毕业设计说明书（论文）中文摘要随着社会的发展，许许多多的雷达信号和通信信号的处理问题往往设计到非常高带宽的射频（RF）信号。由香农/乃奎斯特采样定理可知，信号的采样速率必须达到信号的带宽的两倍以上，这对很多问题来说，这是一个非常巨大的挑战，采样率越来越大，硬件实现变得困难，而且先采样再压缩的方式将导致资源被大量的浪费。针对这个问题，压缩采样理论应运而生，它的核心思想是将压缩和采样同步进行，现测量信号的非自适应线性投影，然后根据相应的重构算法，由我们测量到的测量值进而重构信号。压缩采样具有明显的优点，这样处理的数据量是远远小于传统奈奎斯特采样定理所得到的数据量，有效地解决了奈奎斯特采样定理对采样速率的限制，让我们采集高分辨率信号成为可能。在本文中，我们设计了一个模/信转换系统，仿真并实现了随机解调/信转换系统，以多频信号为研究对象，进行实验，通过实验仿真分析了稀疏度和采样率对信号重构的影响。33108
毕业论文关键词： 压缩采样  随机解调  模信转换  多频信号  稀疏信号
毕业设计说明书（论文）外文摘要
Title   Multitone Signal compressive sampling and high resolution estimation
Abstract
With the development of information technology and people's increasing demand for information, the bandwidth of signal become wider. According to the traditional signal processing framework, the required sampling rate increases, and hardware implementation becomes difficult, and the way of sampling and then compressing wastes a lot of resources. To solve this problem, the compressive sampling theory comes into being, the core idea is to combine compressing and sampling. Firstly we measure the non-adaptive linear projection of the signal, then we use reconstruction algorithm to reconstruct the original signal. Using compressive sampling the amount of measurement data is far less than the amount using conventional sampling methods. Compressive sampling breakthrough the bottleneck of Shannon sampling theorem, making it possible to sampling wideband signal. Based on the principle of compressive sampling, and random demodulation, we use the random demodulation-based analog-to-information convert system for sampling the multitone signals, and evaluate its performance through simulation experiments. The simulation results show the relationship between the reconstruction performance and the sparsity of the multitone signals.
Keywords：compressive sensing, random demodulation, analog-to-information conversion, multitone signal, spare signals
目次
1绪论 6
  1.1压缩采样理论概述6
  1.2 国内外研究现状及应用7
      1.2.1 研究现状  7
      1.2.2压缩传感的应用 8
  1.3 压缩采样的基本内容 9
      1.3.1信号的稀疏表示 9
      1.3.2 测量矩阵 10
      1.3.3 信号重构 11
      1.3.4 信号恢复算法介绍 12
  1.4论文的章节安排  15
2 基于随机解调的模/信转换系统 17
  2.1信号的稀疏表示  17
  2.2随机解调的基本原理 17
       2.2.1模拟处理 17
       2.2.2测量矩阵 18
  2.3信号的恢复算法 18
  2.4本章总结 19
3仿真分析  20
  3.1不同稀疏度下的误差分析 21  3.2不同采样率下的误差分析23
  3.3总体分析25
结论 26
1 本文工作总结26
2 研究展望26
致谢 27
参考文献28
1  绪论 多频信号压缩采样与高分辨估计:http://www.751com.cn/tongxin/lunwen_30009.html