本文選題：同頻數(shù)字混合信號(hào)盲分離 + 粒子濾波　； 參考：《解放軍信息工程大學(xué)》2014年碩士論文

【摘要】：隨著通信需求的迅猛增長(zhǎng)以及大量復(fù)雜通信技術(shù)的使用,電磁空間變得復(fù)雜和擁擠,第三方非合作接收中出現(xiàn)了同頻數(shù)字混合信號(hào),給信號(hào)分析和信息獲取工作帶來(lái)了一定困難。粒子濾波盲分離算法是解決該類信號(hào)盲分離的有效途徑之一。本文主要圍繞基于粒子濾波的同頻數(shù)字混合信號(hào)單通道盲分離問(wèn)題展開(kāi)研究,旨在改進(jìn)已有算法中所存在的缺點(diǎn)和不足。主要內(nèi)容如下:1.首先從貝葉斯信號(hào)處理角度給出了傳統(tǒng)濾波問(wèn)題的數(shù)學(xué)描述,對(duì)已有的濾波算法進(jìn)行了簡(jiǎn)要介紹,引入了解決序貫蒙特卡洛信號(hào)處理問(wèn)題的粒子濾波方法,對(duì)其基本原理及優(yōu)缺點(diǎn)進(jìn)行了詳細(xì)分析。其次,建立了單通道接收條件下兩路同頻數(shù)字混合信號(hào)的基帶模型,從貝葉斯估計(jì)的角度,給出了同頻數(shù)字混合信號(hào)盲分離的數(shù)學(xué)描述,為后續(xù)討論奠定了理論基礎(chǔ)。2.針對(duì)調(diào)制參數(shù)非時(shí)變情況下,粒子濾波盲分離算法存在參數(shù)估計(jì)精度低、收斂速度慢等問(wèn)題,提出了一種改進(jìn)的粒子濾波盲分離算法。從優(yōu)化抽樣分布的角度出發(fā),將參數(shù)粒子的抽樣分布建模為Beta分布,有效的提高了抽樣效率,改善了算法的參數(shù)估計(jì)性能和分離性能。為了檢驗(yàn)算法的參數(shù)估計(jì)性能,推導(dǎo)了符號(hào)已知條件下的非時(shí)變參數(shù)聯(lián)合估計(jì)克拉美羅界。3.針對(duì)調(diào)制參數(shù)時(shí)變情況下混合信號(hào)的盲分離問(wèn)題,提出了一種時(shí)變信道下基于粒子濾波的盲分離算法。通過(guò)將時(shí)變調(diào)制參數(shù)建模為一階AR模型,選取先驗(yàn)分布作為參數(shù)粒子的抽樣分布,將傳統(tǒng)粒子濾波盲分離算法擴(kuò)展至調(diào)制參數(shù)時(shí)變情況下的盲分離。為了檢驗(yàn)算法的參數(shù)聯(lián)合估計(jì)性能,推導(dǎo)了符號(hào)已知條件下的時(shí)變參數(shù)聯(lián)合估計(jì)后驗(yàn)克拉美羅界,并給出了性能界的數(shù)值計(jì)算方法。4.針對(duì)粒子濾波盲分離算法計(jì)算復(fù)雜度高的問(wèn)題,提出了一種基于部分采樣的低復(fù)雜度粒子濾波盲分離算法。首先詳細(xì)分析了算法的計(jì)算復(fù)雜度,指出算法計(jì)算量大的原因:符號(hào)粒子采樣過(guò)程的計(jì)算復(fù)雜度與平滑長(zhǎng)度成指數(shù)倍關(guān)系。其次,注意到符號(hào)粒子采樣公式的數(shù)值計(jì)算過(guò)程與經(jīng)典Viterbi譯碼算法類似,借鑒M-算法的思想,在搜索分支路徑過(guò)程中保留部分分支路徑,將算法的計(jì)算復(fù)雜度變?yōu)榕c平滑長(zhǎng)度成多項(xiàng)式關(guān)系,例如當(dāng)平滑長(zhǎng)度等于4時(shí),部分采樣法近似是傳統(tǒng)采樣法的二十分之一。最后給出了算法正確性的理論證明。5.針對(duì)部分采樣法在高信噪比條件下性能損失較大的問(wèn)題,提出了一種基于混合采樣法的粒子濾波盲分離算法。首先分析了高信噪比條件下部分采樣法性能損失較大的原因。其次,結(jié)合傳統(tǒng)采樣法和部分采樣法的優(yōu)勢(shì),提出將平滑區(qū)間劃分為兩部分,各自區(qū)間內(nèi)依據(jù)觀測(cè)值與待抽樣符號(hào)粒子相關(guān)性的大小分別選擇傳統(tǒng)采樣法和部分采樣法進(jìn)行遍歷,有效的實(shí)現(xiàn)了計(jì)算復(fù)雜度與性能之間的折中考慮,并給出了算法正確性的理論證明。
[Abstract]:With the rapid growth of communication demand and the use of a large number of complex communication technologies, the electromagnetic space becomes complex and crowded. It brings some difficulties to signal analysis and information acquisition. Particle filter blind separation algorithm is one of the effective ways to solve this kind of signal blind separation. This paper focuses on the single-channel blind separation of the same frequency digital mixed signals based on particle filter, aiming to improve the shortcomings and shortcomings of the existing algorithms. The main content is as follows: 1. Firstly, the mathematical description of the traditional filtering problem is given from the point of view of Bayesian signal processing, the existing filtering algorithms are briefly introduced, and the particle filter method is introduced to solve the sequential Monte Carlo signal processing problem. The basic principle, advantages and disadvantages are analyzed in detail. Secondly, the baseband model of two-channel digital mixed signals with same frequency under the condition of single channel reception is established. From the point of view of Bayesian estimation, the mathematical description of blind separation of the same frequency digital mixed signals is given, which lays a theoretical foundation for further discussion. A modified particle filter blind separation algorithm is proposed to solve the problems of low parameter estimation accuracy and slow convergence rate in the case of time-invariant modulation parameters. From the point of view of optimizing sampling distribution, the sampling distribution of parameter particles is modeled as Beta distribution, which effectively improves the sampling efficiency and improves the performance of parameter estimation and separation of the algorithm. In order to test the parameter estimation performance of the algorithm, the joint estimator of time-invariant parameters under the condition of known symbols is derived. To solve the problem of blind separation of mixed signals with time-varying modulation parameters, a particle filter based blind separation algorithm in time-varying channels is proposed. By modeling the time-varying modulation parameters as a first-order AR model and selecting the prior distribution as the sampling distribution of the parameter particles, the traditional particle filter blind separation algorithm is extended to the blind separation with time-varying modulation parameters. In order to test the joint parameter estimation performance of the algorithm, the posterior Crameiro bound for joint estimation of time-varying parameters is derived under the condition of known symbols, and the numerical calculation method of the performance bound is given. Aiming at the high computational complexity of particle filter blind separation algorithm, a low complexity particle filter blind separation algorithm based on partial sampling is proposed. Firstly, the computational complexity of the algorithm is analyzed in detail, and the reason for the large computational complexity of the algorithm is pointed out: the computational complexity of the symbolic particle sampling process is exponentially related to the smooth length. Secondly, it is noted that the numerical calculation process of symbolic particle sampling formula is similar to that of classical Viterbi decoding algorithm. By using the idea of M- algorithm, some branch paths are preserved in the process of searching branch paths. The computational complexity of the algorithm is changed to a polynomial relation with the smooth length. For example, when the smoothing length is equal to 4, the partial sampling method is approximately 1/20 of the traditional sampling method. Finally, the theoretical proof of the correctness of the algorithm is given. A particle filter blind separation algorithm based on mixed sampling method is proposed to solve the problem that the performance of partial sampling method loses greatly under the condition of high signal-to-noise ratio (SNR). Firstly, the reason for the performance loss of the partial sampling method under the condition of high SNR is analyzed. Secondly, combining the advantages of traditional sampling method and partial sampling method, the smooth interval is divided into two parts. The traditional sampling method and partial sampling method are selected to traverse according to the magnitude of correlation between observed values and the symbol particles to be sampled in each interval. The trade-off between computational complexity and performance is realized effectively. The correctness of the algorithm is proved theoretically.
【學(xué)位授予單位】：解放軍信息工程大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 欒海妍;江樺;劉小寶;;利用粒子濾波與支持向量機(jī)的數(shù)字混合信號(hào)單通道盲分離[J];應(yīng)用科學(xué)學(xué)報(bào);2011年02期

