Due to the nonlinear problem existing in the system modeling of mane uvering target tracking,a novel heterogeneous sensor fusion algorithm based on cubature Kalman filter(CKF) is pr oposed.Considering the accuracy and real time in target tracking realization,two realization structures,centralized mea surements fusion and distributed state fusion,are respectively constructed under the framework of CKF.In the architecture of cent ralized measurements fusion,the optimal weighted method is used to deal with the azimuth observations from radar and inf rared sensors,and then the combination of the fusion result and the radial distance information fused from radar is taken as a group of new measurements.Finally,the CKF algorithm is used for maneuvering target tracking based on new measurements. In the framework of distributed state fusion,in order to achieve the dimension augmenting of the infrared sensor meas urements,the radial distance information from radar is fused,and the fusion result is defined as the virtual distance measur ement of all infrared sensors,and then the CKFs based on different groups of measurements are applied to obtain the final s tate estimation of the maneuvering target by means of the distributed weighted fusion.The performance of the two fusion architectures is compared in the simulation scene,and the theoretical analysis and experimental results show the feasibilit y and efficiency of the proposed algorithm.