• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>OCEANS 2016 MTS/IEEE Monterey >Temporal analysis of stationary Markov a-sub-Gaussian noise
【24h】

Temporal analysis of stationary Markov a-sub-Gaussian noise

机译：静止马尔可夫A-Sub-Gaussian噪声的时间分析

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

摘要

In warm shallow waters, snapping shrimp noise is the dominant source of ambient noise at medium-to-high frequencies. The noise process is impulsive and exhibits memory. The latter property causes outliers to cluster together, thus resulting in bursty impulsive noise. When tuned to snapping shrimp data, the stationary α-sub-Gaussian noise with memory order m (αSGN(m)) model tracks the former's temporal amplitude statistics very well. In comparison to contemporary impulsive noise models, αSGN(m) offers a far more realistic model for snapping shrimp noise. To develop a more deeper understanding of αSGN(m), we perform an impulsive event analysis on its realizations. This is accomplished by initially mapping impulsive data to a point process in time. The resulting time-series is then analyzed via first-order interval and counting analysis. We compare our results with those of snapping shrimp noise and highlight the pros and cons of adopting the αSGN(m) model. Moreover, our results also offer us a reliable way to tune the order m of αSGN(m).
机译：在温暖的浅水中，捕捉虾噪声是中高频率的环境噪声的主导来源。噪声过程是脉冲的并且展示记忆。后者属性导致聚集在一起的异常值，从而导致突发的脉冲噪声。当调谐到捕捉虾数据时，静止的α-亚高斯噪声具有存储器顺序M（αSgn（m））模型非常好地跟踪前者的时间幅度统计信息。与当代脉冲噪声模型相比，αsgn（m）提供了一个用于捕捉虾噪声的更现实的模型。为了开发对αSGN（M）的更深入了解，我们对其实现进行了冲动的事件分析。这是通过最初将脉冲数据映射到点处理的时间来实现的。然后通过一阶间隔和计数分析分析所得到的时间序列。我们将结果与捕捉虾噪声的结果进行比较，并突出采用αsgn（m）模型的优缺点。此外，我们的结果还为我们提供了一种可靠的方法来调整αsgn（m）的命令m。

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