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UAV-enabled Integrated Sensing and Communication: Tracking Design and Optimization

Yifan Jiang, Qingqing Wu, Wen Chen, Kaitao Meng

TL;DR

The paper tackles UAV-enabled ISAC for joint target tracking and communication by formulating an EKF-based tracking framework and optimizing UAV trajectories to minimize a weighted sum of predicted PCRBs for relative position and velocity. An SCA-based algorithm solves the non-convex trajectory problem, and a special measurement-MSE-dominant case yields an optimal relative motion state with a fixed elevation angle and zero relative velocity. Results show that the proposed method closely approximates the theoretical optimum in the measurement-MSE-dominant regime and reveals three trade-offs between sensing and communication imposed by the fixed elevation angle. This work advances high-midelity, UAV-aided ISAC by incorporating velocity estimation and providing practical trajectory design insights for reliable beam maintenance and real-time response.

Abstract

Integrated sensing and communications (ISAC) enabled by unmanned aerial vehicles (UAVs) is a promising technology to facilitate target tracking applications. In contrast to conventional UAV-based ISAC system designs that mainly focus on estimating the target position, the target velocity estimation also needs to be considered due to its crucial impacts on link maintenance and real-time response, which requires new designs on resource allocation and tracking scheme. In this paper, we propose an extended Kalman filtering-based tracking scheme for a UAV-enabled ISAC system where a UAV tracks a moving object and also communicates with a device attached to the object. Specifically, a weighted sum of predicted posterior Cramér-Rao bound (PCRB) for object relative position and velocity estimation is minimized by optimizing the UAV trajectory, where an efficient solution is obtained based on the successive convex approximation method. Furthermore, under a special case with the measurement mean square error (MSE), the optimal relative motion state is obtained and proved to keep a fixed elevation angle and zero relative velocity. Numerical results validate that the obtained solution to the predicted PCRB minimization can be approximated by the optimal relative motion state when predicted measurement MSE dominates the predicted PCRBs, as well as the effectiveness of the proposed tracking scheme. Moreover, three interesting trade-offs on system performance resulted from the fixed elevation angle are illustrated.

UAV-enabled Integrated Sensing and Communication: Tracking Design and Optimization

TL;DR

The paper tackles UAV-enabled ISAC for joint target tracking and communication by formulating an EKF-based tracking framework and optimizing UAV trajectories to minimize a weighted sum of predicted PCRBs for relative position and velocity. An SCA-based algorithm solves the non-convex trajectory problem, and a special measurement-MSE-dominant case yields an optimal relative motion state with a fixed elevation angle and zero relative velocity. Results show that the proposed method closely approximates the theoretical optimum in the measurement-MSE-dominant regime and reveals three trade-offs between sensing and communication imposed by the fixed elevation angle. This work advances high-midelity, UAV-aided ISAC by incorporating velocity estimation and providing practical trajectory design insights for reliable beam maintenance and real-time response.

Abstract

Integrated sensing and communications (ISAC) enabled by unmanned aerial vehicles (UAVs) is a promising technology to facilitate target tracking applications. In contrast to conventional UAV-based ISAC system designs that mainly focus on estimating the target position, the target velocity estimation also needs to be considered due to its crucial impacts on link maintenance and real-time response, which requires new designs on resource allocation and tracking scheme. In this paper, we propose an extended Kalman filtering-based tracking scheme for a UAV-enabled ISAC system where a UAV tracks a moving object and also communicates with a device attached to the object. Specifically, a weighted sum of predicted posterior Cramér-Rao bound (PCRB) for object relative position and velocity estimation is minimized by optimizing the UAV trajectory, where an efficient solution is obtained based on the successive convex approximation method. Furthermore, under a special case with the measurement mean square error (MSE), the optimal relative motion state is obtained and proved to keep a fixed elevation angle and zero relative velocity. Numerical results validate that the obtained solution to the predicted PCRB minimization can be approximated by the optimal relative motion state when predicted measurement MSE dominates the predicted PCRBs, as well as the effectiveness of the proposed tracking scheme. Moreover, three interesting trade-offs on system performance resulted from the fixed elevation angle are illustrated.
Paper Structure (13 sections, 2 theorems, 13 equations, 2 figures)

This paper contains 13 sections, 2 theorems, 13 equations, 2 figures.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. EKF-based Tracking Scheme
  4. Signal Model
  5. Radar Signal Model
  6. Radar Measurement Model
  7. Communication Signal Model
  8. Problem Formulation
  9. Proposed Solution and Approximation
  10. Proposed Solution to (P1)
  11. Measurement MSE-only Case
  12. Numerical results
  13. Conclusion

Key Result

Lemma 1

$g(x_{n},0)$ is convex for $x_{n}\in [x_{l}, \infty)$ with with $\overline{\chi} = 2(1+(5c^{2}a_{2}^{2}/\xi(H)))^{\frac{1}{2}}$ and $\xi(H) = 4 a_{1}^{2} H^{2} - 5 c a_{2}^{2}$.

Figures (2)

  • Figure 1: A UAV-enabled ISAC system.
  • Figure 2: Numerical results characterizing the proposed tracking scheme, the fixed elevation angle and the sensing&communication (S&C) performance bounds.

Theorems & Definitions (2)

  • Lemma 1
  • Proposition 1