Gustaf Hendeby, R. Karlsson, F. Gustafsson, N. Gordon
{"title":"非高斯滤波问题中的性能问题","authors":"Gustaf Hendeby, R. Karlsson, F. Gustafsson, N. Gordon","doi":"10.1109/NSSPW.2006.4378821","DOIUrl":null,"url":null,"abstract":"Performance for filtering problems is usually measured using the second-order moment. For non-Gaussian applications, this measure is not always sufficient. In this paper, the Kull-back divergence is extensively used to compare estimated distributions. Several estimation techniques are compared, and methods with ability to express non-Gaussian posterior distributions are shown to give superior performance over classical second-order moment based estimators.","PeriodicalId":388611,"journal":{"name":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","volume":"361 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Performance Issues in Non-Gaussian Filtering Problems\",\"authors\":\"Gustaf Hendeby, R. Karlsson, F. Gustafsson, N. Gordon\",\"doi\":\"10.1109/NSSPW.2006.4378821\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Performance for filtering problems is usually measured using the second-order moment. For non-Gaussian applications, this measure is not always sufficient. In this paper, the Kull-back divergence is extensively used to compare estimated distributions. Several estimation techniques are compared, and methods with ability to express non-Gaussian posterior distributions are shown to give superior performance over classical second-order moment based estimators.\",\"PeriodicalId\":388611,\"journal\":{\"name\":\"2006 IEEE Nonlinear Statistical Signal Processing Workshop\",\"volume\":\"361 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 IEEE Nonlinear Statistical Signal Processing Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NSSPW.2006.4378821\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE Nonlinear Statistical Signal Processing Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NSSPW.2006.4378821","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance Issues in Non-Gaussian Filtering Problems
Performance for filtering problems is usually measured using the second-order moment. For non-Gaussian applications, this measure is not always sufficient. In this paper, the Kull-back divergence is extensively used to compare estimated distributions. Several estimation techniques are compared, and methods with ability to express non-Gaussian posterior distributions are shown to give superior performance over classical second-order moment based estimators.
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