红外与毫米波学报2011，Vol.30Issue(1)：74-79,6.
基于模糊函数特征优化的雷达辐射源个体识别
Feature optimization of ambiguity function for radar emitter recognition
摘要
Abstract
Ambiguity function (AF) modeling of radar signals is a powerful approach to feature extraction and recognition of radar emitters. An AF subspace based optimization framework is proposed to identify radar emitters by exploring unintentional modulation on pulse (UMOP) features. First, near-zero Doppler cuts of AF were extracted as a preliminary feature subset. Then, two kinds of cut-concatenation schemes were designed to construct two different pairs of feature vectors with complementary information respectively, which will facilitate the subsequent feature fusion via canonical correlation analysis (CCA) or discriminative canonical correlation analysis (DCCA). Theoretical analysis and experimental results show that the proposed algorithms not only alleviate the calculation problem in the existing AF based method, but also improve the recognition performance considerably, due to the successful information fusion and redundancy reduction conducted in the AF subset.关键词
雷达辐射源识别/模糊函数/典型相关分析/鉴别典型相关分析/特征融合Key words
radar emitter recognition/ ambiguity function/ canonical correlation analysis/ discriminative canonical correlation analysis/ feature fusion分类
信息技术与安全科学引用本文复制引用
王磊,姬红兵,史亚..基于模糊函数特征优化的雷达辐射源个体识别[J].红外与毫米波学报,2011,30(1):74-79,6.基金项目
国家自然科学基金(60871074) （60871074）
