Aiming at the tracking problem of multi-sensor high-speed and multiple maneuvering targets, a multi-sensor interactive greedy cardinalized probability hypothesis density (MS-IMM-Greedy-CPHD) filter is proposed. In the prediction stage, the interacting multi-mode (IMM) algorithm is used to predict the state, potential distribution and motion model of the target in CPHD filtering; in the update stage of the filter, the greedy measurement partition strategy is used to select the multi-sensor measurement subsets and quasi-partition regions, and the quasi-partition measurement subset is used to predict the target state and potential distribution under different models which is updated interactively. Simulation results show that the proposed MS-IMM-Greedy-CPHD filter can track high maneuvering multi-target stably and effectively. Compared with the multi-sensor cardinalized probability hypothesis density (MS-CPHD) filter, the OSPA error of the proposed method is smaller and the cardinalized estimation is more accurate.