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

Zhiyu Chen, Ming-Min Zhao, Songfu Cai, Ming Lei, Min-Jian Zhao

TL;DR

An integrated sensing, communication, and control framework for UAV-assisted target tracking is proposed, 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.

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.

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

TL;DR

An integrated sensing, communication, and control framework for UAV-assisted target tracking is proposed, 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.

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.
Paper Structure (14 sections, 3 theorems, 85 equations, 15 figures, 1 algorithm)

This paper contains 14 sections, 3 theorems, 85 equations, 15 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Communication Model
  4. Sensing and Measurement Model
  5. Control System Model
  6. Problem Formulation
  7. Joint Optimization of Mobile Target Tracking Control and Wireless Beamforming
  8. Extended Kalman Filtering
  9. Joint Control and Beamforming Design
  10. Numerical Results
  11. Conclusion
  12. Proof of Lemma \ref{['lemma-1']}
  13. Proof of Lemma \ref{['lemma-2']}
  14. Proof of Theorem \ref{['theorem-1']}

Key Result

Lemma 1

For any given control inputs $\{\bold{u}_{n-1+i}\}_{i\in\mathcal{I}}$, the sensing-centric and communication-centric feasibility-check problems, i.e., (S-check) and (C-check), yield identical outcomes, i.e.,

Figures (15)

  • Figure 1: UAV-assisted mobile target tracking system.
  • Figure 2: Illustration of a typical closed-loop feedback control system with LQR traget tracking controller.
  • Figure 3: Illustration of the proposed ISCC framework.
  • Figure 4: Case 1: $\bold{p}^{U}[1]=(0,100)$ m
  • Figure 5: Case 2: $\bold{p}^{U}[1]=(0,150)$ m
  • ...and 10 more figures

Theorems & Definitions (3)

  • Lemma 1
  • Lemma 2
  • Theorem 1