Research on LOS angular rate estimation of strap-down seeker based on modified adaptive cubature Kalman filter

Aiming at the adverse effects of rapid changes in measurement noise variance on the filtering performance of strap-down seeker, a modified adaptive square-root cubature Kalman filter (MASCKF) is proposed. This paper establishes a LOS angular rate decoupling model and incorporates a chi-square test-based detection step into the filtering algorithm. When the noise variance changes rapidly, the state estimation value and covariance matrix value are corrected to improve the problem of delayed updating of the noise variance estimate, which leads to a decrease in filtering effectiveness. The simulation outcomes indicate that the MASCKF algorithm is capable of effectively reducing the influence of rapid noise changes on the estimation of noise variance. it exhibits better filtering estimation performance when dealing with the problem of rapid changes in measurement noise variance of seekers, and has practical engineering research significance.