本文關(guān)鍵詞：稀疏欠采樣陣列信號處理方法研究　出處：《電子科技大學》2014年博士論文　論文類型：學位論文

  更多相關(guān)文章： 稀疏 過水平采樣 波達方向估計 陣列波束綜合 波束形成

【摘要】：壓縮感知理論是一種利用信號的稀疏特性從欠采樣測量值中恢復信號的技術(shù)，該理論不受傳統(tǒng)Nyquist采樣定理約束，因而具有廣泛的學術(shù)研究和工業(yè)應(yīng)用價值。目前，壓縮感知理論重點研究了特定字典情況下的稀疏信號重建算法模型、重建條件等，而針對壓縮感知理論在陣列信號處理中的具體應(yīng)用仍有許多待深入研究的內(nèi)容。 過水平采樣是另一種降低采樣率的方法，其也被稱為事件驅(qū)動采樣，，即由信號本身決定何時采樣。過水平采樣可以在獲取信息的同時有效地減少采樣樣本數(shù)，實現(xiàn)高效節(jié)能地獲取、探測與估計信息的目的。但目前過水平采樣理論、信號重建算法及其在信號處理中的具體應(yīng)用還不成熟。 極化陣列既可以獲得信號的空間信息，又可以獲得信號的極化信息，完備的電磁信息為陣列性能的提高奠定了物理基礎(chǔ)，使得在稀疏極化域條件下進一步改善陣列信號處理性能成為可能。但現(xiàn)有針對極化陣列的波達方向（Direction-of-Arrival, DOA）估計算法因待估參數(shù)多而存在計算量大的問題。 針對以上情況，本文重點研究了基于壓縮感知理論的應(yīng)用算法、壓縮感知理論和過水平采樣理論的聯(lián)合優(yōu)化算法，以及針對極化陣列的信號處理相關(guān)算法。本論文的主要工作如下： 1.針對非相關(guān)信號和相干信號同時存在的情況下，提出一種基于信號子空間方法和子空間塊稀疏重建理論的DOA估計方法。所提算法可以有效地估計信號的DOA、不受陣列幾何結(jié)構(gòu)的限制，且具有超載能力。進一步研究了色噪聲環(huán)境下的非高斯信號DOA估計問題，提出了基于四階累積量(Fourth-order Cumulants, FOC)的DOA估計算法。另外，在利用陣列方向分集的情況下，提出了一種基于雙約束彈性樹搜索正交匹配追蹤的DOA估計算法。 2.提出一種基于稀疏重建理論和過水平采樣理論的聯(lián)合優(yōu)化DOA估計算法。首先針對模擬信號進行過水平采樣，并研究了該欠采樣方案可以利用簡單的1-比特模數(shù)轉(zhuǎn)換器硬件電路實現(xiàn)。同時基于欠采樣測量數(shù)據(jù)，將DOA估計問題描述為壓縮感知稀疏重建框架下的一個代價函數(shù)。該算法可以降低陣列信號處理系統(tǒng)的采樣速率和硬件成本。 3.提出一種基于稀疏重建理論的波束綜合算法，首先將壓縮感知的稀疏重建理論和凸優(yōu)化引入到線性陣列的波束綜合問題。進一步研究基于迭代重加權(quán)1范數(shù)最小化和凸優(yōu)化的陣列波束圖綜合算法，并將這一方法拓展到由陣列方向性所構(gòu)建的二維陣列的波束綜合問題，最后研究了關(guān)于極化陣列的波束綜合問題及基于稀疏表示的圓陣波束形成問題。 4.提出一種針對稀疏極化域陣列的低復雜度DOA估計算法。首先引入了部分校正分布式電磁矢量傳感器陣列的測量模型，并推導出由關(guān)于四維參數(shù)退化到關(guān)于二維參數(shù)的譜搜索代價函數(shù)。同時將該低復雜度DOA估計方法拓展到關(guān)于未校正極化陣列的DOA估計問題。最后介紹了一種類似ESPRIT的低復雜度DOA估計算法，并給出了理論分析。
[Abstract]:Compressed sensing theory is a kind of sampling measurement technology to restore the signal from the less use of signal sparse characteristics, this theory is not affected by the traditional Nyquist sampling theorem constraint, thus it has a wide range of academic research and industrial application value. At present, the research model of sparse signal reconstruction algorithm specific dictionary under the theory of compressed sensing, reconstruction conditions so, the specific application of compressed sensing theory in array signal processing is still a lot to be studied.
Horizontal sampling is another method to reduce the sampling rate, which is also known as event driven by the sampling signal itself determines when sampling. Over the level of access to information at the same time sampling can effectively reduce the sample number, efficiency gains, detection and estimation of information. But the current level of sampling the theory of signal reconstruction algorithm and its application in signal processing is not mature.
Polarized array can get the spatial information signal, and can obtain the polarization information signal, complete information to improve the electromagnetic performance of the array provided the physical basis, the conditions to further improve the polarization domain sparse array signal processing becomes possible. But the existing performance for polarized array DOA (Direction-of-Arrival, DOA) estimation algorithm for the estimated parameters and the problems of large amount of calculation.
In view of the above situation, this paper focuses on the application algorithm based on compressed sensing theory, the joint optimization algorithm of compressive sensing theory and over level sampling theory, and the related algorithm of signal processing for polarization array.
1. for uncorrelated signals and coherent signals exist at the same time, a method is proposed based on the estimation of signal subspace method and subspace block sparse reconstruction theory DOA. The proposed algorithm can effectively estimate the signal from DOA, array geometry constraints, and has overload ability. Further study of the non Gauss signal DOA colored noise estimation is proposed based on four order cumulant (Fourth-order, Cumulants, FOC) DOA estimation algorithm. In addition, in the case of using array diversity, we propose a double elastic constraint tree search based on orthogonal matching pursuit DOA estimation algorithm.
2. proposed a joint optimization of DOA based on sparse reconstruction theory and theory of horizontal sampling estimation algorithm. Firstly the analog signal level sampling, and studied the undersampling scheme can be implemented by hardware circuit of 1- bit analog-to-digital converter is simple. At the same time sampling based on measured data, the DOA estimation problem is described as a price compressed sensing sparse reconstruction under the framework of function. This algorithm can reduce the sampling rate and the hardware cost of array signal processing system.
3. propose a pattern synthesis algorithm based on sparse reconstruction theory, the compressed sensing sparse reconstruction theory and convex optimization problem into the pattern synthesis of linear array. Further research on the iterative reweighted 1 minimization and convex optimization algorithm based on array beam pattern synthesis, and this method is extended to two-dimensional array pattern synthesis problem constructed by the array direction of the problem, formed at the end of the beam synthesis problems about polarization array and circular array beamforming based on sparse representation.
4. we propose a low complexity DOA for sparse array polarization domain estimation algorithm. Firstly, partial correction of distributed electromagnetic vector sensor array measurement model, and deduced by a four-dimensional parameter degradation to spectral search cost function on the two-dimensional parameters. At the same time the low complexity DOA estimation method is extended to a polarization correction array DOA estimation problem. Finally introduces the low complexity DOA a similar ESPRIT estimation algorithm, and the theoretical analysis is given.

【學位授予單位】：電子科技大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN911.7

【參考文獻】

相關(guān)期刊論文 前2條

1 徐振海,王雪松,肖順平,莊釗文;極化敏感陣列信號檢測:部分極化情形[J];電子學報;2004年06期

2 徐振海,王雪松,肖順平,莊釗文;極化敏感陣列濾波性能分析:完全極化情形[J];電子學報;2004年08期

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