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Improved Direction-of-Arrival Estimation Using Wavelet Based Denoising Techniques

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Abstract

In this paper, we explore the use of wavelet based denoising techniques to improve the Direction-of-Arrival (DOA) estimation performance of array processors at low SNR. Traditional single sensor wavelet denoising techniques are not suitable for this application since they fail to preserve the intersensor signal correlation. We propose two correlation preserving techniques for denoising multi-sensor signals: (1) the Temporal Wavelet Array Denoising (TWAD) technique developed by Rao and Jones [IEEE Trans. Signal Processing, vol. 48, pp. 1225–1234, 2000], and (2) a new Spatial Wavelet Array Denoising (SWAD) technique. It is shown that SWAD offers the advantage of a significant reduction in computational complexity at the cost of a slight reduction in SNR gain. The denoised array data is used for DOA estimation by the MUSIC algorithm. Simulation results are presented for MUSIC (without denoising), TWAD-MUSIC, and SWAD-MUSIC, to illustrate the improvement in DOA estimation performance brought about by denoising.

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References

  1. R. O. Schmidt, “Multiple Emitter Location and Signal Parameter Estimation,” Proceedings RADC, Spectral Estimation Workshop, Rome, New York, 1979, pp. 243–258.

  2. A. Paulraj, R. Roy and T. Kailath, “A Subspace Rotation Approach to Signal Parameter Estimation,” Proc. IEEE, vol. 74, 1986, pp. 1044–1045.

    Article  Google Scholar 

  3. P. Stoica and A. Nehorai, “MUSIC, Maximum Likelihood and Cramer–Rao Bound,” IEEE Trans. Acoust. Speech Signal Process., vol. 37, 1989, pp. 720–741.

    Article  MathSciNet  MATH  Google Scholar 

  4. D. L. Donoho, “De-noising by Soft Thresholding,” IEEE Trans. Inf. Theory, vol. 41, 1995, pp. 613–627.

    Article  MathSciNet  MATH  Google Scholar 

  5. D. L. Donoho and I. M. Johnstone, “Ideal Spatial Adaption via Wavelet Shrinkage,” Biometrika, vol. 81, 1994, pp. 425–455.

    Article  MathSciNet  MATH  Google Scholar 

  6. A. M. Rao and D. L. Jones, “A Denoising Approach to Multisensor Signal Estimation,” IEEE Trans. Signal Process., vol. 48, 2000, pp. 1225–1234.

    Article  Google Scholar 

  7. R. Sathish and G. V. Anand, “Spatial wavelet packet denoising for improved DOA estimation,” Proceedings of the 14th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing (MLSP), 29 Sept.–1 Oct. 2004, pp. 745–754.

  8. S. M. Kay, Statistical Signal Processing. Vol. I. Estimation Theory, Englewood Cliffs, New Jersey: Prentice-Hall, 1995.

    Google Scholar 

  9. D. L. Donoho,“Unconditional Bases are Optimal Bases for Data Compression and for Statistical Estimation,” Applied Computational Harmonic Analysis, 1993, pp. 100–115.

  10. J. W. Brewer, “Kronecker Products and Matrix Calculus in System Theory,” IEEE Trans. Circuits Syst., vol. 25, 1978, pp. 772–776.

    Article  MathSciNet  MATH  Google Scholar 

  11. J. Pearl, “On Coding and Filtering Stationary Signals by Discrete Fourier Transform,” IEEE Trans. Inf. Theory, vol. 19, 1973, pp. 229–232.

    Article  MathSciNet  MATH  Google Scholar 

  12. P. Stoica and R. Moses, Introd ction to Spectral Analysis, Englewood Cliffs, New Jersey: Prentice-Hall, 1997, Chapter 6.

    MATH  Google Scholar 

  13. D. B. Percival and A. T. Walden, Wavelet Methods for Time Series Analysis, Cambridge, UK: Cambridge University Press, 2000, Chapter 6.

    MATH  Google Scholar 

  14. A. L. Swindlehurst and T. Kailath, “A Performance Analysis of Subspace-Based Methods in the Presence of Model Errors. Part I. The MUSIC Algorithm,” IEEE Trans. Signal Process., vol. 40, 1992, pp. 1758–1774.

    Article  MATH  Google Scholar 

  15. H. Srinath and V. U. Reddy, “Analysis of MUSIC Algorithm with Sensor Gain and Phase Perturbations,” Signal Process., vol. 23, 1991, pp. 245–256.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Biomedical Imaging Group, Room No. 4139, STI-LIB, Station-17, EPFL, ECUBLENS-1015, Lausanne, Switzerland

    R. Sathish

  2. Department of Electrical Communication Engineering, Indian Institute of Science, Bangalore, 560012, India

    G. V. Anand

Authors
  1. R. Sathish
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  2. G. V. Anand
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Sathish, R., Anand, G.V. Improved Direction-of-Arrival Estimation Using Wavelet Based Denoising Techniques. J VLSI Sign Process Syst Sign Image Video Technol 45, 29–48 (2006). https://doi.org/10.1007/s11265-006-9770-9

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  • DOI: https://doi.org/10.1007/s11265-006-9770-9

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