A Reduced-Order Multiple-Model Adaptive Identification Algorithm of Missile Guidance Law

To address the problem of identifying missile guidance laws and guidance parameters, a reduced-order multiple-model adaptive identification algorithm is proposed, in which the guidance parameters to be identified are used as state quantities to expand the dimensionality of the state equations, and the guidance parameters are continuously adjusted by filtering and estimation to make them close to their true values. A minimum sampling variance resampling particle filtering algorithm is used for state estimation of nonlinear multiple model sets. Finally, a variety of guidance laws are simulated and verified. The results show that the reduced-order multiple-model adaptive identification algorithm effectively improves the computational accuracy and the adaptability of the algorithm to multiple guidance laws.