基于改进MKELM的红外空间锥体目标识别

doi:10.12305/j.issn.1001-506X.2024.10.02

摘要/Abstract

摘要：

针对远距离探测时仅能获取目标的红外辐射强度序列、样本量有限、信噪比低而导致目标识别困难的问题, 提出一种基于改进多核极限学习机(multiple kernel extreme learning machine, MKELM)的红外空间锥体目标识别方法。首先对红外辐射强度序列进行变分模态分解(variational mode decomposition, VMD)并重构, 然后对重构序列进行时域特征提取, 最后采用鲸鱼优化算法(whale optimization algorithm, WOA)优化MKELM的参数组合, 在仿真生成的空间锥体目标红外辐射强度序列数据集上进行目标分类识别实验。实验结果验证了所提算法的有效性, 同时表明所提方法具有较好的识别准确性和鲁棒性。

关键词: 红外辐射强度序列, 空间目标识别, 变分模态分解, 鲸鱼优化算法, 多核极限学习机

Abstract:

An infrared spatial cone-shaped target recognition method based on improved multiple kernel extreme learning machine (MKELM) is proposed in order to solve the problems that infrared radiation intensity sequence is the only data available at long-range detection, the sample size is limited and the signal-to-noise ratio (SNR) is usually low which lead to the difficulty of target recognition. Firstly, variational mode decomposition (VMD) and reconstruction are performed on infrared radiation intensity sequence. Then, time-domain features are extracted based on reconstructed sequences. Finally, whale optimization algorithm (WOA) is used to find the optimal combination of parameters for MKELM, and target recognition experiment is carried out on the simulated spatial cone-shaped target infrared radiation intensity sequence dataset by using improved MKELM. The experimental results verify the effectiveness, recognition accuracy and robustness of the proposed method.

Key words: infrared radiation intensity sequence, spatial target recognition, variational mode decomposition (VMD), whale optimization algorithm (WOA), multiple kernel extreme learning machine (MKELM)

中图分类号:

TP391
TN219

王彩云, 常韵, 李晓飞, 王佳宁, 吴钇达, 张慧雯. 基于改进MKELM的红外空间锥体目标识别[J]. 系统工程与电子技术, 2024, 46(10): 3257-3264.

Caiyun WANG, Yun CHANG, Xiaofei LI, Jianing WANG, Yida WU, Huiwen ZHANG. Infrared spatial cone-shaped target recognition based on improved MKELM[J]. Systems Engineering and Electronics, 2024, 46(10): 3257-3264.

