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Structure of Data Fusion and Nonlinear Statistical Track Data Fusion in Cooperative Engagement Capability
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 Title & Authors
Structure of Data Fusion and Nonlinear Statistical Track Data Fusion in Cooperative Engagement Capability
Jung, Hyoyoung; Byun, Jaeuk; Lee, Saewoom; Kim, Gi-Sung; Kim, Kiseon;
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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;Nonlinear system;Statistical linearization;
 Cited by
Q. Gan and C. J. Harris, "Comparison of two measurement fusion methods for Kalman filter based multisensor data fusion," IEEE Trans. AES, vol 37, no. 1, pp. 273-279, Jan. 2001.

A. G. O. Mutambara, "Information based estimation for both linear and nonlinear systems," in Proc. American Control Conf. 1999, vol. 2, pp. 1329-1333, Jun. 1999.

C. Chong and S. Mori, "Convex combination and covariance intersection algorithms in distributed fusion," Information Fusion, vol. 1, pp. WeA2-11-18, 2001.

L. Duan, X. Huang, K. Feng, B. Luo, and Y. Li, "An adaptive information dissemination of decentralized warship cooperative engagement with constrained bandwidth based on a geodetic coordinate system," ELSEVIER Simulation Modeling Practice and Theory, vol. 18, no. 8, pp. 1130-1144, Sept. 2010. crossref(new window)

S. J. Julier and J. K. Uhlmann, "Unscented filtering and nonlinear estimation," in Proc. IEEE, 92(S), pp. 401-422, Mar. 2004. crossref(new window)

V. Shin, Y. Lee, and T. Choi, "Generalized Millman's formula and its application for estimation problems," Signal Processing, vol. 86, pp. 257-266, Feb. 2006. crossref(new window)

Y. Bar-Shalom and L. Campo, "The effective of the common process noise on the two-sensor fused-track covariance," IEEE Trans. AES, vol. 22, pp. 803-805, Nov. 1986.

A. Gelb, Applied optimal estimation, MIT Press, 1974.

X. R. Li and V. P. Jilkov, "Survey of maneuvering tartget tracking. part I: Dynamic models," IEEE Trans. AES, vol 39, no.4, Oct. 2003.

S. Lee, E. Kim, H. Jung, G. Kim, K. Kim, "Experimental research on radar and ESM measurement fusion technique using probabilistic data association for cooperative target tracking," J-KICS, vol. 37C, no. 6, pp. 355-364, May, 2012.