M. Marinov, Borislav Ganev, N. Djermanova, T. Tashev
{"title":"Analysis of Sensors Noise Performance Using Allan Deviation","authors":"M. Marinov, Borislav Ganev, N. Djermanova, T. Tashev","doi":"10.1109/ET.2019.8878552","DOIUrl":null,"url":null,"abstract":"The paper presents the noise analysis of different sensor types using Allan Variance (AV). Compared to the conventional variance that assesses the variation around the mean value of the aggregate data surveyed, AV estimates variations by averaging measurements for different periods. This approach often leads to the possibility of directly distinguishing the different noise types and to better convergence of the process of assessing their levels. An important advantage of this method is that there is no need for any further transformations. According to IEEE recommendations, the AV approach is the preferred method for identifying stochastic error and for determining the type of noise in different types of inertial sensors. The purpose of this work is to study the applicability of the AV analysis method for efficient noise analysis for other types of sensors such as CO2 and MEMS pressure sensors.","PeriodicalId":306452,"journal":{"name":"2019 IEEE XXVIII International Scientific Conference Electronics (ET)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE XXVIII International Scientific Conference Electronics (ET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ET.2019.8878552","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
Abstract
The paper presents the noise analysis of different sensor types using Allan Variance (AV). Compared to the conventional variance that assesses the variation around the mean value of the aggregate data surveyed, AV estimates variations by averaging measurements for different periods. This approach often leads to the possibility of directly distinguishing the different noise types and to better convergence of the process of assessing their levels. An important advantage of this method is that there is no need for any further transformations. According to IEEE recommendations, the AV approach is the preferred method for identifying stochastic error and for determining the type of noise in different types of inertial sensors. The purpose of this work is to study the applicability of the AV analysis method for efficient noise analysis for other types of sensors such as CO2 and MEMS pressure sensors.