Three-Dimensional RF Source Localization Using Reflection and an Improved Particle Filter

Document Type : Research Article


Department of Electrical Engineering, Faculty of Engineering, University of Zabol, Zabol, Iran


This study uses an obstacle map for three-dimensional radio frequency (RF) source localization with reflection. The received signal strength indicator (RSSI) and the angle of arrival (AOA) are the observation requirements for the three-dimensional localization. In the first step of the localization, an unmanned aerial vehicle (UAV) is used to obtain AOA and locate the three-dimensional reflection using a two-dimensional map. Then, the path loss function is used and the reflection angle alongside the distance between the receiver and RF source is estimated based on RSSI. This information 
is integrated with the information from the two-dimensional map to estimate the RF source location in the three-dimensional space. The possible RF source locations in three-dimensional space are obtained, and it is shown that the possible locations of the RF source for one reflection in the three-dimensional make a circle, so three reflections are required for three-dimensional RF source localization. An improved particle filter is used to estimate RF source location while using the Kullback-Leibler distance (KLD) criteria and local search to improve the method performance with proper estimation speed and accuracy. The simulation results show that the improved particle filter has an adequate estimation with optimal particle number and higher execution speed than the initial particle filter.


Main Subjects

  1. Bai, H. Xu, J. Li, X. Gao, F. Qin, X. Zheng, Coal mine personnel positioning algorithm based on improved adaptive unscented Kalman filter with wireless channel fading and unknown noise statistics, Transactions of the Institute of Measurement and Control, 44(6) (2022) 1217-1227.
  2. R. Puji, W.H. Ari, W. Risma, Wireless Nurse Call System Using IoT Implementation, Journal of Electrical and Electronics Engineering, 14(1) (2021) 11-16.
  3. Murray, S.F. Hasan, Present state of the art in post disaster victim localization, in: 2020 IEEE 5th International Symposium on Telecommunication Technologies (ISTT), IEEE, 2020, pp. 51-56.
  4. Cacace, N. Mimmo, L. Marconi, An ARVA sensor simulator, Robot Operating System (ROS) The Complete Reference (Volume 5), (2021) 233-266.
  5. Mimmo, P. Bernard, L. Marconi, Avalanche victim search via robust observers, IEEE Transactions on Control Systems Technology, 29(4) (2020) 1450-1461.
  6. Berioli, J.M. Chaves, N. Courville, P. Boutry, J.L. Fondere, H. Skinnemoen, H. Tork, M. Werner, M. Weinlich, WISECOM: A rapidly deployable satellite backhauling system for emergency situations, International Journal of Satellite Communications and Networking, 29(5) (2011) 419-440.
  7. Demiane, S. Sharafeddine, O. Farhat, An optimized UAV trajectory planning for localization in disaster scenarios, Computer networks, 179 (2020) 107378.
  8. Sinha, I. Guvenc, Impact of antenna pattern on TOA based 3D UAV localization using a terrestrial sensor network, IEEE Transactions on Vehicular Technology, 71(7) (2022) 7703-7718.
  9. T. Le, X. Huang, C. Ritz, E. Dutkiewicz, A. Bouzerdoum, D. Franklin, Hybrid TOA/AOA localization with 1D angle estimation in UAV-assisted WSN, in: 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS), IEEE, 2020, pp. 1-6.
  10. Tomic, M. Beko, R. Dinis, P. Montezuma, Distributed algorithm for target localization in wireless sensor networks using RSS and AoA measurements, Pervasive and Mobile Computing, 37 (2017) 63-77.
  11. Yu, I. Sharp, Y.J. Guo, Ground-based wireless positioning, John Wiley & Sons, 2009.
  12. Tian, Y. Liu, Q. Hu, A NLOS Mitigation Algorithm for TOA Based Localization in Mixed LOS/NLOS Environments, in: International Conference on Autonomous Unmanned Systems, Springer, 2022, pp. 2843-2853.
  13. Wang, K. Gu, Y. Wu, W. Dai, Y. Shen, NLOS effect mitigation via spatial geometry exploitation in cooperative localization, IEEE Transactions on Wireless Communications, 19(9) (2020) 6037-6049.
  14. Gentner, T. Jost, W. Wang, S. Zhang, A. Dammann, U.-C. Fiebig, Multipath assisted positioning with simultaneous localization and mapping, IEEE Transactions on Wireless Communications, 15(9) (2016) 6104-6117.
  15. Haidari, H. Moradi, M. Shahabadi, S.M. Dehghan, A reflection-based rf source localization algorithm, International Journal of Robotics and Automation, 34(3) (2019).
  16. Haidari, H. Moradi, M. Shahabadi, S.M. Dehghan, RF source localization using reflection model in NLOS condition, in: 2016 4th International Conference on Robotics and Mechatronics (ICROM), IEEE, 2016, pp. 601-606.
  17. Zaidi, I. Bouazzi, M. Usman, M.Z. Mohammed Shamim, N. Singh, V.K. Gunjan, Cooperative Scheme ToA-RSSI and Variable Anchor Positions for Sensors Localization in 2D Environments, Complexity, 2022 (2022).
  18. Zekavat, R.M. Buehrer, Handbook of position location: Theory, practice and advances, John Wiley & Sons, 2011.
  19. Chu, Z. Lu, D. Gesbert, L. Wang, X. Wen, Vehicle localization via cooperative channel mapping, IEEE Transactions on Vehicular Technology, 70(6) (2021) 5719-5733.
  20. Zhang, L. Chen, M. Feng, T. Jiang, Toward reliable non-line-of-sight localization using multipath reflections, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 6(1) (2022) 1-25.
  21. Wang, K. Zhao, Z. Zheng, A 3D indoor positioning method of wireless network with single base station in multipath environment, Wireless Communications and Mobile Computing, 2022 (2022).
  22. Haidari, H. Moradi, S. Dehghan, RF source localization using obstacles map and reflections, International Journal of Industrial Electronics Control and Optimization, 4(2) (2021) 181-190.
  23. W. Li, G.S. Bastos, A hybrid self-adaptive particle filter through KLD-sampling and SAMCL, in: 2017 18th International Conference on Advanced Robotics (ICAR), IEEE, 2017, pp. 106-111.
  24. Budiarto, K. Horihata, K. Haneda, J.-i. Takada, Experimental study of non-specular wave scattering from building surface roughness for the mobile propagation modeling, IEICE transactions on Communications, 87(4) (2004) 958-966.
  25. Li, S. Sun, T.P. Sattar, Adapting sample size in particle filters through KLD-resampling, Electronics Letters, 49(12) (2013) 740-742.