基于线性约束最大熵学习算法的自适应波束形成

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) Pub Date : 2017-10-01 DOI:10.1109/ICCKE.2017.8167926

M. Hajiabadi, H. Khoshbin, G. Hodtani

{"title":"基于线性约束最大熵学习算法的自适应波束形成","authors":"M. Hajiabadi, H. Khoshbin, G. Hodtani","doi":"10.1109/ICCKE.2017.8167926","DOIUrl":null,"url":null,"abstract":"The Gaussian noise profile has been demonstrated to be an inaccurate model in several antenna beamforming problems. Many available beamformers are based on second-order statistics and their efficiency degrades significantly due to impulsive noise existed in the received signal. Therefore, 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-order-moment-based beamformers.","PeriodicalId":151934,"journal":{"name":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","volume":"705 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Adaptive beamforming based on linearly constrained maximum correntropy learning algorithm\",\"authors\":\"M. Hajiabadi, H. Khoshbin, G. Hodtani\",\"doi\":\"10.1109/ICCKE.2017.8167926\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Gaussian noise profile has been demonstrated to be an inaccurate model in several antenna beamforming problems. Many available beamformers are based on second-order statistics and their efficiency degrades significantly due to impulsive noise existed in the received signal. Therefore, 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-order-moment-based beamformers.\",\"PeriodicalId\":151934,\"journal\":{\"name\":\"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)\",\"volume\":\"705 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCKE.2017.8167926\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCKE.2017.8167926","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 1

摘要

在一些天线波束形成问题中，高斯噪声分布已被证明是一个不准确的模型。许多现有的波束形成器都是基于二阶统计量的，由于接收信号中存在脉冲噪声，其效率会显著降低。因此，需要关注非高斯噪声环境下的波束形成问题。根据信息理论学习(ITL)准则在非高斯环境下的鲁棒性，提出了一种线性约束版本的最大熵学习算法，以解决存在非高斯和脉冲噪声的波束形成问题。仿真结果表明，该自适应波束形成器与传统二阶矩波束形成器相比具有精度高、抗干扰能力强的特点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Adaptive beamforming based on linearly constrained maximum correntropy learning algorithm

The Gaussian noise profile has been demonstrated to be an inaccurate model in several antenna beamforming problems. Many available beamformers are based on second-order statistics and their efficiency degrades significantly due to impulsive noise existed in the received signal. Therefore, 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-order-moment-based beamformers.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2017 7th International Conference on Computer and Knowledge Engineering (ICCKE)

自引率

0.00%

发文量