Radar Micro-Doppler Parameter Estimation Method Based on Gated Dual-Path Dynamic-Wavelet Convolutional Network

Abstract

Micro-Doppler parameter estimation is crucial for moving targets. However, conventional methods face limitations like inadequate time-frequency (TF) resolution and poor generalization, while existing deep learning approaches often treat TF analysis as a fixed preprocessing step. To overcome these challenges, this paper introduces a radar micro-Doppler parameter estimation method based on a gated dual-path dynamic-wavelet convolutional network (GDWCN). The GDWCN is an end-to-end deep learning framework that maps raw radar signals to micro-motion parameters by integrating clutter suppression, gated dual-path module, feature extraction, and parameter regression. Its core innovation is a gated dual-path module that combines dynamic convolution and learnable wavelet convolution, selecting the optimal processing path based on input signal characteristics. For the Inspire 2 drone, GDWCN reduced the mean absolute error (MAE) of frequency estimation by approximately 38% compared to the enhanced time-frequency micro-Doppler network, and its relative error by approximately 69% compared to the short-time Fourier transform (STFT), and 58% over the local maximum synchroextracting transform. Ablation studies further confirm the efficacy of the clutter suppression module and the attention mechanism.

Keywords

parameter estimation / radar signal processing / deep learning / time-frequency analysis