PERSON]); writing--review and editing, P.R., P.S. ([PERSON]), G.K. and P.S. ([PERSON]); visualization, P.R., G.K. and P.S. ([PERSON]); supervision, P.R. ; project administration, P.R. ; funding acquisition, P.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.