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雷达动目标短时稀疏分数阶傅里叶变换域检测方法
引用本文:陈小龙,关键,于晓涵,何友.雷达动目标短时稀疏分数阶傅里叶变换域检测方法[J].电子学报,2017,45(12):3030-3036.
作者姓名:陈小龙  关键  于晓涵  何友
作者单位:1. 海军航空工程学院电子信息工程系, 山东烟台 264001; 2. 海军航空工程学院信息融合研究所, 山东烟台 264001
基金项目:国家自然科学基金(61501487;61531020),国防科技基金(2102024),航空科学基金(20162084005;20162084006),中国科协“青年人才托举工程”(YESS20160115),“泰山学者”专项经费资助
摘    要:复杂背景下的动目标检测技术是雷达目标探测的关键技术和难点之一,亟需发展和研究高时频分辨率、大数据量高效以及适用于多分量信号分析的方法和手段.该文结合经典时频分析技术和高分辨稀疏域信号处理的优势,提出短时稀疏分数阶傅里叶变换(ST-SFRFT)并用于雷达动目标检测和参数估计,实现时变信号高分辨时频表示的同时,改善SCR,提高复杂环境下雷达动目标检测的性能.实测对海雷达数据验证表明,所提方法在抗杂波以及参数估计精度等方面较经典时频动目标检测方法有明显优势.

关 键 词:雷达动目标检测  稀疏表示  时频分析  分数阶傅里叶变换  稀疏时频分布  
收稿时间:2017-04-06

Radar Detection for Moving Target in Short-Time Sparse Fractional Fourier Transform Domain
CHEN Xiao-long,GUAN Jian,YU Xiao-han,HE You.Radar Detection for Moving Target in Short-Time Sparse Fractional Fourier Transform Domain[J].Acta Electronica Sinica,2017,45(12):3030-3036.
Authors:CHEN Xiao-long  GUAN Jian  YU Xiao-han  HE You
Affiliation:1. Department of Electronic and Information Engineering, Naval Aeronautical and Astronautical University, Yantai, Shandong 264001, China; 2. Institute of Information Fusion, Naval Aeronautical and Astronautical University, Yantai Shandong 264001, China
Abstract:Moving target detection in complex environment is one of the key points and difficulties in radar target detection.It is necessary to develop theory and method of high time-frequency resolution,high efficiency for big data,and suitable for multi-signals analysis.In this paper,the concept of short-time sparse fractional Fourier transform (ST-SFRFF) is proposed in association with the advantages of classical time-frequency analysis and high-resolution signal processing in sparse domain.The ST-SFRFT is then used for radar moving target detection and parameter estimation,which can achieve high-resolution representation of time-varying signal and improve signai-to-clutter ratio as well.The detection performance of moving target in complex environment is improved.Real experiment using a marine radar was carried out and the results indicate that the proposed method shows distinct advantages in anti-clutter and parameter estimation over classical time-frequency-based moving target detection methods.
Keywords:radar moving target detection  sparse representation  time-frequency analysis  fractional Fourier transform  sparse time-frequency distribution
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