Using Image Registration Method to Register UAV

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Abstract:

For the UAV’s characteristics that is the date of camera shooting and photographing and digital map contain information, a UAV target location method is presented based on image matching. Firstly, using the artificial reconnaissance registration method that has improved, the UAV aerial images that consider digital map coordinates as the target are registered to obtain registration information. Then, the UAV video frame resolution containing the target is improved by improving the method of super-resolution reconstruction. Finally, making use of the Gaussian pyramid matching algorithm based on optical flow, the video frames are registered with the aerial to complete the target. By MATLAB and C++ simulation, achieving the above algorithm. The results proved that this method achieve the target location without the UAV position and attitude information, while improving the precision and speed of target location, that is an effective method for target location in the battlefield.

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1675-1679

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December 2014

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© 2015 Trans Tech Publications Ltd. All Rights Reserved

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[1] MaoZhaojun, WangDeihu. A Model of Target Position for UAV Based attitude Measuring /Laser Range-Finder[J]. Fire Control & Command Control. 2003, 28(5): 334-337.

Google Scholar

[2] Srikanth Saripalli, James F. Montgomery. Vision- based Autonomous Landing of an Unmanned Aerial Vehicle[C]. Proceeding of IEEE International Conference on Robotics and Automation. May2005.

DOI: 10.1109/robot.2002.1013656

Google Scholar

[3] FanBangquan, DuanLianfei, ZhaoBingai, etal. UAV Reconnaissance Target Locationn Technology[M]. Beijing: National Defense Industry Press , 2014, 6, 21-29.

Google Scholar

[4] A. J. Patti, M. I. Sezan, A. M. Tekalp. Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time. IEEE Trans IP, 1997, 6(8): 1064-1076.

DOI: 10.1109/83.605404

Google Scholar

[5] ZhuoLi, WangSuyu,LiXiaoguang. Image/Video Super Resolution[M]. Beijing: Posts&Telecom press , 2011, 100-102.

Google Scholar

[6] Waske B and van der Linden S. Classifying multilevel imagery from SAR and optical sensors by decision fusion[J]. IEEE Transactions on Geoscience and Remo-te Sensing, 2008, 46: 1457-1466.

DOI: 10.1109/tgrs.2008.916089

Google Scholar

[7] LvWenTao, LvGaohuan. Application of Scale Invariant Feature Transform to SAR image matching[J]. Information and Electronic Engineering, 2010, 8(4): 388-392.

Google Scholar

[8] WangSuyu , ZhuoLi , ShenLansun , etal. A SimPle & Efefctive Video Sequence SuPer-Resolution Algorithm[J]. Journal of Beijing University of Technology, 2009, 35(6): 742-747.

Google Scholar