MLICA-Based Separation Algorithm for Complex Sinusoidal Signals with PDF Parameter Optimization
Summary :
Blind source separation (BSS) techniques are required for various signal decomposing issues. Independent component analysis (ICA), assuming only a statistical independence among stochastic source signals, is one of the most useful BSS tools because it does not need a priori information on each source. However, there are many requirements for decomposing multiple deterministic signals such as complex sinusoidal signals with different frequencies. These requirements may include pulse compression or clutter rejection. It has been theoretically shown that an ICA algorithm based on maximizing non-Gaussianity successfully decomposes such deterministic signals. However, this ICA algorithm does not maintain a sufficient separation performance when the frequency difference of the sinusoidal waves becomes less than a nominal frequency resolution. To solve this problem, this paper proposes a super-resolution algorithm for complex sinusoidal signals by extending the maximum likelihood ICA, where the probability density function (PDF) of a complex sinusoidal signal is exploited as a priori knowledge, in which the PDF of the signal amplitude is approximated as a Gaussian distribution with an extremely small standard deviation. Furthermore, we introduce an optimization process for this standard deviation to avoid divergence in updating the reconstruction matrix. Numerical simulations verify that our proposed algorithm remarkably enhances the separation performance compared to the conventional one, and accomplishes a super-resolution separation even in noisy situations.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E95-B No.11 pp.3556-3562
- Publication Date
- 2012/11/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E95.B.3556
- Type of Manuscript
- PAPER
- Category
- Sensing
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Tetsuhiro OKANO, Shouhei KIDERA, Tetsuo KIRIMOTO, "MLICA-Based Separation Algorithm for Complex Sinusoidal Signals with PDF Parameter Optimization" in IEICE TRANSACTIONS on Communications,
vol. E95-B, no. 11, pp. 3556-3562, November 2012, doi: 10.1587/transcom.E95.B.3556.
Abstract: Blind source separation (BSS) techniques are required for various signal decomposing issues. Independent component analysis (ICA), assuming only a statistical independence among stochastic source signals, is one of the most useful BSS tools because it does not need a priori information on each source. However, there are many requirements for decomposing multiple deterministic signals such as complex sinusoidal signals with different frequencies. These requirements may include pulse compression or clutter rejection. It has been theoretically shown that an ICA algorithm based on maximizing non-Gaussianity successfully decomposes such deterministic signals. However, this ICA algorithm does not maintain a sufficient separation performance when the frequency difference of the sinusoidal waves becomes less than a nominal frequency resolution. To solve this problem, this paper proposes a super-resolution algorithm for complex sinusoidal signals by extending the maximum likelihood ICA, where the probability density function (PDF) of a complex sinusoidal signal is exploited as a priori knowledge, in which the PDF of the signal amplitude is approximated as a Gaussian distribution with an extremely small standard deviation. Furthermore, we introduce an optimization process for this standard deviation to avoid divergence in updating the reconstruction matrix. Numerical simulations verify that our proposed algorithm remarkably enhances the separation performance compared to the conventional one, and accomplishes a super-resolution separation even in noisy situations.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E95.B.3556/_p
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@ARTICLE{e95-b_11_3556,
author={Tetsuhiro OKANO, Shouhei KIDERA, Tetsuo KIRIMOTO, },
journal={IEICE TRANSACTIONS on Communications},
title={MLICA-Based Separation Algorithm for Complex Sinusoidal Signals with PDF Parameter Optimization},
year={2012},
volume={E95-B},
number={11},
pages={3556-3562},
abstract={Blind source separation (BSS) techniques are required for various signal decomposing issues. Independent component analysis (ICA), assuming only a statistical independence among stochastic source signals, is one of the most useful BSS tools because it does not need a priori information on each source. However, there are many requirements for decomposing multiple deterministic signals such as complex sinusoidal signals with different frequencies. These requirements may include pulse compression or clutter rejection. It has been theoretically shown that an ICA algorithm based on maximizing non-Gaussianity successfully decomposes such deterministic signals. However, this ICA algorithm does not maintain a sufficient separation performance when the frequency difference of the sinusoidal waves becomes less than a nominal frequency resolution. To solve this problem, this paper proposes a super-resolution algorithm for complex sinusoidal signals by extending the maximum likelihood ICA, where the probability density function (PDF) of a complex sinusoidal signal is exploited as a priori knowledge, in which the PDF of the signal amplitude is approximated as a Gaussian distribution with an extremely small standard deviation. Furthermore, we introduce an optimization process for this standard deviation to avoid divergence in updating the reconstruction matrix. Numerical simulations verify that our proposed algorithm remarkably enhances the separation performance compared to the conventional one, and accomplishes a super-resolution separation even in noisy situations.},
keywords={},
doi={10.1587/transcom.E95.B.3556},
ISSN={1745-1345},
month={November},}
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TY - JOUR
TI - MLICA-Based Separation Algorithm for Complex Sinusoidal Signals with PDF Parameter Optimization
T2 - IEICE TRANSACTIONS on Communications
SP - 3556
EP - 3562
AU - Tetsuhiro OKANO
AU - Shouhei KIDERA
AU - Tetsuo KIRIMOTO
PY - 2012
DO - 10.1587/transcom.E95.B.3556
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E95-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2012
AB - Blind source separation (BSS) techniques are required for various signal decomposing issues. Independent component analysis (ICA), assuming only a statistical independence among stochastic source signals, is one of the most useful BSS tools because it does not need a priori information on each source. However, there are many requirements for decomposing multiple deterministic signals such as complex sinusoidal signals with different frequencies. These requirements may include pulse compression or clutter rejection. It has been theoretically shown that an ICA algorithm based on maximizing non-Gaussianity successfully decomposes such deterministic signals. However, this ICA algorithm does not maintain a sufficient separation performance when the frequency difference of the sinusoidal waves becomes less than a nominal frequency resolution. To solve this problem, this paper proposes a super-resolution algorithm for complex sinusoidal signals by extending the maximum likelihood ICA, where the probability density function (PDF) of a complex sinusoidal signal is exploited as a priori knowledge, in which the PDF of the signal amplitude is approximated as a Gaussian distribution with an extremely small standard deviation. Furthermore, we introduce an optimization process for this standard deviation to avoid divergence in updating the reconstruction matrix. Numerical simulations verify that our proposed algorithm remarkably enhances the separation performance compared to the conventional one, and accomplishes a super-resolution separation even in noisy situations.
ER -
English
