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
Untuk menyelesaikan masalah gabungan multisensor teragih, kaedah linear optimum boleh digunakan dalam model hingar Gaussian. Dalam amalan, taburan hingar saluran biasanya bukan Gaussian, mungkin berekor berat, menjadikan kaedah linear gagal. Dengan menggabungkan alat klasik gabungan linear optimum dan kaedah statistik teguh, algoritma gabungan teguh MAD dua peringkat (MADRF) dicadangkan. Ia berkesan melakukan kedua-dua bunyi saluran Gaussian yang tercemar secara simetri dan tidak simetri dengan parameter pencemaran yang berbeza-beza dalam julat yang luas.
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Salinan
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
Salinan
@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},}
Salinan
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 -