本文關(guān)鍵詞：支持向量機(jī)在頻率估計(jì)算法中的應(yīng)用研究　出處：《解放軍信息工程大學(xué)》2014年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 支持向量機(jī) 頻率估計(jì) 統(tǒng)計(jì)學(xué)習(xí)理論 結(jié)構(gòu)風(fēng)險(xiǎn)最小化準(zhǔn)則 α穩(wěn)定分布 線性調(diào)頻信號(hào)

【摘要】：動(dòng)態(tài)頻譜聚合、機(jī)會(huì)使用其他業(yè)務(wù)空閑頻譜資源的重要前提是不能干擾授權(quán)用戶的正常通信。這首先需要對(duì)某固定頻段進(jìn)行實(shí)時(shí)頻譜感知,即通過(guò)對(duì)此頻段不間斷的搜索、判別與分析,實(shí)時(shí)檢測(cè)授權(quán)用戶是否正在使用該頻段,如果正在使用,進(jìn)一步提取其信號(hào)的調(diào)制方式、頻率以及帶寬等特征參數(shù),從而全面評(píng)估此頻段頻譜的特性及頻率占用情況,找出適合通信的“頻譜空洞”,在不影響已有通信的前提下伺機(jī)工作。其中授權(quán)用戶信號(hào)的頻率估計(jì)是整個(gè)頻譜感知過(guò)程的重要環(huán)節(jié),它的正確與否直接反映了授權(quán)用戶對(duì)頻譜的使用情況,從而客觀地描述了此頻段頻譜的利用率。然而由于受到信號(hào)參數(shù)時(shí)變性、地理位置、傳輸距離等因素的影響,數(shù)據(jù)的小樣本、低信噪比(Signal-to-Noise Ratio, SNR)特性很大程度上制約了頻譜感知的性能。統(tǒng)計(jì)學(xué)習(xí)理論(Statistical Learning Theory, SLT)是一門(mén)專門(mén)研究小樣本情況下機(jī)器學(xué)習(xí)規(guī)律的理論,它的出現(xiàn)為解決數(shù)據(jù)的小樣本、低SNR問(wèn)題帶來(lái)一定的契機(jī)。支持向量機(jī)(Support Vector Machine, SVM)作為其具體實(shí)現(xiàn)方式,充分體現(xiàn)了結(jié)構(gòu)風(fēng)險(xiǎn)最小化(Structural Risk Minimization, SRM)思想的精髓。它具有較好的泛化能力、高維處理能力和非線性處理能力,并且能夠克服人工神經(jīng)網(wǎng)絡(luò)等機(jī)器學(xué)習(xí)方法存在的模型過(guò)擬合以及算法存在局部極小值等一些難以解決的具體問(wèn)題。本文將SVM應(yīng)用于頻率估計(jì)相關(guān)問(wèn)題的解決,重點(diǎn)針對(duì)基于離散Fourier變換(Discrete Fourier Transform, DFT)和基于相位頻率估計(jì)算法,以及線性調(diào)頻信號(hào)和Gaussian噪聲條件下的頻率估計(jì)問(wèn)題等方面展開(kāi)研究,并最終給出了基于SVM的頻譜感知系統(tǒng)設(shè)計(jì)方案。主要工作和創(chuàng)新點(diǎn)如下：1.針對(duì)基于DFT的頻率估計(jì)算法存在計(jì)算量與估計(jì)性能之間的矛盾,本文將最小二乘支持向量機(jī)回歸(Least Squares Support Vector Regression, LS-SVR)看成一個(gè)線性內(nèi)插器,同時(shí)將內(nèi)插范圍縮小到已知離散幅度譜最大值左右相鄰兩根譜線之間,提出了一種基于LS-SVR的DFT內(nèi)插頻率估計(jì)算法。2.針對(duì)影響基于相位頻率估計(jì)算法的兩個(gè)主要因素：相位噪聲模型的近似性和相位解繞過(guò)程的不正確性,本文從接收信號(hào)實(shí)際相位與時(shí)間序列之間存在的線性關(guān)系出發(fā),充分發(fā)揮SLT對(duì)小樣本數(shù)據(jù)良好的學(xué)習(xí)能力與泛化能力,提出一種基于支持向量機(jī)回歸(Support Vector Regression, SVR)的相位解繞與頻率估計(jì)算法。3.針對(duì)噪聲分布未知條件下的頻率估計(jì)問(wèn)題,本文從SLT出發(fā),利用調(diào)制信息的有限字符集特性構(gòu)造關(guān)于頻率的SRM函數(shù),將參數(shù)估計(jì)問(wèn)題轉(zhuǎn)化為求分類問(wèn)題的極值,從而提出一種基于最小二乘支持向量機(jī)分類(Least Squares Support Vector Classification, LS-SVC)的星座圖頻率估計(jì)算法。4.針對(duì)線性調(diào)頻信號(hào)實(shí)際相位與時(shí)間序列滿足的二次關(guān)系，本文選用二次多項(xiàng)式核函數(shù)完成相位解繞過(guò)程。并利用SVR較強(qiáng)的非線性處理能力,準(zhǔn)確估計(jì)線性調(diào)頻信號(hào)的瞬時(shí)頻率(Instantaneous Frequency, IF)、瞬時(shí)頻率變化率(Instantaneous Frequency Rate, IFR)以及初始相位。5.針對(duì)國(guó)家科技重大專項(xiàng)“新一代無(wú)線寬帶移動(dòng)網(wǎng)”的子課題"IMT-A頻譜聚合技術(shù)研發(fā)”中頻譜感知的需求,本文設(shè)計(jì)了基于SVM的頻譜感知系統(tǒng)設(shè)計(jì)方案。
[Abstract]:Dynamic spectrum aggregation, an important prerequisite for other business opportunities to use the idle spectrum resources is not normal communication interference to licensed users. This first needs to carry on the real-time spectrum sensing of a fixed frequency, namely the frequency of uninterrupted search, identification and analysis, real-time detection of authorized users is the use of the band, if you are using, further extraction the signal modulation, frequency and bandwidth parameters, thus the comprehensive assessment of the characteristics and frequency spectrum occupancy, to find a suitable communication "spectrum hole", without affecting the existing communication under the work. The authorized user to signal frequency estimation is an important part of the spectrum sensing process, it correct or not directly reflect the use of authorized users of the spectrum, so as to objectively describe the use of the frequency spectrum but due rate. By signal time-varying parameters, geographical location, transmission distance and