基于辐射累积和空间反演的空中弱目标检测算法
文献类型：期刊论文
| 作者 | 马天磊 ; 史泽林; 尹健; 徐保树; 刘云鹏
|
| 刊名 | 红外与激光工程
 |
| 出版日期 | 2015 |
| 卷号 | 44期号:11页码:3500-3506 |
| 关键词 | 红外图像序列 弱目标检测 辐射能量累积 恒虚警判决 |
| ISSN号 | 1007-2276 |
| 其他题名 | Dim air target detection based on radiation accumulation and space Inversion |
| 产权排序 | 1 |
| 中文摘要 | 背景辐射嗓声是弱信号检测面临的难点问题。提出了一种显著提升信嗓比实现匀速运动弱目标的有效检测算法。建立目标坐标空间和速度空间，以不同速度矢量控制图像叠加，形成提升了信嗓比的新的图像序列并构成图像空间;利用恒虚警判决法在图像空间中检测候选目标点;根据候选目标点所对应的坐标向量和速度向量分别映射到坐标空间和速度空间，由两个空间中出现的峰值判定目标点。实际红外成像系统实拍实验表明，算法能将信嗓比提升至接近原图的倍，目标检测概率和虚警概率都明显优于所对比的弱目标检测算法。 |
| 英文摘要 | Background radiation noise interference is a difficult technica1 problem for dim signal detection. dim target detection algorithm was proposed which can significant1y improve signal-to-noise ratio (SNR) to achieve uniformly motion dim target detection successfully. Firstly, a coordinate space and a velocity space were established. Then the original image sequence was stacked a10ng different velocity vectors to acquire a new image sequence with SNR improved and the new image sequence forms an image space. Secondly, quasi-target points in the image space were detected by constant false-alarm ratio (CFAR) judging. Finally, velocity vectors and coordinate vectors of quasi-target points were mapped to the velocity space and the coordinate space respectively. As a result, two local peaks from the spaces will confirm true target points. Experiments of real images from actual IR imaging system show that proposed algorithm can improve SNR approximately up to times of original image SNR, and the proposed algorithm is demonstrably superior to compared algorithms on detection probability and false alarm probability. |
| 收录类别 | EI ; CSCD |
| 语种 | 中文 |
| CSCD记录号 | CSCD:5586576 |
| 源URL | [http://ir.sia.cn/handle/173321/17422]  |
| 专题 | 沈阳自动化研究所_光电信息技术研究室 |
| 推荐引用方式 GB/T 7714 | 马天磊,史泽林,尹健,等. 基于辐射累积和空间反演的空中弱目标检测算法[J]. 红外与激光工程,2015,44(11):3500-3506. |
| APA | 马天磊,史泽林,尹健,徐保树,&刘云鹏.(2015).基于辐射累积和空间反演的空中弱目标检测算法.红外与激光工程,44(11),3500-3506. |
| MLA | 马天磊,et al."基于辐射累积和空间反演的空中弱目标检测算法".红外与激光工程 44.11(2015):3500-3506. |
入库方式： OAI收割
来源：沈阳自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。