{"title":"Distributed radar tracking using the double debiased distributed Kalman filter","authors":"A. Charlish, F. Govaers, W. Koch","doi":"10.1109/RADAR.2014.6875764","DOIUrl":null,"url":null,"abstract":"The distributed Kalman filter requires the measurement covariances of remote radar nodes to be known at all radar nodes. This is not possible for a radar network, as the true measurement covariances depend on the radar-target geometry and the fluctuating signal-to-noise ratio. This paper tackles this problem using the double debiased distributed Kalman filter (D3KF) which utilizes a radar model to form a hypothesis on the global covariance. The scheme also transmits debiasing matrices, that account for the mismatch between the assumed and encountered measurement covariance. The scheme is evaluated in a radar network scenario, where it is demonstrated to achieve close to the optimal performance of a centralized Kalman filter (CKF). In contrast to a CKF, the D3KF does not transmit the complete measurement data and is not dependent on the transmission rate of the communication channels to the fusion center.","PeriodicalId":127690,"journal":{"name":"2014 IEEE Radar Conference","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Radar Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RADAR.2014.6875764","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The distributed Kalman filter requires the measurement covariances of remote radar nodes to be known at all radar nodes. This is not possible for a radar network, as the true measurement covariances depend on the radar-target geometry and the fluctuating signal-to-noise ratio. This paper tackles this problem using the double debiased distributed Kalman filter (D3KF) which utilizes a radar model to form a hypothesis on the global covariance. The scheme also transmits debiasing matrices, that account for the mismatch between the assumed and encountered measurement covariance. The scheme is evaluated in a radar network scenario, where it is demonstrated to achieve close to the optimal performance of a centralized Kalman filter (CKF). In contrast to a CKF, the D3KF does not transmit the complete measurement data and is not dependent on the transmission rate of the communication channels to the fusion center.