# Temporal Micro-Doppler Spectrogram-based ViT Multiclass Target Classification

arXiv: https://arxiv.org/abs/2511.11951 · HTML: https://mlsanity.com/paper/2511.11951/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Builds on (most cited first, 3 of 3)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition](https://mlsanity.com/paper/2011.08981/) (2020)
- [Generative and Explainable AI for High-Dimensional Channel Estimation](https://mlsanity.com/paper/2504.10775/) (2025)

## Abstract (arXiv)

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.