Self-tuned LMS Algorithm for Sinusoidal Time Delay Tracking
In this paper the problem of estimating the time delay
between two spatially separated noisy sinusoidal signals by system
identification modeling is addressed. The system is assumed to be
perturbed by both input and output additive white Gaussian noise. The
presence of input noise introduces bias in the time delay estimates.
Normally the solution requires a priori knowledge of the input-output
noise variance ratio. We utilize the cascade of a self-tuned filter with
the time delay estimator, thus making the delay estimates robust to
input noise. Simulation results are presented to confirm the superiority
of the proposed approach at low input signal-to-noise ratios.
[1] G.C. Carter, "Time delay estimation for passive sonar signal processing,"
IEEE Trans. ASSP, vol.ASSP-29, no.3, pp.463--470, 1981.
[2] P.L. Feintuch, N.J. Bershad and F.A. Reed, "Time delay estimation using
LMS algorithm-dynamic behavior," IEEE Trans. ASSP, vol.ASSP-29,
no.3, pp.571--576, 1981.
[3] F.A. Reed, P.L. Feintuch and N.J. Bershad, "Time delay estimation using
LMS algorithm-static behavior," IEEE Trans. ASSP, vol.ASSP-29, no.3,
pp.561--571, 1981.
[4] R.E. Ziemer and W.H. Trenter, Principles of Communications: Systems
Modulation and Noise, Wiley, NY, 2002.
[5] H.C. So, "Noisy input-output system identification approach for time
delay estimation," Signal Processing, vol.82, no.10, pp.1471--1475,
2002.
[6] J. Gamba and T. Shimamura, "Sinusoidal time delay tracking by a
self-tuned LMS filter with interpolation based system response
coefficient ratios," presented at the 2005 IEEE-EURASIP Workshop on
Nonlinear Signal and Image Processing, Sapporo Convention Center,
Sapporo, Japan, May 18--20, 2005.
[7] B. Widrow et al., "Adaptive noise canceling: Principles and applications,"
Proc. IEEE, vol.63, pp.1692--1716, 1975.
[8] Y.T. Chan, J.M. Riley and J.B. Plant, "Modeling time delay and its
application to nonstationary delays," IEEE Trans. ASSP, vol.ASSP-29,
no.4, pp.577--581, 1981.
[9] J.T. Ricard and J.R. Zeidler, "Second-order output statistics of the
adaptive line enhancer," IEEE Trans. ASSP, vol.ASSP-27, no.1,
pp.31--39, 1979.
[10] S. Haykin, Adaptive Filter Theory, Prentice-Hall, Englewood Cliffs, NJ,
2002.
[1] G.C. Carter, "Time delay estimation for passive sonar signal processing,"
IEEE Trans. ASSP, vol.ASSP-29, no.3, pp.463--470, 1981.
[2] P.L. Feintuch, N.J. Bershad and F.A. Reed, "Time delay estimation using
LMS algorithm-dynamic behavior," IEEE Trans. ASSP, vol.ASSP-29,
no.3, pp.571--576, 1981.
[3] F.A. Reed, P.L. Feintuch and N.J. Bershad, "Time delay estimation using
LMS algorithm-static behavior," IEEE Trans. ASSP, vol.ASSP-29, no.3,
pp.561--571, 1981.
[4] R.E. Ziemer and W.H. Trenter, Principles of Communications: Systems
Modulation and Noise, Wiley, NY, 2002.
[5] H.C. So, "Noisy input-output system identification approach for time
delay estimation," Signal Processing, vol.82, no.10, pp.1471--1475,
2002.
[6] J. Gamba and T. Shimamura, "Sinusoidal time delay tracking by a
self-tuned LMS filter with interpolation based system response
coefficient ratios," presented at the 2005 IEEE-EURASIP Workshop on
Nonlinear Signal and Image Processing, Sapporo Convention Center,
Sapporo, Japan, May 18--20, 2005.
[7] B. Widrow et al., "Adaptive noise canceling: Principles and applications,"
Proc. IEEE, vol.63, pp.1692--1716, 1975.
[8] Y.T. Chan, J.M. Riley and J.B. Plant, "Modeling time delay and its
application to nonstationary delays," IEEE Trans. ASSP, vol.ASSP-29,
no.4, pp.577--581, 1981.
[9] J.T. Ricard and J.R. Zeidler, "Second-order output statistics of the
adaptive line enhancer," IEEE Trans. ASSP, vol.ASSP-27, no.1,
pp.31--39, 1979.
[10] S. Haykin, Adaptive Filter Theory, Prentice-Hall, Englewood Cliffs, NJ,
2002.
@article{"International Journal of Electrical, Electronic and Communication Sciences:55498", author = "Jonah Gamba", title = "Self-tuned LMS Algorithm for Sinusoidal Time Delay Tracking", abstract = "In this paper the problem of estimating the time delay
between two spatially separated noisy sinusoidal signals by system
identification modeling is addressed. The system is assumed to be
perturbed by both input and output additive white Gaussian noise. The
presence of input noise introduces bias in the time delay estimates.
Normally the solution requires a priori knowledge of the input-output
noise variance ratio. We utilize the cascade of a self-tuned filter with
the time delay estimator, thus making the delay estimates robust to
input noise. Simulation results are presented to confirm the superiority
of the proposed approach at low input signal-to-noise ratios.", keywords = "LMS algorithm, Self-tuned filter, Systemidentification, Time delay estimation,.", volume = "2", number = "4", pages = "606-7", }