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An Improved Wavelet Denoising Algorithm for Wideband Radar Targets Detection

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Abstract

A novel denoising technique based on wavelet transform modulus maxima (WTMM) is proposed for processing wideband radar spread targets detection signal in a clutter environment. Combined with the improved adaptive Bayes–Shrink threshold and Lipschitz exponents, we propose the path pruned approach at each scale terms as full-scale to split the signal. The estimation of WTMM over each scale has been optimized, thus, the signal and the noise can be split effectively. Additionally, to improve the computational efficiency, a fast method based on a piecewise polynomial interpolation algorithm is applied for the split signal reconstruction. Statistical results are quite promising and perform better than the conventional denoising algorithms: compared with the classical WTMM algorithm, the improved WTMM full-scale denoising algorithm not only increases the signal-to-noise (SNR) ratio by over 10 % but also reduces the processing time by 88 % and reduces the root-mean-square-error (RMSE) by over 35 %. More generally, the proposed algorithm has better performance than that of several typical algorithms in its denoising quality and singularity detection.

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References

  1. V.K. Anandan, P. Balamuralidhar, P.B. Rao, A.R. Jain, A method for adaptive moments estimation technique applied to MST radar echoes, in Proc. Prog. Electromagn. Res. Symp. (1996), pp. 360–365

    Google Scholar 

  2. V.K. Anandan, G.R. Reddy, P.B. Rao, Spectral analysis of atmospheric signal using higher orders spectral estimation technique. IEEE Trans. Geosci. Remote Sens. 39(9), 1890–1895 (2001)

    Article  Google Scholar 

  3. V.K. Anandan, C.J. Pan, T. Rajalakshmi, G.R. Reddy, Multitaper spectral analysis of atmospheric radar signal. Ann. Geophys. 22(11), 3995–4003 (2004)

    Article  Google Scholar 

  4. A. Antoniadis, G. Oppenheim Lecture Notes in Statistics Wavelets and Statistics (Springer, Berlin, 1995)

    Book  Google Scholar 

  5. L. Birgé, P. Massart, From Model Selection to Adaptive Estimation (Université Paris-Sud, Département de mathématiques, Paris, 1995)

    Google Scholar 

  6. S.G. Chang, B. Yu, M. Vetterli, Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9(9), 1532–1546 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  7. E. Conte, A.D. Maio, G. Ricci, GLRT-based adaptive detection algorithms for range-spread targets. IEEE Trans. Signal Process. 49(7), 1336–1348 (2001)

    Article  Google Scholar 

  8. G.L. Cui, L.J. Kong, X.B. Yang, The Rao and Wald tests designed for distributed targets with polarization MIMO radar in Compound-Gaussian clutter. Circuits Syst. Signal Process. 31(1), 237–254 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  9. G.L. Cui, L.J. Kong, X.B. Yang, GLRT-based detection algorithm for polarimetric MIMO radar against SIRV clutter. Circuits Syst. Signal Process. 31(3), 1033–1048 (2012)

    Article  MathSciNet  Google Scholar 

  10. D.L. Donoho, De-noising by soft-thresholding. IEEE Trans. Inf. Theory 41(3), 613–627 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  11. D.L. Donoho, I.M. Johnstone, Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425–455 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  12. D.L. Donoho, I.M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90(432), 1200–1224 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  13. A. Grossmann, J. Morlet, Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM J. Math. 15(4), 723–736 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  14. M. Han, Fast reconstruction algorithm based on Hermite interpolation from modulus maxima of wavelet transform. J. Syst. Simul. 17(23), 2616–2619 (2005)

    Google Scholar 

  15. G.Y. Luo, Wavelet Notch filter design of spread-spectrum communication systems for high-precision wireless positioning. Circuits Syst. Signal Process. 31(2), 651–668 (2012)

