Intelligent Recognition of Flight Pattern Based on IFPRM-SBLFS Deep Learning

Shiyan SUN; Lin LI; Huimin ZHU; Zhangsong SHI; Weige LIANG

doi:10.12382/bgxb.2024.0893

您当前的位置：

首页 >

文章列表页 >

Intelligent Recognition of Flight Pattern Based on IFPRM-SBLFS Deep Learning

更新时间：2025-09-05

- Intelligent Recognition of Flight Pattern Based on IFPRM-SBLFS Deep Learning
- Acta Armamentarii Vol. 46, Issue 5, Pages: 240893(2025)
- 作者机构：
  
  海军工程大学, 湖北武汉 430033
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2024.0893
  CLC：
- Received：25 September 2024，
  
  Published Online：07 May 2025，
  
  Published：31 May 2025
- 稿件说明：
移动端阅览
Shiyan SUN, Lin LI, Huimin ZHU, et al. Intelligent Recognition of Flight Pattern Based on IFPRM-SBLFS Deep Learning[J]. Acta Armamentarii, 2025, 46(5): 240893.
DOI：

Shiyan SUN, Lin LI, Huimin ZHU, et al. Intelligent Recognition of Flight Pattern Based on IFPRM-SBLFS Deep Learning[J]. Acta Armamentarii, 2025, 46(5): 240893. DOI： 10.12382/bgxb.2024.0893.

摘要

在实际监测任务中

及时有效地识别飞行模式至关重要。然而

现有的飞行模式识别方法主观性强、模式单一

限制了在复杂情况下的飞行监控能力

在实际应用中有局限性

进而导致模式边界定位不精确、识别精度低。为此提出一种基于敏感边界和长飞行序列的飞行模式智能识别方法(Intelligent Flight Pattern Recognition Method for Sensitive Boundaries and Long Flight Sequences

IFPRM-SBLFS)

以对飞行模式进行智能识别。为了更好地探索多模式飞行参数的空间关系

设计自适应图嵌入

针对不同持续时间的飞行模式提出去噪深度多尺度自动编码器

以及用于减轻模型损失的分类加权焦点损失和回归联合时空交集损失。为验证所提方法的优越性

采集多架民用航班的真实参数

涵盖11种飞行模式

通过人工标注构建飞行模式数据集。仿真计算结果表明:新模型能够在连续飞行架次中自动区分不同的飞行模式

并准确提取模式边界

识别准确率达到了99.07%

且无需任何预处理或后处理;新的智能识别方法可以有效提高精确度和敏感边界的飞行模式识别效果。

Abstract

Timely and effective identification of aircraft flight patterns is crucial in monitoring task. However

the existing flight pattern recognition methods have limitations in practical applications due to strong subjectivity and single pattern

which limits the flight monitoring capability in complex situations

and in turn leads to imprecise pattern boundary positioning and low recognition accuracy. For this reason

a flight pattern intelligent recognition method based on sensitive boundaries and long flight sequences is proposed for the intelligent recognition of flight states. In order to better explore the spatial relationships of multi-modal flight parameters

an adaptive graph embedding is designed. A denoising depth multi-scale autoencoder is proposed for the flight patterns at different durations

as well as the classification-weighted focal point loss and regression-joint spatio-temporal intersection loss for mitigating model loss. In order to verify the superiority of the proposed method

the real parameters of several civil flights covering 11 flight patterns are collected

and a flight state dataset is constructed by manual labelling. The results show that the proposed model is able to automatically distinguish different flight patterns in consecutive flight sorties and accurately extract the mode boundaries without any pre-processing or post-processing

with an identification accuracy of 99.07%. The intelligent recognition method can effectively improve the recognition accuracy and the flight state recognition of sensitive boundaries.

关键词

Keywords

references

HU C Y , WU J Y , SUN C , et al. Intelligent temporal detection network for boundary-sensitive flight regime recognition [J ] . Engineering Applications of Artificial Intelligence , 2023 , 126 : 106949 .

