Two Simplified Recursive Gauss–Newton Algorithms for Direct Amplitude and Phase Tracking of a Real Sinusoid

  title={Two Simplified Recursive Gauss–Newton Algorithms for Direct Amplitude and Phase Tracking of a Real Sinusoid},
  author={Jun Zheng and Kenneth Wing-Kin Lui and Wing-Kin Ma and Hing-Cheung So},
  journal={IEEE Signal Processing Letters},
In this letter, the problem of adaptive tracking the amplitude and phase of a noisy sinusoid with known frequency is addressed. Based on approximating the recursive Gauss-Newton approach, two computationally simple algorithms, which provide direct parameter estimates, are devised and analyzed. Simulation results show that the proposed methods can attain identical estimation performance as their original one. 
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Publications referenced by this paper.
Showing 1-6 of 6 references

Soderstrom, Theory and Practice of Recursive Identification

T. L. Ljung
View 4 Excerpts
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MAP/ML Estimation of the Frequency and Phase of a Single Sinusoid in Noise

IEEE Transactions on Signal Processing • 2007
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Unbiased estimation of the phase of a sinusoid

2004 IEEE International Conference on Acoustics, Speech, and Signal Processing • 2004