This paper presents a new approach to radar target classification based on one-dimensional scattering centers matching.
本文提出了一种新的基于目标一维散射中心匹配的雷达目标识别方法。
Spectrum analysis is the most important branch in radar signal processing and significant of radar target classification and feature detection.
谱分析是雷达信号处理的重要组成部分，对目标分类、特征检测均有重要意义。
In classification stage, five kernel-based classifications are used and compared, and fusion methods are designed for wide-band polarimetric radar target classification.
在分类器设计环节，比较五种核非线性分类器，并根据宽带极化雷达目标散射数据的特点，使用融合分类的方法对目标进行分类。
The classification mechanism of support vector machine (SVM) was analyzed in detail. The one dimensional image of radar target was recognized by SVM.
研究了支撑矢量机的分类机理，并利用支撑矢量机对雷达目标一维像进行了识别。
For inverse synthetic aperture radar (ISAR), the multipath effects introduce two artifacts in its reconstructed images, which greatly hinders radar target recognition and classification.
对于逆合成孔径雷达(ISAR)而言，多径效应会在ISAR的二维目标重建像上引入两个伪像，这将会影响雷达的目标识别与分类。
A novel SVM for the radar target recognition is proposed in this paper, the mechanism of SVM is particularly analyzed and the classification algorithm is established.
文中将支撑矢量机的概念引入雷达的目标一维像识别中，对其机理作了详细地分析，建立了相应的支撑矢量机分类器算法。
Joint target detection and classification scheme for radar non-cooperative target recognition (NCTR) was proposed.
提出一种雷达识别非合作目标的检测和分类一体化方案。
The main motive of this thesis is to investigate target classification of HF over-the-horizon (OTH) radar based on multiple frequency feature and poles.
本文主要研究基于多频和极点特征的高频超视距雷达目标识别方法。
The wavelet neural network is applied in target identification of step frequency MMW radar. The results of experiment indicate that the method is valuable for target classification.
将所提出的小波神经网络用于毫米波频率步进雷达目标一维距离像识别。
The Study of Target Classification Method Based on Low-Resolution Radar Return Sequences Outline Image;
低分辨雷达目标智能识别一直是雷达目标识别研究领域的难点。
The Study of Target Classification Method Based on Low-Resolution Radar Return Sequences Outline Image;
低分辨雷达目标智能识别一直是雷达目标识别研究领域的难点。
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