Remote Interference Source Localization: A Multi-UAV-Based Cooperative Framework

WU Guangyu; GU Jiangchun

doi:10.1049/cje.2021.00.310

Volume 31 Issue 3

May 2022

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Article Navigation > Chinese Journal of Electronics > 2022 > 31(3): 442-455

WU Guangyu and GU Jiangchun, “Remote Interference Source Localization: A Multi-UAV-Based Cooperative Framework,” Chinese Journal of Electronics, vol. 31, no. 3, pp. 442-455, 2022, doi: 10.1049/cje.2021.00.310

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WU Guangyu and GU Jiangchun, “Remote Interference Source Localization: A Multi-UAV-Based Cooperative Framework,” Chinese Journal of Electronics, vol. 31, no. 3, pp. 442-455, 2022, doi: 10.1049/cje.2021.00.310

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Remote Interference Source Localization: A Multi-UAV-Based Cooperative Framework

doi: 10.1049/cje.2021.00.310

WU Guangyu^1
,,
GU Jiangchun^2
,

1.
Department of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
2.
School of Communication Engineering, Army Engineering University of PLA, Nanjing 210000, China

Funds: This work was supported by the National Key Scientific Instrument and Equipment Development Project (61827801).

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Author Bio:
is currently pursuing the M.S. degree with the Department of Computer Science and Technology, University of Science and Technology of China, Hefei, China. He received the B.S. degree with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China. His research interests include machine learning, mobile computing, and the Internet of things. (Email: gywu@mail.ustc.edu.cn)

(corresponding author) received the B.S. degree in electronic and information engineering from Xidian University, Xi’an, China, in 2018, and the M.S. degree in information and communication engineering from the College of Communications Engineering, Army Engineering University of PLA, Nanjing, China, in 2020, where he is currently pursuing the Ph.D. degree. His research interests include UAV communications, convex optimization techniques, and reinforcement learning. (Email: gujiangchungjc@sina.com)
Received Date: 2021-08-29
Accepted Date: 2022-02-10

Available Online: 2022-03-05

Publish Date: 2022-05-05

Abstract

Abstract

Interference source localization with high accuracy and time efficiency is of crucial importance for protecting spectrum resources. Due to the flexibility of unmanned aerial vehicles (UAVs), exploiting UAVs to locate the interference source has attracted intensive research interests. The off-the-shelf UAV-based interference source localization schemes locate the interference sources by employing the UAV to keep searching until it arrives at the target. This obviously degrades time efficiency of localization. To balance the accuracy and the efficiency of searching and localization, this paper proposes a multi-UAV-based cooperative framework alone with its detailed scheme, where search and remote localization are iteratively performed with a swarm of UAVs. For searching, a low-complexity Q-learning algorithm is proposed to decide the direction of flight in every time interval for each UAV. In the following remote localization phase, a fast Fourier transformation based location prediction algorithm is proposed to estimate the location of the interference source by fusing the searching result of different UAVs in different time intervals. Numerical results reveal that in the proposed scheme outperforms the state-of-the-art schemes, in terms of the accuracy, the robustness and time efficiency of localization.
- Unmanned aerial vehicle,
- Reinforcement learning,
- Remoate localization,
- Wireless communications

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References(30)

