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arXiv · 2603.14829

A Spatio-Temporal-Frequency Transformer Framework for Near-Field Target Recognition

Zongyao Zhao·Zhaolin Wang·Lincong Han·Jing Jin·Kaibin Huang
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Abstract

A target recognition framework relying on near-field integrated sensing and communication (ISAC) systems is proposed. By exploiting the distance-dependent spatial signatures provided by the near-field spherical wavefront, high-accuracy sensing is realized in a bandwidth-efficient manner. A spatio--temporal--frequency (STF) transformer framework is introduced for target recognition using electromagnetic features found in the wireless channel response. In particular, a lightweight spatial encoder is employed to extract features from the antenna array for each frame and subcarrier. These features are then fused by a time-frequency transformer head with positional embeddings to model temporal dynamics and cross-subcarrier correlations. Simulation results demonstrate that strong target recognition performance can be achieved even with limited bandwidth resources.

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Zongyao Zhao, Zhaolin Wang, Lincong Han, Jing Jin, Kaibin Huang. 2026-03-16. A Spatio-Temporal-Frequency Transformer Framework for Near-Field Target Recognition. https://arxiv.org/abs/2603.14829

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