【摘要】：信號(hào)參數(shù)估計(jì)是陣列信號(hào)研究中一個(gè)重要的組成部分,隨著實(shí)際應(yīng)用需求的增加,各領(lǐng)域?qū)τ趨?shù)估計(jì)系統(tǒng)的要求也越來(lái)越高,因而為得到更精確的信源定位,參數(shù)估計(jì)算法的研究備受重視。而傳統(tǒng)的以MUSIC算法及ESPRIT算法為代表的算法,經(jīng)過(guò)改進(jìn)后已具備比較高的估計(jì)分辨率以及精度。但是這些算法對(duì)于信噪比以及快拍等條件要求比較苛刻,并且相干信源情況下得不到準(zhǔn)確的估計(jì)結(jié)果。而近年來(lái)興起的壓縮感知理論,通過(guò)稀疏重構(gòu)的方式實(shí)現(xiàn)信號(hào)參數(shù)的估計(jì),算法的優(yōu)點(diǎn)在于,僅僅需要陣列的單次或少次快拍的數(shù)據(jù),并且具有天然的解相干能力。本文對(duì)這類算法的個(gè)別關(guān)鍵技術(shù)進(jìn)行了討論以及分析,主要的工作如下:1.給出傳統(tǒng)信號(hào)參數(shù)估計(jì)的模型,介紹了壓縮感知理論的相關(guān)理論基礎(chǔ),在此基礎(chǔ)上介紹了基于壓縮感知理論的信號(hào)參數(shù)估計(jì)模型,并且分析了與傳統(tǒng)模型的區(qū)別與聯(lián)系,為后續(xù)的研究奠定了理論的基礎(chǔ)。2.針對(duì)標(biāo)量陣列的DOA估計(jì),介紹了幾種基于壓縮感知理論的常見(jiàn)的DOA估計(jì)算法,并通過(guò)仿真驗(yàn)證壓縮感知算法相對(duì)于傳統(tǒng)估計(jì)算法在例如分辨率和相干信源估計(jì)上的優(yōu)勢(shì),對(duì)比分析了幾種算法的估計(jì)性能,并簡(jiǎn)要分析了算法的優(yōu)劣勢(shì),為實(shí)際工程應(yīng)用中合理的選擇適當(dāng)?shù)乃惴ㄌ峁┝死碚撘罁?jù)。3.針對(duì)極化敏感陣列的多參數(shù)估計(jì),本文介紹了極化敏感陣元的結(jié)構(gòu)以及陣列信號(hào)的接收數(shù)據(jù)模型,并在此基礎(chǔ)上將壓縮感知理論推廣到極化敏感陣列信號(hào)多參數(shù)估計(jì)中。對(duì)信號(hào)的接收數(shù)據(jù)模型進(jìn)行重新建模,分別根據(jù)不同的極化陣元研究?jī)煞N信號(hào)的稀疏表示形式及多參數(shù)估計(jì)算法,實(shí)現(xiàn)了電磁極化信號(hào)的空域到達(dá)角,以及極化信息的估計(jì),并通過(guò)仿真實(shí)驗(yàn)證明相對(duì)于傳統(tǒng)算法本文算法性能有所提高,簡(jiǎn)要分析兩種算法的應(yīng)用范圍及算法估計(jì)性能等。4.針對(duì)存在互耦效應(yīng)下的陣列信號(hào)參數(shù)估計(jì),介紹互耦誤差陣列的信號(hào)接收數(shù)據(jù)模型,并且針對(duì)未知互耦信息的情況,分別利用激勵(lì)矩陣和陣列導(dǎo)向矢量變換兩個(gè)角度研究互耦誤差存在條件下的信號(hào)的稀疏表示形式以及信源定位算法,提高了未知互耦信息條件下信源方位估計(jì)的準(zhǔn)確性,最后通過(guò)仿真對(duì)比兩種處理方式下算法的估計(jì)性能。
[Abstract]:Signal parameter estimation is an important part of array signal research. With the increasing demand of practical application, the requirement of parameter estimation system in various fields is higher and higher, so more accurate source location can be obtained. The research of parameter estimation algorithm has attracted much attention. The traditional algorithms represented by music algorithm and Esprit algorithm have higher resolution and accuracy after improvement. However, these algorithms are demanding for SNR and rapid-shoot conditions, and can not get accurate estimation results in the case of coherent sources. In recent years, the compression sensing theory, which realizes the estimation of signal parameters by sparse reconstruction, has the advantage that it only needs the data of single or few shot of the array, and it has the natural ability of decoherence. In this paper, some key techniques of this algorithm are discussed and analyzed. The main work is as follows: 1. This paper gives the model of traditional signal parameter estimation, introduces the related theoretical basis of compression perception theory, and then introduces the signal parameter estimation model based on compression perception theory, and analyzes the difference and relation between the model and the traditional model. For the subsequent research laid the theoretical foundation. 2. For the DOA estimation of scalar array, several common DOA estimation algorithms based on compressed sensing theory are introduced, and the advantages of compressed sensing algorithms compared with traditional estimation algorithms such as resolution and coherent source estimation are verified by simulation. The estimation performance of several algorithms is compared and analyzed, and the advantages and disadvantages of the algorithms are briefly analyzed, which provides a theoretical basis for the reasonable selection of appropriate algorithms in practical engineering applications. In this paper, the structure of polarization-sensitive array elements and the receiving data model of array signals are introduced, and the theory of compression sensing is extended to the multi-parameter estimation of polarization-sensitive array signals. The received data model is remodeled and the sparse representation of two signals and multi-parameter estimation algorithms are studied according to different polarization array elements respectively. The spatial arrival angle and polarization information estimation of electromagnetic polarization signal are realized. The simulation results show that the performance of this algorithm is better than that of the traditional algorithm. The application scope of the two algorithms and the estimation performance of the two algorithms are analyzed briefly. 4. For the parameter estimation of array signal with mutual coupling effect, the signal receiving data model of mutual coupling error array is introduced, and the case of unknown mutual coupling information is discussed. The sparse representation of signals with mutual coupling error and the source location algorithm are studied by using the excitation matrix and array steering vector transform respectively. The accuracy of the source azimuth estimation under the condition of unknown mutual coupling information is improved. Finally, the estimation performance of the two algorithms is compared by simulation.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN820.15

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本文編號(hào)：2125184