帧内最大相似像在HRRP目标识别中的应用
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摘要
由于高分辨距离像(HRRP)具有便于获取、处理方便的优势,基于HRRP的雷达目标识别技术一直是雷达自动目标识别技术研究的热点。HRRP的几何结构特征能够直接反映目标的物理结构,在利用帧内最大相似像缓和HRRP的方位敏感性的基础上,HRRP的等效散射中心维数、等效目标尺寸等几何结构特征的提取,有效实现了三类不同飞机目标的识别。实验表明,在基于几何结构特征的HRRP目标识别中,与平均向量的帧中心提取方法相比,帧内最大相似像具有更好的识别效果。
Radar target recognition based on high resolution range profile(HRRP) has always been an important research area of radar automatic target recognition(RATR) due to the convenient acquisition and processing of HRRP. The geometric structure features of HRRP can directly reflect the physical structure of the target.Firstly, the frame maximum likelihood profile was used to relieve the azimuth sensitivity of HRRP, and then the geometric structure features of HRRP such as the equivalent scattering center dimension and equivalent target size etc, were extracted to realize the classification of three different air craft targets effectively. Experiments show that compared with the mean vector, the frame maximum likelihood profile has a better recognition performance in RATR based on the geometric structure features.
Radar target recognition based on high resolution range profile(HRRP) has always been an important research area of radar automatic target recognition(RATR) due to the convenient acquisition and processing of HRRP. The geometric structure features of HRRP can directly reflect the physical structure of the target.Firstly, the frame maximum likelihood profile was used to relieve the azimuth sensitivity of HRRP, and then the geometric structure features of HRRP such as the equivalent scattering center dimension and equivalent target size etc, were extracted to realize the classification of three different air craft targets effectively. Experiments show that compared with the mean vector, the frame maximum likelihood profile has a better recognition performance in RATR based on the geometric structure features.
引文
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[2]Zhou Y,Yu X L,Cui M L,et al.Radar target recognition based on multiple features fusion with D-S theory[C]//The10th International Conference on Electronic Measurement&Instruments.2011:243-247.
[3]Pilcher C M,Khotanzad A.Maritime ATR using classifier combination and high resolution range profiles[J].IEEE Transactions on Aerospace and Electronic Systems,2011,47(4):2558-2573.
[4]毕莉,赵锋,高勋章,等.基于一维像序列的进动目标尺寸估计研究[J].电子与信息学报,2010,32(8).
[5]Xu X T,Wen T N.Research on air target classification based on extraction of physical features of high-resolution radar range profiles[C]//Proceedings of 2011 IEEE CIE International Conference on Radar.2011,2:1668-1671.
[6]Doo S H,Smith G E,Baker C J.On variability in high resolution range profiles[C]//2013 International Conference on Radar.2013:505-510.
[7]但波,姜永华,李敬军,等.雷达高分辨距离像自适应角域划分方法[J].系统工程与电子技术,2014,36(11).
[8]Du L,Liu H W,Bao Z.Using the amplitude fluctuation property of target HRRP for radar automatic target recognition[C]//2006 CIE International Conference on Radar.2006.
[9]Schimpf H,Fuchs H H.Analysis of ATR features for non-cooperative ground-based classification of ships[C]//11th International Radar Symposium.2010:148-152.
[10]Blacknell D,Griffiths H.Radar Automatic Target Recognition(ATR)and Non-Cooperative Target Recognition(NCTR)[M/OL].London,UK:IET,2013.
[11]Liao K,Yang W L.Extraction of radar target length based on high resolution range profile[C]//2010 International Conference on Electrical and Control Engineering.2010.