This paper considers a networked tracking architecture in 6G integrated sensing and communication (ISAC) systems, where multiple base stations (BSs) cooperatively transmit radio signals and process received echo signals to track multiple moving targets. Compared to the single-BS counterpart, networked tracking allows the moving targets to be associated with different BSs over time such that the wireless resources can be dynamically allocated among BSs based on target locations. However, networked tracking imposes new challenges for algorithm design and resource allocation. In this paper, we first design the networked Kalman Filter (NKF) that is suitable for multi-BS based tracking, then characterize the posterior Cramer-Rao bound (PCRB) under this NKF, and last design the beamforming vectors of all the BSs to minimize the tracking PCRB. Numerical results show that our dynamic beamforming design can properly associate the targets to the suitable BSs at various sensing blocks and reduce the tracking mean-squared error (MSE).

翻译：本文考虑6G通感一体化（ISAC）系统中的协同追踪架构，其中多个基站（BS）协作发射无线电信号并处理接收到的回波信号以追踪多个移动目标。与单基站方案相比，协同追踪允许移动目标在不同时间关联至不同基站，从而可根据目标位置在基站间动态分配无线资源。然而，协同追踪给算法设计与资源分配带来了新挑战。本文首先设计适用于多基站追踪的协同卡尔曼滤波器（NKF），继而推导该NKF框架下的后验克拉美-罗界（PCRB），最后设计所有基站的波束赋形向量以最小化追踪PCRB。数值结果表明，所提动态波束赋形设计能在不同感知块中合理地将目标关联至合适基站，并降低追踪均方误差（MSE）。