{"title":"基于投影的rao - blackwelzed粒子滤波在条件共轭状态空间模型中的参数估计","authors":"Milan Papez","doi":"10.1109/SSP.2018.8450730","DOIUrl":null,"url":null,"abstract":"Particle filters constitute today a well-established class of techniques for state filtering in non-linear state-space models. However, online estimation of static parameters under the same framework represents a difficult problem. The solution can be found to some extent within a category of state-space models allowing us to perform parameter estimation in an analytically tractable manner, while still considering non-linearities in data evolution equations. Nevertheless, the well-known particle path degeneracy problem complicates the computation of the statistics that are required to estimate the parameters. The present paper proposes a simple and efficient method which is experimentally shown to suffer less from this issue.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Projection-Based Rao-Blackwellized Particle Filter to Estimate Parameters in Conditionally Conjugate State-Space Models\",\"authors\":\"Milan Papez\",\"doi\":\"10.1109/SSP.2018.8450730\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Particle filters constitute today a well-established class of techniques for state filtering in non-linear state-space models. However, online estimation of static parameters under the same framework represents a difficult problem. The solution can be found to some extent within a category of state-space models allowing us to perform parameter estimation in an analytically tractable manner, while still considering non-linearities in data evolution equations. Nevertheless, the well-known particle path degeneracy problem complicates the computation of the statistics that are required to estimate the parameters. The present paper proposes a simple and efficient method which is experimentally shown to suffer less from this issue.\",\"PeriodicalId\":330528,\"journal\":{\"name\":\"2018 IEEE Statistical Signal Processing Workshop (SSP)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE Statistical Signal Processing Workshop (SSP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSP.2018.8450730\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE Statistical Signal Processing Workshop (SSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSP.2018.8450730","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Projection-Based Rao-Blackwellized Particle Filter to Estimate Parameters in Conditionally Conjugate State-Space Models
Particle filters constitute today a well-established class of techniques for state filtering in non-linear state-space models. However, online estimation of static parameters under the same framework represents a difficult problem. The solution can be found to some extent within a category of state-space models allowing us to perform parameter estimation in an analytically tractable manner, while still considering non-linearities in data evolution equations. Nevertheless, the well-known particle path degeneracy problem complicates the computation of the statistics that are required to estimate the parameters. The present paper proposes a simple and efficient method which is experimentally shown to suffer less from this issue.