压缩感知理论在激光雷达中的应用
时间:2018-08-27 14:26 来源:毕业论文 作者:毕业论文 点击:次
| 摘要激光雷达工作频率高，与普通微波雷达相比，成像分辨率显著提高。近年来，激光雷达技术飞速发展，成像效果越来越好，所需采集处理的数据也越来越庞大，这对硬件提出了很高的要求，花费巨大。压缩感知理论利用了信号的稀疏特性，在采集信号时无需遵守奈奎斯特定理，只需要采集少量必要的信号，就能精确地重建原始信号，该理论对激光雷达的发展影响巨大。本论文首先讨论了激光雷达的基本原理，然后进行对信号的稀疏表示、压缩测量、精确重建的研究，重点讨论了两种基本重建方法，并用正弦信号进行模拟仿真，通过比较信噪比等参量来验证压缩感知的可行性。最后将压缩感知和激光雷达相结合，模拟一个近似的雷达信号，在稀疏基础上得到的采样数减小的测量量，得出压缩感知在激光雷达上有广泛的应用。27504 关键字：激光雷达、压缩感知、稀疏分解、信号重建 毕业论文设计说明书外文摘要 Title Compressed sensing theory in the application of laser radar Abstract Laser radar operating frequency is high,imaging resolution is sighicantly improved compared with conventional microwave.In recent years, the laser radar technology and imaging effect is better and better,ts for hardware,the required data collection proces has became increasingly large,which made high and cost demands on the hardware.Compressed sensing theory using the sparse characteristic of the signal, the collected signal without obey the Nyquist specific reason, only need to collect a few necessary signal, can accurately reconstruct the original signal, the theory has had a huge impact on the development of laser radar. This paper first discusses the basic principle of laser radar, then,the measurement of sparse representation of signal, compression, accurate reconstruction research, focuses on the two basic reconstruction method, and simulated with sine signal,verify the feasibility of the compression perception by comparing the signal to noise ratio and other parameters.Finally combining compression perception and laser radar with an approximation of the radar signal, based on the sparse sampling number in reduce the amount of measurement, it is concluded that compression perception on the laser radar has a wide range of applications. Keywords: Laser radar, compressed sensing, signal sparse decomposition,signal reconstruction 目 次 1 绪论 1 1.1 论文的研究背景和意义 1 1.2 相关研究现状 1 1.2.1 压缩感知的研究现状 1 1.2.2 激光雷达的发展现状 2 1.3 论文的结构安排 3 2 激光雷达工作原理 4 2.1 激光雷达原理及组成 4 2.2 激光雷达信号 5 3 压缩感知基本理论 7 3.1 引言 7 3.2 压缩感知理论基本原理 7 3.3 信号的稀疏变换 8 3.4 测量矩阵 9 3.5 重建算法 10 3.5.1 最小L1范数的典型凸优化算法 11 3.5.2 正交匹配算法 12 3.6 压缩感知理论在激光雷达成像中的应用 14 4 模拟仿真 15 4.1 有噪声正弦信号的压缩感知模拟仿真 15 (责任编辑：qin) |