王志森 , 张召悦 , 冯朝辉 , 等 . 终端区飞行轨迹聚类分析及异常轨迹识别 [J ] . 科学技术与工程 , 2022 , 29 ( 9 ): 3807 - 3813 .

WANG Z S , ZHANG Z Y , FENG C H , et al. Cluster analysis and abnormal trajectory identification of flight trajectory in terminal area [J ] . Science Technology and Engineering , 2022 , 29 ( 9 ): 3807 - 3813 . (in Chinese)

ZHANG Q L , MOTT J H , JOHNSON M E , et al. Development of a reliable method for general aviation flight phase identification [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 23 ( 8 ): 11729 - 11738 .

任济寰 , 吴祥 , 薄煜明 , 等 . 基于增强上下文信息长短期记忆网络的弹道轨迹预测 [J ] . 兵工学报 , 2023 , 44 ( 2 ): 462 - 471 . DOI: 10.12382/bgxb.2021.0489 http://doi.org/10.12382/bgxb.2021.0489 根据己方观测数据进行弹道轨迹预测是现代陆军实施精准打击的重要一环。针对现有弹道轨迹预测方法存在精度不足且实时性不强的问题,提出一种新的增强上下文信息长短期记忆(CE-LSTM)网络轨迹预测模型,进行弹道轨迹的长期精准预测。在LSTM网络可逼近任意非线性函数且具备长期记忆能力的基础上,构建隐藏层输出混合单元提取短时上下文信息,进一步逼近弹体运动状态;通过建立不同条件下的弹道轨迹的数据集,训练得到具备最优超参数的CE-LSTM网络。实验结果表明,与弹道微分方程组的数值积分解法以及高斯混合模型相比,CE-LSTM网络在预测的精度上优于其他2种方法,预测速度提高了3~10倍,且具备较强的泛化能力。

REN J H , WU X , BO Y M , et al. Ballistic trajectory prediction based on long-term and short-term memory network with enhanced contextual information [J ] . Acta Armamentarii , 2023 , 44 ( 2 ): 462 - 471 . (in Chinese)

SHEN L , WEI Y N , WANG Y Z , et al. AFMF: time series anomaly detection framework with modified forecasting [J ] . Knowledge-Based Systems , 2024 , 296 : 111912 .

LI W H , CHEN Z Y , HE G L . A novel weighted adversarial transfer network for partial domain fault diagnosis of machinery [J ] . IEEE Transactions on Industrial Informatics , 2021 , 17 ( 3 ): 1753 - 1762 .

LI J , WANG Y , ZI Y Y , et al. Whitening-net: a generalized network to diagnose the faults among different machines and conditions [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2022 , 33 ( 10 ): 5845 - 5858 .

WU J Y , ZHAO Z B , SUN C , et al. Learning from class-imbalanced data with a model-agnostic framework for machine intelligent diagnosis [J ] . Reliability Engineering & System Safety , 2021 , 216 : 107934 .

LEE D H . Implementation of tactical maneuvers with maneuver libraries [J ] . Chinese Journal of Aeronautics , 2020 , 33 ( 1 ): 255 - 270 .

WU J Y , SUN C , ZHANG C , et al. Deep clustering variational network for helicopter regime recognition in HUMS [J ] . Aerospace Science and Technology , 2022 , 124 : 107553 .

FAN Y Q , SHEN G M , XU X H , et al. Flight track pattern recognition based on few labeled data with outliers [J ] . Journal of Electronic Imaging , 2021 , 30 ( 3 ): 031204 .

ALVAREZMONTOYA J , CARVAJALCASTRILLON A , SIERRA-PEREZ J . In-flight and wireless damage detection in a UAV composite wing using fiber optic sensors and strain field pattern recognition [J ] . Mechanical Systems and Signal Processing , 2020 , 136 : 106526 .

ZHOU S H , ZHOU Y L , XU Z Z , et al. The landing safety prediction model by integrating pattern recognition and Markov chain with flight data [J ] . Neural Computing and Applications , 2019 , 31 ( S1 ): 147 - 159 .

