The cubature Kalman filter (CKF) algorithm is not suitable for non-Gaussian environments. The cubature particle filter (CPF) algorithm can solve the problem of the CKF algorithm, but it will introduce the problem of a large computational complexity. To solve the above problems, a Double-Layer Cubature Kalman Filter (DLCKF) algorithm is proposed. The DLCKF algorithm uses the state estimation of the inner CKF to replace the state transition density function of the outer CKF and updates the weights of each deterministic sampling point of the outer CKF with the latest measurements. Finally, the state estimation at each time is obtained. Simulation results show that, compared with the CKF and the CPF, the proposed algorithm not only has a low computational complexity, but also has very good estimation accuracy.