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Title Structure of Data Fusion and Nonlinear Statistical Track Data Fusion in Cooperative Engagement Capability
Journal Name J-KICS
First Author Hyoyoung Jung
Coauthor Jaeuk Byun, Saewoom Lee, Gi-Sung Kim, Kiseon Kim
Publication Date 2014.01.01 Link Link icon
Acceptance Rate (%) Date 2014-07-09 11:11
As the importance of Cooperative Engagement Capability and network-centric warfare has been dramatically increasing, it is necessary to develop distributed tracking systems. Under the development of distributed tracking systems, it requires tracking filters and data fusion theory for nonlinear systems. Therefore, in this paper, the problem of nonlinear track fusion, which is suitable for distributed networks, is formulated, four algorithms to solve the problem of nonlinear track fusion are introduced, and performance of introduced algorithms are analyzed. It is a main problem of nonlinear track fusion that cross-covarinaces among multiple platforms are unknown. Thus, in order to solve the problem, two techniques are introduced; a simplification technique and a approximation technique. The simplification technique that help to ignore cross-covariances includes two algorithms, i.e. the sample mean algorithm and the Millman formula algorithm, and the approximation technique  to obtain approximated cross-covariances utilizes two approaches, by using analytical linearization and statistical linearization based on the sigma point approach. In simulations, BCS fusion is the most efficient scheme because it reduces RMSE by approximating cross-covariances with low complexity.

Decentralized tracking system, Cooperative engagement capability, Data fusion, Nonlinearsystem, Statistical linearization

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