強噪聲背景下正弦信號頻率估計算法研究
發(fā)布時間：2019-06-21 00:31
【摘要】：從強噪聲中準確提取單一正弦信號的頻率是通信系統(tǒng)、信號處理等領域一個非常重要的問題。目前,強噪聲背景下正弦信號頻率估計已經成功應用于雷達探測、語音信號處理、聲納地震、通信系統(tǒng)中的信號恢復、橋梁振動檢測、生物醫(yī)學檢測以及電子通信技術等眾多領域,引起越來越多的學者的關注和重視。因此,對正弦信號頻率估計研究具有極其重要的理論意義和實際應用價值。頻率是正弦信號最重要的參數(shù)和最本質的特征,頻率估計研究是信號處理領域的一個經典課題。本論文對幾類正弦信號頻率估計算法進行研究,包括經典的DFT算法、最大似然估計法、PHD算法、MC算法、TSA算法、Rife算法以及傅里葉系數(shù)插值迭代算法,分別在算法原理及計算量的基礎上比較了算法的性能,通過仿真實驗,得出頻率估計均方根誤差與信噪比之間的關系,并與頻率估計均方根誤差克拉美-羅下限做了比較。并且在分析總結各種算法的基礎上,提出了相應的改進算法。本文的主要研究工作及結論如下:1、提出了基于正弦信號LP性質的分段自相關頻率估計新算法。該算法解決了兩步自相關頻率估計算法(TSA算法)估計性能和計算量不能兼顧的缺點,在計算量較小的情況下逼近TSA2算法的性能,彌補了 TSA1改進后帶來的計算量增加的問題。并對分段自相關算法進行了仿真實驗,用結果分析其算法效果較好。2、提出了一種基于FFT的正弦信號頻率估計新算法。通過分析Rife算法和傅里葉系數(shù)迭代算法的性能可知,Rife算法計算簡單、精度不高,而傅里葉系數(shù)迭代算法需要兩次迭代之后才會到達精度要求,每次迭代的計算量大。結合Rife算法和傅里葉系數(shù)迭代算法這兩種算法的優(yōu)缺點,本文新提出了一種改進后的高精度頻率估計算法。并對此方法進行了仿真實驗,結果分析其算法效果較好。
[Abstract]:The frequency of extracting a single sinusoidal signal from strong noise is a very important problem in the fields of communication system, signal processing and so on. At present, sinusoidal signal frequency estimation under the background of strong noise has been successfully applied to radar detection, speech signal processing, Sonar earthquake, signal recovery in communication system, bridge vibration detection, biomedical detection and electronic communication technology, which has attracted more and more scholars' attention and attention. Therefore, the study of sinusoidal signal frequency estimation is of great theoretical significance and practical application value. Frequency is the most important parameter and essential feature of sinusoidal signal. Frequency estimation is a classical topic in the field of signal processing. In this paper, several kinds of sinusoidal signal frequency estimation algorithms are studied, including classical DFT algorithm, maximum likelihood estimation method, PHD algorithm, MC algorithm, TSA algorithm, Rife algorithm and Fourier coefficient interpolation iterative algorithm. The performance of the algorithm is compared on the basis of algorithm principle and computation. Through simulation experiments, the relationship between root mean square error and signal to noise ratio of frequency estimation is obtained. It is compared with the root mean square error of frequency estimation. Based on the analysis and summary of various algorithms, the corresponding improved algorithms are proposed. The main research work and conclusions of this paper are as follows: 1. A new piecewise autocorrelation frequency estimation algorithm based on sinusoidal signal LP property is proposed. The algorithm solves the disadvantage that the performance and computation of two-step autocorrelation frequency estimation algorithm (TSA algorithm) can not be taken into account, and approaches the performance of TSA2 algorithm when the amount of computation is small, which makes up for the problem of increasing the amount of computation caused by the improvement of TSA1. The piecewise autocorrelation algorithm is simulated, and the results show that the algorithm is effective. 2, a new sinusoidal signal frequency estimation algorithm based on FFT is proposed. By analyzing the performance of Rife algorithm and Fourier coefficient iterative algorithm, it can be seen that the calculation of Rife algorithm is simple and the accuracy is not high, while the Fourier coefficient iterative algorithm needs two iterations to meet the accuracy requirements, and the computation of each iteration is large. Based on the advantages and disadvantages of Rife algorithm and Fourier coefficient iterative algorithm, an improved high precision frequency estimation algorithm is proposed in this paper. The simulation experiment of this method is carried out, and the results show that the algorithm is effective.
【學位授予單位】：哈爾濱工程大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN911.23
本文編號：2503647
[Abstract]:The frequency of extracting a single sinusoidal signal from strong noise is a very important problem in the fields of communication system, signal processing and so on. At present, sinusoidal signal frequency estimation under the background of strong noise has been successfully applied to radar detection, speech signal processing, Sonar earthquake, signal recovery in communication system, bridge vibration detection, biomedical detection and electronic communication technology, which has attracted more and more scholars' attention and attention. Therefore, the study of sinusoidal signal frequency estimation is of great theoretical significance and practical application value. Frequency is the most important parameter and essential feature of sinusoidal signal. Frequency estimation is a classical topic in the field of signal processing. In this paper, several kinds of sinusoidal signal frequency estimation algorithms are studied, including classical DFT algorithm, maximum likelihood estimation method, PHD algorithm, MC algorithm, TSA algorithm, Rife algorithm and Fourier coefficient interpolation iterative algorithm. The performance of the algorithm is compared on the basis of algorithm principle and computation. Through simulation experiments, the relationship between root mean square error and signal to noise ratio of frequency estimation is obtained. It is compared with the root mean square error of frequency estimation. Based on the analysis and summary of various algorithms, the corresponding improved algorithms are proposed. The main research work and conclusions of this paper are as follows: 1. A new piecewise autocorrelation frequency estimation algorithm based on sinusoidal signal LP property is proposed. The algorithm solves the disadvantage that the performance and computation of two-step autocorrelation frequency estimation algorithm (TSA algorithm) can not be taken into account, and approaches the performance of TSA2 algorithm when the amount of computation is small, which makes up for the problem of increasing the amount of computation caused by the improvement of TSA1. The piecewise autocorrelation algorithm is simulated, and the results show that the algorithm is effective. 2, a new sinusoidal signal frequency estimation algorithm based on FFT is proposed. By analyzing the performance of Rife algorithm and Fourier coefficient iterative algorithm, it can be seen that the calculation of Rife algorithm is simple and the accuracy is not high, while the Fourier coefficient iterative algorithm needs two iterations to meet the accuracy requirements, and the computation of each iteration is large. Based on the advantages and disadvantages of Rife algorithm and Fourier coefficient iterative algorithm, an improved high precision frequency estimation algorithm is proposed in this paper. The simulation experiment of this method is carried out, and the results show that the algorithm is effective.
【學位授予單位】：哈爾濱工程大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN911.23
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