An Adaptive Fading CKF-Based Strapdown Rotating Projectile Line-of-Sight Rate Filtering Algorithm

In response to the strong coupling between line-of-sight angular rate and attitude error in the output of the strapdown rotating projectile, a method based on adaptive fading volume covariance Kalman filtering (AFCKF) is proposed. To achieve the decoupling of line-of-sight angular rate, the relative motion characteristics of missile and target are considered. A state model is constructed, which includes an estimation model of line-of-sight angular rate in the terminal guidance phase, and a measurement model including line-of-sight angle and attitude angular is established based on geometric relationships. To solve the problem of filter failure caused by the divergence of the line-of-sight angular rate estimation results in traditional CKF for rotating projectile, a fading factor based on residual sequence is introduced to adjust the predicted state covariance for quick convergence of the estimation results. To verify the effectiveness of AFCKF, typical interferences of attitude angle and measurement angle are considered. Simulation results show that the mean estimation errors of line-of-sight elevation rate and azimuth rate of the proposed method are 30.41% and 42.18% of the traditional EKF, respectively, effectively improving the accuracy of line-of-sight angular rate estimation for rotating projectil