2 涂世龍;鄭輝;;同頻不同速率數(shù)字調(diào)制混合信號(hào)的單通道盲分離[J];電路與系統(tǒng)學(xué)報(bào);2010年03期

3 涂世龍;陳越新;鄭輝;;利用糾錯(cuò)編碼的同頻調(diào)制混合信號(hào)單通道盲分離[J];電子與信息學(xué)報(bào);2009年09期

4 崔榮濤;李輝;萬(wàn)堅(jiān);戴旭初;;一種基于過(guò)采樣的單通道MPSK信號(hào)盲分離算法[J];電子與信息學(xué)報(bào);2009年03期

5 魏安全;沈連豐;;CFE下FRESH濾波器性能分析及CFE校正[J];電子與信息學(xué)報(bào);2008年04期

6 萬(wàn)堅(jiān);朱中梁;;一種基于盲信號(hào)提取的同頻干擾抵消算法[J];信息工程大學(xué)學(xué)報(bào);2007年04期

7 蔡權(quán)偉,魏平,肖先賜;基于模型擬合的重疊信號(hào)盲分離方法[J];電子學(xué)報(bào);2005年10期

8 張曉冬,王橋,吳樂(lè)南;利用脊的特征進(jìn)行信號(hào)盲分離[J];電子學(xué)報(bào);2004年07期

，

本文編號(hào)：2021377