图/表 7

图1

表1

图2

图3

表2

图4

图5

参考文献 33

1	韩金辉, 魏艳涛, 彭真明, 等. 红外弱小目标检测方法综述[J]. 红外与激光工程, 2022, 51 (4): 438- 461.
	HAN J H , WEI Y T , PENG Z M , et al. Infrared dim and small target detection: a review[J]. Infrared and Laser Engineering, 2022, 51 (4): 438- 461.
2	WU D Y , LU H Z , HU M F , et al. Independent random recurrent neural networks for infrared spatial point targets classification[J]. Applied Sciences, 2019, 9 (21): 4622. doi: 10.3390/app9214622
3	BAGNALL A , LINES J , BOSTROM A , et al. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances[J]. Data Mining and Know-ledge Discovery, 2017, 31, 606- 660. doi: 10.1007/s10618-016-0483-9
4	YE L , KEOGH E . Time series shapelets: a novel technique that allows accurate, interpretable and fast classification[J]. Data Mining and Knowledge Discovery, 2011, 22 (1-2): 149- 182. doi: 10.1007/s10618-010-0179-5
5	SILBERMAN G L . Parametric classification techniques for theater ballistic missile defense[J]. Johns Hopkins APL Technical Digest, 1998, 19 (3): 323.
6	张兵. 光学图像末制导中的点目标检测与识别算法研究[D]. 长沙: 国防科学技术大学, 2005.
	ZHANG B. Point target detection and recognition algorithms in optical image terminal homing system[D]. Changsha: National University of Defense Technology, 2005.
7	李鑫. 基于XCS的目标灰度时间序列特征提取方法研究[D]. 长沙: 国防科学技术大学, 2017.
	LI X. Research on feature extraction methods of time series of gray level of target based on XCS[D]. Changsha: National University of Defense Technology, 2017.
8	刘赛. 弹道导弹红外辐射特性测量及弹头目标识别技术研究[D]. 长春: 中国科学院大学, 2023.
	LIU S. Research on infrared radiation characteristic measurement and warhead recognition technology of ballistic missiles[D]. Changchun: Chinese Academy of Sciences, 2023.
9	ZHAO B D , LU H Z , CHEN S F , et al. Convolutional neural networks for time series classification[J]. Journal of Systems Engineering and Electronics, 2017, 28 (1): 162- 169. doi: 10.21629/JSEE.2017.01.18
10	KARIM F , MAJUMDAR S , DARABI H , et al. LSTM fully convolutional networks for time series classification[J]. IEEE Access, 2017, 6, 1662- 1669.
11	ZHENG Y , LIU Q , CHEN E H , et al. Exploiting multi-channels deep convolutional neural networks for multivariate time series classification[J]. Frontiers of Computer Science, 2016, 10, 96- 112. doi: 10.1007/s11704-015-4478-2
12	CUI Z C, CHEN W L, CHEN Y X. Multi-scale convolutional neural networks for time series classification[EB/OL]. [2023-04-16]. https://arXiv.org/abs/1603.06995.
13	SHEN L , WANG Y Z . TCCT: tightly-coupled convolutional transformer on time series forecasting[J]. Neurocomputing, 2022, 480, 131- 145. doi: 10.1016/j.neucom.2022.01.039
14	YANG C H H, TSAI Y Y, CHEN P Y. Voice2series: reprogramming acoustic models for time series classification[C]//Proc. of the International Conference on Machine Learning, 2021: 11808-11819.
15	冯洁琼. 基于红外辐射变化波形的弹道目标识别神经网络算法研究[D]. 长沙: 国防科学技术大学, 2016.
	FENG J Q. Research on neural network algorithm for ballistic target recognition based on waveform of infrared radiation[D]. Changsha: National University of Defense Technology, 2016.
16	DENG Q Q , LU H Z , HU M F , et al. Exo-atmospheric infrared objects classification using recurrence-plots-based convolutional neural networks[J]. Applied Optics, 2019, 58 (1): 164- 171. doi: 10.1364/AO.58.000164
17	YU B Z, LU H Z, TAO H M. A deep learning method with wavelet packet transform for infrared target recognition[C]//Proc. of the IEEE International Conference on Signal Processing, Communications and Computing, 2021.
18	ZHANG S H, CHEN X, RAO P, et al. Visualization of radiation intensity sequences for space infrared target recognition[C]//Proc. of the Earth and Space: From Infrared to Terahertz, 2023, 12505: 546-553.
19	张晔, 侯毅, 欧阳克威, 等. 单变量序列数据分类方法综述[J]. 系统工程与电子技术, 2023, 45 (2): 313- 335.
	ZHANG Y , HOU Y , OUYANG K W , et al. Survey of univariate sequence data classification methods[J]. Systems Engineering and Electronics, 2023, 45 (2): 313- 335.
20	王彩云, 赵焕玥, 李晓飞, 等. 基于改进Delaunay三角剖分的空间目标红外辐射成像方法[J]. 激光与红外, 2020, 50 (2): 161- 167.
	WANG C Y , ZHAO H Y , LI X F , et al. IR radiation imaging method of space target based on improved Delaunay triangulation[J]. Laser & Infrared, 2020, 50 (2): 161- 167.
21	DENG Q Q , LU H Z , XIAO S Z , et al. Analysis of infrared signatures of exo-atmosphere micro-motion objects based on inertial parameters[J]. Infrared Physics & Technology, 2018, 88, 32- 40.
22	李享, 李劲东, 王玉莹, 等. 中段飞行弹道导弹表面温度与辐射特性计算[J]. 红外技术, 2022, 44 (2): 134- 139.
	LI X , LI J D , WANG Y Y , et al. Calculation of temperature and radiation characteristics of midcourse ballistic missiles[J]. Infrared Technology, 2022, 44 (2): 134- 139.
23	唐文博. 复杂场景下弹道中段雷达目标仿真与识别研究[D]. 哈尔滨: 哈尔滨工业大学, 2021.
	TANG W B. Research on target simulation and recognition of midcourse ballistic radar in complex scene[D]. Harbin: Harbin Institute of Technology, 2021.
24	于秉志, 卢焕章, 陶华敏, 等. 引入探测器特性的空间目标红外灰度序列仿真研究[J]. 激光与红外, 2021, 51 (8): 1018- 1024.
	YU B Z , LU H Z , TAO H M , et al. A simulation study of space target infrared grayscale series with detector characteristics[J]. Laser & Infrared, 2021, 51 (8): 1018- 1024.
25	冉运超. 红外探测器成像真实感建模与仿真方法研究及评价[D]. 武汉: 华中科技大学, 2017.
	RAN Y C. Research on method of improving synthetic infrared image fidelity and quality evaluation[D]. Wuhan: Huazhong University of Science and Technology, 2017.
26	MA Y , HU M F , LU H Z , et al. Recurrent neural networks for discrimination of exo-atmospheric targets based on infrared radiation signature[J]. Infrared Physics & Technology, 2019, 96, 123- 132.
27	王维高, 魏云冰, 滕旭东. 基于VMD-SSA-LSSVM的短期风电预测[J]. 太阳能学报, 2023, 44 (3): 204- 211.
	WANG W G , WEI Y B , TENG X D . Short-term wind power forecasting based on VMD-SSA-LSSVM[J]. Acta Energiae Solaris Sinica, 2023, 44 (3): 204- 211.
28	HUANG G B , ZHOU H M , DING X J , et al. Extreme learning machine for regression and multiclass classification[J]. IEEE Trans.on Systems, Man, and Cybernetics, Part B(Cybernetics), 2011, 42 (2): 513- 529.
29	LI Z B , JIANG W L , ZHANG S , et al. A hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition and wavelet kernel extreme learning machine methods[J]. Sensors, 2021, 21 (8): 2599.
30	夏悠然, 管军, 易文俊. 基于改进粒子群优化极限学习机的弹丸参数辨识[J]. 系统工程与电子技术, 2023, 45 (2): 521- 529.
	XIA Y R , GUAN J , YI W J . Projectile parameter identification: extreme learning machine optimized by improved particle swarm[J]. Systems Engineering and Electronics, 2023, 45 (2): 521- 529.
31	MIRJALILI S , LEWIS A . The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95, 51- 67.
32	WANG Z G, YAN W Z, OATES T. Time series classification from scratch with deep neural networks: a strong baseline[C]//Proc. of the International Joint Conference on Neural Networks, 2017: 1578-1585.
33	DEMPSTER A , PETITJEAN F , WEBB G I . ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels[J]. Data Mining and Knowledge Discovery, 2020, 34 (5): 1454- 1495.

