Trajectory Optimization in Single and Dual-UAV Bearing-Only Target Localization

2026-06-08

AI Summary

This work addresses the issue of degenerate observation geometry in bearings-only target localization by proposing a Fisher Information Matrix (FIM)-based trajectory optimization method for unmanned aerial vehicles. To mitigate geometric degeneracy, a spectrally weighted FIM objective function is introduced to enhance optimization gradients. In the two-vehicle scenario, a sine term of the line-of-sight intersection angle is incorporated to improve triangulation geometry. Kinematic constraints and a modified particle swarm optimization algorithm—augmented with particle normalization—are jointly employed to ensure physically feasible trajectories. Experimental results demonstrate that the proposed approach reduces the median localization error by 99.21% for a single UAV and achieves a 69.70% improvement in the two-UAV case compared to conventional FIM-based methods, exhibiting superior performance in long-range and extended-duration maneuvering target localization.

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.

Problem

• bearing-only localization
• trajectory optimization
• UAV
• target localization
• observation geometry

Innovation

• Fisher Information Matrix
• trajectory optimization
• bearing-only localization
• dual-UAV coordination
• improved PSO