MIMO-OFDM ISAC网络中的位置与速度估计精度：Fisher信息分析
Position and Velocity Estimation Accuracy in MIMO-OFDM ISAC Networks: A Fisher Information Analysis
AI总结：
本文提出理论框架，基于Fisher信息分析推导了MIMO-OFDM ISAC网络中目标位置与速度估计的CRLBs，揭示协作感知的性能增益及系统参数影响。
AI中文摘要：
本文提出了一个理论框架，用于推导由多个协作且分布式的多输入多输出（MIMO）基站（BSs）组成的、基于正交频分复用（OFDM）的通信感知一体化（ISAC）网络中目标位置与速度估计精度的信息论边界。利用Fisher信息分析，我们推导了单站和双站配置下Cramér-Rao下界（CRLBs）的闭式表达式。该框架随后被扩展至协作场景，包括具有多个协调单站传感器和多站配置的网络，实现了目标位置与速度的联合估计。我们系统地考察了估计精度如何依赖于基站数量、带宽、天线配置和网络几何结构等关键系统参数。数值结果突出了协作感知带来的性能增益，并为未来ISAC系统的设计提供了指导。
英文摘要：
This paper presents a theoretical framework to derive information-theoretic bounds on the estimation accuracy of target position and velocity in orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) networks composed of multiple cooperative and distributed multiple-input multiple-output (MIMO) base stations (BSs). Leveraging Fisher information analysis, we derive closed-form expressions for the Cramér-Rao lower bounds (CRLBs) in both monostatic and bistatic configurations. The framework is then extended to cooperative settings, including networks with multiple coordinated monostatic sensors and multistatic configurations, enabling joint estimation of target position and velocity. We systematically examine how estimation accuracy depends on key system parameters such as the number of BSs, bandwidth, antenna configuration, and network geometry. Numerical results highlight the performance gains enabled by cooperative sensing and provide insights to guide the design of future ISAC systems.
