Boost-Phase Trajectory Inference From Space-Borne LOS Measurements With a Sieve Method

Abstract

Boost-phase trajectory inference is one of the major objectives of the space-borne missile early-warning system. Traditional methods can be classified as either profile-based or profile-free methods. The profile-based methods are accurate but inadaptable to the types of missiles, whereas the profile-free methods are adaptable but inaccurate. To integrate the strengths of the profile-based and profile-free methods, a multimodel trajectory inference approach is proposed. First, a general net acceleration model (GNAM) containing only type-free prior information is constructed by the method of sieves. Then, a new kind of net acceleration profile is proposed by incorporating type-dependent prior information into the GNAM. After that, the multimodel approach is proposed following the Bayesian framework. Simulations indicate that the approach is accurate in estimation and capable for type identification.