李少毅1, 卫孟杰1, 杨俊彦1,2, 杨曦1(), 孟中杰1
收稿日期:
2024-03-20
修回日期:
2024-05-09
接受日期:
2024-05-23
出版日期:
2024-05-31
发布日期:
2024-05-29
通讯作者:
杨曦
E-mail:nwpuyx@163.com
基金资助:
Shaoyi LI1, Mengjie WEI1, Junyan YANG1,2, Xi YANG1(), Zhongjie MENG1
Received:
2024-03-20
Revised:
2024-05-09
Accepted:
2024-05-23
Online:
2024-05-31
Published:
2024-05-29
Contact:
Xi YANG
E-mail:nwpuyx@163.com
Supported by:
摘要:
随着红外干扰对抗、人工智能、无人协同等技术的不断发展,红外成像制导武器越来越多地面临强背景干扰、强人工干扰、强战术动作干扰等强对抗作战环境,传统的单一波段红外成像探测技术已无法满足复杂战场环境下的目标识别与抗干扰任务,由此红外多波段成像末制导技术受到了广泛关注,按功能阶段划分其主要包括红外多波段图像融合、目标检测和多波段抗干扰技术等。目前,对红外多波段成像末制导技术的研究主要集中在红外多波段图像融合方面,但相关综述较少。从末制导技术现状、多波段信息运用技术现状和未来技术发展方向等角度对红外多波段成像末制导技术进行了综述。首先,对红外多波段成像制导技术的发展现状做出了总结,分析了多波段红外场景特性与波段选段,介绍了国内外现役多波段成像制导武器,并总结了目前红外多波段制导武器面临的主要问题;其次,重点对国内外研究集中的图像融合技术进行了分类和分析;然后,从多波段信息运用角度,介绍了基于红外多波段图像的目标检测、目标识别、抗干扰技术发展现状;最后,对红外多波段成像末制导技术的未来发展趋势进行了展望,分析了群体智能协同化、多模态智能运用与信息融合和探感导一体化智能对抗的重要性。
中图分类号:
李少毅, 卫孟杰, 杨俊彦, 杨曦, 孟中杰. 红外多波段成像末制导技术研究现状与展望[J]. 航空学报, 2024, 45(20): 630427.
Shaoyi LI, Mengjie WEI, Junyan YANG, Xi YANG, Zhongjie MENG. Research status and prospects of infrared multi-band imaging terminal guidance technology[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(20): 630427.
表 3
各国红外多波段成像制导武器性能
武器型号 | 国家 | 导引系统 | 成像探测器类型 | 红外探测波段/ | 跟踪能力 | 飞行马赫数 | 射程/km | 发射平台 |
---|---|---|---|---|---|---|---|---|
NSM[ | 挪威 | 高精度惯导系统+GPS卫星辅助导航+激光雷达高度表+双波段红外成像导引头+地形匹配 | 凝视型前视焦平面红外成像传感器 | 导引头视角: | 0.7~0.95 | 200 | 舰艇、陆基车辆 | |
Python-5[ | 以色列 | 双波段红外寻的+双波段光电成像导引头 | 导引头视角: | 4 | 20 | 战斗机 | ||
A-DARTER[ | 南非/巴西 | 中端机载雷达/头盔瞄准指令制导+惯性导航;末端双波段红外寻的制导 | 导引头视角: 跟踪速率:120 (°)/s | 3 | 23 | 战斗机 | ||
RVV-MD[ | 俄罗斯 | 全角度被动双波段红外寻的 | 导引头视角: 离轴发射角: | 2.5 | 0.3~40 | 战斗机 | ||
MAA-1B[ | 巴西 | 双波段红外寻的 | 导引头视角: 跟踪速率: 离轴发射角: | 3.5 | 12 | 战斗机 | ||
西北风ATAM[ | 法国 | 双波段红外寻的 | 四元阵列双色被动红外导引头 | 2.6 | 6 | 三脚架、陆基车辆、直升机、军舰 | ||
MICA-IR[ | 法国 | 惯性导航+多波段红外寻的 | 机电扫描的双波段红外成像焦平面阵列探测器 | 导引头视角: 跟踪速率: | 4 | 80 | 战斗机、潜艇、舰艇 |
表 4
基于空间域的图像融合方法优缺点
算法 | 优点 | 缺点 |
---|---|---|
加权融合法 | ① 结构简单、运算速度快、适用于图像实时处理 ② 信息损耗小、附加噪声小、能够提高融合图像信噪比 | ① 当融合图像灰度值差异较大时,融合效果不佳、图像拼接痕迹明显 ② 依赖权值的选择,权值选取不当会模糊图像已有的边缘、纹理等信息,使图像对比度降低 |
主成分分析法 | ① 适用于所有波段的图像融合,能够用于多个波段之间的变换 ② 经主成分变换之后的图像的光谱性保持较好,且分辨率较高 | ① 在融合过程中,只考虑了统计特征,而没有考虑各波段图像的特点 ② 计算量较大,可操作性较差 ③ 受大噪声的影响较大 |
非负矩阵分解法 | ① 可以很好的表达原始图像的特征及结构,且分解值为非负,具有一定的稀疏性和线性无关性 ② 为基于部分的分解方法,符合人类的“局部构成整体”思维 | ① 全局特征提取能力有限,融合图像对比度较低,视觉效果较差 ② 算法复杂度较高,在对高维数据进行分解时,计算耗时长 ③ 对噪声敏感以及鲁棒性较差 |
伪彩色图像融合法 | ① 将灰度图像转换为彩色图像,增强了图像的视觉效果 ② 可以根据不同的应用需求调整彩色映射策略,以突出不同的特征 | ① 光谱失真比较严重,且有波段限制 ② 伪彩色处理可能会导致图像中的细节信息模糊,且融合图像质量受不同波段分辨率差异的影响 |
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