A Novel Accurate Source Number Estimation Method Based on GBSA-MDL Algorithm

  • Taha Bouras
  • Di He
  • Fei Wen
  • Peilin Liu
  • Wenxian Yu
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 237)


Several classical source number estimation methods have been proposed in the past based on information theoretic criteria such as minimum description length (MDL). However, in most known real applications there is a scenario in which the number of sensors goes to infinity at the same speed as the number of snapshots (general asymptotic case) which yields to a blind performance for the classical MDL and results in an inaccurate source number estimation. Accordingly, in this work, the Galaxy Based Search Algorithm (GBSA) is modified and applied with the MDL criteria in order to optimize and correct the detection of source number under such sample-starving case. Simulation results show that the proposed GBSA-MDL based method gives reliable results compared to several used source number estimation methods.


Source number estimation methods Minimum Description Length (MDL) General asymptotic case Optimization Galaxy Based Search Algorithm (GBSA) 



This research work is supported by the Important National Science and Technology Specific Project of China under Grant No. 2016ZX03001022-006, the Shanghai Science and Technology Committee under Grant No. 16DZ1100402, and the National Natural Science Foundation of China under Grant No. 91438113.


  1. 1.
    Xie, W., Wen, F., Liu, J., Wan, Q.: Simultaneously source association, DOA and fading coefficients estimation for multipath signals. IEEE Trans. Signal Process. 65(11), 2773–2786 (2017)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Wen, F., Wan, Q., Fan, R., Wei, H.: Improved MUSIC algorithm for multiple noncoherent subarrays. IEEE Signal Process. Lett. 21(5), 527–530 (2014)CrossRefGoogle Scholar
  3. 3.
    Wen, F., Xie, W., Chen, X., Liu, P.: DOA estimation for noncircular sources with multiple noncoherent subarrays. IEEE Commun. Lett. (2017).
  4. 4.
    Xie, W., Wang, C., Wen, F., Liu, J., Wan, Q.: DOA and gain-phase errors estimation for noncircular sources with a central symmetric array. IEEE Sens. J. 17(10), 3068–3078 (2017)CrossRefGoogle Scholar
  5. 5.
    Wong, C., Klukas, R., Messier, G.: Using WLAN infrastructure for angle-of-arrival indoor user location. In: Proceedings of the 68th Semi-Annual IEEE Vehicular Technology (VTC 2008), pp. 1–5, September 2008Google Scholar
  6. 6.
    Schmidt, R.O.: Multiple emitter location and signal parameter estimation. IEEE Trans. Antenna Propag. 34(3), 276–280 (1986)CrossRefGoogle Scholar
  7. 7.
    Roy, R., Kailath, T.: ESPRIT-estimation of signal parameters via rotational invariance techniques. IEEE Trans. Signal Process. 37(7), 984–995 (1989)CrossRefzbMATHGoogle Scholar
  8. 8.
    Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control AC-19(6), 716–723 (1974)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(1978), 461–464 (1987)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Valaee, S., Kabal, P.: An information theoretic approach to source enumeration in array signal processing. IEEE Trans. Signal Process. 52(5), 1171–1178 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Wu, H.T.: Source number estimators using transformed Gerschgorin radii. IEEE Trans. Signal Process. 43(6), 1325–1333 (1995)CrossRefGoogle Scholar
  12. 12.
    Wax, M., Kailath, T.: Detection of signals by information theoretic criteria. IEEE Trans. Acoust. Speech Signal Process. 33(2), 387–392 (1985)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Huang, L., So, H.C.: Source enumeration via MDL criterion based on linear shrinkage estimation of noise subspace covariance matrix. IEEE Trans. Signal Process. 61, 4806–4821 (2013)CrossRefGoogle Scholar
  14. 14.
    Huang, L., Long, T., Mao, E., So, H.C.: MMSE-based MDL method for accurate source number estimation. IEEE Signal Process. Lett. 16, 798–801 (2009)CrossRefGoogle Scholar
  15. 15.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Goldberg, D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Boston (1989)zbMATHGoogle Scholar
  17. 17.
    Mehrabian, A.R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecol. Inform. 1, 355–366 (2006)CrossRefGoogle Scholar
  18. 18.
    Kennedy, J., Eberhart, E.C.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
  19. 19.
    Shah-Hosseini, H.: Otsu’s criterion-based multilevel thresholding by a nature-inspired metaheuristic called galaxy-based search algorithm. In: Third World Nature and Biologically Inspired Computing (NaBIC) (2011). 2013 MECSGoogle Scholar
  20. 20.
    Shah-Hosseini, H.: Principal components analysis by the galaxy-based search algorithm: a novel metaheuristic for continuous, optimisation. Int. J. Comput. Sci. Eng. 6(1/2), 132–140 (2011)CrossRefGoogle Scholar
  21. 21.
    Sardari, F., Moghaddam, M.E.: An object tracking using modified galaxy based search algorithm. Swarm Evol. Comput. 30, 27–38 (2016)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Navigation and Location-Based ServicesShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China

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