给出了一种结合幂变换的高分辨率距离像 (High Resolution Range Profile,简称 HRRP)的预处理新方法。根据时域 -频域能量等价性 ,利用功率平均形成一种频域平均模板。基于美国 MSTAR(Moving and Stationary TargetAcquisition and Reco...给出了一种结合幂变换的高分辨率距离像 (High Resolution Range Profile,简称 HRRP)的预处理新方法。根据时域 -频域能量等价性 ,利用功率平均形成一种频域平均模板。基于美国 MSTAR(Moving and Stationary TargetAcquisition and Recognition)展开更多
提出了一种改进的ReliefF算法,并将其用于雷达高分辨距离像(high resolution range profile,HRRP)目标识别。与传统ReliefF算法相比,新算法通过在每类目标中等距离间隔抽取相同数量样本的方式进行权值累积,降低了样本数量及分布差异等...提出了一种改进的ReliefF算法,并将其用于雷达高分辨距离像(high resolution range profile,HRRP)目标识别。与传统ReliefF算法相比,新算法通过在每类目标中等距离间隔抽取相同数量样本的方式进行权值累积,降低了样本数量及分布差异等因素对特征权值的影响,得到了更稳定有效的特征权值。利用此权值不但可降低特征向量维数,并可对最小距离分类器加权,提高目标识别率。最后,对5种不同飞机实测数据的识别结果表明本算法可达到83%的平均识别率。展开更多
Radar high-resolution range profile （HRRP） has received intensive attention from the radar automatic target recognition （RATR） community. Usually, since the initial phase of a complex HRRP is strongly sensitive to...Radar high-resolution range profile （HRRP） has received intensive attention from the radar automatic target recognition （RATR） community. Usually, since the initial phase of a complex HRRP is strongly sensitive to target position variation, which is referred to as the initial phase sensitivity in this paper, only the amplitude information in the complex HRRP, called the real HRRP in this paper, is used for RATR, whereas the phase information is discarded. However, the remaining phase information except for initial phases in the complex HRRP also contains valuable target discriminant information. This paper proposes a novel feature extraction method for the complex HRRP. The extracted complex feature vector, referred to as the complex feature vector with difference phases, contains the difference phase information between range cells but no initial phase information in the complex HRRR According to the scattering center model, the physical mechanism of the proposed complex feature vector is similar to that of the real HRRP, except for reserving some phase information independent of the initial phase in the complex HRRP. The recognition algorithms, frame-template establishment methods and preprocessing methods used in the real HRRP-based RATR can also be applied to the proposed complex feature vector-based RATR. Moreover, the components in the complex feature vector with difference phases approximate to follow Gaussian distribution, which make it simple to perform the statistical recognition by such complex feature vector. The recognition experiments based on measured data show that the proposed complex feature vector can obtain better recognition performance than the real HRRP if only the cell interval parameters are properly selected.展开更多
雷达数据生成在目标识别等任务中发挥重要的作用。现有雷达数据生成方法包括电磁仿真、视线追踪等,存在对模型误差敏感、计算量大等问题。本文面向雷达HRRP(high resolution range profile)数据提出一种基于深度生成网络的雷达数据生成...雷达数据生成在目标识别等任务中发挥重要的作用。现有雷达数据生成方法包括电磁仿真、视线追踪等,存在对模型误差敏感、计算量大等问题。本文面向雷达HRRP(high resolution range profile)数据提出一种基于深度生成网络的雷达数据生成方法,在模型先验信息未知的情况下,由雷达HRRP数据集训练得到深度生成网络,从而实现雷达HRRP数据的快速生成。实测数据处理结果表明该方法生成HRRP与数据集中HRRP极为相似,生成HRRP可以应用于增强雷达HRRP数据集、改善数据不平衡问题等。展开更多
Automatic target recognition （ATR） is an important function for modern radar. High resolution range profile （HRRP） of target contains target struc- ture signatures, such as target size, scatterer distribu- tion, e...Automatic target recognition （ATR） is an important function for modern radar. High resolution range profile （HRRP） of target contains target struc- ture signatures, such as target size, scatterer distribu- tion, etc, which is a promising signature for ATR. Sta- tistical modeling of target HRRPs is the key stage for HRRP statistical recognition, including model selection and parameter estimation. For statistical recognition al- gorithms, it is generally assumed that the test samples follow the same distribution model as that of the train- ing data. Since the signal-to-noise ratio （SNR） of the received HRRP is a function of target distance, the as- sumption may be not met in practice. In this paper, we present a robust method for HRRP statistical recogni- tion when SNR of test HRRP is lower than that of train- ing samples. The noise is assumed independent Gaus- sian distributed, while HRRP is modeled by probabilistic principal component analysis （PPCA） model. Simulated experiments based on measured data show the effective- ness of the proposed method.展开更多