    Article  Google Scholar 

  16. S. Majumdar, H. Parthasarathy, Wavelet-based transistor parameter estimation. Circuits Syst. Signal Process. 29(3), 953–970 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  17. S. Majumdar, H. Parthasarathy, Perturbation approach to Ebers–Moll modeled transistor amplifier circuit. Circuits Syst. Signal Process. 29(3), 431–448 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  18. S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989)

    Article  MATH  Google Scholar 

  19. S.G. Mallat, W.L. Hwang, Singularity detection and processing with wavelets. IEEE Trans. Inf. Theory 38(2), 617–643 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  20. S.G. Mallat, Z.F. Zhang, Matching pursuits with time–frequency dictionaries. IEEE Trans. Signal Process. 41(12), 3397–3415 (1993)

    Article  MATH  Google Scholar 

  21. S.G. Mallat, S. Zhong, Characterization of signals from multiscale edges. IEEE Trans. Pattern Anal. Mach. Intell. 14(7), 710–732 (1992)

    Article  Google Scholar 

  22. B. Mohammed, E. Hassan, FPGA-implementation of discrete wavelet transform with application to signal denoising. Circuits Syst. Signal Process. 31(3), 987–1015 (2012)

    Article  MATH  Google Scholar 

  23. F. Pascal, Y. Chitour, P. Forster, P. Larzabal, Covariance structure maximum-likehood estimates in compound Gaussian noise existence and algorithm analysis. IEEE Trans. Signal Process. 56(1), 34–48 (2008)

    Article  MathSciNet  Google Scholar 

  24. Z. Ping, Z. Chun, Four-channel tight wavelet frames design using Bernstein polynomial. Circuits Syst. Signal Process. 31(5), 1847–1861 (2012)

    Article  Google Scholar 

  25. X.F. Shuai, L.J. Kong, J.Y. Yang, AR-model-based adaptive detection of range-spread targets in compound Gaussian clutter. Signal Process. 91(4), 750–758 (2011)

    Article  MATH  Google Scholar 

  26. M. Sudipta, P. Harish, Wavelet based transistor parameter estimation using second order Volterra model. Circuits Syst. Signal Process. 30(6), 1289–1311 (2011)

    Article  MATH  Google Scholar 

  27. F.B. Tuteur, Wavelet transformations in signal detection, in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Process. (1988), pp. 1435–1438

    Google Scholar 

  28. D.N. Vizireanu, A fast, simple and accurate time-varying frequency estimation method for single-phase electric power systems. Measurement 45(5), 1331–1333 (2012)

    Article  Google Scholar 

  29. D.N. Vizireanu, S.V. Halunga, Simple, fast and accurate eight points amplitude estimation method of sinusoidal signals for DSP based instrumentation. J. Instrum. 7(4), 4001 (2012)

    Article  Google Scholar 

  30. D.N. Vizireanu, R.O. Preda, Is “five-point” estimation better than “three-point” estimation. Measurement (2012, accepted)

  31. A. Witkin, Scale-space filtering: a new approach to multi-scale description, in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Process., vol. 9 (1984), pp. 150–153

    Google Scholar 

  32. Y. Yang, Y.S. Wei, Neighboring coefficients preservation for signal denoising. Circuits Syst. Signal Process. 31(2), 827–832 (2012)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors are very grateful to the anonymous referees for their careful reading and helpful comments. This work is supported in part by the National Natural Science Foundation of China (10876006), to a certain degree and it also benefited by support by the Fundamental Research Funds for the Central Universities (ZYGX2009J017, ZYGX2011J013).

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Authors and Affiliations

  1. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu City, China

    Xiandong Meng, Zhiming He & Ganzhong Feng

  2. Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu City, China

    Bo Xiao

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  1. Xiandong Meng
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  2. Zhiming He
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Correspondence to Xiandong Meng.

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Meng, X., He, Z., Feng, G. et al. An Improved Wavelet Denoising Algorithm for Wideband Radar Targets Detection. Circuits Syst Signal Process 32, 2003–2026 (2013). https://doi.org/10.1007/s00034-013-9549-8

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  • DOI: https://doi.org/10.1007/s00034-013-9549-8

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