Unmanned aerial vehicles trajectory analysis considering missing data
Abstract
Researches very often deal with the problem of missing data. This issue is caused by impossibility of data obtaining, its distortion or concealment. The goal of present paper is to recover missing data and to analyse Unmanned Aerial Vehicles (UAV) trajectory based on the degree of deviation from pre-planned trajectory. The range probability approach is used to assess flight situation. The results of trajectory analysis for real position data of UAV are demonstrated.
Keyword : unmanned aerial vehicle, trajectory, data processing, data recovery, flight situation, spline interpolation
How to Cite
Wang, B., Kharchenko, V., Kukush, A., & Kuzmenko, N. (2019). Unmanned aerial vehicles trajectory analysis considering missing data. Transport, 34(2), 155-162. https://doi.org/10.3846/transport.2019.8544
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Twisk, J.; De Vente, W. 2002. Attrition in longitudinal studies: how to deal with missing data, Journal of Clinical Epidemiology 55(4): 329–337. https://doi.org/10.1016/S0895-4356(01)00476-0
Ambrosius, F. 2005. Interpolation of 3D Surfaces for Contact Modeling. University of Twente, Enschede, The Netherlands. 51 p. Available from Internet: https://www.ram.ewi.utwente.nl/aigaion/attachments/single/190
Chowdhary, G.; Jategaonkar, R. 2010. Aerodynamic parameter estimation from flight data applying extended and unscented Kalman filter, Aerospace Science and Technology 14(2): 106–117. https://doi.org/10.1016/j.ast.2009.10.003
EC. 2012. Towards a European Strategy for the Development of Civil applications of Remotely Piloted Aircraft Systems (RPAS). SWD(2012) 259 Final. European Commission (EC). 29 p. Available from Internet: http://register.consilium.europa.eu/pdf/en/12/st13/st13438.en12.pdf
ERSG. 2013. Roadmap for the Integration of Civil Remotely-Piloted Aircraft Systems into the European Aviation System. Final report from the European RPAS Steering Group (ERSG). 16 p. Available from Internet: https://ec.europa.eu/docsroom/documents/10484/attachments/1/translations/en/renditions/pdf
Harchenko, V. P.; Kukush, A. G.; Ostroumov, I. V. 2007. Optimizaciya kolichestva izmerenij koordinat pri mnogoal’ternativnoj klassifikacii situacij vozdushnogo dvizheniya, Kibernetika i vychislitel’naya tehnika 153: 52–59. (in Russian).
Harchenko, V. P.; Prusov, D. E. 2012. Osnovni pryncypy suchasnoi’ klasyfikacii’ bezpilotnyh aviacijnyh system, Proceedings of the National Aviation University 53(4): 5–12. (in Ukrainian). https://doi.org/10.18372/2306-1472.53.3477
Hardier, G.; Bucharles, A. 2010. On-line parameter identification for in-flight aircraft monitoring, in ICAS 2010: 27th International Congress of the Aeronautical Sciences, 19–24 September 2010, Nice, France, 1–12.
Hayhurst, K. J.; Maddalon, J. M.; Miner, P. S.; Szatkowski, G. N.; Ulrey, M. L.; DeWalt, M. P.; Spitzer, C. R. 2007. Preliminary Considerations for Classifying Hazards of Unmanned Aircraft Systems. Technical Report NASA/TM-2007-214539. National Aeronautics and Space Administration (NASA), Langley Research Center, Hampton, Virginia, US. 78 p. Available from Internet: https://ntrs.nasa.gov/search.jsp?R=20070008225
ICAO. 2008. Performance-Based Navigation (PBN) Manual. Doc 9613. AN/937. International Civil Aviation Organization (ICAO) 294 p. Available from Internet: https://www.icao.int/SAM/Documents/2009/SAMIG3/PBN%20Manual%20-%20Doc%209613%20Final%205%2010%2008%20with%20bookmarks1.pdf
ICAO. 2011. Unmanned Aircraft Systems (UAS). Cir 328. AN/190. International Civil Aviation Organization (ICAO). 38 p. Available from Internet: http://www.icao.int/Meetings/UAS/Documents/Circular%20328_en.pdf
George, E. A.; Tiwari, G.; Yadav, R. N.; Peters, E.; Sadana, S. 2013. UAV systems for parameter identification in agriculture, in 2013 IEEE Global Humanitarian Technology Conference: South Asia Satellite (GHTC-SAS), 23–24 August 2013, Trivandrum, India, 270–273. https://doi.org/10.1109/GHTC-SAS.2013.6629929
Kanchana, S, Thanamani, A. S. 2014. Classification of efficient imputation method for analyzing missing values, International Journal of Computer Trends and Technology 12(4): 193–195. https://doi.org/10.14445/22312803/IJCTT-V12P138
Kharchenko, V.; Kuzmenko, N. 2013. Unmanned aerial vehicle collision avoidance using digital elevation model, Proceedings of the National Aviation University 54(1): 21–25. https://doi.org/10.18372/2306-1472.54.3858
Kharchenko, V. P.; Kuzmenko, N. S.; Kukush, A. G.; Ostroumov, I. V. 2016. Multi-parametric data recovery for unmanned aerial vehicle navigation system, in 2016 4th International Conference on Methods and Systems of Navigation and Motion Control (MSNMC), 18–20 October 2016, Kiev, Ukraine, 295–299. https://doi.org/10.1109/MSNMC.2016.7783165
Kharchenko, V.; Kuzmenko, N.; Mykhatsky, O.; Savchenko, O. 2014. Experimental unmanned aerial vehicle flight data measurement and their post-processing analysis, Proceedings of the National Aviation University 58(1): 11–16. https://doi.org/10.18372/2306-1472.58.6632
Kharchenko, V; Prusov, D. 2012. Analysis of unmanned aircraft systems application in the civil field, Transport 27(3): 335–343. https://doi.org/10.3846/16484142.2012.721395
Ostroumov, I. V.; Kukush, O. G.; Harchenko, V. P. 2007. Bagatoal’ternatyvna klasyfikaciya sytuacij povitryanogo stanu u vypadku, koly shhil’nosti rozpodilu jmovirnosti vidomi netochno, Proceedings of the National Aviation University 31(1): 73–77. (in Ukrainian). https://doi.org/10.18372/2306-1472.31.1439
Royston, P. 2004. Multiple imputation of missing values, The Stata Journal: Promoting Communications on Statistics and Stata 4(3): 227–241. https://doi.org/10.1177/1536867X0400400301
Seber, G. A. F.; Lee, A. J. 2003. Linear Regression Analysis. Wiley. 582 p.
Sujit, P. B.; Saripalli, S.; Sousa, J. B. 2013. An evaluation of UAV path following algorithms, in 2013 European Control Conference (ECC), 17–19 July 2013, Zurich, Switzerland, 3332–3337. https://doi.org/10.23919/ECC.2013.6669680
Twisk, J.; De Vente, W. 2002. Attrition in longitudinal studies: how to deal with missing data, Journal of Clinical Epidemiology 55(4): 329–337. https://doi.org/10.1016/S0895-4356(01)00476-0