Research on UAV Swarm Radiation Source Localization Method Based on Dynamic Formation Optimization
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摘要: 在障碍物密集、结构复杂的城市场景中，无人机编队进行辐射源定位常受到信号衰减、多径效应和建筑物遮挡等因素的影响，导致现有方法定位精度不高。针对这一问题，本文提出了一种基于动态队形调整的无人机编队辐射源定位方法。该方法通过优化无人机编队的几何构型，有效降低路径损耗与干扰，从而提升定位性能。具体而言，系统利用接收信号强度实时评估信号质量，并在编队运动过程中根据环境变化自适应调整队形，以优化信号传播路径。同时，结合几何定位精度因子、均方根误差等指标，对编队结构进行动态优化，从而提高距离估计与定位解算的可靠性。实验结果表明，相比传统方法，该方法在复杂城市环境中能够更快收敛并显著提升定位精度，定位误差降低了80%以上。同时，所提方法能够有效适应动态环境变化，展现出较强的鲁棒性与实用价值。Abstract:
In dense and structurally complex urban environments, UAV swarm–based radiation source localization is often degraded by signal attenuation, multipath propagation, and building obstructions. To overcome these limitations, this paper proposes a dynamic formation–adjustment localization method for UAV swarms. By optimizing the geometric configuration of the swarm, the method reduces path loss and interference, thereby enhancing localization accuracy. Received signal strength is used to assess signal quality in real time, enabling adaptive formation adjustments that improve signal propagation. Moreover, geometric dilution of precision and root mean square error metrics are integrated to further refine swarm geometry and improve distance estimation reliability. Experimental results show that the proposed method converges faster and significantly improves localization accuracy in complex urban environments, reducing errors by over 80%. The method also adapts effectively to environmental variations, demonstrating strong robustness and practical applicability. Objective UAV swarm localization and formation control in urban environments are challenged by obstacles, signal attenuation, and rapidly changing conditions, which limit the reliability of traditional methods. To overcome these issues, this study proposes a radiation source localization approach that integrates Received Signal Strength Indicator–based sensing with dynamic formation adjustment, aiming to improve localization accuracy and enhance system robustness in complex urban scenarios. Methods The proposed method uses Received Signal Strength Indicator measurements to estimate the distance to the radiation source and dynamically adjusts the UAV swarm formation to reduce localization errors. These adjustments are driven by real-time feedback incorporating relative positions, signal strength, and environmental variations. Localization accuracy is further enhanced through a multi-sensor fusion strategy that integrates GPS, IMU, and depth camera data. A data-quality assessment mechanism evaluates signal reliability and triggers formation adaptation when the signal falls below a predefined threshold. Overall, the optimization process minimizes positioning errors and improves the robustness of the localization system. Results and Discussions Simulation experiments in a ROS-based environment were conducted to evaluate the proposed UAV swarm localization method under urban obstacles and multipath conditions. The swarm began in a hexagonal formation and dynamically adjusted its geometry according to environmental changes and localization confidence ( Fig. 3 -4 ). As shown inFig. 5 , localization errors fluctuated during initialization but quickly converged to below 1 m after 150 s. Formation comparisons (Fig. 6 ) showed that symmetric structures such as hexagonal and triangular formations maintained errors under 0.5 m, while asymmetric formations (T and Y-shape) produced deviations up to 4.9 m. Further comparisons (Fig. 7 ) indicated that traditional RSSI saturated near 15 m, DOA fluctuated between 5–14 m, and TDOA failed due to synchronization issues, whereas the proposed method achieved sub-meter accuracy within 60 s and remained robust throughout the mission. These results demonstrate that combining RSSI-based distance estimation with dynamic formation adjustment substantially improves localization accuracy, convergence speed, and adaptability in complex environments.Conclusions This paper tackles the challenge of UAV swarm localization in complex urban environments by integrating RSSI-based distance estimation with dynamic formation adjustment and multi-sensor fusion. ROS-based simulations validate the effectiveness of the proposed method, showing that: (1) localization errors converge rapidly to sub-meter levels, reaching below 1 m within 150 s even in NLoS conditions; (2) symmetric formations, such as hexagonal and triangular structures, outperform asymmetric ones, reducing errors by up to 67% compared with fixed Y-shaped formations; and (3) relative to traditional RSSI, DOA, and TDOA approaches, the proposed method achieves faster convergence, higher stability, and greater robustness. -
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