本文選題：時(shí)頻域多分量信號(hào) + 時(shí)頻分析�。� 參考：《電子科技大學(xué)》2016年博士論文

【摘要】：在過(guò)去的幾十年里,隨著電磁設(shè)備研發(fā)的多樣性及應(yīng)用的廣泛性,采集到的信號(hào)形式與種類(lèi)也越來(lái)越復(fù)雜,分布越來(lái)越密集,在同一信道中存在多個(gè)信號(hào)的情況變得越來(lái)越普遍,這種情況為后續(xù)的信號(hào)處理研究增加了一些新的問(wèn)題。由于廣泛的應(yīng)用背景和巨大的現(xiàn)實(shí)意義,盡管時(shí)頻域多分量信號(hào)的分析識(shí)別問(wèn)題是一個(gè)極端欠定的病態(tài)問(wèn)題,但仍然得到了很多研究人員的青睞和投入。盡管在研究過(guò)程中,研究人員針對(duì)生電、機(jī)械振動(dòng)、語(yǔ)音等自然信號(hào)和雷達(dá)、通信等人工信號(hào)的分析識(shí)別問(wèn)題,分別定義了確定性信號(hào)模型、非平穩(wěn)信號(hào)模型、非線(xiàn)性時(shí)間序列模型和狀態(tài)空間模型等等各種模型,提出了各種時(shí)頻變換方法、矩陣分解方法和參數(shù)估計(jì)重構(gòu)方法。但歸根結(jié)底,所有這些研究都是針對(duì)特定的應(yīng)用場(chǎng)景的,而在更為復(fù)雜的情況下或者更為廣泛的應(yīng)用背景下常常存在失效的問(wèn)題。針對(duì)這些問(wèn)題,本文在前人現(xiàn)有研究成果的基礎(chǔ)上,對(duì)時(shí)頻域多分量信號(hào)的分析識(shí)別問(wèn)題進(jìn)行了更加深入地研究和探討,提出了一些新的思路和算法,進(jìn)而擴(kuò)展了這個(gè)方向上的研究?jī)?nèi)容,具體包括:1.針對(duì)在多個(gè)線(xiàn)性調(diào)頻信號(hào)的全盲分析識(shí)別中對(duì)較弱分量信號(hào)的檢測(cè)失效問(wèn)題,本文將譜峭度的盲檢測(cè)技術(shù)和分?jǐn)?shù)階傅里葉變換算法相結(jié)合,在現(xiàn)有研究的基礎(chǔ)上提出了基于分?jǐn)?shù)階傅里葉譜峭度的變換新算法,并將其應(yīng)用于較低信噪比下對(duì)多個(gè)非等功率分量信號(hào)的檢測(cè)估計(jì)之中。該算法首先討論了分?jǐn)?shù)階傅里葉變換的圓特性,然后將譜峭度的盲檢測(cè)技術(shù)引入分?jǐn)?shù)階傅里葉域,定義了分?jǐn)?shù)階傅里葉譜峭度的概念并推導(dǎo)了它的一些特性,進(jìn)而將這些特性用于多個(gè)線(xiàn)性調(diào)頻信號(hào)的盲的分析識(shí)別中。最后,理論分析和仿真結(jié)果都驗(yàn)證了所提算法在較低信噪比下對(duì)較弱分量信號(hào)的檢測(cè)性能優(yōu)于其它算法;2.針對(duì)在多個(gè)非線(xiàn)性調(diào)頻信號(hào)的全盲分析識(shí)別中對(duì)多個(gè)相近分量信號(hào)的檢測(cè)失效問(wèn)題,將分?jǐn)?shù)階傅里葉譜峭度和短時(shí)分?jǐn)?shù)階傅里葉變換算法相結(jié)合,在現(xiàn)有研究的基礎(chǔ)上提出了基于分?jǐn)?shù)階傅里葉譜峭度的自適應(yīng)短時(shí)分?jǐn)?shù)階傅里葉變換新算法,并將其用于多個(gè)相近非線(xiàn)性調(diào)頻信號(hào)的檢測(cè)估計(jì)之中。該算法首先推導(dǎo)了高斯旋轉(zhuǎn)窗的譜密度和非圓特性,并將分?jǐn)?shù)階傅里葉譜峭度的特性用于修正后的最優(yōu)旋轉(zhuǎn)高斯窗的參數(shù)選擇,從而定義了基于分?jǐn)?shù)階傅里葉譜峭度的自適應(yīng)短時(shí)分?jǐn)?shù)階傅里葉變換新算法;在此基礎(chǔ)上,將基于分?jǐn)?shù)階傅里葉譜峭度的時(shí)頻分割算法用于時(shí)頻變換后對(duì)多個(gè)非線(xiàn)性調(diào)頻信號(hào)的盲檢測(cè)提取之中。最后,理論分析和仿真結(jié)果驗(yàn)證了所提算法的時(shí)頻聚焦性?xún)?yōu)于其它算法,因此可以識(shí)別非常接近的多個(gè)非線(xiàn)性調(diào)頻信號(hào);3.針對(duì)全盲條件下多個(gè)非平穩(wěn)非線(xiàn)性時(shí)間序列的分析識(shí)別問(wèn)題,將高階統(tǒng)計(jì)技術(shù)和非平穩(wěn)分割技術(shù)、相空間重構(gòu)技術(shù)以及單通道獨(dú)立分量分析算法相結(jié)合,在現(xiàn)有研究的基礎(chǔ)上提出了基于高階單通道獨(dú)立分量分析的變換新算法,并將其應(yīng)用于多個(gè)非平穩(wěn)非線(xiàn)性時(shí)間序列的全盲分離識(shí)別之中。該算法主要分為三個(gè)步驟:首先通過(guò)應(yīng)用基于高階啟發(fā)式的非平穩(wěn)檢測(cè)和分割算法成功得到高階平穩(wěn)的非線(xiàn)性時(shí)間子序列;然后通過(guò)選取合適的重構(gòu)參數(shù)將分割后的非線(xiàn)性時(shí)間子序列有效重構(gòu)為多維軌跡矩陣,并應(yīng)用基于高階奇異值分解的坐標(biāo)變換方法將該矩陣轉(zhuǎn)換成偽多通道的瞬時(shí)線(xiàn)性混合模型;最后應(yīng)用盲源分離方法將其中感興趣的信號(hào)分量分離并提取出來(lái)。理論分析和仿真顯示都驗(yàn)證了所提算法不僅可以有效分離多個(gè)非平穩(wěn)非線(xiàn)性時(shí)間序列,而且對(duì)噪聲和重構(gòu)參數(shù)的魯棒性也優(yōu)于傳統(tǒng)的單通道獨(dú)立分量分析算法;4.針對(duì)數(shù)字通信中多個(gè)相位調(diào)制信號(hào)的共信道盲分析識(shí)別問(wèn)題,在現(xiàn)有研究的基礎(chǔ)上提出了基于高階奇異值分解的盲源分離新算法,并將其應(yīng)用于共信道碼速率不同的多個(gè)數(shù)字相位調(diào)制信號(hào)的分離估計(jì)之中。該算法首先通過(guò)過(guò)采樣和矩陣重排將多個(gè)數(shù)字相位調(diào)制信號(hào)的盲分析識(shí)別問(wèn)題轉(zhuǎn)化成相位變化的多個(gè)周期信號(hào)的盲分離問(wèn)題,然后采用高階奇異值分解算法估計(jì)各個(gè)相位調(diào)制信號(hào)的碼元波形;最后應(yīng)用盲源分離方法直接估計(jì)出各個(gè)相位調(diào)制信號(hào)的符號(hào)序列。仿真顯示所提算法在一定程度上可以解決共信道多個(gè)數(shù)字相位調(diào)制信號(hào)的盲分離問(wèn)題,且對(duì)噪聲干擾和非等功率影響具有一定的魯棒性。
