A new algorithm for the estimation of the frequency of a complex exponential in additive Gaussian noise

Sam Reisenfeld; Elias Aboutanios

doi:10.1109/LCOMM.2003.815637

A new algorithm for the estimation of the frequency of a complex exponential in additive Gaussian noise

Sam Reisenfeld, Elias Aboutanios

Research output: Contribution to journal › Article › peer-review

52 Citations (Scopus)

Abstract

This letter presents a new algorithm for the precise estimation of the frequency of a complex exponential signal in additive, complex, white Gaussian noise. The discrete Fourier transform (DFT)-based algorithm performs a frequency interpolation on the results of an N point complex fast Fourier transform. For large N and large signal to noise ratio, the frequency estimation error variance obtained is 0.063 dB above the Cramer-Rao Bound. The algorithm has low computational complexity and is well suited for real time applications.

Original language	English
Pages (from-to)	549-551
Number of pages	3
Journal	IEEE Communications Letters
Volume	7
Issue number	11
DOIs	https://doi.org/10.1109/LCOMM.2003.815637
Publication status	Published - 2003
Externally published	Yes

Keywords

Discrete Fourier transform (DFT)
Fast Fourier transform (FFT)
Frequency estimation
Interpolation

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10.1109/LCOMM.2003.815637

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abstract = "This letter presents a new algorithm for the precise estimation of the frequency of a complex exponential signal in additive, complex, white Gaussian noise. The discrete Fourier transform (DFT)-based algorithm performs a frequency interpolation on the results of an N point complex fast Fourier transform. For large N and large signal to noise ratio, the frequency estimation error variance obtained is 0.063 dB above the Cramer-Rao Bound. The algorithm has low computational complexity and is well suited for real time applications.",

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AB - This letter presents a new algorithm for the precise estimation of the frequency of a complex exponential signal in additive, complex, white Gaussian noise. The discrete Fourier transform (DFT)-based algorithm performs a frequency interpolation on the results of an N point complex fast Fourier transform. For large N and large signal to noise ratio, the frequency estimation error variance obtained is 0.063 dB above the Cramer-Rao Bound. The algorithm has low computational complexity and is well suited for real time applications.

KW - Discrete Fourier transform (DFT)

KW - Fast Fourier transform (FFT)

KW - Frequency estimation

KW - Interpolation

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A new algorithm for the estimation of the frequency of a complex exponential in additive Gaussian noise

Abstract

Keywords

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