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

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.