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

TL;DR

This paper explores an OFDM-based ISAC imaging method using inverse virtual aperture to detect and image extended moving targets in vehicular scenarios, balancing sensing accuracy and communication efficiency.

Contribution

It introduces a novel IVA imaging approach with motion compensation for extended targets using 5G NR waveforms in vehicular environments.

Findings

High image contrast achieved in simulations

RMSE of target centroid estimated accurately

Trade-off between sensing and communication performance analyzed

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.