• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Autoregressive Modeling of Raman Spectra for Detection and Classification of Surface Chemicals
【24h】

Autoregressive Modeling of Raman Spectra for Detection and Classification of Surface Chemicals

机译：拉曼光谱的自回归建模，用于表面化学物质的检测和分类

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 considers the problem of detecting and classifying surface chemicals by analyzing the received Raman spectrum of scattered laser pulses received from a moving vehicle. An autoregressive (AR) model is proposed to model the spectrum and a two-stage (detection followed by classification) scheme is used to control the false alarm rate. The detector decides whether the received spectrum is from pure background only or background plus some chemicals. The classification is made among a library of possible chemicals. The problem of mixtures of chemicals is also addressed. Simulation results using field background data have shown excellent performance of the proposed approach when the signal-to-noise ratio (SNR) is at least $-10$ dB.
机译：本文通过分析从行驶中的车辆接收到的散射激光脉冲的拉曼光谱来考虑检测和分类表面化学物质的问题。提出了一种自回归（AR）模型来对频谱建模，并使用两阶段（检测后再分类）方案来控制误报率。检测器确定接收的光谱是仅来自纯背景还是来自背景加某些化学物质。在可能的化学品库中进行分类。还解决了化学物质混合的问题。使用现场背景数据进行的仿真结果表明，当信噪比（SNR）至少为$ -10 $ dB时，该方法具有出色的性能。

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