[Abstract]:In the past few decades, with the diversity of the development of electromagnetic devices and the wide range of applications, the forms and types of signals collected are becoming more and more complex and more and more dense. The presence of multiple signals in the same channel becomes more and more common. This situation has added some new problems to the follow-up signal processing research. Although the analysis and recognition of multi component signals in time and frequency domain is an extremely ill defined and ill conditioned problem, it still gets the favor and input of many researchers. Although in the process of research, researchers are aiming at natural signals such as electricity, machinery vibration, voice and other natural signals and radar, communication and so on. In the analysis and recognition of the signal, the deterministic signal model, the non-stationary signal model, the nonlinear time series model and the state space model are defined respectively. Various time-frequency transformation methods, matrix decomposition methods and parameter estimation reconstruction methods are proposed. All these studies are aimed at specific applications. In the context of more complex situations or more extensive application background, there are often failures. In this paper, based on the existing research results of previous studies, this paper makes a further research and Discussion on the problem of analysis and recognition of multi component signals in time-frequency domain, and puts forward some new ideas and algorithms. The research contents of this direction are expanded, including: 1. for the detection and failure of weak component signals in all blind analysis and recognition of multiple linear frequency modulation signals, this paper combines the blind detection technique of spectral kurtosis with the fractional Fu Liye transform algorithm, and proposes a fractional Fu Liye based on the existing research. A new algorithm for spectral kurtosis is applied to the detection and estimation of multiple non equal power component signals at low signal to noise ratio. The algorithm first discusses the circular characteristics of fractional Fourier transform, and then introduces the blind detection technique of spectral kurtosis to the fractional Fourier domain, and defines the concept of fractional Fourier spectral kurtosis. Some of its characteristics are applied to the blind analysis and recognition of multiple linear frequency modulation signals. Finally, both theoretical analysis and simulation results demonstrate that the proposed algorithm is superior to other algorithms for weak component signals under low signal to noise ratio; 2. With the combination of fractional Fourier spectrum kurtosis and short-time fractional Fourier transform algorithm, a new adaptive short time fractional Fourier transform algorithm based on fractional Fourier spectral kurtosis is proposed on the basis of the existing research, and it is applied to multiple similar Nonlinear FM signals. The algorithm first derives the spectral density and non circular characteristics of the Gauss rotating window, and uses the characteristic of the fractional Fourier spectral kurtosis to select the parameters of the modified optimal rotating Gauss window, and then defines a new adaptive short time fractional Fourier transform algorithm