IEICE TRANSACTIONS on Communications
Separation of Mixtures of Complex Sinusoidal Signals with Independent Component Analysis
Summary :
ICA (Independent Component Analysis) has a remarkable capability of separating mixtures of stochastic random signals. However, we often face problems of separating mixtures of deterministic signals, especially sinusoidal signals, in some applications such as radar systems and communication systems. One may ask if ICA is effective for deterministic signals. In this paper, we analyze the basic performance of ICA in separating mixtures of complex sinusoidal signals, which utilizes the fourth order cumulant as a criterion of independency of signals. We theoretically show that ICA can separate mixtures of deterministic sinusoidal signals. Then, we conduct computer simulations and radio experiments with a linear array antenna to confirm the theoretical result. We will show that ICA is successful in separating mixtures of sinusoidal signals with frequency difference less than FFT resolution and with DOA (Direction of Arrival) difference less than Rayleigh criterion.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E94-B No.1 pp.215-221
- Publication Date
- 2011/01/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E94.B.215
- Type of Manuscript
- PAPER
- Category
- Wireless Communication Technologies
Authors
Keyword
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Tetsuo KIRIMOTO, Takeshi AMISHIMA, Atsushi OKAMURA, "Separation of Mixtures of Complex Sinusoidal Signals with Independent Component Analysis" in IEICE TRANSACTIONS on Communications,
vol. E94-B, no. 1, pp. 215-221, January 2011, doi: 10.1587/transcom.E94.B.215.
Abstract: ICA (Independent Component Analysis) has a remarkable capability of separating mixtures of stochastic random signals. However, we often face problems of separating mixtures of deterministic signals, especially sinusoidal signals, in some applications such as radar systems and communication systems. One may ask if ICA is effective for deterministic signals. In this paper, we analyze the basic performance of ICA in separating mixtures of complex sinusoidal signals, which utilizes the fourth order cumulant as a criterion of independency of signals. We theoretically show that ICA can separate mixtures of deterministic sinusoidal signals. Then, we conduct computer simulations and radio experiments with a linear array antenna to confirm the theoretical result. We will show that ICA is successful in separating mixtures of sinusoidal signals with frequency difference less than FFT resolution and with DOA (Direction of Arrival) difference less than Rayleigh criterion.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E94.B.215/_p
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@ARTICLE{e94-b_1_215,
author={Tetsuo KIRIMOTO, Takeshi AMISHIMA, Atsushi OKAMURA, },
journal={IEICE TRANSACTIONS on Communications},
title={Separation of Mixtures of Complex Sinusoidal Signals with Independent Component Analysis},
year={2011},
volume={E94-B},
number={1},
pages={215-221},
abstract={ICA (Independent Component Analysis) has a remarkable capability of separating mixtures of stochastic random signals. However, we often face problems of separating mixtures of deterministic signals, especially sinusoidal signals, in some applications such as radar systems and communication systems. One may ask if ICA is effective for deterministic signals. In this paper, we analyze the basic performance of ICA in separating mixtures of complex sinusoidal signals, which utilizes the fourth order cumulant as a criterion of independency of signals. We theoretically show that ICA can separate mixtures of deterministic sinusoidal signals. Then, we conduct computer simulations and radio experiments with a linear array antenna to confirm the theoretical result. We will show that ICA is successful in separating mixtures of sinusoidal signals with frequency difference less than FFT resolution and with DOA (Direction of Arrival) difference less than Rayleigh criterion.},
keywords={},
doi={10.1587/transcom.E94.B.215},
ISSN={1745-1345},
month={January},}
Copy
TY - JOUR
TI - Separation of Mixtures of Complex Sinusoidal Signals with Independent Component Analysis
T2 - IEICE TRANSACTIONS on Communications
SP - 215
EP - 221
AU - Tetsuo KIRIMOTO
AU - Takeshi AMISHIMA
AU - Atsushi OKAMURA
PY - 2011
DO - 10.1587/transcom.E94.B.215
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E94-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2011
AB - ICA (Independent Component Analysis) has a remarkable capability of separating mixtures of stochastic random signals. However, we often face problems of separating mixtures of deterministic signals, especially sinusoidal signals, in some applications such as radar systems and communication systems. One may ask if ICA is effective for deterministic signals. In this paper, we analyze the basic performance of ICA in separating mixtures of complex sinusoidal signals, which utilizes the fourth order cumulant as a criterion of independency of signals. We theoretically show that ICA can separate mixtures of deterministic sinusoidal signals. Then, we conduct computer simulations and radio experiments with a linear array antenna to confirm the theoretical result. We will show that ICA is successful in separating mixtures of sinusoidal signals with frequency difference less than FFT resolution and with DOA (Direction of Arrival) difference less than Rayleigh criterion.
ER -
English
