Optimizing Multi-AAV Cooperative Tracking for Real-Time Applications in Network-Challenged Environments
Lin, Na Wang, Zhijiang Zhao, Liang Hawbani, Ammar Liu, Zhi Guizani, Mohsen
Lin, Na
Wang, Zhijiang
Zhao, Liang
Hawbani, Ammar
Liu, Zhi
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
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Research Projects
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Journal Issue
Abstract
Autonomous aerial vehicles (AAVs) have found widespread utility in the field of multi-target tracking (MTT) due to their inherent advantages, such as ease of deployment, flexible maneuverability, and cooperative communication capabilities. AAVs can perform tasks ranging from regional surveillance to tracking and search-and-rescue operations in hazardous environments. Nonetheless, the issue of how to efficiently coordinate multiple AAVs to track diverse mobile targets remains a critical concern. This paper focuses on MTT with multi-AAV, aiming at optimizing system performance in scenarios where network availability is limited. In contrast to treating sensor perception as a monolithic process, we propose a scheme for cooperative sensing and data processing. This scheme is designed to reduce system response latency in environmental information sensing and multidimensional data processing for multiple AAVs. Furthermore, in contrast to assuming linear target movement and single-step target position prediction, we introduce a multi-agent deep reinforcement learning (MADRL) framework combined with multi-step prediction extended Kalman Filter (MP-EKF). This framework is tailored to enhance tracking precision, especially when targets’ trajectories are curved, which can reduce AAV flight displacement if the target’s position can be predicted multiple steps later. In addition, unlike using latency as a real-time application to measure the “freshness” of information, the Age of Information (AoI) is introduced for considering the waiting time of transmission and calculation between multiple AAVs. This assessment method is utilized to comprehensively evaluate and mitigate data latency within the MADRL algorithm. Finally, extensive simulation experiments demonstrate that the proposed scheme significantly outperforms both baseline methods and state-of-the-art approaches in terms of AoI, system latency, and energy consumption.
Citation
N. Lin, Z. Wang, L. Zhao, A. Hawbani, Z. Liu and M. Guizani, "Optimizing Multi-AAV Cooperative Tracking for Real-Time Applications in Network-Challenged Environments," in IEEE Transactions on Computers, doi: 10.1109/TC.2025.3566867
Source
IEEE Transactions on Computers
Conference
Keywords
Autonomous aerial vehicles (AAVs), Multi-target tracking (MTT), Multi-step prediction, Multi-agent deep reinforcement learning (MADRL), Path planning, Cooperative tracking
Subjects
Source
Publisher
IEEE