2022
DOI: 10.3390/info14010018
|View full text |Cite
|
Sign up to set email alerts
|

Instantaneous Frequency Estimation of FM Signals under Gaussian and Symmetric α-Stable Noise: Deep Learning versus Time–Frequency Analysis

Abstract: Deep learning (DL) and machine learning (ML) are widely used in many fields but rarely used in the frequency estimation (FE) and slope estimation (SE) of signals. Frequency and slope estimation for frequency-modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sound navigation and ranging (SONAR), and radio detection and ranging (RADAR) measurements. This work proposed a novel frequency estimation technique for instantaneous linear FM (LFM) s… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
3

Citation Types

0
3
0

Year Published

2023
2023
2025
2025

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(3 citation statements)
references
References 37 publications
0
3
0
Order By: Relevance
“…The traditional IF estimation methods can mainly be divided into two kinds: timefrequency methods and non-time-frequency methods [6][7][8][9][10]. Empirical mode decomposition (EMD) is a representative non-time-frequency method [11].…”
Section: Introductionmentioning
confidence: 99%
“…The traditional IF estimation methods can mainly be divided into two kinds: timefrequency methods and non-time-frequency methods [6][7][8][9][10]. Empirical mode decomposition (EMD) is a representative non-time-frequency method [11].…”
Section: Introductionmentioning
confidence: 99%
“…To address the parameter estimation under impulsive noise environment, scholars have proposed many methods, such as, fractional lower-order statistics [3][4][5], nonlinear transform [6][7][8], tracking differentiator [9,10], convolutional neural networks [11,12]. In [3], an improved fractional lower order LVD (FLO-LVD) for the impulsive noise is proposed, which can overcome the influence of cross-terms and achieve higher estimation accuracy.…”
Section: Introductionmentioning
confidence: 99%
“…It effectively eliminates high-amplitude impulsive noise, but suffers a significant performance degradation under low GSNR and strong impulsive noise environments. In [12], a deep learning-based parameter estimation method of LFM signal is proposed, which uses deep neural networks and convolutional neural networks to eliminate the impact of impulsive noise on LFM signal parameter estimation. However, this method requires a considerable amount of time to train the model.…”
Section: Introductionmentioning
confidence: 99%