This paper proposes a novel distributed multi-target fusion mechanism based on Generalized Covariance Intersection (GCI) and Average Marginal Density (AMD). There are several inevitable defects in traditional multi-sensor tracking methods. For instance, the track-correlation performance is sensitive to the setting of correlation parameters, and the calculated amount of track correlation increases exponentially with the number of targets, etc. To solve these problems, a robust distributed fusion method suitable for multiple targets is proposed. Firstly, we approximated the local multi-target posterior probability density as a product distribution with its AMD. Secondly, considering the unknown correlation between different radar nodes, the GCI fusion algorithm was employed to perform distributed fusion. Since the track correlation process was embedded in the track fusion process, the distributed fusion mechanism performed track correlation and track fusion at the same time. Finally, we derived the closed-form solution of GCI fusion with AMDs. The proposed fusion algorithm is implemented by using Gaussian mixture model, and the experimental result shows that its performance is superior to that of the traditional fusion algorithm.