Parameters estimation of LFM signal based on Gaussian-weighted fractional Fourier transform

Peng WANG; Tian-shuang QIU; Jing-chun LI; Hai-feng TAN

doi:10.11959/j.issn.1000-436x.2016077

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Parameters estimation of LFM signal based on Gaussian-weighted fractional Fourier transform

academic paper | 更新时间：2024-06-05

- Parameters estimation of LFM signal based on Gaussian-weighted fractional Fourier transform
- Journal of Communications Vol. 37, Issue 4, Pages: 107-115(2016)
- 作者机构：
  
  1. 大连理工大学电子信息与电气工程学部，辽宁大连116024
  2. 国家无线电监测中心，北京100037
  3. 北京邮电大学信息与通信工程学院，北京100876
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61139001);The National Natural Science Foundation of China(61172108);The National Natural Science Foundation of China(81241059)
- DOI：10.11959/j.issn.1000-436x.2016077
  CLC： TN911.7
- Online First：2016-04，
  
  Published：25 April 2016
- 稿件说明：
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Peng WANG, Tian-shuang QIU, Jing-chun LI, et al. Parameters estimation of LFM signal based on Gaussian-weighted fractional Fourier transform[J]. Journal of Communications, 2016, 37(4): 107-115.
DOI：

Peng WANG, Tian-shuang QIU, Jing-chun LI, et al. Parameters estimation of LFM signal based on Gaussian-weighted fractional Fourier transform[J]. Journal of Communications, 2016, 37(4): 107-115. DOI： 10.11959/j.issn.1000-436x.2016077.

摘要

针对低占空比下传统算法参数估计性能下降的问题，提出了一种高斯加权分数阶傅里叶变换(GFRFT

Gaussian-weighted fractional Fourier transform)参数估计方法。给出了时限信号GFRFT的定义并推导了其模值平方的特性，研究了高斯白噪声背景下GFRFT 的输出信噪比并给出了闭式表达式，进行了仿真实验并讨论说明了该方法的适用条件。仿真结果表明，该方法在低占空比的情况下可以有效地提高参数估计精度。

Abstract

To overcome the performance degradation of conventiona methods in low duty ratio condition

a novel me-thod of parameters estimation for LFM signal based on the Gaussian-weighted fractional Fourier transform (GFRFT) was proposed. Firstly

the GFRFT definition was given and the LFM signal GFRFT with finite duration was derived. Secondly

the statistical characteristics of the GFRFT for the LFM signal under the Gaussian white noise were studied

and a closed mathematical expression of output signal-to-noise ratio was derived. Finally

simulation experiments are conducted

and the applicable condition of the GFRFT is also discussed

which demonstrates that the proposed method can effectively improve parameters estimation performance

especially in the low duty ratio condition.

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references

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