基于神经网络的符号化飞行动作识别

doi:10.12305/j.issn.1001-506X.2022.03.04

Abstract

Abstract:

Flight action recognition is the basis of many key technologies, such as flight training evaluation and air combat intelligent decision-making. It is of great significance to realize fast and efficient flight action recognition. Thus, a method based on symbolic neural network is proposed to realize the efficient recognition of basic flight actions and complex flight actions. Firstly, the idea of differential segmentation is used to slice the flight parameters, and then the convolution neural network (CNN) and long-short term memory (LSTM) neural network are used to realize the modular processing of flight actions, which effectively replaces the traditional method of logical reasoning for the original data. And the method can use the basic flight action to segment the flight data. It has good scalability and can process a large number of flight data quickly. Finally, the simulation experiments of thirteen basic flight actions, two complex flight actions and the whole flight data are carried out. The simulation results show that the method has good recognition performance.

Key words: action recognition, differential segmentation, convolution neural network (CNN), long-short term memory (LSTM) network

CLC Number:

V249

Wei FANG, Yu WANG, Wenjun YAN, Chong LIN. Symbolized flight action recognition based on neural network[J]. Systems Engineering and Electronics, 2022, 44(3): 737-745.

Figures/Tables 22

Fig.1

Table 1

Fig.2

Table 2

Fig.3

Fig.4

Fig.5

Table 3

Table 4

Table 5

Fig.6

Fig.7

Fig.8

Fig.9

Table 6

Table 7

Fig.10

Table 8

Fig.11

Fig.12

Fig.13

Fig.14

References 35

1	KELLER J . DARPA eyes artificial intelligence (AI) and unmanned aircraft in jet fighter dogfighting[J]. Military & Aerospace Electronics, 2019, 30 (6): 30.
2	黄长强. 未来空战过程智能化关键技术研究[J]. 航空兵器, 2019, 26 (1): 11- 19.
	HUANG C Q . Research on key technologies of intelligent air combat process in the future[J]. Aeronautical Weapon, 2019, 26 (1): 11- 19.
3	曹建彪, 王业光, 王世鹏, 等. 基于飞行任务的飞行品质评估方法[J]. 飞机设计, 2021, 41 (2): 37- 41.
	CAO J B , WANG Y G , WANG S P , et al. Flight quality assessment method based on flight mission[J]. Aircraft Design, 2021, 41 (2): 37- 41.
4	JING L , QI S J , WANG F . Unmanned aerial vehicle flying qua-lities flight test evaluation technique in approaching and landing phase[J]. IOP Conference Series Materials Science and Engineering, 2020, 887 (1): 12- 42.
5	孟光磊, 张慧敏, 朴海音, 等. 自动化飞行训练评估中的战机机动动作识别[J]. 北京航空航天大学学报, 2020, 46 (7): 1268- 1273.
	MENG G G , ZHANG H M , PIAO H Y , et al. Recognition of flighter maneuver in automatic flight training evaluation[J]. Journal of Beijing University of Aeronautics and Astronsutics, 2020, 46 (7): 1268- 1273.
6	倪世宏, 史忠科, 谢川, 等. 军用战机机动飞行动作识别知识库的建立[J]. 计算机仿真, 2005, 22 (4): 23- 26. doi: 10.3969/j.issn.1006-9348.2005.04.007
	NI S H , SHI Z K , XIE C , et al. Establishment of knowledge base for recognition of manimaneuver flight action of military fighter[J]. Computer Simulation, 2005, 22 (4): 23- 26. doi: 10.3969/j.issn.1006-9348.2005.04.007
7	WANG J , XIONG B , YAN M O . Recognition method of helicopter flight condition based onrandom forest[J]. Computer Engineering and Applications, 2017, 53 (17): 149- 152, 159.
8	CHEN X, ZHANG X G, WANG W J, et al. Multi-objective Monte-Carlo tree search based aerial maneuvering control[C]//Proc. of the IEEE Chinese Guidance, Navigation and Control Conference, 2016: 81-87.
9	RODIN E Y , AMIN M . Maneuver prediction in air combat via artificial neural networks[J]. Computers & Mathematics with Applications, 1992, 24 (3): 95- 112.
10	李丹丹, 路辉, 郎荣玲. 基于主成分分析和SVM的飞参阶段划分研究[J]. 现代电子技术, 2010, 33 (8): 134- 137. doi: 10.3969/j.issn.1004-373X.2010.08.042
	LI D D , LU H , LANG R L . Research on stage division of flying parameter based on principal component analysis and SVM[J]. Modern Electronic Technology, 2010, 33 (8): 134- 137. doi: 10.3969/j.issn.1004-373X.2010.08.042
11	HUANG C Q , DONG K S , HUANG H Q , et al. Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization[J]. Journal of Systems Engineering and Electronics, 2018, 29 (1): 86- 97. doi: 10.21629/JSEE.2018.01.09
12	XU X M , YANG R N , FU Y . Situation assessment for air combat based on novel semi-supervised naive Bayes[J]. Journal of Systems Engineering and Electronics, 2018, 29 (4): 768- 779. doi: 10.21629/JSEE.2018.04.11
13	CHUA L O , ROSKA T . The CNN paradigm[J]. IEEE Trans.on Circuits & Systems I: Fundamental Theory & Applications, 1993, 40 (3): 147- 156.
