Abstract
The high-speed translational motion of space targets will cause the micro-Doppler to shift, tilt, and fold, which brings great difficulty to the extraction of micromotion features. Translational motion must be compensated in advance to extract the authentic characteristics of micro-Doppler curves. To solve the problem of translational motion compensation, an estimation method of translational parameters based on deep learning theory is proposed. A polynomial model describing translational motion is constructed firstly by analyzing its dynamic principle. Meanwhile, the parametric characterization of micromotion signal is deduced by taking the cone target as an example. On this basis, two regression networks that can estimate acceleration and velocity respectively from the time-frequency graph are built using transfer learning. The translational motion compensation is accomplished with high accuracy and low computation complexity. The proposed method can also achieve satisfactory results in the presence of high intensity noise and discontinuous scattering points. Finally, the effectiveness and robustness of the proposed method are validated by simulations.
Highlights
In the course of translational motion, a space target needs to spin around its symmetry axis in order to maintain the attitude stability
We study the problem of translational motion compensation for space targets
ESTIMATION OF ACCELERATION We can obtain the precompensated radar echo signal from (13), the signal is converted into 2-D timefrequency graph by short-time Fourier transform (STFT)
Summary
In the course of translational motion, a space target needs to spin around its symmetry axis in order to maintain the attitude stability. Guo estimated the Doppler center to compensate translational motion after keystone transform [5] This kind of methods does not consider the instantaneous state of frequency, but focuses on the overall aggregation extent. Xu obtained the translational parameters of group targets based on Radon transform [8] This kind of parameterization methods can intuitively observe the frequency modulation features of the signal and the processing effects of each step. Gu estimated the translational acceleration by the delayed-conjugated multiplication processing and achieved the residual velocity by searching the maximum spectral peak [12] This kind of methods eliminates the polynomial phase step by step according to the signal characteristics, and only the m-D modulation term is retained.
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