Networked Tracking of Multiple Moving Targets in 6G Network

2026-04-21

AI Summary

This work addresses the challenges of resource allocation and base station handover in multi-base-station cooperative tracking within integrated sensing and communication systems for 6G. The authors propose a novel cooperative tracking architecture that incorporates a networked Kalman filter tailored for multi-base-station scenarios and, for the first time, jointly optimizes target-to-base-station association and beamforming design to minimize the posterior Cramér–Rao bound—an information-theoretic lower bound on tracking error. By dynamically optimizing each base station’s beamforming vectors based on this bound, the framework enables efficient allocation of sensing resources. Experimental results demonstrate that the proposed method effectively guides target association toward the optimal base station, significantly reducing the mean squared tracking error across consecutive sensing intervals.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Computer Vision: Motion & Tracking

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Tracking, profiling, and countermeasures against them
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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).

Problem

• networked tracking
• multiple moving targets
• 6G ISAC
• resource allocation
• multi-BS cooperation

Innovation

• networked Kalman filter
• posterior Cramer-Rao bound
• dynamic beamforming
• multi-BS tracking
• ISAC