...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Neural Networks, IEEE Transactions on >Blind Extraction of Global Signal From Multi-Channel Noisy Observations
【24h】

Blind Extraction of Global Signal From Multi-Channel Noisy Observations

机译：从多通道噪声观测中盲提取全局信号

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We propose a novel efficient method of blind signal extraction from multi-sensor networks when each observed signal consists of one global signal and local uncorrelated signals. Most of existing blind signal separation and extraction methods such as independent component analysis have constraints such as statistical independence, non-Gaussianity, and underdetermination, and they are not suitable for global signal extraction problem from noisy observations. We developed an estimation algorithm based on alternating iteration and the smart weighted averaging. The proposed method does not have strong assumptions such as independence or non-Gaussianity. Experimental results using a musical signal and a real electroencephalogram demonstrate the advantage of the proposed method.
机译：当每个观察到的信号由一个全局信号和局部不相关信号组成时，我们提出了一种从多传感器网络中提取盲信号的有效方法。现有的大多数盲信号分离和提取方法（例如独立分量分析）都具有诸如统计独立性，非高斯性和欠确定性之类的约束，并且它们不适合从嘈杂的观测中提取全局信号。我们开发了一种基于交替迭代和智能加权平均的估计算法。所提出的方法没有诸如独立性或非高斯性的强烈假设。使用音乐信号和真实脑电图的实验结果证明了该方法的优势。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号