Networked Tracking of Multiple Moving Targets in 6G Network

TL;DR

This paper proposes a networked tracking framework for 6G ISAC systems, introducing a multi-BS Kalman filter, analyzing bounds, and optimizing beamforming to improve multi-target tracking accuracy.

Contribution

It develops a networked Kalman filter for multi-BS tracking, characterizes the PCRB, and designs beamforming to enhance tracking performance in 6G systems.

Findings

Dynamic beamforming reduces tracking MSE.

Proper target-BS association improves tracking accuracy.

The proposed NKF outperforms single-BS approaches.

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).