14	SANDHYA R S , APPARAO N G , USHA S V . Kinematic joint descriptor and depth motion descriptor with convolutional neural networks for human action recognition[J]. Materials Today: Proceedings, 2021, 37 (2): 3164- 3173.
15	JIAN Q Z , XIAO L G . Deep convolution neural networks for twitter sentiment analysis[J]. IEEE Access, 2018, 6, 23253- 23260. doi: 10.1109/ACCESS.2017.2776930
16	REN S Q , HE K M , GIRSHICK R , et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans.on Pattern Analysis & Machine Intelligence, 2017, 39 (6): 1137- 1149.
17	HOCHREITER S , SCHMIDHUBER J . Long short-term memory[J]. Neural Computation, 1997, 9 (8): 1735- 1780. doi: 10.1162/neco.1997.9.8.1735
18	HUANG M, TAO W, LIANG X, et al. Application of improved long-short-term memory network in human morphology detection[C]//Proc. of the IEEE 7th International Conference on Computer Science and Network Technology, 2019: 111-114.
19	BIN Y , YANG Y , SHEN F M , et al. Describing video with attention-based bidirectional LSTM[J]. IEEE Trans.on Cybernetics, 2019, 49 (7): 2631- 2641. doi: 10.1109/TCYB.2018.2831447
20	WU C , CHEN L , WANG G B , et al. Spatiotemporal scenario generation of traffic flow based on LSTM-GAN[J]. IEEE Access, 2020, 8, 186191- 186198. doi: 10.1109/ACCESS.2020.3029230
21	MA L , TIAN S . A hybrid CNN-LSTM model for aircraft 4D tra-jectory prediction[J]. IEEE Access, 2020, 8, 134668- 134680. doi: 10.1109/ACCESS.2020.3010963
22	WU J L , HE Y , YU L C , et al. Identifying emotion labels from psychiatric social texts using a bi-directional LSTM-CNN model[J]. IEEE Access, 2020, 8, 66638- 66646. doi: 10.1109/ACCESS.2020.2985228
23	BELLAMY J C . Airborne navigational recorders[J]. Navigation Journal of the Institute of Navigation, 1952, 3 (6): 182- 188.
24	ROBERTS C A . The status of flight recorders in modern aircraft[J]. Journal of Air Law & Commerce, 1977, 43 (2): 271- 287.
25	史浩宇, 赵克新. 无人作战飞机自主空战战术动作的分层识别方法[J]. 火力与指挥控制, 2019, 44 (12): 51- 55, 60. doi: 10.3969/j.issn.1002-0640.2019.12.011
	SHI H Y , ZHAO K X . Hierarchical identification method for autonomous air combat tactical movements of unmanned combat aircraft[J]. Fire Control & Command Control, 2019, 44 (12): 51- 55, 60. doi: 10.3969/j.issn.1002-0640.2019.12.011
26	AUSTIN F , CARBONE G , HINZ H , et al. Game theory for automated maneuvering during air-to-air combat[J]. Journal of Guidance Control & Dynamics, 1990, 13 (6): 1143- 1149.
27	彭阳, 彭晓明. 一种无人机飞行动作质量分析方法[J]. 飞行力学, 2017, 35 (6): 65- 69. doi: 10.3969/j.issn.1002-0853.2017.06.015
	PENG Y , PENG X M . A method for quality analysis of UAV flight action[J]. Journal of Flight Mechanics, 2017, 35 (6): 65- 69. doi: 10.3969/j.issn.1002-0853.2017.06.015
28	方伟, 王玉, 闫文君, 等. 基于微分思想和卷积神经网络的飞行动作识别[J]. 中国电子科学研究院学报, 2021, 16 (4): 347- 353. doi: 10.3969/j.issn.1673-5692.2021.04.008
	FANG W , WANG Y , YAN W J , et al. Flight action recognition based on differential idea and convolutional neural network[J]. Journal of China Academy of Electronic Sciences, 2021, 16 (4): 347- 353. doi: 10.3969/j.issn.1673-5692.2021.04.008
29	XIE C , KUMAR A . Finger vein identification using convolutional neural network and supervised discrete hashing[J]. Pattern Recognition Letters, 2019, 119 (3): 148- 156.
30	VENKATA R M , ARUNKUMAR R . A weight based feature extraction model on multifaceted multimedia bigdata using convolutional neural network[J]. International Information and Engineering Technology Association, 2020, 25 (6): 725- 729.
31	XU K , GONG H , LIU F . Vehicle detection based on improved multitask cascaded convolutional neural network and mixed image enhancement[J]. IET Image Processing, 2020, 14 (17): 4621- 4632. doi: 10.1049/iet-ipr.2020.1005
32	SHI Q Q , LI W , TAO R , et al. Ship classification based on multifeature ensemble with convolutional neural network[J]. Remote Sensing, 2019, 11 (4): 419- 440. doi: 10.3390/rs11040419
33	GRAVES A , SCHMIDHUBER J . Framewise phoneme classification with bidirectional LSTM and other neural network architectures[J]. Neural Networks, 2005, 18 (6): 602- 610.
34	孟光磊, 陈振, 罗元强. 基于动态贝叶斯网络的机动动作识别方法[J]. 系统仿真学报, 2017, 29 (S1): 140- 145.
	MENG G L , CHEN Z , LUO Y Q . Maneuvering action recognition method based on dynamic Bayesian network[J]. Journal of System Simulation, 2017, 29 (S1): 140- 145.
35	李鸿利, 单征, 郭浩然. 基于MDTW的飞行动作识别算法[J]. 计算机工程与应用, 2015, 51 (9): 267- 270. doi: 10.3778/j.issn.1002-8331.1307-0018
	LI H L , SHAN Z , GUO H R . Flight action recognition algorithm based on MDTW[J]. Computer Engineering and Applications, 2015, 51 (9): 267- 270. doi: 10.3778/j.issn.1002-8331.1307-0018