文摘给出了一种结合幂变换的高分辨率距离像 (High Resolution Range Profile,简称 HRRP)的预处理新方法。根据时域 -频域能量等价性 ,利用功率平均形成一种频域平均模板。基于美国 MSTAR(Moving and Stationary TargetAcquisition and Recognition)
文摘提出了一种改进的ReliefF算法,并将其用于雷达高分辨距离像(high resolution range profile,HRRP)目标识别。与传统ReliefF算法相比,新算法通过在每类目标中等距离间隔抽取相同数量样本的方式进行权值累积,降低了样本数量及分布差异等因素对特征权值的影响,得到了更稳定有效的特征权值。利用此权值不但可降低特征向量维数,并可对最小距离分类器加权,提高目标识别率。最后,对5种不同飞机实测数据的识别结果表明本算法可达到83%的平均识别率。
基金the National Natural Science Foundation of China(Grant No.60302009)the National Defense Advanced Research Foundation of China(Grant No.413070501)
文摘Radar high-resolution range profile （HRRP） has received intensive attention from the radar automatic target recognition （RATR） community. Usually, since the initial phase of a complex HRRP is strongly sensitive to target position variation, which is referred to as the initial phase sensitivity in this paper, only the amplitude information in the complex HRRP, called the real HRRP in this paper, is used for RATR, whereas the phase information is discarded. However, the remaining phase information except for initial phases in the complex HRRP also contains valuable target discriminant information. This paper proposes a novel feature extraction method for the complex HRRP. The extracted complex feature vector, referred to as the complex feature vector with difference phases, contains the difference phase information between range cells but no initial phase information in the complex HRRR According to the scattering center model, the physical mechanism of the proposed complex feature vector is similar to that of the real HRRP, except for reserving some phase information independent of the initial phase in the complex HRRP. The recognition algorithms, frame-template establishment methods and preprocessing methods used in the real HRRP-based RATR can also be applied to the proposed complex feature vector-based RATR. Moreover, the components in the complex feature vector with difference phases approximate to follow Gaussian distribution, which make it simple to perform the statistical recognition by such complex feature vector. The recognition experiments based on measured data show that the proposed complex feature vector can obtain better recognition performance than the real HRRP if only the cell interval parameters are properly selected.
文摘雷达数据生成在目标识别等任务中发挥重要的作用。现有雷达数据生成方法包括电磁仿真、视线追踪等,存在对模型误差敏感、计算量大等问题。本文面向雷达HRRP(high resolution range profile)数据提出一种基于深度生成网络的雷达数据生成方法,在模型先验信息未知的情况下,由雷达HRRP数据集训练得到深度生成网络,从而实现雷达HRRP数据的快速生成。实测数据处理结果表明该方法生成HRRP与数据集中HRRP极为相似,生成HRRP可以应用于增强雷达HRRP数据集、改善数据不平衡问题等。
文摘Automatic target recognition （ATR） is an important function for modern radar. High resolution range profile （HRRP） of target contains target struc- ture signatures, such as target size, scatterer distribu- tion, etc, which is a promising signature for ATR. Sta- tistical modeling of target HRRPs is the key stage for HRRP statistical recognition, including model selection and parameter estimation. For statistical recognition al- gorithms, it is generally assumed that the test samples follow the same distribution model as that of the train- ing data. Since the signal-to-noise ratio （SNR） of the received HRRP is a function of target distance, the as- sumption may be not met in practice. In this paper, we present a robust method for HRRP statistical recogni- tion when SNR of test HRRP is lower than that of train- ing samples. The noise is assumed independent Gaus- sian distributed, while HRRP is modeled by probabilistic principal component analysis （PPCA） model. Simulated experiments based on measured data show the effective- ness of the proposed method.
