Anti-Interference Recognition Algorithm for Aerial Targets in the Complex Confrontation Environment on the Battlefield

doi:10.12382/bgxb.2021.0576

Abstract

Abstract: In the confrontatoin environment with complex background and infrared decoy flares, aerial target recognition by infrared imaging of fortification storming/HEAT missiles remains a challenge. For the problem of aerial target recognition, an anti-interference recognition algorithm with strong discriminative features is proposed, which integrates region generation, feature learning, classification and identification into the anti-interference algorithm to form an abstract recognition framework from the pixel level to region level and then to feature recognition step by step. Firstly, by simulating the typical battlefield environment, a large number of target feature libraries under different confrontation situations are generated, which serve as the basis of the algorithm research. Secondly, region proposals are formed by pixel similarity clustering, and “coarse granularity” location of regions is completed. Then the extracted region features are input into the feature selection module to learn the strong discriminative features. Finally, the selected features are input into the improved multi-classification support vector machines (SVM) for target classification and recognition, and the “finer granularity” locking of the target region is realized. Through the recognition mechanism of gradual refinement, aerial targets in the complex battlefield confrontation environment are recognized. The validation experiments show that the algorithm has an average recognition accuracy rate of 78.63% in the ballistic image datasets under different confrontation situations, which can effectively distinguish between targets and interference, and provide support for the development of algorithms in engineering applications.

Key words: fortificationstorming/HEAT(High-ExplosiveAnti-Tank)missile, infraredtarget, anti-interferenceidentification, featurelearning

CLC Number:

TP391.4

ZHAO Junmin， WEI Jiayi， WU Sijie， LI Xinguo， L Meibo. Anti-Interference Recognition Algorithm for Aerial Targets in the Complex Confrontation Environment on the Battlefield[J]. Acta Armamentarii, 2022, 43(10): 2576-2587.

