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OFDM-Based ISAC Imaging of Extended Targets via Inverse Virtual Aperture Processing

Michael Negosanti, Lorenzo Pucci, Andrea Giorgetti

TL;DR

This work tackles imaging of moving extended targets within an ISAC framework using inverse virtual aperture (IVA) processing in a monostatic MIMO-OFDM system. The authors develop a sensing chain that includes range alignment via cross-correlation and a minimum-variance phase-adjustment step to form a robust range–Doppler (IVA) image, while evaluating a 5G NR upper mid-band scenario with a 3GPP Rel19 vehicle model. Key findings show that increasing the sensing subcarrier fraction $\rho_f$ improves image contrast and reduces the target-centroid RMSE, achieving sub-0.1 m RMSE for $\rho_f \gtrsim 0.8$, and enabling reliable centroid localization with only a small fraction of frame time for sensing ($\rho_t \approx 0.036$). The results provide design guidance for balancing sensing accuracy and communication efficiency in next-generation networks.

Abstract

This work investigates the performance of an integrated sensing and communication (ISAC) system exploiting inverse virtual aperture (IVA) for imaging moving extended targets in vehicular scenarios. A base station (BS) operates as a monostatic sensor using MIMO-OFDM waveforms. Echoes reflected by the target are processed through motion-compensation techniques to form an IVA range-Doppler (cross-range) image. A case study considers a 5G NR waveform in the upper mid-band, with the target model defined in 3GPP Release 19, representing a vehicle as a set of spatially distributed scatterers. Performance is evaluated in terms of image contrast (IC) and the root mean squared error (RMSE) of the estimated target-centroid range. Finally, the trade-off between sensing accuracy and communication efficiency is examined by varying the subcarrier allocation for IVA imaging. The results provide insights for designing effective sensing strategies in next-generation radio networks.

OFDM-Based ISAC Imaging of Extended Targets via Inverse Virtual Aperture Processing

TL;DR

This work tackles imaging of moving extended targets within an ISAC framework using inverse virtual aperture (IVA) processing in a monostatic MIMO-OFDM system. The authors develop a sensing chain that includes range alignment via cross-correlation and a minimum-variance phase-adjustment step to form a robust range–Doppler (IVA) image, while evaluating a 5G NR upper mid-band scenario with a 3GPP Rel19 vehicle model. Key findings show that increasing the sensing subcarrier fraction improves image contrast and reduces the target-centroid RMSE, achieving sub-0.1 m RMSE for , and enabling reliable centroid localization with only a small fraction of frame time for sensing (). The results provide design guidance for balancing sensing accuracy and communication efficiency in next-generation networks.

Abstract

This work investigates the performance of an integrated sensing and communication (ISAC) system exploiting inverse virtual aperture (IVA) for imaging moving extended targets in vehicular scenarios. A base station (BS) operates as a monostatic sensor using MIMO-OFDM waveforms. Echoes reflected by the target are processed through motion-compensation techniques to form an IVA range-Doppler (cross-range) image. A case study considers a 5G NR waveform in the upper mid-band, with the target model defined in 3GPP Release 19, representing a vehicle as a set of spatially distributed scatterers. Performance is evaluated in terms of image contrast (IC) and the root mean squared error (RMSE) of the estimated target-centroid range. Finally, the trade-off between sensing accuracy and communication efficiency is examined by varying the subcarrier allocation for IVA imaging. The results provide insights for designing effective sensing strategies in next-generation radio networks.
Paper Structure (13 sections, 17 equations, 4 figures)

This paper contains 13 sections, 17 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Transmit signal
  4. Sensing signal received
  5. Range and Doppler reconstruction
  6. Image Formation
  7. Range alignment via cross-correlation
  8. Minimum-variance phase-adjustment method
  9. Imaging performance metrics
  10. Numerical Results
  11. Image contrast
  12. RMSE of target-centroid range
  13. Conclusion

Figures (4)

  • Figure 1: Schematic representation of an ISAC–IVA setup with a BS using part of the physical-layer resources to perform imaging of a moving vehicle.
  • Figure 2: IVA image (IPP plane) of the vehicle for $v = 30\,$m/s and $\rho_\mathrm{f} = 1$.
  • Figure 3: $\mathrm{IC}_{\text{mean}}$ of the IVA image for $\Theta_\mathrm{0} = 300^\circ$ as a function of target speed and sensing subcarrier fraction $\rho_\mathrm{f}$.
  • Figure 4: Target–centroid range estimation error as a function of target speed and sensing subcarrier fraction $\rho_{\mathrm f}$.