基于Radon-分数阶傅里叶变换的雷达动目标检测方法

doi:10.3969/j.issn.0372-2112.2014.06.006

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电子学报 ›› 2014, Vol. 42 ›› Issue (6) : 1074-1080. DOI: 10.3969/j.issn.0372-2112.2014.06.006

学术论文

基于Radon-分数阶傅里叶变换的雷达动目标检测方法

陈小龙¹, 刘宁波², 王国庆², 关键¹

作者信息 +

Radar Detection Method for Moving Target Based on Radon-Fourier Fractional Fourier Transform

CHEN Xiao-long¹, LIU Ning-bo², WANG Guo-qing², GUAN Jian¹

Author information +

文章历史 +

摘要

长时间相参积累技术是提高雷达对微弱运动目标探测能力的重要手段之一，本文在分析动目标回波信号距离和多普勒徙动的基础上，提出基于Radon-分数阶傅里叶变换(RFRFT)的长时间相参积累方法.该方法根据预先设定的运动参数搜索范围，提取位于距离-慢时间二维平面中的目标观测值，然后在FRFT域进行匹配和积累，并通过构建的RFRFT域检测单元图实现对非匀速运动目标的检测.该方法能够同时补偿距离和多普勒徙动，有效抑制背景杂波和噪声，提高积累增益.仿真结果表明本文方法具有在强杂波中检测微弱动目标的能力.

Abstract

Long-time coherent integration technique is one of the most important methods for the improvement of radar detection ability of weak moving target.Based on the analysis of range and Doppler migrations of a moving target,a long-time coherent integration method via Radon-fractional Fourier transform (RFRFT) is proposed.The target's observation values in the range-slow time plane are firstly extracted according to the searching area of the preset motion parameters.Then the observation values are matched and accumulated by FRFT and a nonuniformly moving target can be declared by the detection map in the RFRFT domain.The proposed method can compensate the across range unit and Doppler frequency migration effects simultaneously.In addition,the background clutter and noise can be well suppressed,which increases the integration gain.Simulation results show that the proposed method has the ability of detecting weak moving target under strong clutter environment.

导出引用

陈小龙, 刘宁波, 王国庆, 关键. 基于Radon-分数阶傅里叶变换的雷达动目标检测方法[J]. 电子学报, 2014, 42(6): 1074-1080. https://doi.org/10.3969/j.issn.0372-2112.2014.06.006

CHEN Xiao-long, LIU Ning-bo, WANG Guo-qing, GUAN Jian. Radar Detection Method for Moving Target Based on Radon-Fourier Fractional Fourier Transform[J]. Acta Electronica Sinica, 2014, 42(6): 1074-1080. https://doi.org/10.3969/j.issn.0372-2112.2014.06.006

中图分类号： TN959.1+7

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