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

arXiv: https://arxiv.org/abs/2601.16664 · HTML: https://mlsanity.com/paper/2601.16664/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2026.

## Built on by (most cited first, 1 of 1)

- [Semantic Reconstruction and 3-D Detection via Learned Multi-Pair Fusion in RF Imaging](https://mlsanity.com/paper/2608.23249/) (2026)

## Abstract (arXiv)

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.