FlowM-mmWImager: Flow Matching Generative Model for Near-Field mmWave Imaging

Abstract

Millimeter-wave (mmWave) radar has found extensive applications owing to its superior penetration capability and high-resolution imaging performance. In conventional near-field mmWave imaging, meeting the spatial sampling requirements for high-resolution imaging typically necessitates the deployment of oversized antenna arrays or densely spaced transceiver sampling, which leads to increased data acquisition time, significantly elevated data processing complexity, and higher system costs. To overcome this limitation, we present an imaging approach based on a flow matching (FlowM) generative model, termed FlowM-mmWImager, which enables high-quality target reconstruction by directly processing the 16-times undersampled raw radar echo data acquired from a small-aperture antenna array. By incorporating physical constraints derived from target electromagnetic scattering information, FlowM-mmWImager employs a neural-network-modeled velocity field to smoothly transform random Gaussian noise into the target image distribution, achieving high-fidelity radar image generation. To validate the effectiveness of our method, we construct two simulation datasets, SimSAR-MNIST and SimSAR-HWDB, for training and evaluation, and further develop an additional test set, SimSAR-EMNIST, to assess generalization capability. For experimental validation, real-world measurement data comprising common metallic tools are collected using our self-developed mmWave radar system to evaluate the method’s performance in practical scenarios. Experimental results demonstrate that FlowM-mmWImager yields imaging results highly consistent with ground truths in both simulated and real conditions, with an imaging time of less than 1 s, exhibiting strong cross-dataset transferability and practical value.