Targets’ micromotion feature is one of the effective features used for recognition at the middle section of the ballistic curve. Aimed at the problem that a single radar is rather limited in extracting micromotion information, this paper proposes a novel algorithm based on the narrowband radar network to extract precession features. First, a coneshaped target model and a narrowband signal model are established. Then, each scattering point in different perspective is matched by frequency analysis based on transforming nonideal scattering point into ideal scattering point. Finally, by using the microDoppler of conic node to compensate the bottom microDoppler of cone, compensation coefficient is solved when the radar aspect variance is minimal. Furthermore, parameters are obtained by combining the microDoppler of two radars. The simulation results show that the algorithm can extract micromotion parameters and structured parameters accuracy.