{"title":"未知输入传感器网络的分布式联合估计与辨识","authors":"Hua Lan, A. Bishop, Q. Pan","doi":"10.1109/ISSNIP.2014.6827600","DOIUrl":null,"url":null,"abstract":"In this paper we consider the problem of distributed, joint, state estimation and identification for a class of stochastic systems with unknown inputs (UI). A distributed Expectation-Maximization (EM) algorithm is presented to estimate the local state at each sensor node by using the local observations in the E-step, and three different consensus schemes are proposed to diffuse the local state estimate to each sensor's neighbours and to derive the global state estimate at each node. In the M-step, each sensor identifies the local unknown inputs by using the global state estimate. A numerical example of target tracking in distributed sensor network is given to verify the three different distributed EM algorithms compared with the centralized EM based measurement-level and track-level fusion.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Distributed joint estimation and identification for sensor networks with unknown inputs\",\"authors\":\"Hua Lan, A. Bishop, Q. Pan\",\"doi\":\"10.1109/ISSNIP.2014.6827600\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we consider the problem of distributed, joint, state estimation and identification for a class of stochastic systems with unknown inputs (UI). A distributed Expectation-Maximization (EM) algorithm is presented to estimate the local state at each sensor node by using the local observations in the E-step, and three different consensus schemes are proposed to diffuse the local state estimate to each sensor's neighbours and to derive the global state estimate at each node. In the M-step, each sensor identifies the local unknown inputs by using the global state estimate. A numerical example of target tracking in distributed sensor network is given to verify the three different distributed EM algorithms compared with the centralized EM based measurement-level and track-level fusion.\",\"PeriodicalId\":269784,\"journal\":{\"name\":\"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSNIP.2014.6827600\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSNIP.2014.6827600","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Distributed joint estimation and identification for sensor networks with unknown inputs
In this paper we consider the problem of distributed, joint, state estimation and identification for a class of stochastic systems with unknown inputs (UI). A distributed Expectation-Maximization (EM) algorithm is presented to estimate the local state at each sensor node by using the local observations in the E-step, and three different consensus schemes are proposed to diffuse the local state estimate to each sensor's neighbours and to derive the global state estimate at each node. In the M-step, each sensor identifies the local unknown inputs by using the global state estimate. A numerical example of target tracking in distributed sensor network is given to verify the three different distributed EM algorithms compared with the centralized EM based measurement-level and track-level fusion.