系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (3): 762-770.doi: 10.12305/j.issn.1001-506X.2022.03.07

• 电子技术 • 上一篇    下一篇

脉冲噪声下基于NAT函数的LFM信号参数估计

金艳, 赵大地*, 姬红兵   

  1. 西安电子科技大学电子工程学院, 陕西 西安 710071
  • 收稿日期:2021-02-18 出版日期:2022-03-01 发布日期:2022-03-10
  • 通讯作者: 赵大地
  • 作者简介:金艳(1978—), 女, 副教授, 博士, 主要研究方向为非高斯信号处理、现代信号处理、统计信号处理、信号检测与估计、通信信号侦测|赵大地(1997—), 男, 硕士研究生, 主要研究方向为非高斯噪声下信号处理方法研究|姬红兵(1963—), 男, 教授, 博士研究生导师, 博士, 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
  • 基金资助:
    国家自然科学基金(61871301)

Parameter estimation of LFM signals based on NAT functions in impulsive noise

Yan JIN, Dadi ZHAO*, Hongbing JI   

  1. School of Electronic Engineering, Xidian University, Xi'an 710071, China
  • Received:2021-02-18 Online:2022-03-01 Published:2022-03-10
  • Contact: Dadi ZHAO

摘要:

针对传统的线性调频(linear frequency modulation, LFM)信号参数估计方法在脉冲噪声环境中无法准确提取参数信息的问题, 设计了两种非线性幅值变换函数(nonlinear amplitude transformation, NAT), 即attenuation-NAT(A-NAT)函数与increasing bounded-NAT(IB-NAT)函数, 推导证明了大幅值脉冲样本经A-NAT或IB-NAT变换后, 存在有界的二阶统计量, 且LFM信号变换后仅幅值发生变化, 相位信息不变。对经过NAT变换后的含噪信号进行吕氏分布(Lv's distribution, LVD)分析, 根据LVD域的峰值坐标即可实现LFM信号的参数估计。由实验结果可知, 所提方法无需获取噪声先验信息, 在强脉冲噪声条件下, 仍可准确提取信号参数信息, 具有良好的鲁棒性。

关键词: 脉冲噪声, 非线性幅值变换, 参数估计, 线性调频信号

Abstract:

To address the problem of performance degradation or even failure of traditional linear frequency modulation signal (LFM) parameter estimation methods under impulsive noise, this paper constructs two new nonlinear amplitude transformation (NAT) functions, namely the attenuation-NAT(A-NAT) function and the increasing bounded-NAT(IB-NAT) function. The derivation proves that the second-order moments of the large amplitude pulse samples are bounded by A-NAT or IB-NAT. Moreover, only the amplitude of the LFM signal changes after the proposed transformation, and the phase information remains unchanged. Perform Lv's distribution (LVD) analysis on the noisy signal after NAT transformation, and the parameter estimates of the LFM signal could be obtained based on the peak coordinates. Experimental results show that the proposed methods are robust, and they do not need the prior information of the impulsive noise, and can still accurately extract the signal parameter information even under strong impulse noise conditions.

Key words: impulsive noise, nonlinear amplitude transformation, parameter estimation, linear frequency modulation (LFM) signal

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