本文關(guān)鍵詞：低復(fù)雜度陣列信號波達(dá)方向估計算法研究　出處：《西安電子科技大學(xué)》2014年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 陣列信號處理 波達(dá)方向估計 子空間投影 二維波達(dá)方向估計 降維算法 稀疏重構(gòu)

【摘要】：陣列信號波達(dá)方向估計是陣列信號處理領(lǐng)域中具有重要應(yīng)用價值的研究課題。近年來,低運算量、低復(fù)雜度的陣列信號波達(dá)方向估計算法受到了廣泛關(guān)注。陣列信號波達(dá)方向估計將多個傳感器設(shè)置在空間的不同位置組成傳感器陣列,利用各個信號在空間位置上的差異,通過對多通道接收機輸出數(shù)據(jù)的處理,獲得空間各個信號源來波所對應(yīng)的方向。在波達(dá)方向估計的發(fā)展歷程中,基于傳統(tǒng)理論和經(jīng)典方法提出的有關(guān)算法既解決了一些實際問題,同時為該領(lǐng)域的發(fā)展提供了新的思路。由于波達(dá)方向估計的重要性使得該領(lǐng)域的研究經(jīng)久不衰,并且對處理算法的性能提出了更高的要求。本文主要針對低運算量、低復(fù)雜度的陣列信號波達(dá)方向估計進(jìn)行深入研究,主要研究成果有:1.針對傳統(tǒng)基于子空間的波達(dá)方向估計算法中子空間估計運算量大的問題,提出了無需協(xié)方差矩陣特征值分解的快速波達(dá)方向估計算法。首先將子空間投影(SP)算法引入波達(dá)方向估計,將其與MUSIC算法相結(jié)合形成了子空間投影MUSIC算法;將子空間投影算法中大矩陣乘法運算用小矩陣乘積代替,提出了較低運算復(fù)雜度和存儲量的簡化子空間投影MUSIC算法;通過協(xié)方差矩陣分解簡化子空間投影處理,得到低復(fù)雜度和存儲量的高效子空間投影MUSIC算法。2.針對傳統(tǒng)二維MUSIC算法中二維譜峰搜索運算量大的問題,提出只需一維譜峰搜索且無需角度配對的低復(fù)雜度二維波達(dá)方向估計算法。首先將降維MUSIC算法引入到二維波達(dá)方向估計領(lǐng)域,與求根法相結(jié)合,得到運算量更低的求根RD-MUSIC算法;將模值約束加入到RD-MUSIC算法的約束條件中,使方向向量的約束性更強,在保持低運算量的同時提高了角度估計性能;用直接求導(dǎo)法對RD-MUSIC算法進(jìn)行求解,有效增強了方向向量中各元素應(yīng)滿足條件的約束性,同樣在保持低運算量的同時進(jìn)一步提高了角度估計性能。3.將陣列信號利用過完備基進(jìn)行稀疏表示,可將波達(dá)方向估計問題轉(zhuǎn)化成聯(lián)合求解陣列信號在過完備基下的稀疏系數(shù)過程。提出兩種基于單測量矢量的協(xié)方差矩陣稀疏重構(gòu)波達(dá)方向估計方法,均無需已知信號數(shù)量,具有較低的運算復(fù)雜度和較高的角度估計性能。為了使算法具有相干信號估計能力,第一種算法采用了對協(xié)方差矩陣進(jìn)行空間平滑處理的方式;第二種算法采用了構(gòu)造協(xié)方差矩陣重構(gòu)向量的方式。4.為了使稀疏重構(gòu)波達(dá)方向估計方法只需少量樣本就能達(dá)到較好的角度估計效果,采用了貝葉斯稀疏重構(gòu)方法。介紹了多測量矢量稀疏貝葉斯學(xué)習(xí)(MSBL)算法和快速序列稀疏貝葉斯學(xué)習(xí)(FSBL)算法。分別將多種波達(dá)方向估計稀疏表示模型代入不同的貝葉斯稀疏重構(gòu)算法進(jìn)行仿真實驗,并對多種不同算法的特點和性能進(jìn)行了分析和總結(jié)。
[Abstract]:DOA estimation of array signals is an important research topic in the field of array signal processing. Low-complexity DOA estimation algorithms for array signals have attracted wide attention. The DOA estimation of array signals sets multiple sensors in different locations in space to form sensor arrays. By processing the output data of multi-channel receiver, the direction of each signal source is obtained. In the development of DOA estimation. The algorithm based on traditional theory and classical method solves some practical problems. At the same time, it provides a new way of thinking for the development of this field. Due to the importance of DOA estimation, the research in this field has not declined. And the performance of the processing algorithm is higher. This paper mainly focuses on the low computation, low complexity of array signal DOA estimation. The main research results are as follows: 1. Aiming at the problem that the traditional subspace-based DOA estimation algorithm has a large amount of computation. A fast DOA estimation algorithm without eigenvalue decomposition of covariance matrix is proposed. Firstly, the subspace projection algorithm is introduced into DOA estimation. The subspace projection MUSIC algorithm is formed by combining it with MUSIC algorithm. In this paper, a simplified subspace projection MUSIC algorithm with lower computational complexity and storage capacity is proposed, in which the large matrix multiplication is replaced by the small matrix product in the subspace projection algorithm. The subspace projection is simplified by covariance matrix decomposition. We obtain a high-efficient subspace projection MUSIC algorithm with low complexity and storage capacity. 2. Aiming at the problem of the large computation of two-dimensional spectral peak search in the traditional two-dimensional MUSIC algorithm. A low complexity 2D DOA estimation algorithm is proposed, which requires only one dimensional spectral peak search and no angle pairing. Firstly, the reduced dimension MUSIC algorithm is introduced into the field of 2D DOA estimation, which is combined with the root-finding method. The root finding RD-MUSIC algorithm with lower computation is obtained. The modular value constraint is added to the constraint condition of RD-MUSIC algorithm, which makes the direction vector more restrictive, and improves the performance of angle estimation while maintaining low computation load. The direct derivation method is used to solve the RD-MUSIC algorithm, which effectively enhances the constraint of each element in the direction vector. At the same time, the performance of angle estimation is improved. 3. The array signal is represented sparsely by over-complete basis. The DOA estimation problem can be transformed into solving the sparse coefficient process of array signals under overcomplete basis. Two methods of DOA estimation based on covariance matrix sparse reconstruction based on single measurement vector are proposed. In order to make the algorithm have the ability of coherent signal estimation, it has lower computational complexity and higher angle estimation performance without the need of known number of signals. In the first algorithm, the covariance matrix is processed by spatial smoothing. The second algorithm uses the method of constructing covariance matrix reconstruction vector. 4. In order to make sparse reconstruction DOA estimation method only a small number of samples can achieve a better angle estimation effect. The Bayesian sparse reconstruction method is used, and the multi-measure vector sparse Bayesian learning algorithm and the fast sequence sparse Bayesian learning algorithm (FSBL) are introduced. Algorithm. A variety of DOA sparse representation models are replaced by different Bayesian sparse reconstruction algorithms for simulation experiments. The characteristics and performance of different algorithms are analyzed and summarized.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN911.7

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