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Reseach Article

Multiresolution Transform based Denoising in Direction Finding

Published on September 2017 by K. Gowri, P. Palnisamy
International Conference on Microelectronics, Circuits and System
Foundation of Computer Science USA
MICRO2016 - Number 1
September 2017
Authors: K. Gowri, P. Palnisamy
a5c2a63e-cb34-4d23-8d41-8543bbfe440e

K. Gowri, P. Palnisamy . Multiresolution Transform based Denoising in Direction Finding. International Conference on Microelectronics, Circuits and System. MICRO2016, 1 (September 2017), 31-38.

@article{
author = { K. Gowri, P. Palnisamy },
title = { Multiresolution Transform based Denoising in Direction Finding },
journal = { International Conference on Microelectronics, Circuits and System },
issue_date = { September 2017 },
volume = { MICRO2016 },
number = { 1 },
month = { September },
year = { 2017 },
issn = 0975-8887,
pages = { 31-38 },
numpages = 8,
url = { /proceedings/micro2016/number1/28440-6108/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Microelectronics, Circuits and System
%A K. Gowri
%A P. Palnisamy
%T Multiresolution Transform based Denoising in Direction Finding
%J International Conference on Microelectronics, Circuits and System
%@ 0975-8887
%V MICRO2016
%N 1
%P 31-38
%D 2017
%I International Journal of Computer Applications
Abstract

In this paper, multi-resolution transforms based denoising followed by an improved method of Direction of Arrival (DOA) estimation is investigated. The predominant subspace method, Multiple Signal Classification (MUSIC) algorithm is very practical and efficient for direction of arrival estimation, but it fails to determine the direction at low Signal to Noise Ratio (SNR). The pre-eminence of MUSIC algorithm is used to upgrade the resolution of direction of arrival under adverse noisy situations. The noise is suppressed and thereby the gain of the received signal from sensors is improved by ridgelet transform and GHM (Geronimo J. S, Hardin D. P and Massopust P. R) multiwavelet transform based denoising. The simulation results of denoising and pseudo spectrum of the algorithm delivers improved performance in terms of root mean square error (RMSE), spectrum function, bias and gain. SNR, snapshots, array elements are the input parameters.

References
  1. Harry. L. Van Trees, 2002. Detection, Estimation and Modulation Theory. Optimum Array Processing, Wiley, 1-13.
  2. Ralph. O. Schmidt, 1986. Multiple Emitter Location and Signal Parameter Estimation, IEEE Trans. on Antennas and Propagation, 34(3), 276–280.
  3. Roy. R and Kailath. T, 1989. Estimation of Signal Parameters via Rotational Invariance Techniques. IEEE Trans. on Acoust. , Speech, Signal Processing, 60(12) 984–995.
  4. Wei. X, Yuan. Y and Ling. Q, Dec 2012. DOA Estimation using a Greedy Block Coordinate Descent Algorithm. IEEE Trans. Signal Processing, 60(12), 6382–6394.
  5. Ottersten. B, Viberg. M and Kailath. T, 1991. Performance Analysis of the Total Least Squares ESPRIT Algorithm. IEEE Transactions on Signal Processing, 39(5), 1122–1135.
  6. Capon. J, 1979. Maximum Likelihood Spectral Estimation. Nonlinear Methods Spectral Anal. , 34, 155–179.
  7. Zhang. L, Dong. W, Zhang. D and Shi. G, 2010. Two-Stage Image Denoising by Principal Component Analysis with Local Pixel Grouping. Pattern Recognition, 43, 1531–1549.
  8. Do. M. N, Vetterli. M, Jan. 2003. The Finite Ridgelet Transform for Image Representation. IEEE Transactions on Image Processing, 12(1), 16–28.
  9. Chen. G. Y and Kegl. B, 2007. Image Denoising with Complex Ridgelets. Pattern Recognition, 40, 578–585.
  10. Wang. H, Peng J and Wu W, Oct. 2002. Fusion Algorithm for Multisensory Images Based on Discrete Multiwavelets Transform. IEEE Proc. Vis. Image signal Processing, 149(5), 283–289.
  11. Strang. G, Strela. V, 1995. Short Wavelets and Matrix Dilation Equations. IEEE Trans. signal Processing, 43, 108–115.
  12. Sathish. R and Anand. G. V, 2014 . Wavelet Array Denoising for Improved Direction of Arrival Estimation. Bangalore: IISc, Technical Report Tr-PME-2003-2014. DRDO-IISc Program on Mathematical Engineering.
  13. Candes. E. J, 1998. Ridgelets: Theory and Applications. Ph. D Dissertation: Dept. Statistics, Stanford Univ. , Stanford, CA,
  14. Yang. S, Wang. M and Jiao. L, 2007. Geometrical Multi-Resolution Network Based on Ridgelet Frame. Signal Processing,Elsevier, 87, 750–761.
  15. Geronimo. J. S, Hardin. D. P. and Massopust P. R. , 1994. Fractal Functionsand Wavelet Expressions based on Several Scaling Functions. J. Approx. Theory, 78, 373–409.
  16. Strela. V, Heller. P. N, Strang. Topiwala. G, P and Heil. C, 1999. The Application of Multiwavelet Filter Banks to Image Processing. IEEE Transactions on Image Processing, 8, 548–563.
  17. Tham J. Y, shen. L, Lee S. L and Tan H. H, Feb. 2000. A General Approach for Analysis and Application of Discrete Multiwavelet Transforms. IEEE Trans. Signal Processing, 48(2), 457–464.
  18. Xinxing. Y and Licheng. J, Jan. 1999. Adaptive Multiwavelet Prefilter. Electronic letters, 35(1), 11–13.
  19. Serdean. C. V, Ibrahim. M. K, Moemeni. A and Al-akaidi M. M, 2007. Wavelet and Multiwavelet Watermarking. IET Image Processing, 1( 2), 223–230.
  20. Jiao. L, Pan. J. and Fang. Y, Sep. 2001. Multiwavelet Neural Network and its Approximation Properties. IEEE Trans. Neural Networks, 12(5), 1060–1066.
  21. Donoho. D. L and Johnstone. I. M, 1995. Denoising by Soft Thresholding. IEEE Transactions on Information Theory, 41, 613–627.
  22. Starck. J. L, Candes E. J and Donoho D. L, June 2002. The Curvelet Transform for Image Denoising. IEEE Transactions on Image Processing, 11(6), 670–684.
Index Terms

Computer Science
Information Sciences

Keywords

Multiresolution Transform Ridgelet Transform Ghm Multiwavelet Transform Doa Estimation Denoising Subspace Methods