ZHU Hongyan, GUO Kai, CHEN Shuo. Fusion of Gaussian Mixture Models for Maneuvering Target Tracking in the Presence of Unknown Cross-correlation[J]. Chinese Journal of Electronics, 2016, 25(2): 270-276. doi: 10.1049/cje.2016.03.012
Citation: ZHU Hongyan, GUO Kai, CHEN Shuo. Fusion of Gaussian Mixture Models for Maneuvering Target Tracking in the Presence of Unknown Cross-correlation[J]. Chinese Journal of Electronics, 2016, 25(2): 270-276. doi: 10.1049/cje.2016.03.012

Fusion of Gaussian Mixture Models for Maneuvering Target Tracking in the Presence of Unknown Cross-correlation

doi: 10.1049/cje.2016.03.012
Funds:  This work is supported by the National Natural Science Foundation of China (No.61203220) and the National Basic Research Program of China(973 Program) (No.2013CB329405).
  • Received Date: 2014-03-28
  • Rev Recd Date: 2014-09-23
  • Publish Date: 2016-03-10
  • The paper addresses the problem of estimation fusion for maneuvering target tracking in the presence of unknown cross-correlation. To improve the fusion accuracy, two major points are concerned. Firstly, the Interacting multiple model (IMM) estimator is performed for each sensor, and the local estimate is represented by a Gaussian mixture model instead of a Gaussian density to keep more details of the local tracker. Next, a close-formed solution of fusing two Gaussian mixtures in the Covariance intersection (CI) framework is derived to cope with the unknown cross-correlation of local estimation errors. Experimental results demonstrate that the proposed approach provides some improvements in the fusion accuracy over the competitive methods.
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