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.
@article{arxiv.2603.14829,
title = {A Spatio-Temporal-Frequency Transformer Framework for Near-Field Target Recognition},
author = {Zongyao Zhao and Zhaolin Wang and Lincong Han and Jing Jin and Kaibin Huang},
journal= {arXiv preprint arXiv:2603.14829},
year = {2026}
}