Direction of arrival estimation of sources with intersecting signature in time–frequency domain using a combination of IF estimation and MUSIC algorithm

  • Nabeel Ali KhanEmail author
  • Sadiq Ali
  • Mokhtar Mohammadi
  • Muhammad Haneef


Time–frequency (TF) approaches are frequently employed for source localization at low signal to noise ratio. However, TF approaches fail to achieve the desired performance for sparsely sampled signals or signals corrupted by heavy noise in an under-determined scenario when sources are not TF separable. In this study, we propose a new TF method for direction of arrival (DOA) estimation of sources with closely spaced and overlapping TF signature. The proposed method uses a combination of a high-resolution time–frequency distribution and instantaneous frequency estimation method for extraction of sources with intersecting and closely spaced time–frequency signatures. Once sources are extracted, their DOAs are estimated using a well known multiple signal classification (MUSIC) algorithm. Experimental results demonstrate that the proposed source localization method achieves better performance as compared to the conventional time–frequency MUSIC algorithm.


High resolution TFDs Instantaneous frequency estimation MUSIC Direction of arrival estimation Adaptive directional time–frequency distribution 



  1. Amin, M. G., & Zhang, Y. (2000). Direction finding based on spatial time–frequency distribution matrices. Digital Signal Processing, 10(4), 325–339.CrossRefGoogle Scholar
  2. Belouchrani, A., & Amin, M. (1999). Time–frequency MUSIC. IEEE Signal Processing Letters, 6, 109–110.CrossRefGoogle Scholar
  3. Belouchrani, A., Amin, M., Thirion-Moreau, N., & Zhang, Y. (2013). Source separation and localization using time–frequency distributions: An overview. IEEE Signal Processing Magazine, 30(6), 97–107.CrossRefGoogle Scholar
  4. Boashash, B. (2003). Time frequency analysis: A comprehensive reference. Amsterdam: Elsevier.Google Scholar
  5. Boashash, B., & Aïssa-El-Bey, A. (2018). Robust multisensor time-frequency signal processing: A tutorial review with illustrations of performance enhancement in selected application areas. Digital Signal Processing, 77, 153–186.MathSciNetCrossRefGoogle Scholar
  6. Boashash, B., Khan, N. A., & Ben-Jabeur, T. (2015). Time-frequency features for pattern recognition using high-resolution TFDs: A tutorial review. Digital Signal Processing, 40, 1–30.MathSciNetCrossRefGoogle Scholar
  7. Boashash, B., Aïssa-El-Bey, A., & Al-Saad, M. F. (2018) Multisensor time–frequency signal processing matlab package: An analysis tool for multichannel non-stationary data. SoftwareX.Google Scholar
  8. Chabriel, G., Kleinsteuber, M., Moreau, E., Shen, H., Tichavsky, P., & Yeredor, A. (2014). Joint matrices decompositions and blind source separation: A survey of methods, identification, and applications. IEEE Signal Processing Magazine, 31, 34–43.CrossRefGoogle Scholar
  9. Guo, L., Zhang, Y., Wu, Q., & Amin, M. (2015). Doa estimation of sparsely sampled nonstationary signals. In IEEE China summit and international conference on signal and information processing (ChinaSIP), pp. 300–304.Google Scholar
  10. Heidenreich, P., Cirillo, L., & Zoubir, A. (2009). Morphological image processing for FM source detection and localization. Signal Processing, 89(6), 1070–1080.CrossRefzbMATHGoogle Scholar
  11. Kassis, C. E., Picheral, J., & Mokbel, C. (2010). Advantages of nonuniform arrays using root-MUSIC. Signal Processing, 90(2), 689–695.CrossRefzbMATHGoogle Scholar
  12. Khan, N. A., & Ali, S. (2018). Sparsity-aware adaptive directional time–frequency distribution for source localization. Circuits, Systems, and Signal Processing, 37(3), 1223–1242.MathSciNetCrossRefGoogle Scholar
