An effective method for identifying inertia characteristics
of cone-shaped space target based on deep learning is proposed. The inertia
ratio is determined by the time-varying scattering fields from the cone-shaped
targets. The multistatic method is introduced to reduce
the evaluation time of time-varying scattering
fields. The micro-Doppler spectrogram (MDS) dataset is
constructed by time-frequency analysis with numerical simulation method, point
scattering model, and experimental tests. The compressed dataset is further
achieved by truncated singular value decomposition (SVD). Finally, the
micro-motion parameter identification model is constructed to identify the
inertia ratio for the cone-shaped space target. The interaction loss function and the
feed-forward denoising convolutional neural networks (DnCNNs) are employed to improve the identification accuracy. Parameters identification of the precession
frequency, precession angle, spin frequency, and inertia ratio with both
simulation and experiment datasets demonstrate the validity of the proposed
method.