References

［1］周卫文，康美玲，周泽强.红外诱饵对成像制导导弹影响规律研究［J］.红外与激光工程，2019，48(12):118-124.
ZHOU W W，KANG M L，ZHOU Z Q.Research of infrared flares influence mechanism on the imaging guidance missile［J］.Infrared and Laser Engineering，2019，48(12):118-124.(in Chinese)
［2］马贤杰，李国平，王洪静，等.国外红外导引头及红外诱饵发展历程与展望［J］.航天电子对抗，2020，36(3):58-64.
MA X J，LI G P，WANG H J，et al.Development roadmap and direction of infrared seekers and infrared decoys［J］.Aerospace Electronic Countermeasure，2020，36(3):58-64. (in Chinese)
［3］杨栋，高德亮，曹耀心，等.红外导引头抗诱饵干扰研究［J］.飞控与探测，2020，3(3):79-85.
YANG D，GAO D L，CAO Y X，et al.Study on anti-jamming technology of IR seeker［J］.Flight Control and Detection，2020，3(3): 79-85. (in Chinese)
［4］赵军民，李鹏，邹汝平.智能攻坚破甲导弹技术发展分析［J］.弹箭与制导学报，2021，41(3):1-5.
ZHAO J M，LI P，ZOU R P.Technical development analysis on intelligent fortification storming /HEAT missiles［J］.Journal of Projectiles，Rockets，Missiles and Guidance，2021，41(3):1-5. (in Chinese)
［5］李召，徐文旭，吴晓鸥.美俄反坦克导弹发展现状及技术需求［J］.飞航导弹，2020(8):35-40，56.
LI Z，XU W X，WU X O.Development status and technical requirements of American and Russian anti tank missiles［J］.Aerodynamic Missile，2020(8):35-40，56. (in Chinese)
［6］ BERG A C，BERG T L，ALIK J.Shape matching and object recognition using low distortion correspondences［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，San Diego，CA，US：IEEE，2005，1:26-33.
［7］张冬青，张纯学，文苏丽.自动目标识别技术在导弹上的应用研究［J］.战术导弹技术，2010(5):1-6.
ZHANG D Q，ZHANG C X，WEN S L.Research on the application of automatic target recognition on missile［J］.Tactical Missile Technology，2010(5):1-6. (in Chinese)
［8］赵丹辉，王俊敏，张宏宇，等.精确制导武器导航与末制导技术发展综述［J］.飞航导弹，2021(2):64-70.
ZHAO D H，WANG J M，ZHANG H Y，et al.Overview of the development of precision guided weapon navigation and terminal guidance technology［J］.Aerodynamic Missile，2021(2):64-70. (in Chinese)
［9］张秀蓉.复合导引头目标跟踪技术及实现［D］.西安：西安电子科技大学，2020.
ZHANG X R.Target tracking technology and implementation of compound seeker［D］.Xi'an:Xidian University，2020. (in Chinese)
［10］张坤，胡斌星，许新鹏，等.基于对数极坐标变换的快速相关匹配算法［J］.飞行力学，2020，38(2):61-65.
ZHANG K，HU B X，XU X P，et al.A fast correlation matching algorithm based on log-polar transform［J］. Flight Dynamics，2020，38(2):61-65. (in Chinese)
［11］冉洪成.基于对数极坐标的图像匹配综述［J］.现代计算机，2020(4):65-69.
RAN H C.Review of image matching based on logarithmic polar coordinates［J］.Modern Computer，2020(4):65-69. (in Chinese)
［12］贾迪，朱宁丹，杨宁华，等.图像匹配方法研究综述［J］.中国图象图形学报，2019，24(5):677-699.
JIA D，ZHU N D，YANG N H，et al.Image matching methods［J］.Journal of Image and Graphics，2019，24(5):677-699. (in Chinese)
［13］谢春思，刘志赢，桑雨.基于特征匹配的舰载对陆导弹目标识别模型［J］.系统工程与电子技术，2021，43(8):2244-2253.
XIE C S，LIU Z Y，SANG Y.Target recognition model of ship-to-land missile based on feature matching［J］.Systems Engineering and Electronics，2021，43(8):2244-2253. (in Chinese)
［14］ REN S，HE K，GIRSHICK R，et al.Faster R-CNN: towards real-time object detection with region proposal networks［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence，2017，39(6):1137-1149.
［15］ MOHD M R S，HERMAN S H，SHARIF Z.Application of K-Means clustering in hot spot detection for thermal infrared images［C］∥Proceedings of the IEEE Symposium on Computer Applications & Industrial Electronics.Langkawi，Malaysia:IEEE，2017:107-110.
［16］ GUPTA S. MUKHERJEE A.Infrared image segmentation using enhanced fuzzy C-means clustering for automatic detection systems［C］∥Proceedings of the IEEE International Conference on Fuzzy Systems.Taipei,China:IEEE，2011: 944-949.
［17］ LEI T，JIA X H，ZHANG Y N，et al. Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering［J］.IEEE Transactions on Fuzzy Systems，2018，26(5):3027-3041.
［18］ WANG L P，ZHAN R H，HUANG Y，et al. Joint tracking and classification of extended targets with complex shapes ［J］.Frontiers of Information Technology & Electronic Engineering，2021，22(6):839-862.
［19］ DALAL N，TRIGGS B.Histograms of oriented gradients for human detection［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.San Diego，CA，US: IEEE，2005，1: 886-893.
［20］ HENRIQUES J F，CASEIRO R，MARTINS P,et al.High-speed tracking with kernelized correlation filters［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence,2014，37(3):583-596.
［21］ PEKER M，ALTUN H，KARAKAYA F.Hardware emulation of HOG and AMDF based scale and rotation invariant robust shape detection［C］//Proceedings of the IEEE International Conference on Engineering and Technology.Cairo，Egypt: IEEE，2012，1:1-5.
［22］ ROFFO G，MELZI S，CASTELLANI U，et al.Infinite latent feature selection:a probabilistic latent graph-based ranking approach［C］//Proceedings of the IEEE International Conference on Computer Vision.Venice，Italy:IEEE，2017，1:1398-1406.
［23］ ROFFO G，MELZI S，CASTELLANI U，et al.Infinite feature selection: a graph-based feature filtering approach［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence，2020，1(1):1-15.
［24］ GU Q Q,LI Z H,HAN J W.Generalized fisher score for feature selection［C］//Proceedings of CoRR.abs/1202.3725.US:UaiPress,2012.
［25］王周春.基于机器学习的目标探测与识别技术研究［D］.上海:中国科学院大学(中国科学院上海技术物理研究所),2020.
WANG Z C.Research on target detection and recognition technology based on machine learning［D］.Shanghai:University of Chinese Academy of Sciences(Shanghai Institute of Technical Physics of the Chinese Academy of Sciences)，2020. (in Chinese)
［26］戴小路,汪廷华,胡振威.模糊多核支持向量机研究进展［J］.计算机应用研究，2021，38（10）：2896-2903.
DAI X L,WANG T H,HU Z W.Research progress of fuzzy multiple kernel support vector machine［J］.Application Research of Computers,2021,38(10):2896-2903.(in Chinese)
［27］周玉,任钦差,牛会宾.训练样本数据选择方法研究综述［J］.计算机科学,2020,47(增刊2):402-408.
ZHOU Y,REN Q C,NIU H B.Research on training data sample selection methods［J］.Computer Science,2020,47(S2):402-408. (in Chinese)
［28］吴青，付彦琳.支持向量机特征选择方法综述［J］.西安邮电大学学报，2020，25(5):16-21.
WU Q，FU Y L.Review on support vector machines based on feature selection［J］.Journal of Xi'an University of Posts and Telecommunications，2020，25(5):16-21. (in Chinese)
［29］王霞，董永权，于巧，等.结构化支持向量机研究综述［J］.计算机工程与应用，2020，56(17):24-32.
WANG X，DONG Y Q，YU Q，et al.Review of structural support vector machines［J］.Computer Engineering and Applications，2020，56(17):24-32. (in Chinese)
［30］ ZENG H，CHEUNG Y M.Feature selection and kernel learning for local learning-based clustering［J］.IEEE Transactions on Pattern Analysis and Machine Intelligence，2011,33(8):1532-1547.
［31］李良福，陈卫东，高强，等.基于深度学习的光电系统智能目标识别［J］.兵工学报，2022，43（增刊1）：162-168.
LI L F， CHEN W D， GAO Q， et al. Deep learning-based intelligent target recognition technology for electro-optical system［J］. Acta Armamentarii, 2022,43(S1): 162-168. (in Chinese)

