Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Two-dimensional DOA estimation for multi-path environments by accurate separation of signals using k-medoids clustering

The authors propose a two-dimensional direction-of-arrival (DOA) estimation for multi-path environments, in which there are uncorrelated, partially correlated and coherent signals. The inability to identify non-coherent signals from coherent ones results in a considerable waste of sensors. In this work, an adaptive and automated threshold is considered for the efficient separation of noise, non-coherent signals, and coherent groups. First, non-coherent signals, coherent groups, and noise subspace are separated using k-medoids clustering. After determining the number of sources, non-coherent signals and coherent groups, non-coherent DOAs are estimated separately. The number of coherent signals in each coherence group is determined by the minimum descriptive length criterion. Finally, coherent DOAs are estimated in each group by constructing a coherent estimation matrix. The proposed method does not require any prior information such as knowing the number of signals or the covariance matrix of uncorrelated signals. The simulation results show that the proposed method is able to distinguish between the non-coherent and coherent signals, even at low signal-to-noise ratios and a small number of snapshots. Also, in terms of detection probability and estimation accuracy, it shows an improvement of over 1.2 and 83%, respectively, compared with the conventional forward−backward spatial smoothing scheme.

References

    1. 1)
      • 19. Couto, D., Zipfel, C.: ‘Regulation of pattern recognition receptor signalling in plants’, Nat. Rev. Immunol., 2016, 16, (9), p. 537.
    2. 2)
      • 11. Tao, H., Xin, J., Wang, J., et al: ‘Two-dimensional direction estimation for a mixture of noncoherent and coherent signals’, IEEE Trans. Signal Process., 2015, 63, (2), pp. 318333.
    3. 3)
      • 24. Sekizawa, S.: ‘Estimation of arrival directions using MUSIC algorithm with a planar array’. Int. Conf. on Universal Personal Communications, IEEE, 1998.
    4. 4)
      • 20. Wagstaff, K., Cardie, C., Rogers, S., et al: ‘Constrained K-means clustering with background knowledge’. Int. Conf. on Machine Learning, San Francisco, 2001.
    5. 5)
      • 1. Gay, S.L., Benesty, J.: ‘Acoustic signal processing for telecommunication’ (Springer Science & Business Media, New York, USA, 2012).
    6. 6)
      • 15. Jin, X., Han, J.: ‘K-medoids clustering’, in Sammut, C., Webb, G.I.: (Eds.) ‘Encyclopedia of machine learning’ (Springer, New York, USA, 2017), pp. 2448.
    7. 7)
      • 6. Gan, L., Luo, X.: ‘Direction-of-arrival estimation for uncorrelated and coherent signals in the presence of multipath propagation’, IET Microw. Antennas Propag., 2013, 7, (9), pp. 746753.
    8. 8)
      • 25. Chen, Y.-M.: ‘On spatial smoothing for two-dimensional direction-of-arrival estimation of coherent signals’, IEEE Trans. Signal Process., 1997, 45, (7), pp. 16891696.
    9. 9)
      • 3. Qi, C., Wang, Y., Zhang, Y., et al: ‘Spatial difference smoothing for DOA estimation of coherent signals’, IEEE Signal Process. Lett., 2005, 12, (11), pp. 800802.
    10. 10)
      • 12. Agatonović, M., Stanković, Z., Dončov, N., et al: ‘Application of artificial neural networks for efficient high-resolution 2d DOA estimation’, Radioengineering, 2012, 21, (4), pp. 11781186.
    11. 11)
      • 13. Pillai, S.U.: ‘Array signal processing’ (Springer Science & Business Media, New York, USA, 2012).
    12. 12)
      • 9. Fang, W.-H., Lee, Y.-C., Chen, Y.-T.: ‘Maximum likelihood 2-D DOA estimation via signal separation and importance sampling’, IEEE Antennas Wirel. Propag. Lett., 2016, 15, pp. 746749.
    13. 13)
      • 23. Bora, M., Jyoti, D., Gupta, D., et al: ‘Effect of different distance measures on the performance of K-means algorithm: An experimental study in MATLAB’, arXiv preprint arXiv:1405.7471, 2014.
    14. 14)
      • 21. Ives, Z.G.: ‘Technical perspective: k-Shape: Efficient and accurate clustering of time series’, ACM SIGMOD Record, 2016, 45, (1), pp. 6868.
    15. 15)
      • 8. Chen, F.-J., Kwong, S., Kok, C.-W.: ‘Esprit-like two-dimensional DOA estimation for coherent signals’, IEEE Trans. Aerosp. Electron. Syst., 2010, 46, (3), pp. 14771484.
    16. 16)
      • 26. Hansen, M.H., Yu, B.: ‘Minimum description length model selection criteria for generalized linear models’, Lecture Notes-Monogr. Ser., 2003, 40, pp. 145163.
    17. 17)
      • 7. Wang, H., Liu, K.R.: ‘2-D spatial smoothing for multipath coherent signal separation’, IEEE Trans. Aerosp. Electron. Syst., 1998, 34, (2), pp. 391405.
    18. 18)
      • 22. Devijver, P.A., Kittler, J.: ‘Pattern recognition: A statistical approach’ (Prentice Hall, USA, 1982).
    19. 19)
      • 4. Liu, F., Wang, J., Sun, C., et al: ‘Spatial differencing method for DOA estimation under the coexistence of both uncorrelated and coherent signals’, IEEE Trans. Antennas Propag., 2012, 60, (4), pp. 20522062.
    20. 20)
      • 5. Ye, Z.-F., Zhang, Y.-F., Liu, C.: ‘Direction-of-arrival estimation for uncorrelated and coherent signals with fewer sensors’, IET Microw. Antennas Propag., 2009, 3, (3), pp. 473482.
    21. 21)
      • 10. Wang, G., Xin, J., Zheng, N., et al: ‘Computationally efficient subspace-based method for two-dimensional direction estimation with L-shaped array’, IEEE Trans. Signal Process., 2011, 59, (7), pp. 31973212.
    22. 22)
      • 14. Park, H.-S., Jun, C.-H.: ‘A simple and fast algorithm for K-medoids clustering’, Expert Syst. Appl., 2009, 36, (2), pp. 33363341.
    23. 23)
      • 16. Nadakuditi, R.R., Edelman, A.: ‘Sample eigenvalue based detection of high-dimensional signals in white noise using relatively few samples’, IEEE Trans. Signal Process., 2008, 56, (7), pp. 26252638.
    24. 24)
      • 17. Mather, P., Tso, B.: ‘Classification methods for remotely sensed data’ (CRC Press, USA, 2016).
    25. 25)
      • 18. Schroff, F., Kalenichenko, D., Philbin, J.: ‘FaceNet: A unified embedding for face recognition and clustering’. Proc. of the IEEE Conf. on computer vision and pattern recognition, Boston, 2015.
    26. 26)
      • 2. Rajagopal, R., Rao, P.: ‘Generalised algorithm for DOA estimation in a passive sonar’. IEE Proc. F Radar and Signal Processing, IET, 1993, 140, (1), pp. 1220.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-com.2018.5798
Loading

Related content

content/journals/10.1049/iet-com.2018.5798
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address