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When UAVs Meet ISAC: Real-Time Trajectory Design for Secure Communications

Jun Wu, Weijie Yuan, Lajos Hanzo

arXiv:2306.14140v1eess.SP

TL;DR

Secure UAV communication must handle mobile ground users, while earlier schemes assumed static users and eavesdroppers. The paper uses ISAC echo delays to track Bob and optimize a weighted UAV trajectory; its simulations report effective tracking and a secrecy-rate trade-off, with α = 0.5 achieving the best secure communication performance among the compared weighting choices.

  • Problem

    Prior secure UAV communication work assumed static ground users and eavesdroppers, while practical ground users can move and require real-time location tracking.

    Source: §I · p. 1

  • Method

    An EKF tracks and predicts Bob from ISAC echo-delay measurements, and an iterative successive convex approximation algorithm solves a weighted non-convex real-time trajectory design problem.

    Source: §I · p. 2, B. Real-Time Trajectory Design · p. 4

  • Results

    α = 0.5 achieves the best secure communication performance among the compared weightings, and simulations report that the proposed algorithm tracks Bob efficiently.

    Source: §V · p. 5, §VI · p. 6

  • Takeaways & Limitations

    The simulations support flexible secure communication performance for UAV-aided applications with different communication requirements.

    Source: §VI · p. 6

Abstract

from arXiv · show

The real-time unmanned aerial vehicle (UAV) trajectory design of secure integrated sensing and communication (ISAC) is optimized. In particular, the UAV serves both as a downlink transmitter and a radar receiver. The legitimate user (Bob) roams on ground through a series of unknown locations, while the eavesdropper moves following a fixed known trajectory. To maximize the real-time secrecy rate, we propose an extended Kalman filtering (EKF)-based method for tracking and predicting Bob's location at the UAV based on the delay measurements extracted from the sensing echoes. We then formulate a non-convex real-time trajectory design problem and develop an efficient iterative algorithm for finding a near optimal solution. Our numerical results demonstrate that the proposed algorithm is capable of accurately tracking Bob and strikes a compelling legitimate vs. leakage rate trade-off.

I. INTRODUCTION

Secure UAV communication must account for mobile ground users, yet prior work assumed static ground users and eavesdroppers. This paper combines ISAC echo-based tracking of Bob with real-time trajectory design; the system model uses delay-derived range measurements.

I. INTRODUCTION

  • The paper addresses the gap left by secure UAV schemes that assumed static ground users and eavesdroppers, although ground users may move.The proposed system tracks Bob while Eve follows a fixed known trajectory.Source: §I · pp. 1–2
  • The proposed framework uses an EKF with delay measurements extracted from ISAC echoes to track and predict Bob’s location for real-time trajectory design.The UAV acts as both a downlink transmitter and a radar receiver.Source: §I · p. 2, §II · p. 2

A. Sensing Model

  • The paper assumes Eve follows a preset path; predicting an unknown Eve trajectory is left for future work.Source: A. Sensing Model · p. 2
  • The UAV receives echoes reflected by Bob, and the propagation delay gives the UAV–Bob range as cτ[n]/2.The measured range adds Gaussian noise to that range.Source: A. Sensing Model · p. 2

B. Communication Model

The communication model specifies free-space UAV links to Bob and Eve, with a fixed transmit-power approximation. It defines Bob’s received rate and Eve’s leakage rate as the communication quantities used for secrecy design.

B. Communication Model

  • The UAV–Bob and UAV–Eve links use the free-space inverse-square path-loss model.Source: B. Communication Model · p. 2
  • Transmit power is approximated as constant because it is far below the power dissipated by UAV propulsion and its variation is negligible.Source: B. Communication Model · p. 2
  • Identifying Bob is outside the main scope; the framework can distinguish him using data association with typical radar cross-section size and object trajectories, or periodic location feedback.Source: B. Communication Model · p. 2
  • The model defines Bob’s achievable rate and Eve’s leakage rate, which together determine the achievable secrecy rate.Source: B. Communication Model · p. 3

C. State Evolution Model

Bob’s state model represents position and velocity across time slots. It assumes approximately constant speed between consecutive slots and includes transition noise.

