Adaptive Beamforming Based on Linearly Constrained Maximum Correntropy Learning Algorithm
Author:
, , , , ,Year
: 2017
Abstract: The Gaussian noise profile has been demonstrated
to be an inaccurate model in several antenna beamforming
problems. Many available beamformers are based on secondorder
statistics and their efficiency degrades significantly due
to impulsive noise existed in the received signal. Hence, a
demand exists for attention to address beamforming problems
under nonGaussian noise environments. According to the robust
performance of information theoretic learning (ITL) criteria in
nonGaussian environments, we propose a linearly constrained
version of maximum correntropy learning algorithm in order
to solve beamforming problem in presence of nonGaussian and
impulsive noises. Simulation results of the proposed adaptive
beamformer are provided to illustrate its accurate and resistant
performance in comparison with conventional second-ordermoment-
based beamformers.
to be an inaccurate model in several antenna beamforming
problems. Many available beamformers are based on secondorder
statistics and their efficiency degrades significantly due
to impulsive noise existed in the received signal. Hence, a
demand exists for attention to address beamforming problems
under nonGaussian noise environments. According to the robust
performance of information theoretic learning (ITL) criteria in
nonGaussian environments, we propose a linearly constrained
version of maximum correntropy learning algorithm in order
to solve beamforming problem in presence of nonGaussian and
impulsive noises. Simulation results of the proposed adaptive
beamformer are provided to illustrate its accurate and resistant
performance in comparison with conventional second-ordermoment-
based beamformers.
Keyword(s): Adaptive filter,beamforming,constrained optimization,
correntropy criterion
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Adaptive Beamforming Based on Linearly Constrained Maximum Correntropy Learning Algorithm
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| contributor author | مجتبی حاجی آبادی | en |
| contributor author | حسین خوش بین قماش | en |
| contributor author | قوشه عابدهدتنی | en |
| contributor author | Mojtaba Hajiabadi | fa |
| contributor author | Hossein Khoshbin Ghomash | fa |
| contributor author | Ghosheh Abed Hodtani | fa |
| date accessioned | 2020-06-06T14:26:32Z | |
| date available | 2020-06-06T14:26:32Z | |
| date copyright | 10/26/2017 | |
| date issued | 2017 | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/3396806 | |
| description abstract | The Gaussian noise profile has been demonstrated to be an inaccurate model in several antenna beamforming problems. Many available beamformers are based on secondorder statistics and their efficiency degrades significantly due to impulsive noise existed in the received signal. Hence, a demand exists for attention to address beamforming problems under nonGaussian noise environments. According to the robust performance of information theoretic learning (ITL) criteria in nonGaussian environments, we propose a linearly constrained version of maximum correntropy learning algorithm in order to solve beamforming problem in presence of nonGaussian and impulsive noises. Simulation results of the proposed adaptive beamformer are provided to illustrate its accurate and resistant performance in comparison with conventional second-ordermoment- based beamformers. | en |
| language | English | |
| title | Adaptive Beamforming Based on Linearly Constrained Maximum Correntropy Learning Algorithm | en |
| type | Conference Paper | |
| contenttype | External Fulltext | |
| subject keywords | Adaptive filter | en |
| subject keywords | beamforming | en |
| subject keywords | constrained optimization | en |
| subject keywords | correntropy criterion | en |
| identifier link | https://profdoc.um.ac.ir/paper-abstract-1065037.html | |
| conference title | (2017 7th International Conference on Computer and Knowledge Engineering (ICCKE | en |
| conference location | مشهد | fa |
| identifier articleid | 1065037 |