Non-Sinusoidal micro-Doppler Estimation Based on Dual-Branch Network

Abstract

The fine state of targets 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 with non-sinusoidal components is poor. In this paper, a neural network is applied to signal extraction for the first time. Inspired by the semantic line detection in computer vision, the extraction of the m-D components is transformed into the network-based time–frequency curves detection problem. Specifically, a novel dual-branch network-based m-D components extraction method is proposed. According to the property of intersected multiple m-D components, 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. In addition, a shuffle attention-fast Fourier convolution (SA-FFC) module is proposed to fuse local and global contexts and focus on key features. 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 for separating the non-sinusoidal m-D components with intersections.