标题： 量化混合雷达融合中的基本权衡：从CRB-率视角 显示英文标题

标题： Fundamental Trade-offs in Quantized Hybrid Radar Fusion: A CRB-Rate Perspective

摘要： 尽管近期进展突显了低分辨率模数转换器（ADC）在集成感知与通信（ISAC）系统中的作用，但在融合单基地和双基地感知系统的混合雷达融合（HRF）中，ADC分辨率的具体影响仍未被充分探索。 HRF中的上行（UL）路径包括同一频带内的直射信号和反射信号，这带来了独特的挑战，尤其是因为反射信号通常比直射信号弱得多，使得HRF系统对ADC分辨率非常敏感。 为了研究量化和ADC分辨率对HRF的影响，我们采用量化Cramér-Rao界（CRB）作为感知精度的度量。 本文推导了专门针对HRF系统和量化通信速率的量化CRB。我们将分析扩展到获得Fisher信息矩阵（FIM）和UL通信速率的下界，这些用于表征量化HRF系统。 利用这些推导出的界限，我们通过CRB-速率边界的角度分析量化HRF系统。我们通过两个优化问题得到CRB-速率边界，其中每个解点代表感知精度和通信速率之间的权衡边界。 广泛的仿真展示了ADC分辨率、动态范围（DR）以及各种系统参数对HRF系统CRB-速率边界的影响力。这些结果为设计高效且高性能的HRF系统提供了关键见解。 显示英文摘要

摘要： While recent advancements have highlighted the role of low-resolution analog-to-digital converters (ADCs) in integrated sensing and communication (ISAC) systems, the specific impact of ADC resolution on hybrid radar fusion (HRF), where we fuse monostatic and bistatic sensing systems remains relatively unexplored. The uplink (UL) paths in HRF, comprising both direct and reflected signals within the same frequency band, pose unique challenges, particularly given that the reflected signal is often significantly weaker than the direct path, making HRF systems susceptible to ADC resolution. To investigate the influence of quantization and ADC resolution on HRF, we employ the quantized Cram\'er-Rao bound (CRB) as a metric for sensing accuracy. This work derives the quantized CRB specifically for HRF systems and the quantized communication rate. We extend our analysis to obtain lower bounds on the Fisher Information Matrix (FIM) and UL communication rate, which we use to characterize quantized HRF systems. Using these derived bounds, we analyze the quantized HRF system through the lens of CRB-rate boundaries. We obtain the CRB-rate boundary through two optimization problems, where each solution point represents a trade-off boundary between the sensing accuracy and the communication rate. Extensive simulations illustrate the influence of ADC resolution, dynamic range (DR), and various system parameters on the CRB-rate boundary of HRF systems. These results offer critical insights into the design of efficient and high-performance HRF systems.