李盛, 刘安, 郭金鼎. 基于智能跑道光栅阵列传感器的机型辨识方法[J]. 应用光学, 2024, 45(2): 453-460. DOI: 10.5768/JAO202445.0208002
引用本文: 李盛, 刘安, 郭金鼎. 基于智能跑道光栅阵列传感器的机型辨识方法[J]. 应用光学, 2024, 45(2): 453-460. DOI: 10.5768/JAO202445.0208002
LI Sheng, LIU An, GUO Jinding. Aircraft type identification method based on FBG sensing array of smart runway[J]. Journal of Applied Optics, 2024, 45(2): 453-460. DOI: 10.5768/JAO202445.0208002
Citation: LI Sheng, LIU An, GUO Jinding. Aircraft type identification method based on FBG sensing array of smart runway[J]. Journal of Applied Optics, 2024, 45(2): 453-460. DOI: 10.5768/JAO202445.0208002

基于智能跑道光栅阵列传感器的机型辨识方法

Aircraft type identification method based on FBG sensing array of smart runway

  • 摘要: 针对当前机型辨识方法易受环境影响的不足,提出了一种新的基于智能跑道光栅阵列传感器的机型辨识方法。利用光栅阵列横向布置光缆采集飞机滑跑通过时的分布式振动响应,通过分析多测区时程脉冲响应特征,确定主辅起落架通过光缆的时差。通过光栅阵列纵向布置光缆感知飞机的滑行轨迹,采用多项式拟合确定飞机滑行速度。基于飞机主辅起落架的测试值与理论值的匹配关系对飞机型号进行辨识,利用某机场试飞及开航2个月内的航班信息进行方法检验。结果显示,提出的机型辨识方法识别准确率可达98.44%,可以对B757、B738、A320和A321机型进行有效辨识。

     

    Abstract: Aiming at the shortcomings that the current aircraft type identification method is easily affected to environmental influences, a novel aircraft type identification method based on fiber optic Bragg grating (FBG) sensing array of smart runway was proposed. The distributed vibration response of the aircraft taxiing was collected by using the FBG array buried horizontally under the pavement. By analyzing the time-history impulse response features of multiple measurement areas, the time differences between the main and auxiliary landing gears passing through the optical cable were determined. The taxiing trajectory of the aircraft was sensed by the FBG array buried longitudinally under the pavement, through which the taxiing speed of the aircraft was determined by polynomial fitting. The aircraft type was identified based on matching relationship between the test value and the theoretical value of the main and auxiliary landing gears of the aircraft. The aircraft information in the test flight and the initial two-month operation of a certain airport were used for method verification. The results show that the identification accuracy of the proposed identification method can reach 98.44%, which can effectively distinguish the B757, B738, A320 and A321 models.

     

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