based on the fractional Fourier spectral kurtosis. The time frequency segmentation algorithm based on fractional Fourier spectral kurtosis is used to extract the blind detection of multiple nonlinear FM signals after the time frequency transformation. Finally, the theoretical analysis and simulation results show that the time frequency focusing of the proposed algorithm is better than other algorithms, so it can identify very close multiple nonlinear FM signals; 3. With the combination of high order statistical technology and non-stationary segmentation, phase space reconstruction and single channel independent component analysis, a new algorithm based on high order single channel independent component analysis is proposed on the basis of the existing research, and the new algorithm is applied to the problem of the analysis and recognition of multiple nonstationary nonlinear time series. The algorithm is divided into three steps: first, a high order stationary nonlinear time sequence is successfully obtained by using a non-stationary detection and segmentation algorithm based on high order heuristic, and then the nonlinear time sequence of the segmented nonlinear time sequence is obtained by selecting the appropriate reconfiguration parameters. The column is effectively reconstructed into a multidimensional trajectory matrix, and the matrix transformation method based on high order singular value decomposition is used to convert the matrix into a pseudo multi channel instantaneous linear mixed model. Finally, the blind source separation method is used to separate and extract the signal components of interest. Both theoretical analysis and simulation display verify that the proposed algorithm is not only effective. It can effectively separate multiple non-stationary nonlinear time series, and it is more robust to noise and reconstruction parameters than traditional single channel independent component analysis algorithm. 4. for the blind analysis and recognition problem of multiple phase modulation signals in digital communication, the blind algorithm based on high order singular value decomposition is proposed on the basis of the existing research. A new source separation algorithm is applied to the separation estimation of multiple digital phase modulation signals with different cochannel code rates. First, through over sampling and matrix rearrangement, the blind analysis recognition problem of multiple digital phase modulation signals is transformed into a blind separation problem of multiple phase signals with phase change, and then the higher order is adopted. The singular value decomposition algorithm estimates the symbol waveforms of each phase modulation signal. At last, the blind source separation method is used to directly estimate the symbol sequence of each phase modulation signal. The simulation shows that the proposed algorithm can solve the blind separation problem of multiple digital phase modulation signals of common channel to a certain extent, and the noise interference and non equal power can be used. The influence has a certain robustness.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TN911.6

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本文編號(hào)：1947328