other factors, the small sample data, low signal-to-noise ratio (Signal-to-Noise Ratio SNR) features largely restricted the performance of spectrum sensing. The statistical learning theory (Statistical Learning Theory, SLT) is a specialized research on machine learning method under the condition of small samples in theory, it appears to solve the small sample data, bring certain opportunity low SNR problem. Support vector machine (Support Vector Machine, SVM) as its implementation mode, fully embodies the structural risk minimization (Structural Risk, Minimization, SRM). The essence of it has good generalization ability, high the dimension of ability and nonlinear processing ability, and can overcome the artificial neural network in machine learning methods such as the model in the presence of local minima and some difficult to solve the over fitting and algorithm Specific issues. The application of SVM in frequency estimation to solve related problems, focusing on based on the discrete Fourier transform (Discrete Fourier Transform, DFT) and phase estimation algorithm based on frequency and linear frequency modulation signal and Gaussian noise under the condition of frequency estimation problem is studied, and finally gives the design scheme of spectrum sensing system SVM based on the main work and innovation are as follows: 1. aiming at the contradiction between algorithm and estimation performance estimation based on the frequency of DFT, the least squares support vector machine regression (Least Squares Support Vector Regression, LS-SVR) as a linear interpolator, the interpolation range between the maximum known discrete amplitude spectrum about two adjacent spectral lines, we propose a LS-SVR based DFT interpolation algorithm for frequency estimation based on phase frequency estimation for.2. Two main factors: the phase noise model of the algorithm and the approximation of the phase unwrapping process is not correct, the linear relationship between the received signal and the actual phase of time series, give full play to SLT on the learning ability and generalization ability of small sample data, proposed a regression based on support vector machine (Support Vector Regression, SVR) of the phase unwrapping and frequency estimation algorithm for.3. noise distribution under the condition of unknown frequency estimation problem, this paper starting from SLT, SRM function characters using modulation information set about frequency characteristics, the parameter estimation problem into extremum classification problems, and put forward a least squares support vector machine (Least Squares Support Vector classification based on Classification, LS-SVC) constellation.4. frequency estimation algorithm for LFM signal phase and the actual time The two time sequence relationship, the complete polynomial kernel function two phase unwrapping process. And the nonlinear processing ability of SVR strong, accurate instantaneous frequency estimation of LFM signals (Instantaneous, Frequency, IF), the instantaneous frequency rate (Instantaneous Frequency, Rate, IFR) and.5. in the initial phase of major national science and technology projects "a new generation of broadband wireless mobile network project of IMT-A spectrum aggregation technology research" spectrum sensing requirements, this paper designs the design of spectrum sensing system of SVM based on the case.

【學(xué)位授予單位】：解放軍信息工程大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.23;TP181

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本文編號(hào)：1422092