Integrated Sensing, Communication, and Control for UAV-Assisted Mobile Target Tracking

2026-02-05

AI Summary

This work addresses the challenge of simultaneously achieving high tracking accuracy, reliable communication, and control stability in UAV-assisted mobile target tracking, where conventional approaches often fail to balance these competing objectives. The paper presents the first integrated perception–communication–control framework that jointly optimizes UAV trajectory and beamforming within a unified model to enable stable and precise tracking. Key contributions include the incorporation of an extended Kalman filter for state estimation, derivation of a closed-form solution for optimal beamforming under given control inputs, and the use of a relaxed convex approximation to efficiently handle non-convex constraints. Experimental results demonstrate that the proposed method achieves tracking accuracy approaching the non-causal performance bound while maintaining robust communication, significantly outperforming existing decoupled design strategies.

Technology Category

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

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Unmanned aerial vehicles (UAVs) are increasingly deployed in mission-critical applications such as target tracking, where they must simultaneously sense dynamic environments, ensure reliable communication, and achieve precise control. A key challenge here is to jointly guarantee tracking accuracy, communication reliability, and control stability within a unified framework. To address this issue, we propose an integrated sensing, communication, and control (ISCC) framework for UAV-assisted target tracking, where the considered tracking system is modeled as a discrete-time linear control process, with the objective of driving the deviation between the UAV and target states toward zero. We formulate a stochastic model predictive control (MPC) optimization problem for joint control and beamforming design, which is highly non-convex and intractable in its original form. To overcome this difficulty, the target state is first estimated using an extended Kalman filter (EKF). Then, by deriving the closed-form optimal beamforming solution under a given control input, the original problem is equivalently reformulated into a tractable control-oriented form. Finally, we convexify the remaining non-convex constraints via a relaxation-based convex approximation, yielding a computationally tractable convex optimization problem that admits efficient global solution. Numerical results show that the proposed ISCC framework achieves tracking accuracy comparable to a non-causal benchmark while maintaining stable communication, and it significantly outperforms the conventional control and tracking method.

Problem

• UAV
• target tracking
• integrated sensing and communication
• control stability
• communication reliability

Innovation

• Integrated Sensing Communication and Control (ISCC)
• Stochastic Model Predictive Control
• Optimal Beamforming
• Extended Kalman Filter
• Convex Approximation