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

ZHU Hongyan; GUO Kai; CHEN Shuo

doi:10.1049/cje.2016.03.012

Volume 25 Issue 2

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ZHU Hongyan, GUO Kai, CHEN Shuo, “Fusion of Gaussian Mixture Models for Maneuvering Target Tracking in the Presence of Unknown Cross-correlation,” Chinese Journal of Electronics, vol. 25, no. 2, pp. 270-276, 2016, doi: 10.1049/cje.2016.03.012

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ZHU Hongyan, GUO Kai, CHEN Shuo, “Fusion of Gaussian Mixture Models for Maneuvering Target Tracking in the Presence of Unknown Cross-correlation,” Chinese Journal of Electronics, vol. 25, no. 2, pp. 270-276, 2016, doi: 10.1049/cje.2016.03.012

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

doi: 10.1049/cje.2016.03.012

1.
School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China;
2.
China Electronics Technology Group Corporation No. 28 Research Institute, Nanjing 210007, China

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

Abstract

Abstract

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.
- Gaussian mixture model (GMM),
- Estimation fusion,
- Interacting multiple model (IMM),
- Covariance intersection (CI),
- Cross-correlation

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