期刊
MATHEMATICS
卷 10, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/math10071196
关键词
Flight Maneuver Recognition (FMR); unsupervised clustering; phase space reconstruction
类别
资金
- Civil Aviation Flight Technology and Flight Safety Key Laboratory of China [FZ2020ZZ02]
- CAFUC Research Project [CJ202101]
- National Natural Science Foundation of China [U2033213]
- Sichuan Science and Technology Program [2022YFG0027]
This study presents a universal framework for flight maneuver recognition, which can be applied to various flight tasks. By preprocessing, reconstructing, and calculating entropy, it can accurately identify different types of flight maneuvers for multiple aircraft types.
Flight Maneuver Recognition (FMR) refers to the automatic recognition of a series of aircraft flight patterns and is a key technology in many fields. The chaotic nature of its input data and the professional complexity of the identification process make it difficult and expensive to identify, and none of the existing models have general generalization capabilities. A general framework is proposed in this paper, which can be used for all kinds of flight tasks, independent of the aircraft type. We first preprocessed the raw data with unsupervised clustering method, segmented it into maneuver sequences, then reconstructed the sequences in phase space, calculated their approximate entropy, quantitatively characterized the sequence complexity, and distinguished the flight maneuvers. Experiments on a real flight training dataset have shown that the framework can quickly and correctly identify various flight maneuvers for multiple aircraft types with minimal human intervention.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据