Dynamic UAV Swarm Collaboration for Multi-Targets Tracking Under Malicious Jamming: Joint Power, Path and Target Association Optimization

• Published in: IEEE transactions on vehicular technology Vol. 73; no. 4; pp. 5410 - 5425
• Main Authors: Xiang, Lanhua, Wang, Fengyu, Xu, Wenjun, Zhang, Tiankui, Pan, Miao, Han, Zhu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: artificial potential field (APF); Autonomous aerial vehicles; Collaboration; Collision avoidance; Cooperation; Cost function; dynamic collaboration; Energy consumption; Evolutionary algorithms; Interference; Iterative algorithms; Jamming; mean field game; Multi-target tracking (MTT); Multiple target tracking; Optimization; Potential fields; Power control; target association; Target tracking; Trajectory; Trajectory planning; Unmanned aerial vehicles
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2023.3333054

In this paper, the multi-target tracking (MTT) with an unmanned aerial vehicle (UAV) swarm is investigated in the presence of jammers, where UAVs in the swarm communicate with each other to exchange information of targets during tracking. The communication between UAVs suffers from severe interference, including inter-UAV interference and jamming, thus leading to a deteriorated quality of MTT. To mitigate the interference and achieve MTT, we formulate an interference minimization problem by jointly optimizing UAV's sub-swarm division, trajectory, and power, subject to the constraint of MTT, collision prevention, flying ability, and UAV energy consumption. Due to the multiple coupling of sub-swarm division, trajectory, and power, the proposed optimization problem is NP-hard. To solve this challenging problem, it is decomposed into three subproblems, i.e., target association, path plan, and power control. First, a cluster-evolutionary target association (CETA) algorithm is proposed, which involves dividing the UAV swarm into multiple sub-swarms and individually matching these sub-swarms to targets. Second, a jamming-sensitive and singular case tolerance (JSSCT)-artificial potential field (APF) algorithm is proposed to plan trajectory for tracking the targets. Third, we develop a jamming-aware mean field game (JA-MFG) power control scheme, where a novel cost function is established considering the total interference. Finally, to minimize the total interference, a dynamic collaboration approach is designed. Different from traditional alternative iteration algorithms, our proposed dynamic collaboration approach triggers the updates of the sub-swarm division and UAV trajectory, and periodically updates the transmission power. Simulation results validate that the proposed dynamic collaboration approach reduces average total interference, tracking steps, and target switching times by 28%, 33%, and 48%, respectively, comparing to existing baselines.