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

arXiv: https://arxiv.org/abs/2606.09188 · HTML: https://mlsanity.com/paper/2606.09188/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [Revisiting Active Perception](https://mlsanity.com/paper/1603.02729/) (2016)
- [Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing](https://mlsanity.com/paper/2109.13570/) (2021)
- [Online Informative Path Planning for Active Classification Using UAVs](https://mlsanity.com/paper/1609.08446/) (2016)
- [Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning](https://mlsanity.com/paper/2402.04894/) (2024)
- [Adaptive Information Gathering via Imitation Learning](https://mlsanity.com/paper/1705.07834/) (2017)
- [Information Acquisition with Sensing Robots: Algorithms and Error Bounds](https://mlsanity.com/paper/1309.5390/) (2013)
- [3D Trajectory Reconstruction of Moving Points Based on a Monocular Camera](https://mlsanity.com/paper/2502.19689/) (2025)
- [Multi-UAV Trajectory Optimization for Bearing-Only Localization in GPS Denied Environments](https://mlsanity.com/paper/2602.11116/) (2026)

## Abstract (arXiv)

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.