摘要对含有噪声污染的正弦信号进行参数估计是一个十分重要的课题，它在雷达、声纳、通信、语音信号处理、生物医学工程、检测等领域中具有很大的应用价值，而正弦信号最重要的参数和最本质的特征便是频率。频率估计的研究是信号识别处理领域的一个重要内容。自然界最常见的噪声是高斯白噪声，所以如何从混有高斯白噪声的实信号样本中得到正弦信号的频率值一直以来是研究的重点。 频率估计算法可分为基于原信号本身的时域特性和基于变换域的特性两种。基于变换域的频率估计方法通常是考虑信号的频域特性，其在估计性能方面有一定的优势，但实现较为复杂，而时域方法则相对比较简单，特别适用于要求计算量小，快速实时处理实正弦信号的情况。本文基于信号自相关的频率估计这一主题展开研究，主要研究内容如下： 论文针对混有高斯白噪声的正弦信号，研究了扩展自相关的频率估计算法。论文首先根据文献引入 Rim 算法与 TSA 算法，然后研究了改进自相关函数算法并通过仿真分析各方法的性能，并具体分析了改进自相关函数无法达到理想效果的原因。然后在Rim算法的基础上引入了YAN算法， 通过比较YAN算法综合性能在这些方法中性能最优，后又对 YAN算法中的参数 p和q值进行分析以达到 YAN算法的最佳性能。30910
毕业论文关键词: 正弦信号;扩展自相关函数; 频率估计; 均方误差; Rim 算法; TSA 算法; YAN算法
Abstract Parameter estimation of a tone in noise is important in many fields such as radar, sonar, communications, speech signal processing, biomedical engineering, control and measurement. Frequency is the most important parameter and the most essential feature, so its estimation is a classic issue in the field of signal processing. White Gaussian noise is a common noise in the nature, so the frequency estimation of the real sinusoid embedded in white Gaussian noise has been received extensive attention. The method of the frequency estimation can be pided into two categories: based on time-domain and transform domain, such as frequency-domain. For the methods based on frequency-domain, they have advantages in the estimation performance, but they are computationally demanding. While, the methods based on time-domain are relatively simple, and they are especially suitable for applications where the real-time estimation is required. This thesis has made deep research on the correlation-based estimator and the major contributions are as the following: A carrier frequency estimation algorithm based on the expanded autocorrelation in additive white Gaussian noise was proposed，Firstly the introduction Rim algorithm and TSA algorithm and then put forward self-correlation function algorithm through the simulation analysis, the methods of performance improvement, and specific analysis of the improved autocorrelation function is unable to achieve the ideal effect. Then the Rim algorithm based on the introduced Yan algorithm, through the comparison of Yan algorithms in these methods in optimal performance, and Yan algorithm parameters of p and q values were analyzed in order to achieve the Yan algorithm for optimum performance.
Keywords: sinusoid; expanded autocorrelation; frequency estimation; mean square error; Rim algorithm；TSA algorithm；Yan algorithm
目录
1绪论..1
1.1研究背景与意义1
1.2频率估计的国内外发展现状.2
1.3本文研究的重点2
1.4论文概述.3
2基于自相关的频率估计算法.4
2.1引言4
2.2自相关函数的定义.4
2.3RIM算法5
2.4改进自相关函数算法..6 扩展自相关的正弦信号频率估计算法研究:http://www.youerw.com/tongxin/lunwen_26892.html