• 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 >Distributed Detection With Empirically Observed Statistics
【24h】

Distributed Detection With Empirically Observed Statistics

机译：具有经验观察到的统计数据的分布式检测

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

Consider a distributed detection problem in which the underlying distributions of the observations are unknown; instead of these distributions, noisy versions of empirically observed statistics are available to the fusion center. These empirically observed statistics, together with source (test) sequences, are transmitted through different channels to the fusion center. The fusion center decides which distribution the source sequence is sampled from based on these data. For the binary case, we derive the optimal type-II error exponent given that the type-I error decays exponentially fast. The type-II error exponent is maximized over the proportions of channels for both source and training sequences. We conclude that as the ratio of the lengths of training to test sequences alpha tends to infinity, using only one channel is optimal. By calculating the derived exponents numerically, we conjecture that the same is true when alpha is finite under certain conditions. We relate our results to the classical distributed detection problem studied by Tsitsiklis, in which the underlying distributions are known. Finally, our results are extended to the case of m-ary distributed detection with a rejection option.
机译：考虑一个分布式检测问题，其中观察结果的底层分布是未知的;代替这些分布，融合中心可获得嘈杂的经验观察统计信息。这些经验观察到的统计数据与源（测试）序列一起传输到融合中心的不同通道。融合中心决定哪个分布源序列根据这些数据采样。对于二进制案例，我们派生了最佳类型-II错误指数，因为Type-i错误衰减逐步衰减。 II类型错误指数最大化在源和训练序列的比例上最大化。我们得出结论，随着培训长度的比率，测试序列alpha倾向于无穷大，只使用一个通道是最佳的。通过数值计算衍生的指数，我们猜测当在某些条件下alpha有限时，相同是真的。我们将结果与Tsitsiklis研究的经典分布式检测问题相关联，其中底层分布是已知的。最后，我们的结果延伸到M-ARY分布式检测的情况，拒绝选项。

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