This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear featu...This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear feature extracted from kernel principal component analysis （KPCA） respectively, and then utilizes the adaptive feature fusion algorithm which is based on the weighted maximum margin criterion （WMMC） to fuse the features in order to achieve better performance. The linear regression classifier is used in the experiments. The experimental results indicate that the proposed self-fusion algorithm achieves higher recognition rate compared with the traditional PCA and KPCA feature fusion algorithms.展开更多
The normalized central moments are widely used in pattern recognition because of scale and translation invariance. The moduli of normalized central moments of the 1-dimensional complex range profiles are used here as ...The normalized central moments are widely used in pattern recognition because of scale and translation invariance. The moduli of normalized central moments of the 1-dimensional complex range profiles are used here as feature vector for radar target recognition. The common feature extraction method for high resolution range profile obtained by using Fourier-modified direct Mellin transform is inefficient and unsatisfactory in recognition rate And. generally speaking, the automatic target recognition method based on inverse synthetic aperture radar 2-dimensional imaging is not competent for real time object identification task because it needs complicated motion compensation which is sometimes too difficult to carry out. While the method applied here is competent for real-time recognition because of its computational efficiency. The result of processing experimental data indicates that this method is good at recognition.展开更多
高分辨一维距离像(High Resolution Range Profile,HRRP)包含了丰富的目标信息,通过提取HRRP的强散射点分布特征可实现对不同目标的分类和识别。论文充分考虑了HRRP的多域特征和时间依赖性,利用ResNet18网络进行时频域特征提取,并结合...高分辨一维距离像(High Resolution Range Profile,HRRP)包含了丰富的目标信息,通过提取HRRP的强散射点分布特征可实现对不同目标的分类和识别。论文充分考虑了HRRP的多域特征和时间依赖性,利用ResNet18网络进行时频域特征提取,并结合记忆融合网络(Memory Fusion Network,MFN)提出新型深度学习模型MI-MFN(Multi Input-MFN)进行多域特征的融合识别,实现了在不同维度的记忆的跨视图交互,有效地学习和提取HRRP序列特征。实验结果表明,MI-MFN模型的识别准确率可以达到99.9%以上,具有出色的识别性能。展开更多
基金supported in part by the National Natural Science Foundation of China under Grant No. 61033012, No. 611003177, and No. 61070181Fundamental Research Funds for the Central Universities under Grant No.1600-852016 and No. DUT12JR07
文摘This paper proposes a PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm. This algorithm combines the linear feature extracted from principal component analysis （PCA） and nonlinear feature extracted from kernel principal component analysis （KPCA） respectively, and then utilizes the adaptive feature fusion algorithm which is based on the weighted maximum margin criterion （WMMC） to fuse the features in order to achieve better performance. The linear regression classifier is used in the experiments. The experimental results indicate that the proposed self-fusion algorithm achieves higher recognition rate compared with the traditional PCA and KPCA feature fusion algorithms.
文摘The normalized central moments are widely used in pattern recognition because of scale and translation invariance. The moduli of normalized central moments of the 1-dimensional complex range profiles are used here as feature vector for radar target recognition. The common feature extraction method for high resolution range profile obtained by using Fourier-modified direct Mellin transform is inefficient and unsatisfactory in recognition rate And. generally speaking, the automatic target recognition method based on inverse synthetic aperture radar 2-dimensional imaging is not competent for real time object identification task because it needs complicated motion compensation which is sometimes too difficult to carry out. While the method applied here is competent for real-time recognition because of its computational efficiency. The result of processing experimental data indicates that this method is good at recognition.
