An EM Body Model for Device-Free Localization with Multiple Antenna Receivers: A First Study
<|reference_start|>An EM Body Model for Device-Free Localization with Multiple Antenna Receivers: A First Study: Device-Free Localization (DFL) employs passive radio techniques capable to detect and locate people without imposing them to wear any electronic device. By exploiting the Integrated Sensing and Communication paradigm, DFL networks employ Radio Frequency (RF) nodes to measure the excess attenuation introduced by the subjects (i.e., human bodies) moving inside the monitored area, and to estimate their positions and movements. Physical, statistical, and ElectroMagnetic (EM) models have been proposed in the literature to estimate the body positions according to the RF signals collected by the nodes. These body models usually employ a single-antenna processing for localization purposes. However, the availability of low-cost multi-antenna devices such as those used for WLAN (Wireless Local Area Network) applications and the timely development of array-based body models, allow us to employ array-based processing techniques in DFL networks. By exploiting a suitable array-capable EM body model, this paper proposes an array-based framework to improve people sensing and localization. In particular, some simulations are proposed and discussed to compare the model results in both single- and multi-antenna scenarios. The proposed framework paves the way for a wider use of multi-antenna devices (e.g., those employed in current IEEE 802.11ac/ax/be and forthcoming IEEE 802.11be networks) and novel beamforming algorithms for DFL scenarios.<|reference_end|>
@article{rampa2024an,
    title={An EM Body Model for Device-Free Localization with Multiple Antenna
  Receivers: A First Study},
    author={Vittorio Rampa, Federica Fieramosca, Stefano Savazzi, Michele D'Amico},
    journal={2023 IEEE-APS Topical Conference on Antennas and Propagation in
  Wireless Communications (APWC)},
    year={2024},
    doi={10.1109/APWC57320.2023.10297446},
    archivePrefix={arXiv},
    eprint={2405.09558},
    primaryClass={eess.SP cs.CV cs.LG}
}