References

[1]	W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” IEEE Network, vol.34, no.3, pp.134–142, 2020.
[2]	E. Khorov, A. Krasilov, I. Selnitskiy, et al., “A framework to maximize the capacity of 5G systems for ultra-reliable low-latency communications,” IEEE Transactions on Mobile Computing, vol.20, no.6, pp.2111–2123, 2021.
[3]	Q. Wu, G. Ding, Y. Xu, et al., “Cognitive internet of things: A new paradigm beyond connection,” IEEE Internet of Things Journal, vol.1, no.2, pp.129–143, 2014.
[4]	Y. Xu, A. Anpalagan, Q. Wu, et al., “Decision-theoretic distributed channel selection for opportunistic spectrum access: Strategies, challenges and solutions,” IEEE Communications Surveys & Tutorials, vol.15, no.4, pp.1689–1713, 2013.
[5]	J. Q. Wu, G. Ding, J. Wang, et al., “Spatial-temporal opportunity detection for spectrum-heterogeneous cognitive radio networks: Two-dimensional sensing,” IEEE Transactions on Wireless Communications, vol.12, no.2, pp.516–526, 2013.
[6]	L. Wang and Y. Huang, “UAV-based estimation of direction of arrival: An approach based on image processing,” 2020 International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, China, pp.1165–1169, 2020.
[7]	F. Lemic, J. Büsch, M. Chwalisz, et al., “Infrastructure for benchmarking RF-based indoor localization under controlled interference,” The International Conference on Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS'14), Corpus Christi, Texas, USA, pp.26–35, 2014.
[8]	J. Schuette, B. Fell, J. Chapin, et al., “Performance of RF mapping using opportunistic distributed devices, ” 2015 IEEE Military Communications Conference, Tampa, FL, USA, DOI: 10.1109/MILCOM.2015.7357677, 2015.
[9]	Q. Wu, F. Shen, Z. Wang, et al., “3D spectrum mapping based on ROI-driven UAV deployment,” IEEE Network, vol.34, no.5, pp.24–31, 2020.
[10]	G. Sun and J. van de Beek, “Simple distributed interference source localization for radio environment mapping,” 2010 IFIP Wireless Days, Venice, Italy, DOI: 10.1109/WD.2010.5657755, 2010.
[11]	G. Ding, Q. Wu, L. Zhang, et al., “An amateur drone surveillance system based on the cognitive Internet of things,” IEEE Communications Magazine, vol.56, no.1, pp.29–35, 2018.
[12]	D. J. Pack, P. DeLima, G. J. Toussaint, et al., “Cooperative control of UAVs for localization of intermittently emitting mobile targets,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol.39, no.4, pp.959–970, 2009.
[13]	Nan Zhao, Weidang Lu, Min Sheng, et al., “UAV-assisted emergency networks in disasters,” IEEE Wireless Communications, vol.26, no.1, pp.45–51, 2019.
[14]	X. Chen, M. Sheng, N. Zhao, et al., “UAV-relayed covert communication towards a flying warden,” IEEE Transactions on Communications, vol.69, no.11, pp.7659–7672, 2021.
[15]	H. Tsuji, D. Gray, M. Suzuki, et al., “Radio location estimation experiment using array antennas for high altitude platforms,” in Proc. of 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications, Athens, Greece, pp.1–5, 2007.
[16]	F. B. Sorbelli, S. K. Das, C. M. Pinotti, et al., “Range based algorithms for precise localization of terrestrial objects using a drone,” Pervasive Mobile Comput., vol.48, pp.20–42, 2018. doi: 10.1016/j.pmcj.2018.05.007
[17]	H. Bayerlein, P. De Kerret, and D. Gesbert, “Trajectory optimization for autonomous flying base station via reinforcement learning,” 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Kalamata, Greece, pp.1–5, 2018.
[18]	J. Gu, H. Wang, G. Ding, et al., “UAV-enabled mobile radiation source tracking with deep reinforcement learning,” 2020 International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, China, pp.672–678, 2020.
[19]	C. H. Liu, X. Ma, X. Gao, et al., “Distributed energy-efficient multi-UAV navigation for long-term communication coverage by deep reinforcement learning,” IEEE Transactions on Mobile Computing, vol.19, no.6, pp.1274–1285, 2020.
[20]	S. Wu, “Illegal radio station localization with UAV-based Q-learning,” China Communications, vol.15, no.12, pp.122–131, 2018.
[21]	G. Wu, “UAV-Based Interference Source Localization: A Multimodal Q-Learning Approach,” IEEE Access, vol.7, pp.137982–137991, 2019.
[22]	J. Tisdale, A. Ryan, Zu Kim, et al., “A multiple UAV system for vision-based search and localization,” 2008 American Control Conference, Seattle, Washington, USA, pp.1985−1990, 2008.
[23]	K. Maeda, S. Doki, Y. Funabora, et al., “Flight path planning of multiple UAVs for robust localization near infrastructure facilities,” IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, USA, pp.2522−2527, 2018.
[24]	Y. -J. Chen, D. -K. Chang, and C. Zhang, “Autonomous tracking using a swarm of UAVs: A constrained multi-agent reinforcement learning approach,” IEEE Transactions on Vehicular Technology, vol.69, no.11, pp.13702–13717, 2020.
[25]	W. B. Powell, Approximate Dynamic Programming: Solving the Curses of Dimensionality, John Wiley & Sons, Inc., 2011.
[26]	Martin L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley & Sons, Inc., 1995.
[27]	A. Wahbi, A. Roukhe, and L. Hlou, “Enhancing the quality of voice communications by acoustic noise cancellation (ANC) using a low cost adaptive algorithm based Fast Fourier Transform (FFT) and circular convolution,” 2014 9th International Conference on Intelligent Systems: Theories and Applications (SITA-14), Rabat, Morocco, DOI: 10.1109/SITA.2014.6847310, 2014.
[28]	S. S. Agaian, Mei-Ching Chen, and C. L. P. Chen, “Noise reduction algorithms using Fibonacci Fourier transforms,” 2008 IEEE International Conference on Systems, Man and Cybernetics, Singapore, pp.1048–1052, 2008.
[29]	M. K. Pakhira, “A linear time-complexity k-means algorithm using cluster shifting,” 2014 International Conference on Computational Intelligence and Communication Networks, Bhopal, India, pp.1047–1051, 2014.
[30]	B. Farhang-Boroujeny and Y. C. Lim, “A comment on the computational complexity of sliding FFT,” IEEE Transactions on Circuits and Systems Ⅱ: Analog and Digital Signal Processing, vol.39, no.12, pp.875–876, 1992.