MIMO Through-the-Wall Radar Micro-Doppler Signature Representation Under Limited Data Using Heterogeneous Transfer Learning

Identifying indoor individuals using micro-Doppler signature of multiple-input multiple-output (MIMO) through-the-wall radar (TWR), and determining whether they pose a threat holds significant research value in the field of urban security surveillance. However, large-scale TWR human motion data is difficult to collect, which reduces the recognition performance. To address this issue, a MIMO TWR micro-Doppler signature representation method under limited data based on heterogeneous transfer learning is proposed in this letter. The multi-channel TWR human motion Doppler-time maps (DTMs) are first generated, and the trace-ratio group sparse method is then proposed for multi-channel DTM feature augmentation. In addition, a micro-Doppler signature representation method based on optimal transport domain adaptation heterogeneous transfer learning is proposed. By leveraging large-scale millimeter-wave radar human gait data, the proposed method guides the micro-Doppler signature representation to maximize inter-class separation on the TWR DTM set. The effectiveness of the proposed method is validated through a few-shot measured dataset collected for TWR human threat identification.