机载光电侦察吊舱综合信息处理技术发展与分析

李伦平; 刘达

光学与光电技术, 2017, 15 (6): 29, 网络出版: 2018-01-09

机载光电侦察吊舱综合信息处理技术发展与分析

Development and Analysis of Synthetic Information Process Technology for Airborne Electro-Optical Reconnaissance Pod

论文大纲

李伦平 ^*刘达

作者单位

华中光电技术研究所—武汉光电国家实验室, 湖北武汉 430223

摘要

机载光电侦察吊舱为各类侦察应用提供了数据保障,应用需求也从传统的可见光/红外目标跟踪、有源定位、地理跟踪、火炮校射发展到无源定位,并进一步拓展到辅助着舰/着陆、广域探测、多目标记忆跟踪、机器学习与目标智能识别、视觉辅助导航等多项应用领域,机载光电侦察吊舱已经成为了一种高度集成的目标深度侦察工具。对上述应用领域的相关技术现状和发展进行了归纳和总结,指出并分析了当前机载光电侦察吊舱综合信息处理技术领域存在的主要突出问题,包括海量数据与计算资源有限的矛盾、多传感器综合应用深度尚浅、综合信息的误差分析及仿真评估不充分,而未来综合信息处理技术应朝着智能化、标准化和工程化等方向发展,才能有效提高情报搜集和处理效率,进而充分挖掘出机载光电侦察吊舱的应用潜力。

Abstract

Airborne electro-optical reconnaissance pod can provide data protection for reconnaissance applications. Its application field ranges from the traditional fields such as visible light/infrared tracking, active localization, geographic tracking, fire adjustment, to new expanded fields such as passive localization, auxiliary landing, wide area detection, multi targets tracking, machine learning and intelligent identification, visual navigation aids etc. Now, airborne electro-optical reconnaissance pod has become to be a highly integrated tool for target deep reconnaissance. In this paper, the relevant technology of the application fields above are summarized and current problems in the field of synthetic information processing technology are analyzed for airborne electro-optical surveillance pod, including the contradiction between huge data and limited computing resource, low quantitative multi-sensor application, non-sufficient error analysis and simulation evaluation. The future synthetic information processing technology should be developed towards intellectualized, standardized, and practical in order to improve the efficiency of information collection and processing, and exploit the application potential of airborne electro-optical reconnaissance pod.

参考文献

[1] 李文魁, 王俊璞, 金志华, 等. 直升机机载光电吊舱的发展现状及对策［J］. 中国惯性技术学报, 2004, 12(5): 75-80.

LI Wen-kui, WANG Jun-pu, JIN Zhi-hua, et al. Development and countermeasure of airborne optoelectronic pods［J］. Journal of Chinese Inertial Technology, 2004, 12(5): 75-80.

[2] 周前飞, 刘晶红, 熊文卓, 等. 机载光电成像平台的多目标自主定位系统研究［J］. 光学学报, 2015, 35(1): 189-203.

ZHOU Qian-fei, LIU Jing-hong, XIONG Wen-zhuo, et al. Multi-target self-determination orientation system based on airborne photoelectric imaging platform［J］. Acta Optica Sinica, 2015, 35(1): 189-203.

[3] 沈宏海, 黄猛, 李嘉全, 等. 国外先进航空光电载荷的进展与关键技术分析［J］. 中国光学, 2012, 5(1): 20-29.

SHEN Hong-hai, HUANG Meng, LI Jia-quan, et al. Recent progress in aerial electro-optic payloads andtheir key technologies［J］. Chinese Optics, 2012, 5(1): 20-29.

[4] 刘蕴华. 光电吊舱测角精度分析［J］. 现代电子技术, 2012, 35(17): 33-35.

LIU Yun-hua. Study on angle measuring precision of optoelectronic pods［J］. Modern Electronics Technique, 2012, 35(17): 33-35.

[5] X T Yuan, S T Yang, H W Zhu. Region tracking via HMMF in joint feature-spatial space［C］. IEEE Workshop on Motion and Video Computing, Breckenridge, 2005, 2: 72-77.

[6] S T Birchfield, S Rangarajan. Spatiograms versus histograms for region-based tracking［C］. Proceedings of IEEE Computer Society Conference on Computer Vision and attern Recognition(CVPR). San Diego: 2005, 2: 1158-1163.

[7] 杜敬芳. 光电探测系统中目标跟踪算法研究与应用［D］. 成都:电子科技大学, 2016.

