A new adaptive embedded Cubature Kalman Filter (CKF) algorithm based on the Sage-Husa algorithm is proposed for nonlinear target tracking when the distribution of noise is unknown. Firstly, the embedded cubature guidelines are used to improve the conventional Sage-Husa algorithm, thus a noise statistical estimator adaptive to the embedded CKF is obtained for estimating the statistical characteristics of the unknown noise and making correction. Then, a judgment mechanism is introduced into the algorithm to suppress the divergence of the filter. Finally, the result of the maneuvering target tracking simulation verifies the effectiveness of the proposed algorithm.