{"title":"多自由度谐振器的频率分辨率","authors":"K. Moran, B. DeMartini, K. Turner, K. Åstrom","doi":"10.1109/ICSENS.2009.5398243","DOIUrl":null,"url":null,"abstract":"This paper outlines the process for estimating measurable parameters in a multi degree of freedom micro resonator. Thermal mechanical noise provides a baseline limit for frequency resolution of micro resonators. We develop the likelihood function for a linear regression model of 2 degree of freedom resonator. Using the Cramer-Rao inequality we determine the minimum possible covariance to estimate parameters, such as the natural frequency of the system.","PeriodicalId":262591,"journal":{"name":"2009 IEEE Sensors","volume":"248 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Frequency resolution of a multi degree of freedom resonator\",\"authors\":\"K. Moran, B. DeMartini, K. Turner, K. Åstrom\",\"doi\":\"10.1109/ICSENS.2009.5398243\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper outlines the process for estimating measurable parameters in a multi degree of freedom micro resonator. Thermal mechanical noise provides a baseline limit for frequency resolution of micro resonators. We develop the likelihood function for a linear regression model of 2 degree of freedom resonator. Using the Cramer-Rao inequality we determine the minimum possible covariance to estimate parameters, such as the natural frequency of the system.\",\"PeriodicalId\":262591,\"journal\":{\"name\":\"2009 IEEE Sensors\",\"volume\":\"248 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE Sensors\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSENS.2009.5398243\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Sensors","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSENS.2009.5398243","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Frequency resolution of a multi degree of freedom resonator
This paper outlines the process for estimating measurable parameters in a multi degree of freedom micro resonator. Thermal mechanical noise provides a baseline limit for frequency resolution of micro resonators. We develop the likelihood function for a linear regression model of 2 degree of freedom resonator. Using the Cramer-Rao inequality we determine the minimum possible covariance to estimate parameters, such as the natural frequency of the system.