Computationally efficient estimation of sinusoidal frequency at low SNR

We propose a computationally efficient method for estimation of frequency of a single complex sinusoid at low SNR. This method is motivated by the cross-power spectrum method of Nelson (1993) and the weighted phase averager (WPA) methods of Tretter (1985), Kay (1988), and Lovell et al. (1991). We demonstrate that by a simple preprocessing, we can extend the threshold SNR of the WPA significantly. Further, unlike the WPA, the proposed method can be easily extended for estimation of frequencies of multiple sinusoids that are well-separated in frequency. We also derive the variance of the proposed estimator and provide simulation results comparing the proposed method with the WPA.

[1]  Steven Kay Statistically/computationally efficient frequency estimation , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[2]  Steven Kay,et al.  Modern Spectral Estimation: Theory and Application , 1988 .

[3]  B. R. Musicus,et al.  Frequency estimation from phase differences , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[4]  Edward J. Wegman,et al.  Statistical Signal Processing , 1985 .

[5]  G. W. Lank,et al.  A Semicoherent Detection and Doppler Estimation Statistic , 1973, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Steven A. Tretter,et al.  Estimating the frequency of a noisy sinusoid by linear regression , 1985, IEEE Trans. Inf. Theory.

[7]  Douglas Nelson,et al.  Special purpose correlation functions for improved signal detection and parameter estimation , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  Allan Steinhardt,et al.  Thresholds in frequency estimation , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  Brian C. Lovell,et al.  The circular nature of discrete-time frequency estimates , 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.