{"title":"双未知输入多传感器系统的信息融合滤波方案","authors":"Jiahui Lin;Na Wang;Kaishuo Wei;Xueyan Wang","doi":"10.1109/JSEN.2025.3577615","DOIUrl":null,"url":null,"abstract":"An estimating technique for multisensor systems with dual-unknown inputs is presented in this work. The approach separates the estimate procedures of system states and unknown inputs, carrying out state estimation and estimation of the dual-unknown inputs individually for multisensor systems with different unknown inputs in the state and observation equations. The state estimates from individual sensors are fused using the linear minimum variance information fusion criterion (LMVIFC) to produce linearly minimal variance unbiased state estimates after local filtering algorithms are created for each sensor. In order to guarantee the estimates’ objectivity and minimal variation, each sensor simultaneously estimates the unknown inputs in its observation equation independently. The associated estimates are then fused using LMVIFC. The Lyapunov stability theory is used to theoretically demonstrate the filter’s stability under dual-unknown inputs. Last, Monte Carlo simulations are used to assess and contrast the suggested approach with other conventional techniques. The outcomes demonstrate that even in complex scenarios with two distinct kinds of unknown inputs, the suggested approach can precisely and consistently track system states. In a hypersonic aircraft control system, its advantage is further confirmed.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 15","pages":"28664-28676"},"PeriodicalIF":4.3000,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Information Fusion Filtering Scheme for Multisensor Systems With Dual-Unknown Inputs\",\"authors\":\"Jiahui Lin;Na Wang;Kaishuo Wei;Xueyan Wang\",\"doi\":\"10.1109/JSEN.2025.3577615\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An estimating technique for multisensor systems with dual-unknown inputs is presented in this work. The approach separates the estimate procedures of system states and unknown inputs, carrying out state estimation and estimation of the dual-unknown inputs individually for multisensor systems with different unknown inputs in the state and observation equations. The state estimates from individual sensors are fused using the linear minimum variance information fusion criterion (LMVIFC) to produce linearly minimal variance unbiased state estimates after local filtering algorithms are created for each sensor. In order to guarantee the estimates’ objectivity and minimal variation, each sensor simultaneously estimates the unknown inputs in its observation equation independently. The associated estimates are then fused using LMVIFC. The Lyapunov stability theory is used to theoretically demonstrate the filter’s stability under dual-unknown inputs. Last, Monte Carlo simulations are used to assess and contrast the suggested approach with other conventional techniques. The outcomes demonstrate that even in complex scenarios with two distinct kinds of unknown inputs, the suggested approach can precisely and consistently track system states. In a hypersonic aircraft control system, its advantage is further confirmed.\",\"PeriodicalId\":447,\"journal\":{\"name\":\"IEEE Sensors Journal\",\"volume\":\"25 15\",\"pages\":\"28664-28676\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Journal\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11036603/\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/11036603/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Information Fusion Filtering Scheme for Multisensor Systems With Dual-Unknown Inputs
An estimating technique for multisensor systems with dual-unknown inputs is presented in this work. The approach separates the estimate procedures of system states and unknown inputs, carrying out state estimation and estimation of the dual-unknown inputs individually for multisensor systems with different unknown inputs in the state and observation equations. The state estimates from individual sensors are fused using the linear minimum variance information fusion criterion (LMVIFC) to produce linearly minimal variance unbiased state estimates after local filtering algorithms are created for each sensor. In order to guarantee the estimates’ objectivity and minimal variation, each sensor simultaneously estimates the unknown inputs in its observation equation independently. The associated estimates are then fused using LMVIFC. The Lyapunov stability theory is used to theoretically demonstrate the filter’s stability under dual-unknown inputs. Last, Monte Carlo simulations are used to assess and contrast the suggested approach with other conventional techniques. The outcomes demonstrate that even in complex scenarios with two distinct kinds of unknown inputs, the suggested approach can precisely and consistently track system states. In a hypersonic aircraft control system, its advantage is further confirmed.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
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