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LIAO Hong-shu, WEI Ping, LI Wan-chun. SNR Estimation Based on Finite Sample Data[J]. Journal of University of Electronic Science and Technology of China, 2012, 41(3): 364-367. doi: 10.3969/j.issn.1001-0548.2012.03.007
Citation: LIAO Hong-shu, WEI Ping, LI Wan-chun. SNR Estimation Based on Finite Sample Data[J]. Journal of University of Electronic Science and Technology of China, 2012, 41(3): 364-367. doi: 10.3969/j.issn.1001-0548.2012.03.007
shu

SNR Estimation Based on Finite Sample Data

doi: 10.3969/j.issn.1001-0548.2012.03.007
  • 1.

    School of Electronic Engineering,University of Electronic Science and Technology of China Chengdu 611731

  • Received Date: 2010-11-08
  • Rev Recd Date: 2011-05-11
  • Publish Date: 2012-06-15
  • This paper first demonstrats that signal subspace has finite dimensions from signal model. A conclusion is obtained by simulation test that both eigenvalues of signal subspace and noise subspace can't be evaluated precisely due to small size data. Then signal subspace dimensions are estimated accurately with Modified-AIC criterion using likelihood function depending only on noise space eigenvalues. A new robust SNR estimation method is proposed with the application of modified-AIC criterion. Regarding a variety of SNR and data size, simulation results show that the new method can be applied in lower SNR and shorter data samples environment.
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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SNR Estimation Based on Finite Sample Data

doi: 10.3969/j.issn.1001-0548.2012.03.007
  • 1. School of Electronic Engineering,University of Electronic Science and Technology of China Chengdu 611731
  • Received Date: 2010-11-08
  • Rev Recd Date: 2011-05-11
  • Publish Date: 2012-06-15

Abstract: This paper first demonstrats that signal subspace has finite dimensions from signal model. A conclusion is obtained by simulation test that both eigenvalues of signal subspace and noise subspace can't be evaluated precisely due to small size data. Then signal subspace dimensions are estimated accurately with Modified-AIC criterion using likelihood function depending only on noise space eigenvalues. A new robust SNR estimation method is proposed with the application of modified-AIC criterion. Regarding a variety of SNR and data size, simulation results show that the new method can be applied in lower SNR and shorter data samples environment.

LIAO Hong-shu, WEI Ping, LI Wan-chun. SNR Estimation Based on Finite Sample Data[J]. Journal of University of Electronic Science and Technology of China, 2012, 41(3): 364-367. doi: 10.3969/j.issn.1001-0548.2012.03.007
Citation: LIAO Hong-shu, WEI Ping, LI Wan-chun. SNR Estimation Based on Finite Sample Data[J]. Journal of University of Electronic Science and Technology of China, 2012, 41(3): 364-367. doi: 10.3969/j.issn.1001-0548.2012.03.007
shu

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