Research on UAV Swarm Radiation Source Localization Method Based on Dynamic Formation Optimization

WU Sujie; WU Binbin; YANG Ning; WANG Heng; GUO Daoxing; GU Chuan

doi:10.11999/JEIT251023

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WU Sujie, WU Binbin, YANG Ning, WANG Heng, GUO Daoxing, GU Chuan. Research on UAV Swarm Radiation Source Localization Method Based on Dynamic Formation Optimization[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251023

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WU Sujie, WU Binbin, YANG Ning, WANG Heng, GUO Daoxing, GU Chuan. Research on UAV Swarm Radiation Source Localization Method Based on Dynamic Formation Optimization[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251023

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Research on UAV Swarm Radiation Source Localization Method Based on Dynamic Formation Optimization

doi: 10.11999/JEIT251023 cstr: 32379.14.JEIT251023

College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China

Received Date: 2025-09-28
Accepted Date: 2025-12-08
Rev Recd Date: 2025-12-08

Available Online: 2025-12-16

Abstract

Abstract

In dense and structurally complex urban environments, Unmanned Aerial Vehicle (UAV) swarm radiation source localization is affected by signal attenuation, multipath propagation, and building obstructions. To address these limitations, a dynamic formation-optimization method for UAV swarms is proposed. By improving the geometric configuration of the swarm, the method reduces path loss and interference, which strengthens localization accuracy. Received signal strength is used to evaluate signal quality in real time and supports adaptive formation adjustments that improve propagation conditions. Geometric dilution of precision and root mean square error metrics are integrated to refine swarm geometry and improve distance-estimation reliability. Simulation results show that the proposed method converges faster and improves localization accuracy in complex urban environments, reducing errors by more than 80 percent. The method adapts to environmental variation and demonstrates strong robustness and practical value. Objective UAV swarm localization and formation control in urban environments are affected by obstacles, signal attenuation, and rapid variation in the surroundings that reduce the reliability of conventional methods. This study proposes a radiation source localization approach that integrates the Received Signal Strength Indicator (RSSI) with dynamic formation adjustment to improve localization accuracy and strengthen system robustness in complex urban scenarios. RSSI is used once in full form, then referenced consistently. Methods The method uses RSSI measurements to estimate the distance to the radiation source and adjusts UAV swarm formation in real time to reduce localization errors. These adjustments are based on feedback that reflects relative positions, signal strength, and environmental variation. Localization accuracy is strengthened 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 drops below a predefined threshold. This optimization process reduces positioning errors and improves system robustness. Results and Discussions Simulation experiments in a ROS-based environment were conducted to evaluate the UAV swarm localization method under urban obstacles and multipath conditions. The swarm began in a hexagonal formation and adjusted its geometry according to environmental variation and localization confidence (Fig. 3–4). As shown in Fig. 5, localization errors fluctuated during initialization but converged to below 1 m after 150 s. Formation comparisons (Fig. 6) showed that symmetric structures such as hexagonal and triangular formations maintained errors below 0.5 m, whereas asymmetric formations (T and Y shape) produced deviations up to 4.9 m. Further comparisons (Fig. 7) showed that traditional RSSI saturated near 15 m, direction of arrival fluctuated between 5 and 14 m, and time difference of arrival failed due to synchronization problems. The proposed method achieved sub-meter accuracy within 60 s and remained robust throughout the mission. These findings indicate that combining RSSI-based distance estimation with dynamic formation adjustment improves localization accuracy, convergence speed, and adaptability under complex environmental conditions. Conclusions This study addresses UAV swarm localization in complex urban environments by integrating RSSI-based distance estimation, dynamic formation adjustment, and multi-sensor fusion. ROS-based simulations show that: (1) localization errors converge rapidly to sub-meter levels, reaching below 1 m within 150 s under non-line-of-sight conditions; (2) symmetric formations such as hexagonal and triangular configurations outperform asymmetric ones and reduce errors by up to 67 percent compared with fixed Y-shaped formations; and (3) relative to traditional RSSI, direction of arrival, and time difference of arrival approaches, the proposed method shows faster convergence, higher stability, and stronger robustness.
- UAV swarm,
- Radiation source localization,
- Dynamic formation adjustment,
- Received signal strength indicator technology,
- Distributed localization

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References(17)

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