NDLI: Several methods of radar data fusion

Content Provider	IEEE Xplore Digital Library
Author	Yu Xu Yihui Jin Yan Zhou
Copyright Year	2002
Description	Author affiliation: Autom. Dept., Tsinghua Univ., Beijing, China (Yu Xu; Yihui Jin; Yan Zhou)
Abstract	In the processing of radar data, target estimation states fusion is an important problem. Especially under the decentralized model, various estimations of target state which come from local filters with Kalman filter may be fused in order to obtain a more accurate target estimation state. There are two problems to be solved. The first is the fusion criterion, different fusion criteria bring out different results. Three fusion criteria are discussed; one is based on the covariance matrix of estimation error, the second is dependent on the infinite normal number, and the last is related to the element of the state vector. The second problem is the tracking algorithm; it affects the accuracy of the state fusion result. The three criteria and their corresponding algorithms are presented. The algorithms estimating target state are set up with a strong tracking filter other than Kalman or extended Kalman filter; they have better tracking performance so that they can overcome the deficiencies related to Kalman or extended Kalman filters. The reason for this is explained. Comparison results are given.
Sponsorship	Chinese Inst. Electron. (CIE) IEEE EMC Soc. IEEE Beijing Sect. URSI Commission E CIE Committee for URSI IEE Beijing Center
Starting Page	664
Ending Page	667
File Size	207381
Page Count	4
File Format	PDF
ISBN	0780372778
DOI	10.1109/ELMAGC.2002.1177518
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking State estimation Sensor fusion Covariance matrix Kalman filters Radar tracking Sensor systems Nonlinear filters Automation Estimation error
Content Type	Text
Resource Type	Article

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