Ground-Based Videometric Method and System for the Real-Time Measurement of Aircraft Landing Track

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This paper proposes a ground-based videometric method and system for measuring the glide track of landing aircraft in real time. The proposed method is applicable for large-scale measurement via regional relays with multiple cameras. Its measurement ranges from kilometers away to the landing point, and it simultaneously fulfills the real-time measurement of the position and trajectory of aircraft. The real-time measurement result of the actual aircraft landing process shows a deviation from DGPS(Difference Global Positioning System) as small as 20 cm in the measuring region of 1 km. The proposed measurement method for aircraft landing track based on videometrics can establish a new type of landing aid system removed from radar and GPS.

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824-831

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September 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Honglie Li , Junli Guo, The Design of Precision Approach DGPS/RA And Infrared Assistant Landing System, Radio Engineering of China, A 11, 32-33(2002).

Google Scholar

[2] Yong Liu, DeWei Wu, A Flight Test Method Research on Instrument Landing System Applied to a Far Zone, J. Air Force Engineering University, A 8, 23-26 (2003).

Google Scholar

[3] Jiang Feng, XiaoYan Wang, XiPing Liu, Wei Liao, GPS Navigating System for Airplane Automatic Landing, Armament Automation, A 23, 1-5 (2004).

Google Scholar

[4] Omid Shakernia, Yi Mat, T. John Koo, Joao Hespanha, S. Shankar Sastry, Vision Guided Landing of an Unmanned Air Vehicle, in Proceedings of IEEE Conference on Decision and Control Phoenix (IEEE, 1999), pp.4143-4148.

DOI: 10.1109/cdc.1999.828011

Google Scholar

[5] Courtney S. Sharp, Omid Shakernia, S. Shankar Sastry, A Vision System for Landing an Unmanned Aerial Vehicle, in Proceedings of IEEE Conference on Robotics and Automation (IEEE, 2001), pp.1720-1727.

DOI: 10.1109/robot.2001.932859

Google Scholar

[6] So-Ryeok Oh, Kaustubh Pathak, Sunil K. Agrawal, Hemanshu Roy Pota, Matt Garrett, Autonomous Helicopter Landing on a Moving Platform Using a Tether, in Proceedings of IEEE Conference on Robotics and Automation(IEEE, 2005), p.3960~3965.

DOI: 10.1109/robot.2005.1570726

Google Scholar

[7] Frew E, McGee T, Kim Z, Xiao X, Jackson S, Morimoto M, Rathinam S, Padial J, Sengupta R, Vision-Based Road-Following Using a Small Autonomous Aircraft, in Proceedings of IEEE Conference on Aerospace (IEEE, 2004), p.3006~3015.

DOI: 10.1109/aero.2004.1368106

Google Scholar

[8] Etinger S M, Nechyba M C, Vision-Guided Flight Stability and Control for Micro Air Vehicles, in Proceedings of IEEE Conference on Intelligent Robots Systems(IEEE, 2002), pp.2134-2140.

DOI: 10.1109/irds.2002.1041582

Google Scholar

[9] Damien Dusha, Wageeh Boles, Rodney Walker, Fixed-Wing Attitude Estimation Using Computer Vision Based Horizon Detection, presented at the 22th International Unmanned Air Vehicle Systems Conference, Melbourne, Australia, 16-18 April (2007).

DOI: 10.1109/dicta.2007.4426836

Google Scholar

[10] Sungmoon Joo, Corey Ippolito, Khalid Al-Ali, Yoo-Hsiu Yeh, Vision Aided Inertial Navigation with Measurement Delay for Fixed-Wing Unmanned Aerial Vehicle Landing, in Proceedings of IEEE Conference on Aerospace(IEEE, 2008), pp.1-9.

DOI: 10.1109/aero.2008.4526557

Google Scholar

[11] Sungsik Huh, David Hyunchul Shim, A Vision-Based Automatic Landing Method for Fixed-Wing UAVs, presented at the 2nd International Symposium on UAVs, Reno, Nevada, U.S.A., 8-10 June, (2009).

DOI: 10.1007/978-90-481-8764-5_11

Google Scholar

[12] O. A. YAKIMENKO, I. I. KAMINER, W. J. LENTZ, P. A. GHYZEL, Unmanned Aircraft Navigation for Shipboard Landing Using Infrared Vision, IEEE Transaction on Aerospace and Electronic Systems, A 38, pp.1181-1200 (2002).

DOI: 10.1109/taes.2002.1145742

Google Scholar

[13] Hanbury, The morphological top-hat operator generalised to multi-channel images, in Proceedings of the 17th International Conference on Pattern Recognition, (ICPR, 2004), pp.672-675.

DOI: 10.1109/icpr.2004.1334259

Google Scholar

[14] Crossley. Nishihara, Measuring photolithographic overlay accuracy and critical dimensions by correlating binarized Laplacian of Gaussian convolutions, IEEE Transaction on Pattern Analysis and Machine Intelligence, A 10, 17-30 (1998).

DOI: 10.1109/34.3864

Google Scholar

[15] Andres Huertas, and Gerard Medioni, Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks, IEEE Transactions on Pattern Analysis and Machine Intelligence, A 8, 651-664 (1986).

DOI: 10.1109/tpami.1986.4767838

Google Scholar