Parameter Estimation of LFM Signals Based on Compress Transform Function in Impulsive Noise

Yan JIN; Penghui CHEN; Hongbing JI

doi:10.11999/JEIT200342

Volume 43 Issue 2

Feb. 2021

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Yan JIN, Penghui CHEN, Hongbing JI. Parameter Estimation of LFM Signals Based on Compress Transform Function in Impulsive Noise[J]. Journal of Electronics & Information Technology, 2021, 43(2): 277-283. doi: 10.11999/JEIT200342

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Yan JIN, Penghui CHEN, Hongbing JI. Parameter Estimation of LFM Signals Based on Compress Transform Function in Impulsive Noise[J]. Journal of Electronics & Information Technology, 2021, 43(2): 277-283. doi: 10.11999/JEIT200342

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Parameter Estimation of LFM Signals Based on Compress Transform Function in Impulsive Noise

doi: 10.11999/JEIT200342

School of Electronic Engineering, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (61871301)

Received Date: 2020-04-30
Rev Recd Date: 2020-09-22

Available Online: 2020-11-17

Publish Date: 2021-02-23

Abstract

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.
- Impulsive noise,
- Compress Transform (CT) function,
- Fractional Fourier Transform (FrFT),
- Parameter estimation

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References(18)

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