{"title":"基于厄米-高斯正交的非线性卡尔曼滤波","authors":"P. Hušek, J. Stecha","doi":"10.23919/ICCAS50221.2020.9268306","DOIUrl":null,"url":null,"abstract":"Kalman filter became a popular tool for state estimation of linear dynamical systems especially due to its simplicity. However in the case of nonlinear systems its realization is more complicated. Commonly used approach called Extended Kalman filter consists in local approximation based on linearization. Another possibility is to approximate the corresponding integrals using some feasible procedures. In this paper we apply Gauss-Hermite Quadrature for state estimation of nonlinear systems and compare its accuracy with Extended Kalman filter.","PeriodicalId":6732,"journal":{"name":"2020 20th International Conference on Control, Automation and Systems (ICCAS)","volume":"47 1","pages":"637-642"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nonlinear Kalman Filter by Hermite-Gauss Quadrature\",\"authors\":\"P. Hušek, J. Stecha\",\"doi\":\"10.23919/ICCAS50221.2020.9268306\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Kalman filter became a popular tool for state estimation of linear dynamical systems especially due to its simplicity. However in the case of nonlinear systems its realization is more complicated. Commonly used approach called Extended Kalman filter consists in local approximation based on linearization. Another possibility is to approximate the corresponding integrals using some feasible procedures. In this paper we apply Gauss-Hermite Quadrature for state estimation of nonlinear systems and compare its accuracy with Extended Kalman filter.\",\"PeriodicalId\":6732,\"journal\":{\"name\":\"2020 20th International Conference on Control, Automation and Systems (ICCAS)\",\"volume\":\"47 1\",\"pages\":\"637-642\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 20th International Conference on Control, Automation and Systems (ICCAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/ICCAS50221.2020.9268306\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 20th International Conference on Control, Automation and Systems (ICCAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ICCAS50221.2020.9268306","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nonlinear Kalman Filter by Hermite-Gauss Quadrature
Kalman filter became a popular tool for state estimation of linear dynamical systems especially due to its simplicity. However in the case of nonlinear systems its realization is more complicated. Commonly used approach called Extended Kalman filter consists in local approximation based on linearization. Another possibility is to approximate the corresponding integrals using some feasible procedures. In this paper we apply Gauss-Hermite Quadrature for state estimation of nonlinear systems and compare its accuracy with Extended Kalman filter.