基于分数阶傅里叶变换的窄带雷达飞机目标回波特征提取方法

该文研究了常规窄带雷达体制下，利用分数阶傅里叶变换扩展特征域，从而解决直升机、螺旋桨飞机和喷气式飞机3类飞机目标回波分类中的特征提取问题。在现代战场中，直升机、螺旋桨飞机和喷气式飞机具有不同的机动性能，并各自承担着重要的任务。因此，实现这3类飞机的分类具有重大的意义。该文针对3类飞机目标分类的传统特征数目少，包含信息量有限，导致分类性能不够好的问题，基于现有的特征提取方法引入分数阶傅里叶变换(Fractional Fourier Transform, FrFT)，在经过FrFT后的分数域提取3类飞机目标回波的分数阶特征，弥补传统特征的不足。并利用线性相关向量机(Relevance Vector Machine, RVM)的特征选择功能对提取的分数阶特征进行特征选择并分类。基于仿真和实测数据的实验结果证明该文提出的分数阶特征的分类性能较传统时域、多普勒域特征有较大提升。

Feature Extraction Method of Narrow-band Radar Airplane Signatures Based on Fractional Fourier Transform

This paper studies on the feature extraction methods for the classification of helicopter, propeller-driven aircraft, and turbojet using a conventional narrow-band radar system. In the modern battlefield, the helicopter, propeller aircraft and jet aircraft with different motor performances each bear an important task. But the classification performance of the traditional features for the three types of aircraft target classification is not good enough, so the Fractional Fourier Transform (FrFT) is introduced. Based on the existing feature extraction method, the fractional order features of three kinds of aircraft targets are extracted from the fractional domain after FrFT to extend feature domain. Then, the effective features are selected from all extracted features and the classification of the three categories via linear Relevance Vector Machine (RVM) is realized. The experiments demonstrate that the proposed fractional features can improve the classification performance in comparison with some existing features from the time-domain and Doppler-frequency domain.