Trajectory Optimization in Single and Dual-UAV Bearing-Only Target Localization
2026-06-08 • Robotics
RoboticsComputer Vision and Pattern Recognition
AI summaryⓘ
The authors address how drones (UAVs) can better find targets using only direction information (bearing-only). They create a new way to plan drone paths that consider how easy it is to move and improve the viewing angles for locating the target more accurately. By using a special math tool called the Fisher Information Matrix and an improved optimization method, their approach avoids poor viewpoints and works well for one or two drones working together. Simulations show their method significantly reduces errors in pinpointing targets, especially for long-range and moving targets.
bearing-only localizationunmanned aerial vehicle (UAV)trajectory optimizationFisher Information Matrix (FIM)particle swarm optimization (PSO)triangulation geometrysight-line intersection anglemotion constraintsoptical measurement
Authors
Zhijian Xiao, Huayu Huang, Bin Li, Yang Shang, Banglei Guan
Abstract
Bearing-only target localization is a fundamental problem in optical measurement and finds extensive applications in unmanned aerial vehicle (UAV) technology. Effective trajectory planning establishes favorable observation geometries, thereby enhancing the target localization accuracy of bearing-only UAV systems. This paper proposes an trajectory optimization method for unmanned aerial vehicles (UAVs) in bearing-only target localization scenarios. By leveraging the Fisher Information Matrix (FIM), the proposed approach dynamically integrates the geometric configuration and vehicle maneuverability into the optimization framework. Specifically, we introduce a spectrally-weighted FIM objective function that provides better gradient dynamics near degenerate configurations, enabling the planner to rapidly escape from poor observation conditions. For dual-UAV scenarios, an intersection angle sine term is introduced to optimize triangulation geometry by improving the sight-line intersection angle, thereby preventing trajectory aggregation. Furthermore, we propose an improved Particle Swarm Optimization (PSO) algorithm with motion model constraints and particle normalization to ensure the physical feasibility of the trajectory and enhance the compatibility with the objective functions. Simulation results demonstrate that the proposed method reduces the median localization error by 99.21% compared to conventional FIM-based approaches in single-UAV scenarios, and achieves a 69.70% improvement for dual-UAV configurations, exhibits superior performance in long-duration bearing-only target localization of maneuverability targets at extended ranges.