JIAO Q C , WANG X L , WANG L J , et al. Audio features based ADS-CNN method for flight attitude recognition of quadrotor UAV [J ] . Applied Acoustics , 2023 , 211 : 109540 .

殷之平 , 黄勇胜 , 田珈玮 , 等 . 一种基于二维轨迹与时序重要点的飞机机动划分与识别方法 [J ] . 航空工程进展 , 2025 , 16 ( 1 ): 45 - 53 .

YIN Z P , HUANG Y S , TIAN J W , et al. An aircraft manoeuvre classification and identification method based on two-dimensional trajectories and time-series significant points [J ] . Advances in Aeronautical Engineering , 2025 , 16 ( 1 ): 45 - 53 . (in Chinese)

张裕禄 , 毕红葵 , 叶泽浩 , 等 . 基于随机森林的HRGV滑翔段飞行模式识别 [J ] . 战术导弹技术 , 2020 ( 2 ): 1 - 8 ,21.

ZHANG Y L , BI H K , YE Z H , et al. Flight state identification of HRGV gliding segment based on random forest [J ] . Tactical Missile Technology , 2020 ( 2 ): 1 - 8 ,21. (in Chinese)

LEE M , CHUNG M , SHIN Y G . Cephalometric landmark detection via global and local encoders and patch-wise attentions [J ] . Neurocomputing , 2022 , 470 : 182 - 189 .

HE X , ZHOU Y , ZHAO J Q , et al. Swin transformer embedding UNet for remote sensing image semantic segmentation [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 .DOI: 10.1109/TGRS.2022.3144165 https://dx.doi.org/10.1109/TGRS.2022.3144165 .

孟浩 , 田洋 , 孙宇婷 , 等 . 全局注意力关系网络的小样本船舶识别 [J ] . 仪器仪表学报 , 2021 , 42 ( 12 ): 220 - 227 .

MENG H , TIAN Y , SUN Y T , et al. Small sample ship identification based on global attention relation network [J ] . Chinese Journal of Scientific Instrument , 2021 , 42 ( 12 ): 220 - 227 . (in Chinese)

LINT Y , GOYAL P , GIRSHICK R , et al. Focal loss for dense object detection [J ] . IEEE Transactions on Pattern Analysis & Machine Intelligence , 2017 , 99 : 2999 - 3007 .

WEI Z L , DING D L , ZHOU H , et al. A flight maneuver recognition method based on multi-strategy affine canonical time warping [J ] . Applied Soft Computing , 2020 , 95 : 106527 .

LEONI J , ZINNARI F , VILLA E , et al. Flight regimes recognition in actual operating conditions: a functional data analysis approach [J ] . Engineering Applications of Artificial Intelligence , 2022 , 114 : 105016 .

FAN Y Q , SHEN G M , XU X , et al. Flight track pattern recognition based on few labeled data with outliers [J ] . Journal of Electronic Imaging , 2021 ,30: 031204.

Views

589

下载量

CNKI被引量

Alert me when the article has been cited

提交

Tools

Publicity Resources

Prediction of Exterior Ballistics and Impact Point of Mortar Based on Transformer-LSTM Hybrid Neural Network

A Radar Signal Modulation Recognition Method Based on Multi-scale Dual Attention Network

Research on Infrared Target Detection and Tracking in Dark Environments

A Method for Detecting the Camouflaged Small Target in Complex Scene Using Airborne Polarization Remote Sensing

Joint State Equality Constraint Identification and Recursive Filtering Based on Deep Learning

Related Author

Shouhuai YANG

Jiangliu HUANG

Zhihua CHEN

Zhengui HUANG

Mingyu WU

Rongxian QIU

Chun ZHENG

Renlong LIAO

Related Institution

School of Information and Communication Engineering, University of Electronic Science and Technology of China

Wuhan Maritime Communication Research Institute

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications

Advanced Technology Research Institute (Jinan), Beijing Institute of Technology

Postal code：100089
Tel：010-68963060/68962718 Fax：010-68963025 Email：bgxb@cos.org.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备05059581号-4 京公网安备11010802024360号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：44524 Visits Today：504

⁰