{"title":"Distributed Tracking of Extended Target With Orientation Using Variational Bayesian","authors":"Qinqin Jiao","doi":"10.1049/rsn2.70059","DOIUrl":null,"url":null,"abstract":"<p>In this work, we propose an alternative distributed tracking approach for extended target with time-varying orientation in a sensor network. Within the random matrix framework, we employ a Gaussian prior for the orientation, the inverse Gamma priors for the diagonal elements of the extent matrix, and a Gamma prior for the measurement rate. Using the Gamma Gaussian Inverse Gamma Gaussian (GGIGG) state model, we derive a centralised tracking approach based on the variational Bayesian technique. Subsequently, we introduce a distributed variational measurement update that leverages convex combination fusion. Closed-form expressions for the unknown variables are derived under a consensus scheme. The resulting algorithm efficiently computes approximate posterior densities for the kinematic state, extent, orientation, and measurement rate in a distributed manner. The effectiveness of the proposed distributed tracking method is validated through numerical experiments, with results showing that the proposed algorithm outperforms existing method based on the multiplicative error model.</p>","PeriodicalId":50377,"journal":{"name":"Iet Radar Sonar and Navigation","volume":"19 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/rsn2.70059","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iet Radar Sonar and Navigation","FirstCategoryId":"94","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/rsn2.70059","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
In this work, we propose an alternative distributed tracking approach for extended target with time-varying orientation in a sensor network. Within the random matrix framework, we employ a Gaussian prior for the orientation, the inverse Gamma priors for the diagonal elements of the extent matrix, and a Gamma prior for the measurement rate. Using the Gamma Gaussian Inverse Gamma Gaussian (GGIGG) state model, we derive a centralised tracking approach based on the variational Bayesian technique. Subsequently, we introduce a distributed variational measurement update that leverages convex combination fusion. Closed-form expressions for the unknown variables are derived under a consensus scheme. The resulting algorithm efficiently computes approximate posterior densities for the kinematic state, extent, orientation, and measurement rate in a distributed manner. The effectiveness of the proposed distributed tracking method is validated through numerical experiments, with results showing that the proposed algorithm outperforms existing method based on the multiplicative error model.
期刊介绍:
IET Radar, Sonar & Navigation covers the theory and practice of systems and signals for radar, sonar, radiolocation, navigation, and surveillance purposes, in aerospace and terrestrial applications.
Examples include advances in waveform design, clutter and detection, electronic warfare, adaptive array and superresolution methods, tracking algorithms, synthetic aperture, and target recognition techniques.