目标类别	三维模型	微动方式	微动参数			表面材料参数
目标类别	三维模型	微动方式	自旋角速度/(rad/s)	锥旋角速度/(rad/s)	翻滚角速度/(rad/s)	表面材料参数
目标1		进动	3.0π~5.0π	π~3.0π	-	吸收率: 3.6% 发射率: 19.2% 密度: 1 000 kg/m³ 比热容: 1 000 J/(kg·K) 厚度: 0.001 m 初始温度: 300 K
目标2		进动	3.0π~5.0π	π~3.0π	-
目标3		进动	3.0π~5.0π	π~3.0π	-
目标4		翻滚	-	-	0.25π~0.5π
目标5		翻滚	-	-	0.25π~0.5π

方法	识别率					平均识别率
方法	目标1	目标2	目标3	目标4	目标5	平均识别率
ELM	0.690 9	0.314 5	0.340 6	0.836 7	0.901 0	0.616 7
FE-ELM	0.918 3	0.730 3	0.853 7	0.911 5	0.942 8	0.871 3
FE-KELM	0.888 6	0.853 8	0.908 6	0.902 4	0.979 8	0.906 6
VMD-FE-KELM	0.937 1	0.897 1	0.924 3	0.932 9	0.968 6	0.932 0
本文算法	0.969 0	0.946 2	0.953 2	0.947 4	0.984 5	0.960 1

[1]	王磊, 张志勇, 胥辉旗, 曾维贵, 曹司磊. 基于VMD分解和多域联合分布的雷达辐射源识别[J]. 系统工程与电子技术, 2023, 45(8): 2479-2488.
[2]	李广强, 董文超, 朱大庆, 于越, 陈浩, 于双和. 基于改进鲸鱼优化算法的AUV三维路径规划[J]. 系统工程与电子技术, 2023, 45(7): 2170-2182.
[3]	李波, 胡哿郗, 石剑钧, 刘恒畅, 洪涛. 基于多惩罚因子优化VMD的滚动轴承故障特征提取方法[J]. 系统工程与电子技术, 2023, 45(11): 3690-3698.
[4]	付卫红, 周雨菲, 赵文胜. 面向能量差异混合信号的单通道盲源分离算法[J]. 系统工程与电子技术, 2023, 45(10): 3302-3311.
[5]	彭正红, 杨德贵, 王行, 王浩, 朱政亮. 基于趋势估计的微多普勒分离与特征提取算法[J]. 系统工程与电子技术, 2021, 43(12): 3452-3461.
[6]	李亚兰, 金炜东, 葛鹏. 基于VMD和特征融合的辐射源信号识别[J]. 系统工程与电子技术, 2020, 42(7): 1499-1503.
[7]	时维国, 国明. 鲸鱼优化相关向量机的网络控制系统变采样周期调度[J]. 系统工程与电子技术, 2020, 42(10): 2348-2355.
[8]	方章闻, 张金艺, 李科, 姜玉稀. 小样本条件下的通信辐射源半监督特征提取[J]. 系统工程与电子技术, 2020, 42(10): 2381-2389.