Based on generalized covariance intersection(GCI)fusion theory,a computationally efficient distributed multi-sensor multi-target tracking algorithm is proposed in this paper,in which probability hypothesis density(PHD)filters are run at each sen-sor node for filtering.When GCI is used to fuse multiple PHDs,the fusion density is consisted of a large number of fusion hypothe-ses which increase exponentially with the number of Gaussian components.Therefore,in practice GCI fusion is often difficult to calculate.In order to improve the computational efficiency of multi-sensor fusion,the Gaussian components are clustered and then isolated using distance metric in this paper.The distance metric can calculate the density weights of the targets after fusion,and by discarding the fusion assumptions that the weights can be ignored,a simplified approximate density function can be constructed.A-nalysis shows that the proposed fusion algorithm can achieve a multiple improvement in computational efficiency compared to tradi-tional GCI fusion algorithms.The effectiveness of the proposed fusion algorithm is verified through experiments in a simulation sce-nario where 12 targets appeared successively.
关键词
多目标跟踪/广义协方差交集/高斯混合概率假设密度滤波器/传感器融合/计算效率
Key words
multi-target tracking/generalized covariance intersection(GCI)/Gaussian mixture probability hypothesis density(GM-PHD)filter/sensor fusion/computational efficiency
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