MAD Robust Fusion with Non-Gaussian Channel Noise
Summary :
To solve the problem of distributed multisensor fusion, the optimal linear methods can be used in Gaussian noise models. In practice, channel noise distributions are usually non-Gaussian, possibly heavy-tailed, making linear methods fail. By combining a classical tool of optimal linear fusion and a robust statistical method, the two-stage MAD robust fusion (MADRF) algorithm is proposed. It effectively performs both in symmetrically and asymmetrically contaminated Gaussian channel noise with contamination parameters varying over a wide range.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.5 pp.1293-1300
- Publication Date
- 2009/05/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E92.A.1293
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
Authors
Keyword
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Nga-Viet NGUYEN, Georgy SHEVLYAKOV, Vladimir SHIN, "MAD Robust Fusion with Non-Gaussian Channel Noise" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 5, pp. 1293-1300, May 2009, doi: 10.1587/transfun.E92.A.1293.
Abstract: To solve the problem of distributed multisensor fusion, the optimal linear methods can be used in Gaussian noise models. In practice, channel noise distributions are usually non-Gaussian, possibly heavy-tailed, making linear methods fail. By combining a classical tool of optimal linear fusion and a robust statistical method, the two-stage MAD robust fusion (MADRF) algorithm is proposed. It effectively performs both in symmetrically and asymmetrically contaminated Gaussian channel noise with contamination parameters varying over a wide range.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.1293/_p
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@ARTICLE{e92-a_5_1293,
author={Nga-Viet NGUYEN, Georgy SHEVLYAKOV, Vladimir SHIN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={MAD Robust Fusion with Non-Gaussian Channel Noise},
year={2009},
volume={E92-A},
number={5},
pages={1293-1300},
abstract={To solve the problem of distributed multisensor fusion, the optimal linear methods can be used in Gaussian noise models. In practice, channel noise distributions are usually non-Gaussian, possibly heavy-tailed, making linear methods fail. By combining a classical tool of optimal linear fusion and a robust statistical method, the two-stage MAD robust fusion (MADRF) algorithm is proposed. It effectively performs both in symmetrically and asymmetrically contaminated Gaussian channel noise with contamination parameters varying over a wide range.},
keywords={},
doi={10.1587/transfun.E92.A.1293},
ISSN={1745-1337},
month={May},}
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TY - JOUR
TI - MAD Robust Fusion with Non-Gaussian Channel Noise
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1293
EP - 1300
AU - Nga-Viet NGUYEN
AU - Georgy SHEVLYAKOV
AU - Vladimir SHIN
PY - 2009
DO - 10.1587/transfun.E92.A.1293
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2009
AB - To solve the problem of distributed multisensor fusion, the optimal linear methods can be used in Gaussian noise models. In practice, channel noise distributions are usually non-Gaussian, possibly heavy-tailed, making linear methods fail. By combining a classical tool of optimal linear fusion and a robust statistical method, the two-stage MAD robust fusion (MADRF) algorithm is proposed. It effectively performs both in symmetrically and asymmetrically contaminated Gaussian channel noise with contamination parameters varying over a wide range.
ER -
English
