• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >Efficient class-specific models for autoregressive processes with slowly varying amplitude in white noise
【24h】

Efficient class-specific models for autoregressive processes with slowly varying amplitude in white noise

机译：有效的类特定模型，用于自回归过程，白噪声幅度缓慢变化

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

This paper describes an efficient model to describe an autoregressive (AR) signal with slowly-varying amplitude in additive white Gaussian noise (WGN). Even a simple low-order AR model becomes complicated by varying amplitude and additive white noise. However, by approximating the signal amplitude as piecewise-constant, an efficient filtering approach can be applied in order to compute the maximum likelihood (ML) estimate for the entire data record. The model is efficient both in terms of having a compact set of parameters and in the computational sense. Simulation results are provided. The algorithm has applications in signal modeling for underwater acoustic signals, particularly active wideband signals such as explosive sources.
机译：本文介绍了一种有效的模型，用于描述加性高斯白噪声（WGN）中幅度缓慢变化的自回归（AR）信号。即使是简单的低阶AR模型也会因振幅和加性白噪声的变化而变得复杂。但是，通过将信号幅度近似为分段常数，可以应用有效的滤波方法，以便为整个数据记录计算最大似然（ML）估计。在具有紧凑的参数集和计算意义上，该模型都是有效的。提供了仿真结果。该算法在水下声信号，特别是有源宽带信号，例如爆炸源的信号建模中具有应用。

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