In this work, we consider the target detection problem in a multistatic integrated sensing and communication (ISAC) scenario characterized by the cell-free MIMO communication network deployment, where multiple radio units (RUs) in the network cooperate with each other for the sensing task. By exploiting the angle resolution from multiple arrays deployed in the network and the delay resolution from the communication signals, i.e., orthogonal frequency division multiplexing (OFDM) signals, we formulate a cooperative sensing problem with coherent data fusion of multiple RUs' observations and propose a sparse Bayesian learning (SBL)-based method, where the global coordinates of target locations are directly detected. Intensive numerical results indicate promising target detection performance of the proposed SBL-based method. Additionally, a theoretical analysis of the considered cooperative multistatic sensing task is provided using the pairwise error probability (PEP) analysis, which can be used to provide design insights, e.g., illumination and beam patterns, for the considered problem.

翻译：本文研究了一种采用无蜂窝多输入多输出通信网络部署的多基地一体化感知与通信场景中的目标检测问题，其中网络中的多个射频单元相互协作以完成感知任务。通过利用网络中部署的多个阵列提供的角度分辨率以及通信信号（即正交频分复用信号）提供的时延分辨率，我们构建了一个基于多个射频单元观测数据相干融合的协作感知问题，并提出了一种基于稀疏贝叶斯学习的方法，该方法可直接检测目标位置的全局坐标。大量数值结果表明，所提出的基于稀疏贝叶斯学习的方法具有优异的目标检测性能。此外，本文利用成对错误概率分析对所研究的协作多基地感知任务进行了理论分析，该分析可为所研究问题（例如照明与波束模式设计）提供设计指导。