• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国政府科技报告 >Application of Dual-Tree Complex Wavelet Transforms to Burst Detection and RF Fingerprint Classification
【24h】

Application of Dual-Tree Complex Wavelet Transforms to Burst Detection and RF Fingerprint Classification

机译：双树复小波变换在突发检测和射频指纹分类中的应用

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

摘要

This work addresses various Open Systems Interconnection (OSI) Physical (PHY) layer mechanisms to extract and exploit RF waveform features ('fingerprints') that are inherently unique to specific devices and that may be used to provide hardware specific identification (manufacturer, model, and/or serial number). This is addressed by applying a Dual-Tree Complex Wavelet Transform (DT-CWT) to improve burst detection and RF fingerprint classification. A 'Denoised VT' technique is introduced to improve performance at lower SNRs, with denoising implemented using a DT-CWT decomposition prior to Traditional VT processing. A newly developed Wavelet Domain (WD) fingerprinting technique is presented using statistical WD fingerprints with Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) classification. The statistical fingerprint features are extracted from coefficients of a DT-CWT decomposition. Relative to previous Time Domain (TD) results, the enhanced WD statistical features provide improved device classification performance. Additional performance sensitivity results are presented to demonstrate WD fingerprinting robustness for variation in burst location error, MDA/ML training and classification SNRs, and MDA/ML training and classification signal types. For all cases considered, the WD technique proved to be more robust and exhibited less sensitivity when compared with the TD Technique.

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