A Simulation Method Based on Multi-source Sensors for Aircraft Type Identification

doi:10.16182/j.issn1004731x.joss.22-0967

Abstract

Abstract:

Existing simulation methods for aircraft type identification mainly focus on a single sensor and a single target. They do not consider the joint acquisition of aircraft parameters by various sensor devices such as optoelectronics, radar, and electronic detection in real scenarios, leading to the simple simulation scenarios. This paper proposes a simulation platform based on multi-source sensors. Specifically, the platform includes an infrared image simulator that uses a cycleGAN network to generate infrared images of the aircraft, a flight simulator that adopts the three-degree-of-freedom flight control method to generate the movement trajectory of the aircraft, a radar simulator, that applies ellipsoidal equations to simulate radar cross-section of the target, an electromagnetic simulator, which simulates a complex electromagnetic environment through typical signal source collection and random noise introduction. Experimental results on the simulation dataset show that the introduction of multiple sensor information can effectively improve the recognition results of target types and improve the ability of the model to assist decision-making.

Key words: multi-source sensor, aircraft simulation, infrared image simulator, trajectory simulator, radar simulator, electromagnetic simulator

CLC Number:

TP391.9

Gu Shaozhu, Ying Yuxin, Zhang Huajie, Tong Yiqi. A Simulation Method Based on Multi-source Sensors for Aircraft Type Identification[J]. Journal of System Simulation, 2024, 36(1): 149-159.

Figures/Tables 13

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Table 1

Table 2

Table 3

Performance of SSD model with different object distances

目标尺寸(像素)	距离/km	数量	准确率/%
$8 × 8$	>15	419	4.53
$16 × 16$	[10，15)	395	26.83
$32 × 32$	[5，10)	392	89.54
$64 × 64$	[2.5，5)	396	96.46
$128 × 128$	<2.5	395	97.72

