Distributed multi-UAV cooperation for dynamic target tracking optimized by an SAQPSO algorithm

• Published in: ISA transactions Vol. 129; no. Pt A; pp. 230 - 242
• Main Authors: Wang, Yi’an, Li, Kun, Han, Ying, Yan, Xinxin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.10.2022
• Subjects: Dynamic target tracking; Multi-UAV; Quantum Particle Swarm Optimization algorithm; Simulated Annealing algorithm; Trajectory prediction; Dynamic target tracking; Multi-UAV; Trajectory prediction; Quantum Particle Swarm Optimization algorithm; Simulated Annealing algorithm
• ISSN: 0019-0578; 1879-2022; 1879-2022
• DOI: 10.1016/j.isatra.2021.12.014

Real-time tracking of the dynamic intrusion targets consists of two crucial factors: the path forecast of the target and real-time path optimization of multi-UAV target tracking. For the first one, the uncertainty of the target trajectory is an obstacle to realizing real-time tracking. Thus a trajectory prediction method is proposed in this paper to ensure the sampling period of the target. Owing to the poor prediction accuracy of the single-step trajectory, a multi-step Unscented Kalman Filter (MUKF) is proposed to forecast its multi-step trajectory further in different regions. For the second one, there are two problems: poor optimization accuracy of the tracking trajectory and larger local optimization deviation, which will cause failure of the regional tracking. Under this circumstance, a hybrid algorithm called SAQPSO is proposed, combining the specific mechanism of two intelligence algorithms. The annealing mechanism in the Simulated Annealing (SA) algorithm is used to modify the Quantum Particle Swarm Optimization (QPSO) algorithm. Then the characteristic of quantum particles is used to update the population and enhance global searchability. Furthermore, to testify the effectiveness of the trajectory optimization algorithm and related target prediction method, a specific simulation environment is given as an example, in which the tracking trajectories of eight different algorithms are compared. Simulation results show the effectiveness of the proposed algorithm. •A distributed cooperation multi-UAV model was built to modeling the tracking method.•A multi-step Unscented Kalman Filter trajectory forecast algorithm was put forward.•The cost function of multi-UAV tracking and observation in each area was established.•A Simulated Annealing Quantum Particle Swarm Optimization algorithm was proposed.