用逆虚拟孔径技术实现车载场景下移动广域目标的成像
OFDM-Based ISAC Imaging of Extended Targets via Inverse Virtual Aperture Processing
- 基于MIMO-OFDM波形，通过运动补偿构建逆虚拟孔径图像
- 在5G NR上中频段下，目标中心距离估计误差低于0.3米
- 适用于未来车联网感知与通信协同设计
本文研究了基于逆虚拟孔径（IVA）技术的集成感知与通信（ISAC）系统在车辆场景中对移动广域目标的成像性能。基站（BS）采用MIMO-OFDM波形作为单站传感器，通过运动补偿处理目标回波，生成范围-多普勒（交叉方向）的逆虚拟孔径图像。案例研究采用3GPP Release 19中定义的目标模型，将车辆建模为一组空间分布散射体，并使用5G NR上中频段波形进行评估。性能以图像对比度（IC）和目标质心距离估计的均方根误差（RMSE）衡量。结果表明，在给定配置下，目标中心距离估计的RMSE低于0.3米。同时，通过调整子载波分配，分析了感知精度与通信效率之间的权衡，为下一代无线网络中的有效感知策略设计提供了参考。
原文摘要 · Abstract (English)
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.
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