基于算术平均融合的分布式多伯努利扩展目标跟踪

In distributed sensor networks, the inconsistent estimation results of state parameters such as azimuth and axis lengths of the same extended target under different sensors lead to the difficulty of extended target estimation association, which gives rise to challenges to the subsequent density information fusion. Compared with the point target posterior density information, the extended target posterior density contains both centroid state and shape information. Moreover, the Ellipse Distance (ED) is proposed based on the Euclidean distance of centroid and non-Euclidean size-shape metric of shape matrix. The ellipse distance considers both the centroid state and shape information of the extended target, and better realizes the posterior density correlation of the same extended target under different sensors. In addition, in this paper, the approximate Gamma Gaussian Inverse Wishart (GGIW) distribution of fusion space density is derived under the Arithmetic Average (AA) fusion rule, and the AA fusion of posterior information of the same extended target under different sensors is realized. Simulation results show that the proposed algorithm can effectively track multiple extended targets in distributed sensor networks.