Abstract
A particle filter-based algorithm for 3D localization of WSN (Wireless Sensor Network) nodes from an Unmanned Aerial Vehicle (UAV) is proposed. The algorithm uses the information obtained from RSSI measurements taken by a node located on-board the UAV together with its estimated position provided by the UAV navigation system. As the WSN nodes are assumed to be on the ground, Digital Elevation Model (DEM) data has been used to improve the accuracy of the estimation. WSN localization algorithms have typically been validated in short range scenarios. In this paper, field experiments have been carried out to validate the proposed algorithm in medium range scenarios. For this purpose real flights have been conducted with a fixed-wing UAV flying up to 1.5 km away from the transmitter to be located. During the flights the algorithm was running on an on-board embedded computer and the estimated position was sent to the ground control station for monitoring purposes. The results of these experiments are presented in this paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zanca, G., Zorzi, F., Zanella, A., Zorzi, M., Max, M.: Experimental comparison of RSSI-based localization algorithms for indoor wireless sensor networks. In: Workshop on Real-World Wireless Sensor Networks, 2008, pp. 1–5
Peng, R., Sichitiu, M.L.: Probabilistic localization for outdoor wireless sensor networks. ACM SIGMOBILE Mob. Comput. Commun. Rev. 11(1), 53–64 (2007)
Luo, X., O’Brien, W.J., Julien, C.L.: Comparative evaluation of received signal-strength index (RSSI) based indoor localization techniques for construction jobsites. Adv. Eng. Inform. 25(2), 355–363 (2011)
Oguejiofor, O.S., Okorogu, V.N., Abe, A., Osuesu, B.O.: Outdoor localization system using RSSI measurement of wireless sensor network. Int. J. Innov. Technol. Explor. Eng. 2(2), 1–6 (2013)
Sichitiu, M.L., Ramadurai, V., Peddabachagari, P.: Simple algorithm for outdoor localization of wireless sensor networks with inaccurate range measurements. In: International Conference on Wireless Networks, 2003
Benbadis, F., Friedman, T., Dias de Amorim, M., Fdida, S.: GPS-free-free positioning system for wireless sensor networks. In: Second IFIP International Conference on Wireless and Optical Communications Networks, 2005. WOCN 2005, pp. 541–545
Morelli, C., Nicoli, M., Rampa, V., Spagnolini, U., Alippi, C.: Particle filters for Rss-based localization in wireless sensor networks: an experimental study. In: 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006, Vol. 4, pp. IV-957–IV-960
Sichitiu, M.L., Ramadurai, V.: Localization of wireless sensor networks with a mobile beacon. In: 2004 IEEE international conference on mobile Ad-hoc and sensor systems (IEEE Cat. No. 04EX975), pp. 174–183
Amundson, I., Koutsoukos, X.D.: A survey on localization for mobile wireless sensor networks. In: 2nd International Conference on Mobile Entity Localization and Tracking in GPS-less Environments (MELT’09), 2009, pp. 235–254
Caballero, F., Merino, L., Maza, I., Ollero, A.: A particle filtering method for wireless sensor network localization with an aerial robot beacon. In: 2008 International Conference on Robotics and Automation, pp. 596–601, May 2008
Caballero, F., Merino, L., Gil, P., Maza, I., Ollero, A.: A probabilistic framework for entire WSN localization using a Mobile Robot. Rob. Auton. Syst. 56(10), 798–806 (2008)
Korner, F., Speck, R., Goktogan, A.H., Sukkarieh, S.: Autonomous airborne wildlife tracking using radio signal strength. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010, pp. 107–112
Posch, A., Sukkarieh, S.: UAV based search for a radio tagged animal using particle filters. In: Australasian conference on Robotics and Automation (ACRA), 2009
Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Stat. Comput. 10(3), 197–208 (2000)
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)
Bergman, N.: Recursive Bayesian estimation: Navigation and Tracking applications, Linköpings University, Sweden (1999)
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. Massachusetts Institute of Technology, Cambridge (2005)
Bekir, E.: Introduction to Modern Navigation Systems. World Scientific Publishing, Hackensack (2007)
Friis, H.T.: Proceedings of the Institute of Radio Engineers, vol. 34, p. 254 (1946)
DLR, EOWEB (Earth Observation Data Service). http://eoweb.dlr.de:8080/free_SRTM_X-band_data.html. Accessed 20 Mar 2014
DLR, SRTM in DLR Earth Observation Center. http://www.dlr.de/caf/en/desktopdefault.aspx/tabid-5515/9214_read-17716/. Accessed 20 Mar 2014
GRACE, Gravity Recovery and Climate Experiment—Earth gravity definition. http://www.csr.utexas.edu/grace/gravity/gravity_definition.html. Accessed 28 Mar 2014
UNAVCO, Geoid Height Calculator. http://www.unavco.org/community_science/science-support/geoid/geoid.html. Accessed 20 Mar 2014
ARBA MOBILE Product Catalog, Albentia Systems. http://www.albentia.com/Docs/ARBA Mobile—Catalogo de \(\text{ producto }\_\)EN.pdf. Accessed 14 Mar 2014
UAV X-Vision (E500), Elimco. http://www.elimco.com/p_UAV-E500_25.html. Accessed 14 Mar 2014
CloudCapTechnologies, Piccolo II Datasheet. http://www.cloudcaptech.com/Sales and Marketing Documents/Piccolo II Data Sheet.pdf. Accessed 14 Mar 2014
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Cuesta, F., Cordero, M., Díaz, L., Viguria, A., Ollero, A. (2015). A Particle Filter-Based Method for Ground-Based WSN Localization Using an Aerial Robot. In: Koubâa, A., Martínez-de Dios, J. (eds) Cooperative Robots and Sensor Networks 2015. Studies in Computational Intelligence, vol 604. Springer, Cham. https://doi.org/10.1007/978-3-319-18299-5_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-18299-5_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18298-8
Online ISBN: 978-3-319-18299-5
eBook Packages: EngineeringEngineering (R0)