数字信号处理系统－FSK、MSK、ASK调制方式自动识别模块分析和设计
摘 要
数字通信信号调制识别是一种典型的模式识别问题，它涉及到很多复杂的特殊因素。随着通信技术的飞速发展，通信信号的体制和调制样式变得更加复杂多样，信号环境日趋密集，使得常规的识别方法和理论很难适应实际需要，无法有效地对通信信号进行识别，这也给通信信号的识别研究提出了更高的要求。
而且调制方式自动识别在通信对抗中意义重大，它是信号筛选和解调的基础。信号的重要参数的估计，可以更好地去除干扰，为进一步正确识别和解调创造了条件。本文分析了各种不同的调制方式的识别算法，分别从信号的包络或利用高阶累量将信号进行时频分析,或利用通信信号的循环平稳性提取数字信号的特征参数，并根据该特征参数识别不同调制信号。文中具体推导了各种算法的理论过程，并做了大量的仿真验证了算法的可行性。比较分析各种算法：发现不同的算法各有利弊，可对不同的识别环境，采用合适的算法
关键词：调制类型；自动识别；特征参数；参数估计。
Abstract
The classification of modulation types of communication signals is a problem of typical pattern recognition. It involves many perplexing and special factors. With rapidly developing of communication technology, the system and modulation manner of communication signals became more and more complicated and various, and circumstance of signals became increasing denseness. It results in that the routine methods and theory of recognition can hardly satisfy practical requirement and can't effectively recognize for communication signals. So the strict demand has been presented for study on recognition of communication signals
The modulation recognition of digital communication signals is very important in communication countermeasure. It is the foundation of selecting signals and signal demodulation. The estimation of the important parameters of signals will wipe off the interferences, developing good condition for right recognition and demodulation. The article analyzes several algorithms for modulation recognition, through the envelops, or using the high order cumulant, or using time_frequence domain analyzing, or using the parameters picked up on cyclostationary of communication signals to recognize modulation signals. The algorithms are proved in the article and a lot of simulations validate feasibility of the algorithms. Comparing the algorithms, we will find that for these algorithms each has his strong point. We should use them according to the different recognition environments.
Keywords: type of Modulation; classification; character parameters; parameter estimation
目 录
摘 要······························································································································································································· 1
Abstract····························································································································································································· 2
第1章 引 言······································································································································································· 3
1.1 国内外研究现状·································································································································································· 3
1.2 一般调制识别方法的框架结构·········································································································································· 5
1.3 研究存在的问题·································································································································································· 5
第2章 通信信号的自动模式识别··············································································································································· 7
2.1 特征参数提取······································································································································································ 7
2.1.1 A. K. Nandi和E. E. Azzouz等提出的方法········································································································ 7
2.1.2 Y. Yang和S. S. Soliman等提出的方法·············································································································· 8
2.1.3 A. W. Gardner等提出的谱相关识别方法·············································································································· 9
2.2 基于决策理论的信号调制样式的自动识别···················································································································· 10
2.3 信号源分析········································································································································································ 11
2.3.1 FSK信号源···························································································································································· 11
2.3.2 MSK信号源···························································································································································· 12
2.4 本章小结········································································································································································ 15
第3章 AWGN环境下调制类型的自动识别·································································································································· 17
3.1 AWGN信道模型··································································································································································· 17
3.2 时域的特征参数提取方法················································································································································ 17
3.2.1 时域归一化能量分析··············································································································································· 17
3.2.2 梯层电平分析··························································································································································· 18
3.3 变换域的特征参数提取方法············································································································································ 18
3.3.1 基于FFT的个别频率分量分析······························································································································· 18
3.3.2 频域对称性分析······················································································································································· 18
3.3.3 瞬时频率信号的波形分析··································································································································· 18
3.4 基于决策理论的算法···················································································································································· 19
3.5 算法设计············································································································································································ 20
3.6 算法流程图········································································································································································ 20
3.7 在AWGN信道下的实验分析·········································································································································· 21
第4章 非AWGN环境下调制类型的自动识别····························································································································· 23
4.1 Rican信道与Rummler信道模型································································································································· 23
4.1.1 Rican衰落信道···················································································································································· 23
4.1.2 RummIer衰落信道················································································································································ 23
4.2 非AWGN环境下调制类型特征提取·································································································································· 23
4.3 算法设计············································································································································································ 24
4.4 算法流程图········································································································································································ 25
4.5 在非AWGN信道下的实验分析·········································································································································· 26
第5章 结论与展望······································································································································································· 29
致 谢································································································································································································· 30
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附录1 文献综述·············································································································································································· 32
附录2 英文原文·············································································································································································· 35
附录3 英文译文·············································································································································································· 40
附录4 程序代码·············································································································································································· 46
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