The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
ICA (Analisis Komponen Bebas) mempunyai keupayaan yang luar biasa untuk mengasingkan campuran isyarat rawak stokastik. Walau bagaimanapun, kita sering menghadapi masalah mengasingkan campuran isyarat deterministik, terutamanya isyarat sinusoidal, dalam beberapa aplikasi seperti sistem radar dan sistem komunikasi. Seseorang mungkin bertanya sama ada ICA berkesan untuk isyarat deterministik. Dalam makalah ini, kami menganalisis prestasi asas ICA dalam memisahkan campuran isyarat sinusoidal kompleks, yang menggunakan terkumpul tertib keempat sebagai kriteria kebebasan isyarat. Kami secara teorinya menunjukkan bahawa ICA boleh memisahkan campuran isyarat sinusoidal deterministik. Kemudian, kami menjalankan simulasi komputer dan eksperimen radio dengan antena tatasusunan linear untuk mengesahkan hasil teori. Kami akan menunjukkan bahawa ICA berjaya mengasingkan campuran isyarat sinusoidal dengan perbezaan frekuensi kurang daripada resolusi FFT dan dengan perbezaan DOA (Arah Ketibaan) kurang daripada kriteria Rayleigh.
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Salinan
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
Salinan
@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},}
Salinan
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 -