Title :
Comparison of various periodograms for sinusoid detection and frequency estimation
Author :
So, H.C. ; Chan, Y.T. ; Ma, Q. ; Ching, P.C.
Author_Institution :
City Univ. of Hong Kong, Hong Kong
fDate :
7/1/1999 12:00:00 AM
Abstract :
With the advent of the fast Fourier transform (FFT) algorithm, the periodogram and its variants such as the Bartlett´s procedure and Welch method, have become very popular for spectral analysis. However, there has not been a thorough comparison of the detection and estimation performances of these methods. Different forms of the periodogram are studied here for single real tone detection and frequency estimation in the presence of white Gaussian noise. The threshold effect in frequency estimation, that is, when the estimation errors become several orders of magnitude greater than the Cramer-Rao lower bound (CRLB), is also investigated. It is shown that the standard periodogram gives the optimum detection performance for a pure tone while the Welch method is the best detector when there is phase instability in the sinusoid. As expected, since the conventional periodogram is a maximum likelihood estimator of frequency, it generally provides the minimum mean square frequency estimation errors
Keywords :
Gaussian noise; adaptive signal detection; fast Fourier transforms; frequency estimation; least mean squares methods; maximum likelihood detection; spectral analysis; white noise; Bartlett´s procedure; Cramer-Rao lower bound; FFT algorithm; Welch method; detection performance; estimation errors; estimation performance; frequency estimation; maximum likelihood estimator; minimum mean square frequency errors; noisy environment; optimum performance; periodograms comparison; phase instability; single real tone detection; sinusoid detection; spectral analysis; threshold effect; white Gaussian noise; Delay estimation; Detectors; Fast Fourier transforms; Frequency estimation; Gaussian noise; Maximum likelihood detection; Maximum likelihood estimation; Military computing; Phase detection; Signal to noise ratio;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on