Networked Tracking of Multiple Moving Targets in 6G Network
2026-04-21 • Information Theory
Information Theory
AI summaryⓘ
The authors study a system where multiple base stations (BSs) work together to track moving targets by sending and receiving radio signals, which can better manage resources than a single base station. They create a special tracking method called the networked Kalman Filter (NKF) that works across multiple BSs to keep track of targets. They also develop a way to measure tracking accuracy (PCRB) under this method and design how BSs should aim their signals (beamforming) to minimize errors. Their results show this approach helps assign targets to the right BSs and improves tracking accuracy.
6GIntegrated Sensing and Communication (ISAC)Base Station (BS)Networked Kalman Filter (NKF)Posterior Cramer-Rao Bound (PCRB)BeamformingTrackingMean-Squared Error (MSE)Resource Allocation
Authors
Yanmo Hu, Weifeng Zhu, Chenshu Wu, Shuowen Zhang, J. Andrew Zhang, Liang Liu
Abstract
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).