序号	基础类	基本飞行动作	高度	航向角	俯仰角	滚转角	滚转率	转弯率
1	平飞类	直线上升飞行	保持	保持	正保持	零保持	零保持	零保持
		直线水平飞行	增加	保持	零保持	零保持	零保持	零保持
		直线下降飞行	减小	保持	负保持	零保持	零保持	零保持
2	转弯类	右转水平飞行	保持	正变化	零保持	零保持	零保持	变化
		左转水平飞行	保持	负变化	零保持	零保持	零保持	变化
		右转上升飞行	增加	正变化	正变化	零保持	零保持	变化
		右转下降飞行	减小	正变化	负变化	零保持	零保持	变化
		左转上升飞行	增加	负变化	正变化	零保持	零保持	变化
		左转下降飞行	减小	负变化	负变化	零保持	零保持	变化
		垂直上升转弯飞行	增加	零保持	正变化	零保持	零保持	变化
		垂直下降转弯飞行	减小	零保持	负变化	零保持	零保持	变化
3	滚转类	直线滚转飞行	保持	零保持	零保持	变化	变化	零保持
3	滚转类	斜滚转飞行	变化	变化	变化	变化	变化	零保持

序号	复杂飞行动作	所包含的基本飞行动作
1	告警改出	直线水平、垂直上升转弯、直线上升、斜横滚、飞行动作调整段、垂直下降转弯、斜横滚、飞行动作调整段、直线水平
2	筋斗	直线水平、垂直上升转弯、直下降转弯、飞行动作调整段、直线水平
3	大姿态回转	直线水平、右转/左转上升、斜横滚、飞行动作调整段、直线水平
4	殷麦曼	直线水平、垂直上升、直线滚转、飞行动作调整段、直线水平
5	半滚倒转	直线水平、直线滚转、垂直上升、飞行动作调整段、直线水平

