Separation of Phase-Corrupted Multicomponent Nonlinear Chirp Signal

Multicomponent nonlinear chirp signals (NCSs) widely exist in microwave remote sensing. In some applications, it is necessary to separate NCSs containing close components in the time–frequency (TF) domain. However, the phase-corrupted data may cause a defocused TF signature and prevent individual component extraction. To solve the problem, the optimization model is developed to reconstruct and decompose multicomponent NCSs with phase errors and solved by an alternating iterative algorithm. In each iteration, the individual components, phase errors, and regularization parameter are updated by the alternating direction method of multipliers (ADMMs), least-square error criterion (LSC), and the matching pursuit (MP) principle, respectively. Finally, the effectiveness of the proposed method is verified by simulation and real data examples.