With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on ...With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on kernel joint discriminant analysis(KJDA)is proposed.Compared with the traditional feature extraction methods,KJDA possesses stronger discriminative ability in the kernel feature space.K-nearest neighbor(KNN)and kernel support vector machine(KSVM)are applied as feature classifiers to verify the classification effect.Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality,and improve target recognition performance.展开更多
针对红外小目标图像的低分辨率、特征信息少、识别准确率低等问题,提出嵌入空间位置信息和多视角特征提取(Embedded Spatial Location Information and Multi-view Feature Extraction,ESLIMFE)的红外小目标检测模型。首先,随着网络深...针对红外小目标图像的低分辨率、特征信息少、识别准确率低等问题,提出嵌入空间位置信息和多视角特征提取(Embedded Spatial Location Information and Multi-view Feature Extraction,ESLIMFE)的红外小目标检测模型。首先,随着网络深度的增加导致特征图分辨率逐渐减小从而丢失细节信息,因此在骨干网络中嵌入空间位置信息融合注意力机制(Spatial Location Information Fusion,SLIF)弥补小目标特征信息。其次,结合C3模块和动态蛇形卷积提出多视角特征提取(Multi-view Feature Extraction,MVFE)模块,通过在不同视角下提取同一特征来增强小目标的特征表达能力。采用大选择核(Large Selection Kernel,LSK)模块,通过使用不同大小的卷积核提取小目标多尺度信息,以提高对红外小目标定位能力。最后,引入基于注意力的尺度内特征交互(Attention-based Intrascale Feature Interaction,AIFI)模块增强特征之间的交互性。在对空红外小目标数据集上进行实验,实验结果表明,mAP75的检测精度为90.5%,mAP50~95检测精度为74.5%,文中模型能够较好地实现对红外小目标精确检测。展开更多
海杂波背景下的海上小目标是海洋雷达探测的重难点。针对特征空间内海杂波与小目标特征可分性问题,提出了量化特征之间可分性的度量标准——重叠系数。通过开展对海探测试验获取的2~5级海况实测数据,分别提取时域特征相对平均幅度(Relat...海杂波背景下的海上小目标是海洋雷达探测的重难点。针对特征空间内海杂波与小目标特征可分性问题,提出了量化特征之间可分性的度量标准——重叠系数。通过开展对海探测试验获取的2~5级海况实测数据,分别提取时域特征相对平均幅度(Relative Average Amplitude,RAA)、相对峰值峰高(Relative Peak Height,RPH)、时域熵值均值(Time domain Entropy Mean,TEM),频域特征相对多普勒峰高(Relative Doppler Peak Height,RDPH)、相对多普勒向量熵(Relative Vector Entropy,RVE)、频域熵值二阶矩(Second moment of Frequency domain Entropy,SOFE),计算出重叠系数。通过特征检测器进行检测性能对比,低海况下,相对平均幅度、相对峰值峰高、时域熵值均值、相对多普勒峰高、频域熵值二阶矩特征之间重叠系数均在0.3以下,对应特征检测器的检测概率均在85%以上;高海况下其特征之间重叠系数均在0.7以上,对应特征检测器的检测概率均在50%以下。相对多普勒向量熵在4种海况下可分性较小,其对应的特征检测器性能较差。结果验证了重叠系数在一维特征选择的应用可行性,为多特征融合目标检测提供了一定支持。展开更多
基金supported by the National Natural Science Foundation of China(61471191)the Aeronautical Science Foundation of China(20152052026)the Foundation of Graduate Innovation Center in NUAA(kfjj20170313)
文摘With the improvement of radar resolution,the dimension of the high resolution range profile(HRRP)has increased.In order to solve the small sample problem caused by the increase of HRRP dimension,an algorithm based on kernel joint discriminant analysis(KJDA)is proposed.Compared with the traditional feature extraction methods,KJDA possesses stronger discriminative ability in the kernel feature space.K-nearest neighbor(KNN)and kernel support vector machine(KSVM)are applied as feature classifiers to verify the classification effect.Experimental results on the measured aircraft datasets show that KJDA can reduce the dimensionality,and improve target recognition performance.
文摘海杂波背景下的海上小目标是海洋雷达探测的重难点。针对特征空间内海杂波与小目标特征可分性问题,提出了量化特征之间可分性的度量标准——重叠系数。通过开展对海探测试验获取的2~5级海况实测数据,分别提取时域特征相对平均幅度(Relative Average Amplitude,RAA)、相对峰值峰高(Relative Peak Height,RPH)、时域熵值均值(Time domain Entropy Mean,TEM),频域特征相对多普勒峰高(Relative Doppler Peak Height,RDPH)、相对多普勒向量熵(Relative Vector Entropy,RVE)、频域熵值二阶矩(Second moment of Frequency domain Entropy,SOFE),计算出重叠系数。通过特征检测器进行检测性能对比,低海况下,相对平均幅度、相对峰值峰高、时域熵值均值、相对多普勒峰高、频域熵值二阶矩特征之间重叠系数均在0.3以下,对应特征检测器的检测概率均在85%以上;高海况下其特征之间重叠系数均在0.7以上,对应特征检测器的检测概率均在50%以下。相对多普勒向量熵在4种海况下可分性较小,其对应的特征检测器性能较差。结果验证了重叠系数在一维特征选择的应用可行性,为多特征融合目标检测提供了一定支持。
