陈鹏辉,,
姬红兵
西安电子科技大学电子工程学院 西安 710071
基金项目:国家自然科学基金(61871301)
详细信息
作者简介:金艳：女，1978年生，副教授，博士，主要研究方向为现代信号处理、非高斯信号处理、信号检测与估计等
陈鹏辉：男，1995年生，硕士生，主要研究方向为非高斯噪声下信号处理方法
姬红兵：男，1963年生，教授，博士生导师，主要研究方向为光电信号处理、微弱信号检测与识别、医学影像处理等
通讯作者:陈鹏辉 1036843180@qq.com
中图分类号:TN911.7计量
文章访问数:373
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被引次数:0
出版历程
收稿日期:2020-04-30
修回日期:2020-09-22
网络出版日期:2020-11-17
刊出日期:2021-02-23
Parameter Estimation of LFM Signals Based on Compress Transform Function in Impulsive Noise
Yan JIN,Penghui CHEN,,
Hongbing JI
School of Electronic Engineering, Xidian University, Xi’an 710071, China
Funds:The National Natural Science Foundation of China (61871301)
摘要
摘要:针对现有线性调频(LFM)信号参数估计方法在脉冲噪声下性能退化甚至完全失效的问题，该文提出一种脉冲噪声下估计LFM信号参数的新方法。该文构造了一种新的压缩变换(CT)函数，分析了该函数在零点附近的近线性，推导了任意随机变量经该函数变换后的2阶矩有界，证明了函数变换前后LFM信号的初始频率和调频斜率信息不变。将经过函数变换后的信号进行分数阶傅里叶变换(FrFT)，根据FrFT域中峰值坐标和信号参数的关系，寻找变换域中的峰值点，实现信号参数的估计。仿真实验表明，该方法可有效抑制脉冲噪声且能准确估计出信号的参数信息，实现简单，不需要噪声的先验信息，具有良好的稳健性。
关键词:脉冲噪声/
压缩变换函数/
分数阶傅里叶变换/
参数估计
Abstract:In order to solve the problem that existing parameter estimation algorithms of Linear Frequency Modulation (LFM) signals undergo performance degradation or even become invalid in impulsive noise environment, a new method for estimating LFM signal parameters in impulsive noise is proposed in this paper. The paper constructs a new Compress Transform (CT) function, analyzes the approximate linearity of the function near the zero point, derives that the second-order moments are bounded after the proposed transformation for any random variable, and proves that the initial frequency and frequency modulation slope information of an LFM signal are unchanged after the transformation. According to the relationship between the peak coordinates and the signal parameters in the FrFT domain, the peak point in the transform domain is located and the signal parameters estimates can be obtained. Simulation results show that the proposed method can effectively suppress the impulse noise and accurately estimate the parameter information of the signal. This method is simple and robust. Moreover, it does not require the prior information of the impulsive noise.
Key words:Impulsive noise/
Compress Transform (CT) function/
Fractional Fourier Transform (FrFT)/
Parameter estimation
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