Connected Vehicular Network Ground-to-Air Target Tracking Based on Bearings Measurements: Pseudolinear Information-Guided Strategy
Zhihao Xiao, Luyao Zhang, Ning Liu, Tong Ying Guo, Luhan Jin, Yao Mao · IEEE Open Journal of Vehicular Technology · 2026
This paper investigates ground-to-air Unmanned Aerial Vehicle (UAV) tracking using bearing-only line-of-sight (LOS) measurements provided by electro-optical tracking systems (ETS) mounted on a connected vehicular network. The key challenges arise from asymmetric mobility constraints and limited observability in three-dimensional space. To address these issues, a closed-loop tracking framework is proposed that integrates state estimation with ground vehicle coordination. A distributed pseudolinear filter is developed to handle the nonlinear nature of bearing-only observations and improve estimation stability in a decentralized setting. On top of this, an information-theoretic guidance strategy is formulated by adapting the Fisher Information Matrix (FIM) to ground-to-air sensing geometry, enabling explicit quantification of the relationship between sensor positioning and estimation accuracy. The framework employs Voronoi-based spatial decomposition to achieve scalable coordination among multiple ground vehicles, balancing estimation precision with spatial coverage. Extensive simulation results demonstrate that the proposed method enhances tracking accuracy and improves target observability, offering a practical solution for UAV tracking under realistic conditions.