  13. Khan, N. A., & Mohammadi, M. (2018). Reconstruction of non-stationary signals with missing samples using time–frequency filtering. Circuits, Systems, and Signal Processing, 37, 3175–3190.MathSciNetCrossRefGoogle Scholar
  14. Khan, N. A., Ali, S., & Jansson, M. (2018). Direction of arrival estimation using adaptive directional time–frequency distributions. Multidimensional Systems and Signal Processing, 29(2), 503–521.MathSciNetCrossRefGoogle Scholar
  15. Khan, N. A., Mohammadi, M., & Ali, S. (2019). Instantaneous frequency estimation of intersecting and close multi-component signals with varying amplitudes. Signal, Image and Video Processing, 13(3), 517–524.CrossRefGoogle Scholar
  16. Krim, H., & Viberg, M. (1996). Two decades of array signal processing research: The parametric approach. IEEE Signal Processing Magazine, 13, 67–94.CrossRefGoogle Scholar
  17. Matlab code for direction of arrival estimation of close and intersecting sources. Accessed 21 January 2019.
  18. Mohammadi, M., Pouyan, A. A., Khan, N. A., & Abolghasemi, V. (2018a). Locally optimized adaptive directional time-frequency distributions. Circuits, Systems, and Signal Processing, 37, 3154–3174.MathSciNetCrossRefGoogle Scholar
  19. Mohammadi, M., Pouyan, A. A., Khan, N. A., & Abolghasemi, V. (2018b). An improved design of adaptive directional time-frequency distributions based on the radon transform. Signal Processing, 150, 85–89.CrossRefGoogle Scholar
  20. Mu, W., Amin, M. G., & Zhang, Y. (2003). Bilinear signal synthesis in array processing. IEEE Transactions on Signal Processing, 51(1), 90–100.MathSciNetCrossRefzbMATHGoogle Scholar
  21. Ouelha, S., Aïssa-El-Bey, A., & Boashash, B. (2017). Improving doa estimation algorithms using high-resolution quadratic time–frequency distributions. IEEE Transactions on Signal Processing, 65, 5179–5190.MathSciNetCrossRefzbMATHGoogle Scholar
  22. Sharif, W., Chakhchoukh, Y., & Zoubir, A. (2011). Robust spatial time–frequency distribution matrix estimation with application to direction-of-arrival estimation. Signal Processing, 91(11), 2630–2638.CrossRefzbMATHGoogle Scholar
  23. Swindlehurst, A., & Kailath, T. (1992). A performance analysis of subspace-based methods in the presence of model errors. I. The music algorithm. IEEE Transactions on Signal Processing, 40, 1758–1774.CrossRefzbMATHGoogle Scholar
  24. Trees, H. L. V. (2002). Optimum array processing. New York: Wiley Interscience.CrossRefGoogle Scholar
  25. Yang, Y., Dong, X., Peng, Z., Zhang, W., & Meng, G. (2015). Component extraction for non-stationary multi-component signal using parameterized de-chirping and band-pass filter. IEEE Signal Processing Letters, 22(9), 1373–1377.CrossRefGoogle Scholar
  26. Zhang, Y., Ma, W., & Amin, M. (2001). Subspace analysis of spatial time–frequency distribution matrices. IEEE Transactions on Signal Processing, 49, 747–759.CrossRefGoogle Scholar
  27. Zhang, Y. D., Amin, M. G., & Himed, B. (2012). Direction-of-arrival estimation of nonstationary signals exploiting signal characteristics. In 11th International conference on information science, signal processing and their applications (ISSPA), IEEE, pp. 1223–1228.Google Scholar
  28. Zhang, Y., Guo, L., Wu, Q., & Amin, M. (2015). Multi-sensor kernel design for time-frequency analysis of sparsely sampled nonstationary signals. In IEEE radar conference (RadarCon), pp. 0896–0900.Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical EngineeringFoundation UniversityIslamabadPakistan
  2. 2.Department of Electrical EngineeringUniversity of Engineering and TechnologyPeshawarPakistan
  3. 3.Department of Information TechnologyUniversity of Human DevelopmentSulaymaniyahIraq

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