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弹箭与制导学报 >

2019 , Vol. 39 >Issue 6: 25 - 28

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2019.06.007

基于一种改进的最大类间方差的恒虚警算法

  • 李翔 ,
  • 贾杰
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  • 南昌航空大学信息工程学院, 南昌 330000

李翔(1993-),男,安徽滁州人,硕士研究生,研究方向:目标检测。

收稿日期: 2018-11-06

  网络出版日期: 2025-05-12

基金资助

国家自然科学基金(U1431118)

国家基本项目(61263012)

江西省南昌航空大学研究生创新专项基金(YC2017026)

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Constant False Alarm Detector Based on Improved Maximum Between-cluster Variance

  • LI Xiang ,
  • JIA Jie
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  • School of Information Engineering, Nanchang Hangkong University, Nanchang 330000, China

Received date: 2018-11-06

  Online published: 2025-05-12

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摘要

在背景环境先验信息未知的条件下,为提高CFAR检测器在多目标环境下检测性能,提出一种基于改进的最大类间方差方法的CFAR检测器。将均方差与有序差分思想引入类间方差,分析改进的最大类间方差的增量值,由最大增量值处对应的最佳阈值划分出均匀杂波单元与干扰目标单元,利用均匀杂波单元估计背景噪声功率,得到检测门限。仿真结果表明,所提出的算法在均匀环境下CFAR损失更小,检测性能接近CA-CFAR;在多目标干扰环境下抗干扰能力更为稳定。

关键词： 最大类间方差; 一阶差分; 均方差; 恒虚警; 目标检测

本文引用格式

李翔 , 贾杰 . 基于一种改进的最大类间方差的恒虚警算法[J]. 弹箭与制导学报, 2019 , 39(6) : 25 -28 . DOI: 10.15892/j.cnki.djzdxb.2019.06.007

Abstract

To improve the detection performance of CFAR detector in multi-target jamming environment, this paper propose a new CFAR based on the improved maximum between-cluster variance, with the unknown prior information of background environment.We introduce the mean square deviation and ordered difference into the between-cluster variance and analyze the increment of the improved maximum inter-class variance.Then in order to estimate the background noise power and get the detect threshold, we make good use of the uniform clutter unit divided by the optimal threshold corresponding to the maximum increment.Simulation results show that the proposed algorithm has smaller CFAR loss which close to CA-CFAR in uniform environment and has more stable anti-interference ability in the multi-target jamming environment.

Key words： maximum between-cluster variance; first order difference; mean square deviation; constant false alarm; object detection

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参考文献

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