Abstract
Fine target status can be represented by the extracted micro-Doppler
(m-D) components from the radar echo. However, current methods do not
consider the specialty of the m-D components and their performance to
irregular components are poor. In this paper, neural network is applied
to m-D signal extraction for the first time. Specifically, a novel and
effective dual-branch network based m-D components extraction method is
proposed. The dual-branch network consisting of a continuous m-D
components extraction branch and a crossing point detection branch is
designed to obtain components and cross points at the same time. To
solve the error correlation problem of multi-component signals, the
first-order parametric continuous condition and cubic spline
interpolation are employed to obtain complete and smooth components
curves. Simulation and measurement results show that this method of good
robustness is a good candidate to separate the non-sinusoidal m-D
components with intersections.