Aiming at the maneuvering target tracking problem

a novel square-root cubature Kalman filter (SCKF) is proposed by the integration of the adaptive constant acceleration (CA) model and the waveform scheduling.On the basis of the CA model

the approximation relationship between the Jerk and the velocity as well as the acceleration is established in order to make the connection of the state process noise with the state error covariance matrix.As such

the adaptive adjustment of the proposed model is realized.Additionally

the fractional Fourier transform (FrFT) is utilized to rotate the ambiguity function of the transmitted waveform to maintain the orthogonality between the measurement error ellipse and the state prediction error ellipse.Thereby

the optimal transmitting waveform can be obtained and the tracking performance is systematically improved in both of the data processing and the signal processing.The simulation results show that the proposed algorithm possess a simpler structure and higher accuracy than the unscented Kalman filter based on the modified current statistical (CS) model

the SCKF based on the CS model

the SCKF based on the CA model and the interactive multiple model SCKF (IMM-SCKF).