• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Information Theory >Robust minimax detection of a weak signal in noise with a bounded variance and density value at the center of symmetry
【24h】

Robust minimax detection of a weak signal in noise with a bounded variance and density value at the center of symmetry

机译：在对称中心具有有限方差和密度值的噪声中的弱信号的稳健minimax检测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In practical communication environments, it is frequently observed that the underlying noise distribution is not Gaussian and may vary in a wide range from short-tailed to heavy-tailed forms. To describe partially known noise distribution densities, a distribution class characterized by the upper-bounds upon a noise variance and a density dispersion in the central part is used. The results on the minimax variance estimation in the Huber sense are applied to the problem of asymptotically minimax detection of a weak signal. The least favorable density minimizing Fisher information over this class is called the Weber-Hermite density and it has the Gaussian and Laplace densities as limiting cases. The subsequent minimax detector has the following form: i) with relatively small variances, it is the minimum L/sub 2/-norm distance rule; ii) with relatively large variances, it is the L/sub 1/-norm distance rule; iii) it is a compromise between these extremes with relatively moderate variances. It is shown that the proposed minimax detector is robust and close to Huber's for heavy-tailed distributions and more efficient than Huber's for short-tailed ones both in asymptotics and on finite samples.
机译：在实际的通信环境中，经常观察到基本噪声分布不是高斯分布，并且可能在从短尾到重尾的很大范围内变化。为了描述部分已知的噪声分布密度，使用以噪声方差和中心部分的密度色散为上限的分布类别为特征。在Huber意义上的最小极大方差估计的结果被应用于渐近最小弱信号的最小极大检测问题。使此类中的Fisher信息最小化的最不利密度称为Weber-Hermite密度，它具有高斯和拉普拉斯密度作为极限情况。随后的minimax检测器具有以下形式：i）方差相对较小，这是最小L / sub 2 /范数距离规则； ii）方差相对较大，这是L / sub 1 /标准距离规则； iii）这是这些极端情况之间的折中方案，具有相对中等的方差。结果表明，所提出的minimax检测器在渐近分布和有限样本上均具有鲁棒性，并且对于重尾分布具有接近于Huber的效率，并且对于短尾分布而言，其效率比Huber对于短尾分布的效率更高。

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