Нормалізація сенсорних даних за допомогою методу Z-score та Ходжеса-Лемана

Abstract

This paper is devoted to the study and comparative analysis of the Z-score and Hodges-Lehman methods for normalizing sensor data obtained from unmanned aerial vehicles (UAVs). The study aims to assess each method's effectiveness in standardizing data, detecting anomalies, and improving the quality of sensor data, which is critical for ensuring reliable UAV operation. A comparative analysis is carried out to assess the advantages and disadvantages of each method in the context of processing data from different types of sensors installed on UAVs. Particular attention is paid to the impact of emissions and noise on normalization performance, as well as the ability of the methods to preserve important information in the data. The results of the study demonstrate that the Z-score is effective for normalizing data with a normal distribution, while the Hodges-Lehman method shows better results in the presence of outliers and non-normal distribution. It is shown that the choice of the optimal normalization method depends on the characteristics of the sensor data and the specific application of the UAV. The results obtained can be used to develop more efficient UAV sensor data processing systems, which will improve the accuracy of analysis and identify potential problems. A key area for further research is the development of adaptive algorithms that automatically select the optimal normalization method depending on the characteristics of the data.