General high-resolution smoothing techniques for estimating deterministic sinusoidal frequencies from short-record noisy data are presented. These techniques are general in the sense that methods such as the modified least squares Prony method, as well as those which are based on eigenvector decompositions, may be considered as special cases of them. The theoretical basis of these smoothing techniques is discussed, and their performance in the presence of white Gaussian noise at low signal-to-noise ratio (SNR) is examined. It is shown that close to the threshold of the maximum-likelihood method (SNR ≈3 dB) these symmetric smoothing techniques provide better accuracy than any other current method.
|Number of pages||4|
|Journal||Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing|
|State||Published - 1990|
|Event||1990 International Conference on Acoustics, Speech, and Signal Processing: Speech Processing 2, VLSI, Audio and Electroacoustics Part 2 (of 5) - Albuquerque, New Mexico, USA|
Duration: 3 Apr 1990 → 6 Apr 1990