Abstract: A federated fusion algorithm of joint multi-Gaussian mixture multi-Bernoulli (JMGM-MB) filters is proposed to achieve optimal fusion tracking of multiple uncertain maneuvering targets in a hierarchical structure. The JMGM-MB filter achieves a higher level of accuracy than the multi-model Gaussian mixture MB (MM-GM-MB) filter by propagating the state density of each potential target in the interactive multi-model (IMM) filtering manner. Within the hierarchical structure, each sensor node performs a local JMGM-MB filter to capture survival, newborn, and vanishing targets. A notable characteristic of our algo-rithm is a master filter running on the fusion node, which can help identify the origins of state estimates and supplement missed detections. All filters′ outputs are associated as multiple groups of single-target estimates. We rigorously derive the optimal fusion of IMM filters and apply it to merge associated single-target estimates. This optimality is guaranteed by the covariance upper-bounding technique, which can truly eliminate correlations among filters. Simulations demonstrate that the proposed algorithm outperforms the existing centralized and distributed fusion algorithms in linear and heterogeneous sce-narios, and the relative weights of the master and local filters can be adjusted flexibly.