Radar Target MTD 2D-CFAR Algorithm Based on Compressive Detection

Cong Liu, Yunqing Liu, Qi Li, Zikang Wei
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引用次数: 2

Abstract

In order to solve the problem of large data volume brought by the traditional Nyquist sampling theorem in radar signal detection, a compressive detection (CD) model based on compressed sensing (CS) theory is proposed by analyzing the sparsity of the radar target in the range domain. The lower sampling rate completes the compressive sampling of the radar signal on the range field. On this basis, the two-dimensional distribution of the Doppler unit is established by moving target detention moving target detention (MTD), and the detection of the target is achieved with the two-dimensional constant false alarm rate (2D-CFAR) detection algorithm. The simulation experiment results prove that the algorithm can effectively detect the target without the need for reconstruction signals, and has good detection performance.
基于压缩检测的雷达目标MTD 2D-CFAR算法
为了解决传统奈奎斯特采样定理在雷达信号检测中带来的数据量大的问题,通过分析雷达目标在距离域的稀疏性,提出了一种基于压缩感知理论的压缩检测模型。较低的采样率完成了雷达信号在距离场上的压缩采样。在此基础上,通过运动目标滞留(MTD)建立多普勒单元的二维分布,并通过二维恒定虚警率(2D-CFAR)检测算法实现对目标的检测。仿真实验结果证明,该算法可以在不需要重构信号的情况下有效检测目标,具有良好的检测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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