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

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 >

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

Citation:

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

Citation:

PDF( 2644 KB)

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

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, 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 in Fig. 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.
- UAV Swarm,
- Radiation Source Localization,
- Dynamic Formation Adjustment,
- Received Signal Strength Indicator Technology,
- Distributed Localization

FullText(HTML)

References(17)

References

[1]	AMARCHA F A, CHEHRI A, JAKIMI A, et al. Drones optimization for public transportation safety: Enhancing surveillance and efficiency in smart cities[C]. Proceedings of the 2024 IEEE World Forum on Public Safety Technology (WFPST), Herndon, USA, 2024: 153–158. doi: 10.1109/WFPST58552.2024.00023.
[2]	LIU Tao and ZHANG Bohan. A UAV-based remote sensing image automatic monitoring system empowered by artificial intelligence[C]. Proceedings of the 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS), Hassan, India, 2024: 1–5. doi: 10.1109/IACIS61494.2024.10721807.
[3]	LIU Haishi, TSANG Y P, LEE C K M, et al. Internet of UAVs to automate search and rescue missions in post-disaster for smart cities[C]. Proceedings of the 2024 IEEE Intelligent Vehicles Symposium (IV), Jeju Island, Republic of Korea, 2024: 614–619. doi: 10.1109/IV55156.2024.10588641.
[4]	ZHANG Junqi, LU Yehao, WU Yunzhe, et al. PSO-based sparse source location in large-scale environments with a UAV swarm[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(5): 5249–5258. doi: 10.1109/TITS.2023.3237570.
[5]	REN Mingyuan, FU Xiuwen, PACE P, et al. Collaborative data acquisition for UAV-aided IoT based on time-balancing scheduling[J]. IEEE Internet of Things Journal, 2024, 11(8): 13660–13676. doi: 10.1109/JIOT.2023.3339136.
[6]	YE Xinzhe, XUE Wei, CHEN Xiaolong, et al. Cauchy kernel-based AEKF for UAV target tracking via digital ubiquitous radar under the sea–air background[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 3506605. doi: 10.1109/LGRS.2024.3402687.
[7]	SUH U S, HAN S K, and RA W S. Optimal formation of UAV swarm for TDOA-based passive target tracking[J]. Journal of Electrical Engineering & Technology, 2022, 17(1): 551–564. doi: 10.1007/s42835-021-00872-9.
[8]	KANG Zhen, DENG Yihang, YAN Hao, et al. A new method of UAV swarm formation flight based on AOA azimuth-only passive positioning[J]. Drones, 2024, 8(6): 243. doi: 10.3390/drones8060243.
[9]	WANG Yubing, WANG Weijia, ZHANG Xudong, et al. The joint phantom track deception and TDOA/FDOA localization using UAV swarm without prior knowledge of radars' precise locations[J]. Electronics, 2022, 11(10): 1577. doi: 10.3390/electronics11101577.
[10]	ZHANG Yuan, QI Juntong, WU Chong, et al. Indoor UAV formation system based on UWB positioning[C]. Proceedings of the 2023 42nd Chinese Control Conference (CCC), Tianjin, China, 2023: 8545–8550. doi: 10.23919/CCC58697.2023.10240727.
[11]	GÜZEY N. RF source localization using multiple UAVs through a novel geometrical RSSI approach[J]. Drones, 2022, 6(12): 417. doi: 10.3390/drones6120417.
[12]	CHEN Siyuan, ZENG Xiangding, LAEFER D F, et al. Flight path setting and data quality assessments for unmanned-aerial-vehicle-based photogrammetric bridge deck documentation[J]. Sensors, 2023, 23(16): 7159. doi: 10.3390/s23167159.
[13]	滕怀亮, 李本威, 高永, 等. 基于飞行数据的无人机平飞动作质量评价模型[J]. 北京航空航天大学学报, 2019, 45(10): 2108–2114. doi: 10.13700/J.BH.1001-5965.2019.0029. TENG Huailiang, LI Benwei, GAO Yong, et al. Quality evaluation model of unmanned aerial vehicle's horizontal flight maneuver based on flight data[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(10): 2108–2114. doi: 10.13700/J.BH.1001-5965.2019.0029.
[14]	屈耀红, 张峰, 谷任能, 等. 基于距离测量的多无人机协同目标定位方法[J]. 西北工业大学学报, 2019, 37(2): 266–272. doi: 10.3969/j.issn.1000-2758.2019.02.008. QU Yaohong, ZHANG Feng, GU Renneng, et al. Target cooperative location method of multi-UAV based on pseudo range measurement[J]. Journal of Northwestern Polytechnical University, 2019, 37(2): 266–272. doi: 10.3969/j.issn.1000-2758.2019.02.008.
[15]	QUAN Lun, YIN Longji, XU Chao, et al. Distributed swarm trajectory optimization for formation flight in dense environments[C]. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, USA, 2022: 4979–4985. doi: 10.1109/ICRA46639.2022.9812050.
[16]	ZHU Jiandong, DING Ting, and QIAO Lijuan. A closed-form solution for 3D source localization using angles and Doppler shifted frequencies[J]. IEEE Access, 2023, 11: 89581–89590. doi: 10.1109/ACCESS.2023.3305961.
[17]	MA Wen and ZHU Hongyan. Source localization using TDOA with sensor position errors based on constrained total least squares and ADMM[C]. Proceedings of the 2024 27th International Conference on Information Fusion (FUSION), Venice, Italy, 2024: 1–8. doi: 10.23919/FUSION59988.2024.10706425.