[1]	WANG Qiang, WU Letian, LI Hong, WANG Yong, WANG Huan, YANG Wankou. An Infrared Small Target Detection Method via Dual Network Collaboration [J]. Acta Armamentarii, 0, (): 0-0.
[2]	DING Bosheng, ZHANG Ruiheng, XU Lixin, CHENG Huiming. Sand-dust Image Restoration Based on Gray Compensation and Feature Fusion [J]. Acta Armamentarii, 0, (): 0-0.
[3]	JIANG Xinhao, CAI Wei, ZHANG Zhili, JIANG Bo, YANG Zhiyong, WANG Xin. Camouflaged object segmentation based on COSNet [J]. Acta Armamentarii, 0, (): 0-0.
[4]	LI Bo， WANG Bo， HAN Jingye， YANG Zongrui， LI Linyun， FU Changzhi. Infrared Image Moving Object Detection Technology Based on Onboard Computer [J]. Acta Armamentarii, 2022, 43(S1): 66-73.
[5]	ZHANG Boyao， LENG Yanbing. Detection of Metal Surface Scratch Based on YOLOv4 Network Model [J]. Acta Armamentarii, 2022, 43(S1): 214-221.
[6]	ZHANG Liang’an, CHEN Yang, XIE Shenglong, LIU Tongxin. The Detection System for Cracks in Aircraft Protective Grid based on the Technology of Machine Vision and Deep Learning [J]. Acta Armamentarii, 0, (): 0-0.
[7]	HUO Jian, CHEN Huimin, MA Yunfei, GUO Pengyu, YANG Xu, MENG Xiangsheng. Vehicle Targets Recognition Algorithm Based on MEMS Lidar [J]. Acta Armamentarii, 0, (): 0-0.
[8]	QIAN Lizhi, YANG Chuandong, XUE Song, CHEN Dong, LING Chong. Survey on Target Detection of Image Homing Ammunition [J]. Acta Armamentarii, 0, (): 0-0.
[9]	TANG Wei, JIA Fangxiu, WANG Xiaoming. Visible and Infrared Image Adaptive Fusion Based on Bilateral Filter [J]. Acta Armamentarii, 0, (): 0-0.
[10]	YU Bowen， L Ming. Improved YOLOv3 Algorithm and Its Application in Military Target Detection [J]. Acta Armamentarii, 2022, 43(2): 345-354.
[11]	ZHANG Feng, ZHANG Chao, YANG Huamin, WANG Fabin. Deep Learning-based Color Compensation Method for Projected Images in Combat Assistant System [J]. Acta Armamentarii, 2021, 42(11): 2418-2423.
[12]	CAI Hua， SUN Jun， ZHU Ruikun， ZHU Xinli， ZHAO Yiwu. Depth Face Recognition Algorithm Based on Adaptive Circle Margin [J]. Acta Armamentarii, 2021, 42(11): 2424-2432.
[13]	DING Guipeng， TAO Gang, LI Yingchao, PANG Chunqiao, WANG Xiaofeng, DUAN Guiru. Infrared and Visible Images Fusion based on Non-subsampled Contourlet Transform and Guided Filter [J]. Acta Armamentarii, 2021, 42(9): 1911-1922.
[14]	JIANG Shan， DI Xiaoqiang， HAN Cheng. Siamese Network for Visual Tracking with Temporal-spatial Property [J]. Acta Armamentarii, 2021, 42(9): 1940-1950.
[15]	ZHANG Yongmei， LAI Yuping， MA Jianzhe， FENG Chao， SHU Jie. A Video-based Prediction Algorithm for Armored Vehicle and Aircraft Detection/tracking and Trajectory [J]. Acta Armamentarii, 2021, 42(3): 545-554.