A Time-Frequency Representation Method Based on ETF-MDNet for Radar Target Micro-Motion Features

Abstract

This paper proposes a deep learning-based time-frequency representation approach that employs the enhanced time-frequency micro-Doppler network (ETF-MDNet) model to improve the characterization of micro-Doppler features for radar targets, particularly “low, slow, and small” ones. The ETF-MDNet model consists of four key components: the micro-Doppler target signal input module, the basis function selection module, the feature aggregation module, and the energy concentration module. A notable characteristic of this method is its utilization of the inherent adaptive learning capabilities of deep learning, which are combined with an attention mechanism to enhance the aggregation of time-frequency energy. This integration optimizes the method's capacity to represent micro-motion features across both channel and spatial dimensions. Consequently, this approach effectively captures the micro-motion information of the target while suppressing extraneous noise. In comparison to traditional short time Fourier transform, generalized warblet and reassigned spectrogram analysis methods, the proposed method achieves an average enhancement of 31.5% in time-frequency energy concentration, higher time-frequency energy aggregation, and the ability to reveal micro-motion feature details not captured by traditional methods.