随着雷达目标微多普勒现象的发现,目标的微动特性在雷达自动目标识别中逐渐受到了广泛的关注。微动目标回波中包含了精细的目标微多普勒特征信息,因此,可以从其中推断出目标特有的独立特征。而基于目标微动回波时频图的特征更是因为其信息量充足的特点,成为了一种新兴有效的目标分类特征。文中主要研究了飞机目标的韵律频率图(Cadence Frequency Diagram,CFD)特征分类算法,详细叙述了算法的具体步骤。仿真分析了CFD特征在飞机目标分类中的特点和优势,并且研究了相关参数对CFD特征的影响。

Since micro-Doppler phenomenon is observed in radar target detection, micro-motion, as one of significant target characteristics with promising applications in radar target feature extraction and recognition, incur increasing attentions and research interests. The backscatter of target with micro-motions contains fine micro-Doppler features, which is able to determine some unique properties of the target. As the same time, sufficiently many target individual features included in the time-frequency diagram (TFD), which is become an oncoming and useful feature. In this paper , cadence frequency diagram (CFD) feature extraction, which is based on selecting the strongest parts of a cadence-frequency diagram is proposed and studied. Using simulated data we have analyzed this method, which is sound with good classification results.