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access icon free Matching pursuit for direction of arrival estimation in the presence of Gaussian noise and impulsive noise

Two high-resolution direction of arrival (DOA) estimation approaches of non-stationary narrowband signals based on matching pursuit (MP) are developed. The first sensor output is considered as the reference and decomposed by MP. As the MP is a linear decomposition, the obtained MP coefficients contain the steering vector information. So, the MP coefficients corresponding to the leading decomposition atoms are used to develop the MP-MUSIC algorithm for the DOA estimation. In addition, the chosen MP atoms are used to implement the modified spatial time–frequency distribution (STFD) based on Wigner Ville (WV) distribution as well, and this method named MP-WV. It has been demonstrated that these two methods can be applied for underdetermined problems and are robust against Gaussian and impulsive noises. The authors show that using either coefficients or chosen atoms to estimate the DOA in array processing by considering the source discriminative capability outperforms the conventional MUSIC and STFD. Some simulation results showing the performance of the two proposed approaches based on MP, conventional MUSIC and STFD are presented.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
      • 11. Sharif, W., Chakhchoukh, Y., Zoubir, A.M.: ‘Direction-of-arrival estimation of FM sources based on robust spatial time–frequency distribution matrices’. Proc. IEEE Statistical Signal Processing Workshop, Nice, France, June 2011, pp. 537540.
    21. 21)
      • 4. Belouchrani, A., Amin, M.G.: ‘Time–frequency MUSIC’, IEEE Signal Proc. Lett., 1999, 6, (5), pp. 109110 (doi: 10.1109/97.755429).
    22. 22)
      • 6. Schmidt, R.O.: ‘Multiple emitter location and signal parameter estimation’, IEEE Trans. Antennas Propag., 1986, AP-34, (3), pp. 276280 (doi: 10.1109/TAP.1986.1143830).
    23. 23)
      • 1. Belouchrani, A., Amin, M.G.: ‘Blind source separation based on time–frequency signal representation’, IEEE Trans. Signal Proc., 1998, 46, (11), pp. 28882898 (doi: 10.1109/78.726803).
    24. 24)
      • 14. Cohen, L.: ‘Time–frequency distribution – a review’, Proc. IEEE, 1989, 77, (7), pp. 941980 (doi: 10.1109/5.30749).
    25. 25)
      • 20. Mann, S., Haykin, S.: ‘The chirplet transform: physical considerations’, IEEE Trans. Signal Process., 1995, 43, pp. 27452761 (doi: 10.1109/78.482123).
    26. 26)
      • 7. Ghofrani, S., Amin, M.G., Zhang, Y.D.: ‘High-resolution direction finding of non-stationary signals using matching pursuit’, Elsevier Signal Process. J., 2013, 93, pp. 34663478 (doi: 10.1016/j.sigpro.2013.03.016).
    27. 27)
      • 16. Yin, Q., Qian, S., Fenga, A.: ‘Fast refinement for adaptive Gaussian chirplet decomposition’, IEEE Trans. Signal Process., 2002, 50, (6), pp. 12981306 (doi: 10.1109/TSP.2002.1003055).
    28. 28)
      • 18. Li, F., Liu, H.: ‘Performance analysis for DOA estimation algorithms: unification, simplification, and observation’, IEEE Trans. Aerosp. Electron. Syst., 1993, 29, (4), pp. 11701184 (doi: 10.1109/7.259520).
    29. 29)
      • 9. Sharif, W., Chakhchoukh, Y., Zoubir, A.M.: ‘Robust spatial time–frequency distribution matrix estimation with application to direction-of-arrival estimation’, Signal Process., 2011, 91, (11), pp. 26302638 (doi: 10.1016/j.sigpro.2011.05.022).
    30. 30)
      • 13. Qian, S., Chen, D.: ‘Understanding the nature of signals whose power spectrum change with time, joint analysis’, IEEE Signal Process. Mag., 1999, 16, (2), pp. 5267 (doi: 10.1109/79.752051).
    31. 31)
      • 3. Amin, M.G.: ‘Spatial time–frequency distributions for direction finding and blind source separation’. Proc. SPIE Wavelet Conf., 1999, vol. 3723, pp. 6270.
    32. 32)
      • 5. Amin, M.G., Zhang, Y.: ‘Direction finding based on spatial time–frequency distribution matrices’, Digit. Signal Process., 2000, 10, (4), pp. 325339 (doi: 10.1006/dspr.2000.0374).
    33. 33)
      • 12. Zhang, Y., Amin, M.G.: ‘Spatial averaging of time–frequency distributions for signal recovery in uniform linear array’, IEEE Trans. Signal Process., 2000, 48, (10), pp. 28922902 (doi: 10.1109/78.869043).
    34. 34)
      • 2. Sekihara, K., Nagarajan, S., Poeppel, D., Miyashita, Y.: ‘Time–frequency MEG-MUSIC algorithm’, IEEE Trans. Med. Imaging, 1999, 18, (1), pp. 9297 (doi: 10.1109/42.750262).
    35. 35)
      • 22. Bultan, A.: ‘A four-parameter atomic decomposition of chirplets’, IEEE Trans. Signal Process., 1999, 47, (3), pp. 731745 (doi: 10.1109/78.747779).
    36. 36)
      • 19. Mu, W., Amin, M.G., Zhang, Y.: ‘Bilinear signal synthesis in array processing’, IEEE Trans. Signal Process., 2003, 51, (1), pp. 90100 (doi: 10.1109/TSP.2002.806577).
    37. 37)
      • 23. Ghofrani, S., McLernon, D.C.: ‘Auto Wigner-Ville distribution via non adaptive and adaptive signal decomposition’, Signal Process., 2009, 89, pp. 15401549 (doi: 10.1016/j.sigpro.2009.02.004).
    38. 38)
      • 17. Wang, Y., Jiang, Y.: ‘Modified adaptive chirplet decomposition and its efficient implementation’. Eighth Int. Conf. Signal Process. (ICSP), 2006, pp. 1620.
    39. 39)
      • 15. Lu, Y., Demirli, R., Cardoso, G., Saniie, J.: ‘A successive parameter estimation algorithm for chirplet signal decomposition’, IEEE Trans. Ultrason. Ferroelectr. Freq. Control, 2006, 53, (11), pp. 21212131 (doi: 10.1109/TUFFC.2006.152).
    40. 40)
      • 8. Mallat, S.G., Zhang, Z.: ‘Matching pursuit with time–frequency dictionaries’, IEEE Trans. Signal Process., 1993, 41, (12), pp. 33973415 (doi: 10.1109/78.258082).
    41. 41)
      • 10. Sharif, W.: ‘Robust direction-of-arrival estimation for FM sources in the presence of impulsive noise’. Proc. IEEE Int. Conf. Acoustic Speech Signal Processing, Dallas, TX, March 2010, pp. 36623665.
    42. 42)
      • 21. Ghofrani, S., McLernon, D.C., Ayatollahi, A.: ‘Conditional spectral moments in matching pursuit based on the chirplet elementary function’, Digit. Signal Process., 2008, 18, pp. 694708 (doi: 10.1016/j.dsp.2007.10.011).
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