@InProceedings{6952622,
  author = {G. V. Anand and P. V. Nagesha},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Source number estimation in non-Gaussian noise},
  year = {2014},
  pages = {1711-1715},
  abstract = {In this paper a new method of source number estimation in non-Gaussian noise is presented. The proposed signal sub-space identification (SSI) method involves estimation of the array signal correlation matrix and determining the number of positive eigenvalues of the estimated correlation matrix. The SSI method is applied to the problem of estimating the number of plane wave narrowband signals impinging on a uniform linear array. It is shown that the performance of the SSI method in non-Gaussian heavy-tailed noise is significantly better than that of the widely used minimum description length (MDL) method and the recently proposed entropy estimation of eigenvalues (EEE) method based on random matrix theory.},
  keywords = {array signal processing;correlation methods;eigenvalues and eigenfunctions;estimation theory;matrix algebra;source number estimation;nonGaussian noise;signal subspace identification method;SSI method;array signal correlation matrix estimation;positive eigenvalues;plane wave narrowband signals;uniform linear array;minimum description length method;MDL method;entropy estimation of eigenvalues method;EEE method based;random matrix theory;Estimation;Eigenvalues and eigenfunctions;Arrays;Correlation;Signal to noise ratio;Vectors;Non-Gaussian noise;noise variance estimation;signal subspace identification;source number estimation},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569921499.pdf},
}

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