Table 3

Table 4

References 22

1	史志富, 张安, 何胜强. 基于贝叶斯网络的多传感器目标识别算法研究[J]. 传感技术学报, 2007(4): 921-924.
	Shi Zhifu, Zhang An, He Shengqiang. Research on Multisensor Target Recognition Algorithm Based on Bayesian Networks[J]. Chinese Journal of Sensors and Actuators, 2007(4): 921-924.
2	赵静, 王鹏, 丁筱茜, 等. 基于观测器的四旋翼容错控制及仿真研究[J]. 系统仿真学报, 2022, 34(1): 1-10.
	Zhao Jing, Wang Peng, Ding Xiaoqian, et al. Fault Tolerant Control and Simulation of Quadrotor Based on Adaptive Observer[J]. Journal of System Simulation, 2022, 34(1): 1-10.
3	Zhao Bo, Feng Jiashi, Wu Xiao, et al. A Survey on Deep Learning-based Fine-grained Object Classification and Semantic Segmentation[J]. International Journal of Automation and Computing, 2017, 14(2): 119-135.
4	Redmon J, Divvala S, Girshick R, et al. You Only Look Once: Unified, Real-time Object Detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ, USA: IEEE, 2016: 779-788.
5	Kim Sungho, Song Woo-Jin, Kim So-Hyun. Infrared Variation Optimized Deep Convolutional Neural Network for Robust Automatic Ground Target Recognition[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Piscataway, NJ, USA: IEEE, 2017: 195-202.
6	孙艺升. 基于深度学习的飞机飞行轨迹目标识别[C]//第一届空中交通管理系统技术学术年会论文集. 北京: 电子工业出版社, 2018: 288-293.
7	纪永强, 刘通, 徐高正, 等. 基于RCS统计特征的船舶目标识别方法[J]. 舰船科学技术, 2018, 40(13): 129-132.
	Ji Yongqiang, Liu Tong, Xu Gaozheng, et al. The Target Identification Method for Ship Based on RCS Statistical Feature[J]. Ship Science and Technology, 2018, 40(13): 129-132.
8	赵佳欢, 杨海明, 邱令存. 基于循环神经网络的空中目标类型识别[J]. 航天控制, 2020, 38(3): 47-53.
	Zhao Jiahuan, Yang Haiming, Qiu Lingcun. Recognition of Aerial Target Types Based on Recurrent Neural Network[J]. Aerospace Control, 2020, 38(3): 47-53.
9	谢晓方, 刘厚君, 张龙杰, 等. 基于渐进式生成对抗网络的舰船红外图像仿真[J]. 激光与红外, 2021, 51(4): 471-479.
	Xie Xiaofang, Liu Houjun, Zhang Longjie, et al. Infrared Image Simulation for Warship Based on Progressive Generative Adversarial Network[J]. Laser & Infrared, 2021, 51(4): 471-479.
10	初阳, 刘志, 窦林涛. 面向大尺度战场的飞行机动建模仿真技术[J]. 系统仿真学报, 2021, 33(3): 613-621.
	Chu Yang, Liu Zhi, Dou Lintao. Modeling and Simulation Technology of Flight Maneuver for Large Scale Battlefield[J]. Journal of System Simulation, 2021, 33(3): 613-621.
11	温包谦, 王涛, 成坤, 等. 典型空中目标动态RCS特性仿真研究[J]. 弹箭与制导学报, 2020, 40(2): 5-9.
	Wen Baoqian, Wang Tao, Cheng Kun, et al. Simulation Study on Dynamic RCS Characteristics of Typical Aerial Targets[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2020, 40(2): 5-9.
12	刘凯, 许焕武, 陈茂香. 基于参数样本图的雷达辐射源识别[J]. 电子信息对抗技术, 2015, 30(1): 43-47, 58.
	Liu Kai, Xu Huanwu, Chen Maoxiang. Radar Emitter Recognition Based on Radar Parameter Pattern[J]. Electronic Information Warfare Technology, 2015, 30(1): 43-47, 58.
13	孙卫, 王彪. 飞机红外辐射计算及图像仿真[J]. 激光与红外, 2017, 47(6): 728-735.
	Sun Wei, Wang Biao. Calculation and Image Simulation of Aircraft Infrared Radiation[J]. Laser & Infrared, 2017, 47(6): 728-735.
14	黄攀, 杨小冈, 卢瑞涛, 等. 基于GAN的红外飞机数据增强方法[J]. 电光与控制, 2021, 28(11): 84-88.
	Huang Pan, Yang Xiaogang, Lu Ruitao, et al. Data Augmentation of Infrared Aircraft Target Based on Generative Adversarial Network[J]. Electronics Optics & Control, 2021, 28(11): 84-88.
15	Zhu Junyan, Park T, Isola P, et al. Unpaired Image-to-image Translation Using Cycle-consistent Adversarial Networks[C]//2017 IEEE International Conference on Computer Vision (ICCV). Piscataway, NJ, USA: IEEE, 2017: 2242-2251.
16	汪洋, 黄小红, 陈曾平. 基于对数极坐标变换的目标识别算法[J]. 雷达科学与技术, 2009, 7(2): 111-114.
	Wang Yang, Huang Xiaohong, Chen Zengping. A New Algorithm for Target Recognition Based on Log-polar Transform[J]. Radar Science and Technology, 2009, 7(2): 111-114.
17	王佳, 袁俊超, 刘泊材, 等. 典型突防飞行目标RCS特性研究[J]. 现代雷达, 2021, 43(4): 39-45.
	Wang Jia, Yuan Junchao, Liu Bocai, et al. A Study on Typical Maneuverable Penetrating Aircraft Static and Dynamic RCS Characteristics[J]. Modern Radar, 2021, 43(4): 39-45.
18	李尚生, 王旭坤, 付哲泉, 等. 基于目标散射特性的距离-速度欺骗干扰研究[J]. 计算机仿真, 2021, 38(11): 9-13.
	Li Shangsheng, Wang Xukun, Fu Zhequan, et al. Study on Range-velocity Spoofing Based on Target Scattering Characteristics[J]. Computer Simulation, 2021, 38(11): 9-13.
19	Liu Wei, Anguelov D, Erhan D, et al. SSD: Single Shot MultiBox Detector[C]//Computer Vision-ECCV 2016. Cham: Springer International Publishing, 2016: 21-37.
20	Hochreiter S, Schmidhuber Jürgen. Long Short-term Memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
21	Chung Junyoung, Gulcehre Caglar, Cho KyungHyun, et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling[EB/OL]. (2014-12-11) [2022-07-19]. .
22	邓雪, 李家铭, 曾浩健, 等. 层次分析法权重计算方法--分析及其应用研究[J]. 数学的实践与认识, 2012, 42(7)- 93-100.
	Deng Xue, Li Jiaming, Zeng Haojian, et al. Research on Computation Methods of AHP Wight Vector and Its Applications[J]. Mathematics in Practice and Theory, 2012, 42(7): 93-100.

模拟器	光电	空情	雷达	电抗	权重
光电	1	1/3	1/3	1/5	0.08
空情	3	1	1	1/3	0.20
雷达	3	1	1	1/3	0.20
电抗	5	3	3	1	0.52

模拟器	数据类型	模型	准确率/%
光电	红外图像	SSD	62.37
空情	轨迹序列	LSTM	83.17
空情	轨迹序列	GRU	84.17
雷达	RCS序列	LSTM	80.58
雷达	RCS序列	GRU	80.00
电抗	电磁序列	LSTM	68.25
电抗	电磁序列	GRU	84.00
光电、空情、雷达、电抗	红外、轨迹、 RCS、电磁	投票	86.50

场景	缺失比例	准确率
上报信息缺失	0	86.50
	5	83.17
	10	76.33
上报信息不完整	0	86.50
	5	73.50
	10	66.33

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