Temporal Micro-Doppler Spectrogram-based ViT Multiclass Target Classification
This work addresses target classification for radar systems, particularly under challenging conditions like overlaps and occlusions, representing an incremental advancement with a novel method for a known bottleneck.
The paper tackled multiclass target classification using millimeter-wave radar micro-Doppler spectrograms by proposing a Temporal MDS-Vision Transformer (T-MDS-ViT), which achieved superior classification accuracy compared to existing CNN-based methods while improving data efficiency and real-time deployability.
In this paper, we propose a new Temporal MDS-Vision Transformer (T-MDS-ViT) for multiclass target classification using millimeter-wave FMCW radar micro-Doppler spectrograms. Specifically, we design a transformer-based architecture that processes stacked range-velocity-angle (RVA) spatiotemporal tensors via patch embeddings and cross-axis attention mechanisms to explicitly model the sequential nature of MDS data across multiple frames. The T-MDS-ViT exploits mobility-aware constraints in its attention layer correspondences to maintain separability under target overlaps and partial occlusions. Next, we apply an explainable mechanism to examine how the attention layers focus on characteristic high-energy regions of the MDS representations and their effect on class-specific kinematic features. We also demonstrate that our proposed framework is superior to existing CNN-based methods in terms of classification accuracy while achieving better data efficiency and real-time deployability.