DU Jing-fang. Reserch and application of tracking algorithm in photoelectric detection system［D］. Chengdu: University of Electronic Science and Technology of China, 2016.

[8] 周晓尧. 光电探测系统目标定位误差分析与修正问题研究［D］. 长沙:国防科学技术大学, 2011.

ZHOU Xiao-yao. Study on error analysis and correction of target location for electro-optical detection system［D］. Changsha: National University of Defense Technology, 2011.

[9] 石风, 朱有德. 一种无人机载光电吊舱目标定位方法［J］. 计算机与数字工程, 2014, 42(12): 2259-2262+2315.

SHI Feng, ZHU You-de. A target localization method for unmanned-helicopter electro-optic pod［J］. Computer & Digital Engineering, 2014, 42(12): 2259-2262+2315.

[10] 汪阳, 黄克明, 胡军. 基于炸点紫外成像融合的火炮校射方法研究［J］. 兵工学报, 2012, 33(1): 32-34.

WANG Yang, HUANG Ke-ming, HU Jun. Research of on fire adjustment method based on UV Image fusion of burst point［J］. Acta Armamentarii, 2012, 33(1): 32-34.

[11] 林旻序, 乔彦峰, 戴明, 等. 单载荷航空吊舱目标定位方法研究［J］. 红外技术, 2011, 33(10): 593-597.

LIN Min-xu, QIAO Yan-feng, DAI Ming, et al. Research on single payload air pod target location method［J］. Infrared Technology, 2011, 33(10): 593-597.

[12] David salt. An electro-optic precision approach and landing system［C］. SPIE, 1995, 2463: 102-114.

[13] 李博, 王孝通, 徐晓刚, 等. 基于计算机视觉的舰载直升机助降技术研究［J］. 兵工学报, 2007, 28(3): 370-373.

LI Bo, WANG Xiao-tong, XU Xiao-gang, et al. Study on assisted landing technology for shipboard helicopter based on computer vision［J］. Acta Armamentarii, 2007, 28(3): 370-373.

[14] 金仲林. 舰载直升机系留座分布及系留载荷的仿真研究［D］. 南京:南京航空航天大学, 2006.

JIN Zhong-lin. Research on design of mooring bed distribution and simulation of mooring loads for ship-based helicopter［D］. Nanjing: Nanjing University of Aeronautics and Astronautics, 2006.

[15] 张春晓, 林招荣, 姚毅刚, 等. 被动成像广域空中监视系统综述［J］. 航天返回与遥感, 2014, 35(1): 37-45.

ZHANG Chun-xiao, LIN Zhao-rong, YAO Yi-gang, et al. Overview of passive imaging wide area airborne surveillanc［J］. Spacecraft Recovery & Remote Sensing, 2014, 35(1): 37-45.

[16] 许文海, 续元君, 董丽丽, 等. 基于水平集和支持向量机的图像声呐目标识别［J］. 仪器仪表学报, 2012, 33(1): 49-55.

XU Wen-hai, XU Yuan-jun, DONG Li-li, et al. Level-set and SVM based target recognition of image sonar［J］. 2012, 33(1): 49-55.

[17] Y LeCun, B Boser, J S Denker, et al. Backpropagation applied to handwritten zip code recognition［J］. Neural Computation, 1989, (1): 541-551.

[18] E M Kuaaul, T N Baidyk, D C Vunsch, et al. permutation coding techenique for image recognition systems［J］, IEEE Transaction on Neural Networks, 2006, 17(6): 1556-1579.

[19] Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets［J］. Neural Computation, 2006, 18(7): 1527-1554.

[20] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks［C］//Advances in Neural Information Processing Systems. 2012: 1097-1105.

[21] 王若辰. 基于深度学习的目标检测和分割算法研究［D］. 北京:北京工业大学, 2016.

WANG Ruo-chen. Reserach on image target detection and segmentation algorithm based on deep learning［D］. Beijing: Beijing University of Technology, 2016.

[22] 龙海强. 基于深度学习的人脸识别算法［D］. 广州:广州工业大学, 2016.

LONG Hai-qiang. Face recognition algorithm based on deep learning［D］. Guangdong: Guangdong University of Technology, 2016.

李伦平, 刘达. 机载光电侦察吊舱综合信息处理技术发展与分析[J]. 光学与光电技术, 2017, 15(6): 29. LI Lun-ping, LIU Da. Development and Analysis of Synthetic Information Process Technology for Airborne Electro-Optical Reconnaissance Pod[J]. OPTICS & OPTOELECTRONIC TECHNOLOGY, 2017, 15(6): 29.

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