An adaptive squareroot imbedded cubature Kalman filter(ASICKF) method is proposed based on the squareroot imbedded cubature Kalman filter(SICKF) and the strong tracking filter(STF) algorithm to solve the problem that the estimation accuracy of the cubature Kalman filter decreases when the states or parameter of the system is suddenly changed.The STF condition is introduced while SICKF obtains high estimation accuracy.The adaptive fading factor is obtained according to the residual of system output which is introduced into the output covariance root mean square matrix and mutual covariance matrix so that the filter gain can be corrected in real time,and the residual sequence of system output is forced to be orthogonal,so that the ASICKF algorithm has strong tracking ability.In order to verify the performance of the proposed ASICKF algorithm,it is applied to the target tracking under the suddenly changed condition.The simulation results indicate that ASICKF can maintain high estimation accuracy when the state of the system changes suddenly,and the robustness and adaptability of the algorithm are good.