Laser Technology, Volume. 44, Issue 4, 471(2020)
Identification of aircraft wake vortex based on k-nearest neighbor
[1] [1] MURPHY B, O’CALLAGHAN J, FOX M, et al. Overview of the structures investigation for the american airlines flight 587 investigation[C]//46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. New York, USA:IEEE,2013:3369-3373.
[2] [2] GERZ T, HOLZPFEL F, DARRACQ D. Commercial aircraft wake vortices[J]. Progress in Aerospace Sciences, 2002, 38(3): 181-208.
[3] [3] CORNELIUS J O, RUTISHAUSER D K. Enhanced airport capacity through safe, dynamics reductions in aircraft separation[C]//Aircraft VOrtex Spacing System (AVOSS).New York,USA:IEEE,2001:77-103.
[4] [4] HOLZPFEL F, GERZ T, FRECH M, et al. The wake vortex prediction and monitoring system WSVBS Part Ⅰ: Design[J]. Air Traffic Control Quarterly, 2008, 17(9):301-322.
[5] [5] GERZ T, HOLZPFEL F, GERLING W, et al. The wake vortex prediction and monitoring system WSVBS(Part Ⅱ: Performance and ATC integration at Frankfurt airport)[J]. Air Traffic Control Quarterly, 2009, 17(4): 323-346.
[6] [6] LIANG H J, DENG W X, LIANG Y A, et al. Review of aerocraft wake vortex separation dynamic reduction technology[J]. Journal of Ordnance Equipment Engineering, 2018, 39(12):15-19(in Chinese).
[7] [7] HANNON S M, THOMSON J A. Aircraft wake vortex detection and measurement with pulsed solid-state coherent laser radar[J]. Journal of Modern Optics, 1994, 41(11):2175-2196.
[8] [8] HARRIS M, YOUNG R I, KPP F, et al. Wake vortex detection and monitoring[J]. Aerospace Science and Technology, 2002, 6(5):325-331.
[9] [9] XU Sh L, HU Y H, WU Y H. Identification of aircraft wake vortex based on Doppler spectrum features[J]. Journal of Optoelectronics·Laser, 2011, 22(12): 1826-1830(in Chinese).
[10] [10] PAN W J, ZHANG Q Y, ZHANG Q, et al. Identification method of aircraft wake vortex based on Doppler lidar[J]. Laser Technology, 2019, 43(2):233-237(in Chinese).
[11] [11] PAN W J, DUAN Y J, ZHANG Q, et al. Research on aircraft wake vortex recognition using AlexNet[J]. Opto-Electronic Engineering, 2019, 46(7): 190082(in Chinese).
[12] [12] ZHAO L Y, GU R P, WEI Z Q. Calculation of characteristics parameters of dynarnic wake vortex based on lidar echo[J]. Journal of Wuhan University of Science and Technology, 2018, 41(5):388-394(in Chinese).
[13] [13] LI H. Statistical learning method [M]. 2nd ed. Beijng: Tsinghua University Press, 2012:49-58(in Chinese).
[14] [14] COVER T, HART P. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory, 2003, 13(1):21-27.
[15] [15] FAWCETT T. An introduction to ROC analysis[J]. Pattern Recognition Letters, 2006, 27(8):861-874.
[16] [16] RUPPERT D. The elements of statistical learning: Data mining, inference, and prediction[J]. Journal of the American Statistical Association, 2004, 99(466):567-567.
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PAN Weijun, WU Zhengyuan, ZHANG Xiaolei. Identification of aircraft wake vortex based on k-nearest neighbor[J]. Laser Technology, 2020, 44(4): 471
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Received: Jul. 17, 2019
Accepted: --
Published Online: Jul. 16, 2020
The Author Email: PAN Weijun (675702767@qq.com)