C. State Evolution Model

III. PROBLEM FORMULATION

The trajectory design problem maximizes a weighted real-time secrecy objective subject to the UAV’s movement and operating-area constraints. Its weighting factor balances Bob’s communication rate against information leakage to Eve.

III. PROBLEM FORMULATION

  • The optimization maximizes real-time secrecy rate over the UAV trajectory, subject to a finite maximum speed and a rectangular flight area.Source: §III · p. 3
  • The weighting factor α ranges from 0 to 1 and sets the flexible balance between improving Bob’s communication rate and avoiding information leakage.Source: §III · p. 3

IV. PROPOSED ALGORITHM

The proposed algorithm combines EKF-based tracking and prediction of Bob’s real-time location with an iterative method for solving the non-convex trajectory-design problem.

  • The algorithm uses an EKF to track and predict Bob’s real-time location.Source: §IV · p. 3
  • An iterative algorithm is proposed to solve the trajectory-design problem.Source: §IV · p. 3
  • The trajectory-design problem is non-convex.Source: §IV · p. 3

A. EFK-Based Bob Tracking

The paper treats trajectory design as online optimization, requiring Bob’s location and velocity to be tracked at each time slot. It uses an EKF to handle nonlinear range measurements and summarizes its prediction and tracking procedure.

A. EFK-Based Bob Tracking

6) MSE Matrix Update:

The measurement ˆdb[n] relies on the UAV’s real-time location, derived in Section IV-B.

B. Real-Time Trajectory Design

The trajectory-design method addresses a non-convex secrecy-rate objective using successive convex optimization and an iterative procedure based on Bob’s predicted location.

B. Real-Time Trajectory Design

V. NUMERICAL RESULTS

The simulations show that α = 0.5 achieves the best secure-communication performance among the tested weighting factors, while noise, tracking errors, and trajectory choices shape the trade-off between Bob’s rate and Eve’s leakage.

V. NUMERICAL RESULTS

  • α = 0.5 achieves the best secure-communication performance among the tested weighting factors.The comparison considers the legitimate rate at Bob and the secrecy rate.Source: §V · p. 5
  • At higher noise ratios, secure-communication performance worsens; the secrecy-rate CDF is evaluated at different noise levels for α = 0.5.The authors attribute the effect to Eve’s enhanced eavesdropping capability at lower noise levels and report secure communication most of the time when the ratio is at most 3.Source: Figure 3 · p. 5, §V · p. 5
  • The location-estimation RMSE rises in many cases after feedback but can be corrected using subsequent uplink location feedback.The RMSE also becomes high at specific time slots, such as the 79th, when Bob changes direction.Source: §V · p. 5, Figure 4 · p. 6
  • As α increases, the UAV focuses more on Bob’s rate; at α = 0.2 it flies toward the area boundary to reduce leakage, while at α = 0.5 it flies away from Eve while approaching Bob.The simulation uses 10 time slots per tracking period and a 50 m UAV altitude.Source: §V · pp. 5–6

VI. CONCLUSION

The conclusion describes a UAV-aided dynamic ISAC system for serving a roaming ground user in the presence of an eavesdropper. It combines EKF-based location tracking with iterative trajectory optimization and reports efficient tracking and flexible secure-communication performance.

VI. CONCLUSION

  • The system uses a UAV with sensing and communication functions to serve a ground user roaming through unknown locations while an eavesdropper is present.Source: §VI · p. 6
  • An EKF tracks and predicts the user’s real-time location from range measurements extracted from ISAC echoes.Source: §VI · p. 6
  • The authors formulate a weighted real-time trajectory problem and use an iterative successive-convex-optimization algorithm for the resulting non-convex problem.Simulations verify efficient user tracking and flexible secure-communication performance for different communication requirements.Source: §VI · p. 6

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