Localisation of mixed near-field and far-field sources using the largest aperture sparse linear array

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Localisation of mixed near-field and far-field sources using the largest aperture sparse linear array

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© The Institution of Engineering and Technology
Received 07/02/2017, Accepted 29/08/2017, Revised 21/07/2017, Published 05/09/2017

In some applications, the signals received by an array are a mixture of signals emitted by both far-field and near-field sources. This study develops a new cumulant-based multiple signal classification (MUSIC) algorithm for source localisation using a new structural sparse array for scenarios where both far-field and near-field sources coexist. The key feature of this algorithm is that it utilises fourth-order cumulants to compute the virtual covariance matrix and constructs a new special cumulant matrix to acquire the largest number of virtual sensors and the largest array aperture for a given number of sensors. The authors provide a geometric proof to justify the utilisation of the proposed sparse linear array and compute the effective aperture of the array. The proposed algorithm increases resolution ability, direction of arrival (DOA) and range estimation accuracy, and the number of sources to be localised. Moreover, the new method has the main advantage that it does not use the information of all sensors; so that it provides somewhat low computational complexity while it uses many actual and virtual sensors. Monte Carlo simulations are provided to demonstrate the effectiveness of the proposed method.

Inspec keywords: Monte Carlo methods; signal classification; covariance matrices; direction-of-arrival estimation; higher order statistics

Other keywords: Monte Carlo simulations; direction of arrival estimation; virtual covariance matrix; source localisation; range estimation accuracy; structural sparse array; cumulant-based multiple signal classification algorithm; largest aperture sparse linear array; computational complexity

Subjects: Monte Carlo methods; Algebra; Signal processing and detection

References

    1. 1)
      • 1. Krim, H., Viberg, M.: ‘Two decades of array signal processing research: the parametric approach’, IEEE Signal Process. Mag., 1996, 13, (4), pp. 67–94.
    2. 2)
      • 2. Schmidt, R.: ‘Multiple emitter location and signal parameter estimation’, IEEE Trans. Antennas Propag., 1986, 34, (3), pp. 276–280.
    3. 3)
      • 3. Roy, R., Kailath, T.: ‘ESPRIT-estimation of signal parameters via rotational invariance techniques’, IEEE Trans. Acoust., Speech, Signal Process., 1989, 37, (7), pp. 984–995.
    4. 4)
      • 4. Huang, Y.D., Barkat, M.: ‘Near-field multiple source localisation by passive sensor array’, IEEE Trans. Antennas Propag., 1991, 39, (7), pp. 968–975.
    5. 5)
      • 5. Starer, D., Nehorai, A.: ‘Passive localisation of near-field sources by path following’, IEEE Trans. Signal Process., 1994, 42, pp. 677–680.
    6. 6)
      • 6. Challa, R.N., Shamsunder, S.: ‘High-order subspace based algorithms for passive localisation of near-field sources’. Proc. 29th Asilomar Conf. Signals, Systems and Computers, Pacific Grove, CA, 1995, pp. 777–781.
    7. 7)
      • 7. Yuen, N., Friedlander, B.: ‘Performance analysis of higher order ESPRIT for localisation of near-field sources’, IEEE Trans. Signal Process., 1998, 46, pp. 709–719.
    8. 8)
      • 8. Grosicki, E., Abed-Meraim, K., Hua, Y.: ‘A weighted linear prediction method for near-field source localisation’, IEEE Trans. Signal Process., 2005, 53, pp. 3651–3660.
    9. 9)
      • 9. Zhi, W., Chia, M.W.: ‘Near-field source localisation via symmetric subarrays’, IEEE Signal Process. Lett., 2007, 14, (6), pp. 409–412.
    10. 10)
      • 10. Liang, J., Liu, D.: ‘Passive localisation of mixed near-field and far-field sources using two-stage MUSIC algorithm’, IEEE Trans. Signal Process., 2010, 58, (1), pp. 108–120.
    11. 11)
      • 11. He, J., Swamy, M.N.S., Ahmad, M.O.: ‘Efficient application of MUSIC algorithm under the coexistence of far-field and near-field sources’, IEEE Trans. Signal Process., 2012, 60, (4), pp. 2066–2070.
    12. 12)
      • 12. Wang, B., Zhao, Y., Liu, J.: ‘Mixed-order MUSIC algorithm for localisation of far-field and near-field sources’, IEEE Signal Process. Lett., 2013, 20, (4), pp. 311–314.
    13. 13)
      • 13. Wang, B., Liu, J., Sun, X.: ‘Mixed sources localization based on sparse signal reconstruction’, IEEE Signal Process. Lett., 2012, 19, (8), pp. 487–490.
    14. 14)
      • 14. Jiang, J., Duan, F., Chen, J.: ‘Three-dimensional localization algorithm for mixed near-field and far-field sources based on ESPRIT and MUSIC method’, Prog. Electromagn. Res., 2013, 136, pp. 435–456.
    15. 15)
      • 15. Jiang, J., Duan, F., Chen, J., et al: ‘Mixed near-field and far-field sources localization using the uniform linear sensor array’, IEEE Sens., 2013, 13, (8), pp. 3136–3143.
    16. 16)
      • 16. Tichavsky, P., Wong, K.T., Zoltowski, M.D.: ‘Near-field and far-field azimuth elevation angle estimation using a single vector-hydrophone’, IEEE Trans. Signal Process., 2001, 49, (11), pp. 2498–2510.
    17. 17)
      • 17. Liu, G., Sun, X.: ‘Two-stage matrix differencing algorithm for mixed far-field and near-field sources classification and localization’, IEEE Sens., 2014, 14, (6), pp. 1957–1965.
    18. 18)
      • 18. Jiang, J., Duan, F., Wang, X.: ‘An efficient classification method of mixed sources’, IEEE Sens., 2016, 16, (10), pp. 3731–3734.
    19. 19)
      • 19. Dogan, M.C., Mendel, J.M.: ‘Applications of cumulants to array processing – Part I: aperture extension and array calibration’, IEEE Trans. Signal Process., 1995, 43, (5), pp. 1200–1216.
    20. 20)
      • 20. Porat, B., Friedlander, B.: ‘Direction finding algorithms based on higher order statistics’, IEEE Trans. Signal Process., 1999, 39, pp. 2016–2024.
    21. 21)
      • 21. Chevalier, P., Albera, L., Comon, P.: ‘On the virtual array concept for higher order array processing’, IEEE Trans. Signal Process., 2005, 53, pp. 1254–1271.
    22. 22)
      • 22. Jianzhong, L., Wang, Y., Gang, W.: ‘Signal reconstruction for near-field source localisation’, IET Signal Process., 2015, 9, (3), pp. 201–205.
    23. 23)
      • 23. Johnson, R.C., Jasik, H.: ‘Antenna engineering handbook’ (McGraw-Hill, New York, 1984, 3rd edn.), pp. 9–12.
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