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Risk Evaluation Scheme in Automatic Aircraft Carrier Landing Using Artificial Neural Network

Published in Korean (English title shown)

Jihoon Lee, Somang Lee, Seokwon Lee, Donghae Kim, Youdan Kim

3rd Aviation-Specialized University Conference, Seoul, Korea, Feb. 2016

Figure 3. Schematic diagram of artificial neural network for risk evaluation scheme Ⅰ.
Figure 3. Schematic diagram of artificial neural network for risk evaluation scheme Ⅰ.
Figure 2. Landing boxes along the glide path.
Figure 2. Landing boxes along the glide path.

Figures and captions reproduced from the paper (© the authors and original publisher).

Automatic aircraft carrier landing on an aircraft carrier has many risk factors, and it is important to make a proper wave-off decision by evaluating the risk. While a landing signal officer generally takes responsibility for the decision, full automation of carrier landing requires a appropriate risk evaluation scheme. In this paper, two position error-based risk evaluation schemes considering landing environment are proposed using artificial neural network. The artificial neural network takes vertical/horizontal glide path error or corresponding scale factor of ellipse as inputs in addition to root mean square value of distance to glide path, and takes reward or penalty according to the outcome of the landing attempt: successful landing, bolter and crash. Learning data for the network are obtained through Monte Carlo simulation.

Author PDFhttps://jihoonlee91.github.io/papers/pdfs/2016-nn-carrier-landing-risk-assessment-ko.pdfBibTeX@inproceedings{Lee2016, title = {Risk Evaluation Scheme in Automatic Aircraft Carrier Landing Using Artificial Neural Network}, author = {Jihoon Lee and Somang Lee and Seokwon Lee and Donghae Kim and Youdan Kim}, year = {2016}, booktitle = {3rd Aviation-Specialized University Conference, Seoul, Korea, Feb. 2016}, note = {Original Korean title: 인공신경망을 이용한 함상 자동착륙 상황에서의 위험도 평가 기법}, }

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