Lin Zhou;Zheng Zhao;Jiayuan Yan;Yong Jin;Yongjin Huo
{"title":"基于双向联合风险多步预测的多传感器管理方法","authors":"Lin Zhou;Zheng Zhao;Jiayuan Yan;Yong Jin;Yongjin Huo","doi":"10.1109/JSEN.2025.3599187","DOIUrl":null,"url":null,"abstract":"In a multisensor collaborative tracking system, rational multisensor management methods can achieve optimal system performance. However, the complexity and variability of environmental risks will lead to reduced accuracy and safety of the tracking system. Therefore, this article proposes a multisensor management method based on multistep prediction of a bidirectional joint risk to rationally allocate limited sensor resources. First, this article comprehensively considers three risks, including the radiation risk of our multisensors, the risk of detection loss, and the threat risk of opposing targets, meanwhile constructing a bidirectional joint risk model. Second, adaptive weights for the three risks are proposed to adjust the three risks in the above model. Then, based on the framework of time-series prediction, the bidirectional joint risk is predicted. Finally, based on this, the problem of minimizing the multistep prediction bidirectional joint risk is proposed and then achieving the rational allocation of multisensor resources. The simulation results demonstrate that the proposed method is feasible, as it can effectively allocate limited sensor resources in a multirisk environment, improving the accuracy and security of the tracking system.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 19","pages":"37407-37418"},"PeriodicalIF":4.3000,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multisensor Management Method Based on Multistep Prediction of Bidirectional Joint Risk\",\"authors\":\"Lin Zhou;Zheng Zhao;Jiayuan Yan;Yong Jin;Yongjin Huo\",\"doi\":\"10.1109/JSEN.2025.3599187\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In a multisensor collaborative tracking system, rational multisensor management methods can achieve optimal system performance. However, the complexity and variability of environmental risks will lead to reduced accuracy and safety of the tracking system. Therefore, this article proposes a multisensor management method based on multistep prediction of a bidirectional joint risk to rationally allocate limited sensor resources. First, this article comprehensively considers three risks, including the radiation risk of our multisensors, the risk of detection loss, and the threat risk of opposing targets, meanwhile constructing a bidirectional joint risk model. Second, adaptive weights for the three risks are proposed to adjust the three risks in the above model. Then, based on the framework of time-series prediction, the bidirectional joint risk is predicted. Finally, based on this, the problem of minimizing the multistep prediction bidirectional joint risk is proposed and then achieving the rational allocation of multisensor resources. The simulation results demonstrate that the proposed method is feasible, as it can effectively allocate limited sensor resources in a multirisk environment, improving the accuracy and security of the tracking system.\",\"PeriodicalId\":447,\"journal\":{\"name\":\"IEEE Sensors Journal\",\"volume\":\"25 19\",\"pages\":\"37407-37418\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2025-08-21\",\"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/11134136/\",\"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/11134136/","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}
Multisensor Management Method Based on Multistep Prediction of Bidirectional Joint Risk
In a multisensor collaborative tracking system, rational multisensor management methods can achieve optimal system performance. However, the complexity and variability of environmental risks will lead to reduced accuracy and safety of the tracking system. Therefore, this article proposes a multisensor management method based on multistep prediction of a bidirectional joint risk to rationally allocate limited sensor resources. First, this article comprehensively considers three risks, including the radiation risk of our multisensors, the risk of detection loss, and the threat risk of opposing targets, meanwhile constructing a bidirectional joint risk model. Second, adaptive weights for the three risks are proposed to adjust the three risks in the above model. Then, based on the framework of time-series prediction, the bidirectional joint risk is predicted. Finally, based on this, the problem of minimizing the multistep prediction bidirectional joint risk is proposed and then achieving the rational allocation of multisensor resources. The simulation results demonstrate that the proposed method is feasible, as it can effectively allocate limited sensor resources in a multirisk environment, improving the accuracy and security of the tracking system.
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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-Sensors in Industrial Practice