基于Matlab的FMCW（调频连续波）的中频正交处理和脉冲压缩处理 

范文编号:TX048       范文字数:13324,页数:42

摘 要
软件无线电理论中包含（1）正交变换、（2）脉冲压缩两个基本技术原理。对接收到的高频信号进行全数字正交相干检波是十分重要的，它保留了信号复包络的完整信息，包括振幅和相位,因此可以很好的提取有用信号，对其进行后续处理；在匹配滤波器理论指导下提出的人们提出了脉冲压缩概念，脉冲压缩体制较好的解决了作用距离和分辨力之间的矛盾实现了提高雷达距离分辨力的要求。
为了说明第一个原理，本文用线性调频连续（FMCW）信号为例，采用最基本的方法——低通滤波法的实现方法来对其进行简单介绍和仿真分析。
为了说明第二个原理，本文则通过对匹配滤波器原理的介绍以及设计和仿真分析来说明。

关键字  线性调频信号   正交变换   脉冲压缩    匹配滤波

Abstract
Software Radio theory contains (1) orthorhombic transformation and(2) pulse – compression basic theories. It's very important to process digital orthorhombic demodulation. The orthorhombic signal holds all of the information of the received signal, It contains swing and phase of the received signal which can be used to pick-up the available signal. So it is very helpful for the next digital signal procession. In virtue of the theory of matching filter ,some scholar began to say pulse – compression conception The system of pulse – compression better solves the contradiction of effect distance and resolving power. It meets the need of improving the radar effect distance and resolving power.
To illuminate the orthorhombic transformation principle, take FMCW ( frequency modulation continuous wave) for example, We introduce the low passed filter method and emulation the orthorhombic transformation process of FMCW signal in the Matlab.
To  illuminate  the  pulse – compression  principle, We particular expound the theory of matching filter, because ,in fact, the pulse – compression process is just a process of the design of a matching filter.

Key words  FMCW  orthorhombic transformation     pulse – compression
            matching filter

目  录
Abstract   2
引言   4
0.1问题的提出  4
0.2范文的内容   5
第一章 信号采样理论   6
1.1基本采样理论   6
1.2带通信号采样理论   7
第二章 信号正交变换理论   10
2.1 信号正交变换原理   10
2.2数字混频正交变换   14
2.2.1 窄带信号的正交分解与模拟域实现   14
2.2.2数字混频正交变换   16
2.2.3 单通道软件无线电接收机数学模型   17
第三章 信号脉冲压缩原理  20
3.1脉冲压缩概念   20
3.2脉冲压缩的原理   22
3.3匹配滤波器理论   24
第四章FMCW信号处理   25
4.1 FMCW信号   25
4.2 FMCW信号中频正交   28
4.3 FMCW信号脉冲压缩   33
总结   36
参考文献  38
附录1   38
附录2   39

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