Material Identification Method Using Millimeter-Wave Radar Based on Attention Mechanism

Material identification based on intelligent sensing is a fundamental capability that enables next-generation IoT applications such as industrial automation, smart-home perception, and safety detection. Existing technologies often suffer from limited deployment flexibility, poor robustness when dealing with small objects, or insufficient exploitation of material physical properties. To address these issues, this paper proposes a non-contact material identification method based on millimeter-wave radar and micro-vibration sensing. By acoustically exciting micro-vibrations on the target and extracting micro-vibration features from the radar echoes, the method effectively distinguishes different material types. First, we design a same-side excitation detection architecture, in which the acoustic source and radar are placed on the same side of the target. This configuration enhances practical deployment flexibility and mitigates the issue that, under opposite-side setups, the weak echoes from small targets may be obscured by strong reflections from the speaker. Second, we extract three types of micro-vibration features from the radar echoes—frequency, power, and damping—each capturing distinct physical aspects of material properties. Furthermore, to effectively fuse these heterogeneous features, we develop a feature-aware neural network that employs branched modeling and attention mechanisms based on the structural characteristics of different features. The experiments are conducted on eight common material categories, each with multiple object instances and measured under various target-radar distances and observation angles. The results demonstrate that the proposed method achieves an average identification accuracy of 99.0%, providing a practical solution for non-contact material identification in IoT scenarios.