A Novel DFT-Based Algorithm for 2-D Multiple Sinusoidal Frequency Estimation

Frequency estimation of a two dimensional (2-D) multi-component sinusoidal signal in the presence of additive white Gaussian noise (AWGN) is a significant problem in various disciplines such as signal processing, radar/sonar, and wireless communications. This letter presents a novel, fast and accura...

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Bibliographic Details
Published in:IEEE signal processing letters 2024-01, Vol.31, p.1-5
Main Authors: Solak, Veyis, Serbes, Ahmet
Format: Article
Language:English
Subjects:
Algorithms
Classification algorithms
Cramer-Rao bounds
DFT interpolation
direction of arrival estimation
Discrete Fourier transforms
Estimation
Frequency estimation
Multiple frequency estimation
Noise threshold
Radar
Random noise
Signal processing
Signal processing algorithms
Signal to noise ratio
Sine waves
Wireless communications
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Summary:Frequency estimation of a two dimensional (2-D) multi-component sinusoidal signal in the presence of additive white Gaussian noise (AWGN) is a significant problem in various disciplines such as signal processing, radar/sonar, and wireless communications. This letter presents a novel, fast and accurate DFT-based algorithm for the frequency estimation of 2-D multi-component sinusoidal signals. We show that the proposed method attains the Cramér-Rao bound (CRB) when the parameters DFT-shift, iteration number, and minimum DFT frequency separations are chosen appropriately. Comprehensive numerical simulation results show that our algorithm almost reaches the CRB limit after a certain signal-to-noise ratio (SNR) threshold value.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2024.3381043