符号化表示	基本飞行动作	符号化表示	基本飞行动作
0	直线上升飞行	7	左转上升飞行
1	直线水平飞行	8	左转下降飞行
2	直线下降飞行	9	垂直上升转弯飞行
3	右转水平飞行	10	直线滚转飞行
4	左转水平飞行	11	斜滚转飞行
5	右转上升飞行	12	垂直下降转弯飞行
6	右转下降飞行	-	-

层	输出尺寸	参数
填充层_1	(None, 1, 8, 14)	0
卷积层_1	(None, 256, 7, 13)	1 280
丢弃层_1	(None, 256, 7, 13)	0
填充层_2	(None, 256, 7, 17)	0
卷积层_2	(None, 80, 6, 16)	82 000
丢弃层_2	(None, 80, 6, 16)	0
展平层	(None, 7 680)	0
全连接层_1	(None, 256)	1 966 336
丢弃层_3	(None, 256)	0
全连接层_2	(None, 13)	3 341

层	输出尺寸	参数
嵌入层	(None, 115, 16)	208
LSTM层	(None, 16)	2 112
丢弃层	(None, 16)	0
全连接层	(None, 2)	34

Symbolized flight action recognition based on neural network

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 22

References 35

Related Articles 5

Recommended Articles

Metrics

Comments

时间/s	基本动作单元	时间/s	基本动作单元
0~14.33	直线水平飞行	38.01~39.33	直线水平飞行
14.34~26.67	垂直上升转弯飞行	39.34~41.67	左转上升飞行
26.68~27.00	左转上升飞行	41.68~42.33	右转上升飞行
27.01~32.00	左转上升飞行	42.34~43.00	右转上升飞行
32.01~35.00	直线上升飞行	43.01~43.33	左转上升飞行
35.01~35.33	左转上升飞行	43.34~43.66	直线水平飞行
35.34~37.00	直线滚转飞行	43.67~44.33	垂直下降转弯飞行
37.01~37.33	右转上升飞行	44.34~60	直线水平飞行
37.34~38.00	左转上升飞行	-	-

算法	识别率/%
多元动态时间规整识别算法	85.0
动态贝叶斯识别算法	97.2
传统神经网络识别算法	97.0
本文算法	98.7

[1]	Shiyan SUN, Gang ZHANG, Weige LIANG, Bo SHE, Fuqing TIAN. Remaining useful life prediction method of rolling bearing based on time series data augmentation and BLSTM [J]. Systems Engineering and Electronics, 2022, 44(3): 1060-1068.
[2]	Tao SHU, Yichi ZHANG, Rixian DING. Life prediction of bearings in rotating machinery based on grey model and LSTM network [J]. Systems Engineering and Electronics, 2021, 43(8): 2355-2361.
[3]	Shuai ZHAO, Songtao LIU, Huiyang WANG. LPI radar waveform recognition algorithm based on PSO-CNN [J]. Systems Engineering and Electronics, 2021, 43(12): 3552-3563.
[4]	YANG Sihan, PENG Hua, XU Mankun, PAN Yiwei, HOU Xiaoyu. Ultra short wave specific signal spectrogram recognition based on convolution neural network [J]. Systems Engineering and Electronics, 2019, 41(4): 744-751.
[5]	WANG Nianbin, HE Ming, WANG Hongbin, LANG Zeyu. Underwater target feature extraction method based on convolutional neural network [J]. Systems Engineering and Electronics, 2018, 40(6): 1197-1203.

时间/s	基本动作单元	时间/s	基本动作单元
0~4.67	直线水平飞行	41.01~42.00	直线水平飞行
4.68~24.33	垂直转弯上升	42.01~42.33	直线下降飞行
24.34~29.00	垂直转弯下降	42.34~43.33	垂直转弯下降飞行
29.01~29.33	右转下降飞行	43.34~44.00	垂直转弯上升飞行
29.34~29.67	垂直转弯下降飞行	44.01~44.33	直线水平飞行
29.68~30.00	右转下降飞行	44.34~46.33	直线上升飞行
30.01~36.67	垂直转弯下降飞行	46.34~46.67	右转上升飞行
36.68~37.00	直线水平飞行	46.68~47.67	垂直转弯上升飞行
37.01~38.33	垂直转弯上升飞行	47.68~48.33	左转上升飞行
38.34~39.33	直线上升飞行	48.34~49.33	直线水平飞行
39.34~40.67	垂直转弯上升飞行	49.34~50.33	左转上升飞行
40.68~41.00	右转上升飞行	50.34~60.00	直线水平飞行