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On the impact of the antenna radiation patterns in passive radio sensing

Federica Fieramosca, Vittorio Rampa, Stefano Savazzi, Michele D'Amico

TL;DR

The paper addresses how antenna radiation patterns impact passive radio sensing by extending a scalar-diffraction body model to non-isotropic antennas. It introduces a 2-D absorbing-sheet body model with directional pattern weighting, provides $E/E_{0}$ and $V/V_{0}$ formulations, and validates predictions against indoor measurements in mixed-antenna scenarios. Results show that angular filtering from directional antennas mitigates multipath and yields attenuation patterns that align with the diffraction model, enhancing target detection inside versus outside the Fresnel ellipsoid. A likelihood-based detection framework demonstrates improved discriminability with directional antennas, while omnidirectional configurations struggle due to multipath, suggesting practical use in WLAN systems with reconfigurable radiation patterns.

Abstract

Electromagnetic (EM) body models based on the scalar diffraction theory allow to predict the impact of subject motions on the radio propagation channel without requiring a time-consuming full-wave approach. On the other hand, they are less effective in complex environments characterized by significant multipath effects. Recently, emerging radio sensing applications have proposed the adoption of smart antennas with non-isotropic radiation characteristics to improve coverage.This letter investigates the impact of antenna radiation patterns in passive radio sensing applications. Adaptations of diffraction-based EM models are proposed to account for antenna non-uniform angular filtering. Next, we quantify experimentally the impact of diffraction and multipath disturbance components on radio sensing accuracy in environments with smart antennas.

On the impact of the antenna radiation patterns in passive radio sensing

TL;DR

The paper addresses how antenna radiation patterns impact passive radio sensing by extending a scalar-diffraction body model to non-isotropic antennas. It introduces a 2-D absorbing-sheet body model with directional pattern weighting, provides and formulations, and validates predictions against indoor measurements in mixed-antenna scenarios. Results show that angular filtering from directional antennas mitigates multipath and yields attenuation patterns that align with the diffraction model, enhancing target detection inside versus outside the Fresnel ellipsoid. A likelihood-based detection framework demonstrates improved discriminability with directional antennas, while omnidirectional configurations struggle due to multipath, suggesting practical use in WLAN systems with reconfigurable radiation patterns.

Abstract

Electromagnetic (EM) body models based on the scalar diffraction theory allow to predict the impact of subject motions on the radio propagation channel without requiring a time-consuming full-wave approach. On the other hand, they are less effective in complex environments characterized by significant multipath effects. Recently, emerging radio sensing applications have proposed the adoption of smart antennas with non-isotropic radiation characteristics to improve coverage.This letter investigates the impact of antenna radiation patterns in passive radio sensing applications. Adaptations of diffraction-based EM models are proposed to account for antenna non-uniform angular filtering. Next, we quantify experimentally the impact of diffraction and multipath disturbance components on radio sensing accuracy in environments with smart antennas.
Paper Structure (6 sections, 6 equations, 5 figures, 2 tables)

This paper contains 6 sections, 6 equations, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Paper contributions
  3. EM body models
  4. Body-induced effects with mixed antennas
  5. Body detection and model validation
  6. Conclusions

Figures (5)

  • Figure 1: EM model geometry: $2$-D obstacle and antennas.
  • Figure 2: $75$ marked positions (crosses) on a $15\times5$ grid with spacing $0.25$ m along and $0.30$ m across the link. Target is located at position $6$ (drawing not to scale). Corresponding measurement scenario is on the left.
  • Figure 3: Top: maps of the measured attenuation (in dB) for each of the $75$ points of the a) omni-omni, b) omni-dir, and c) dir-dir scenarios. Bottom: measured $A_{\mathrm{S}}^{(m)}$ (dashed) vs. predicted $A_{\mathrm{S}}^{(p)}$ (solid) average attenuations for a target traversing orthogonal to the LOS at $0.25$ m (orange) and $1$ m (violet) away from the TX. Model (\ref{['eq:dE_full_compact-1-1-1']}) with square markers and (\ref{['eq:V_V0_approx-single']}) with cross markers.
  • Figure 4: ROC plots considering the probabilities related to the EM model, and to the measurements from the omni-omni, omni-dir and dir-dir cases. The trivial detector is shown, too.
  • Figure 5: From top to bottom: estimated $\textrm{Pr}(A_{S}|F_{0})$ and $\textrm{Pr}(A_{S}|F_{1})$ from the experimental data and the synthetic (EM) model for the omni-omni (top) and the dir-dir scenarios (bottom). Histograms from experimental data are shown, too.