首页> 外文期刊>Canadian journal of electrical and computer engineering >A high-resolution, multi-template deconvolution algorithm for time-domain UWB channel characterization
【24h】

A high-resolution, multi-template deconvolution algorithm for time-domain UWB channel characterization

机译:用于时域UWB信道表征的高分辨率,多模板反卷积算法

获取原文
获取原文并翻译 | 示例
           

摘要

Time-domain ultra-wideband (UWB) channel characterization with deconvolution has become increasingly popular. However, owing to the nature of the frequency-selective non-line-of-sight channel on a general wideband channel-sounding pulse, the a priori assumption of the CLEAN deconvolution algorithm does not necessarily hold. Moreover, the assumption of a fixed a priori can easily be violated since the radio-wave propagation mechanisms experienced by the sounding pulse can be frequency-selective. This paper first summarizes the use of the CLEAN algorithm in UWB channel characterization, including a discussion about the a priori assumption, enhancements made to the basic algorithm, and the effect of the a priori assumption on channel characteristics. A study of the a priori dependence is then presented along with real-world measurements to demonstrate the shortcomings of the single-template CLEAN algorithm. Finally, a high-resolution, multi-template deconvolution algorithm is proposed to enhance the channel estimation performance. This algorithm incorporates realistic frequency-dependent pulse distortions and is shown to supersede its predecessors by being computationally efficient, simple, accurate, and deployable for point-to-point measurements.
机译:具有反卷积的时域超宽带(UWB)信道表征已变得越来越流行。然而,由于一般宽带信道探测脉冲上的频率选择性非视距信道的性质,CLEAN去卷积算法的先验假设不一定成立。此外,由于探测脉冲所经历的无线电波传播机制可以是频率选择性的,因此容易违反先验固定的假设。本文首先总结了CLEAN算法在UWB信道表征中的使用,包括有关先验假设,对基本算法的增强以及先验假设对信道特性的影响的讨论。然后,对先验依赖性进行了研究,并进行了实际测量,以证明单模板CLEAN算法的缺点。最后,提出了一种高分辨率的多模板反卷积算法,以提高信道估计性能。该算法结合了现实中与频率相关的脉冲失真,并通过计算效率高,简单,准确且可部署用于点对点测量而显示出取代了其前身。

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