本文選題：陣列信號處理　切入點(diǎn)：二維DOA估計　出處：《南京郵電大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：空間信號波達(dá)方向(DOA)估計是陣列信號處理的一個重要分支�；谧涌臻g分析的二維DOA估計能夠?qū)崿F(xiàn)對空間信號更加準(zhǔn)確的定位,并且充分利用陣列數(shù)據(jù)矩陣或其二階統(tǒng)計量的內(nèi)在結(jié)構(gòu)特性,具有分辨率高、復(fù)雜度低、實(shí)時性好等優(yōu)點(diǎn),是DOA估計領(lǐng)域的研究重點(diǎn)之一。隨著二維DOA估計在工程實(shí)踐中的實(shí)用性研究不斷深入,人們對算法的估計精度、分辨率、計算復(fù)雜度等要求越來越高。本文在現(xiàn)有子空間類估計算法研究成果的基礎(chǔ)上,針對二維DOA估計提出了三種改進(jìn)算法,主要工作如下:(1)為了提高面陣下二維DOA估計算法的性能,本文提出了一種基于傳播算子(PM)的改進(jìn)算法。從面陣接收信號中按一定規(guī)則提取相互重合的四個子面陣,由子面陣接收數(shù)據(jù)間的互相關(guān)矩陣創(chuàng)建一個新的數(shù)據(jù)矩陣,再利用PM算法得到旋轉(zhuǎn)不變關(guān)系矩陣,最后得到自動匹配的二維角度估計。本文算法的計算復(fù)雜度要小于傳統(tǒng)面陣ESPRIT算法。仿真結(jié)果表明,在信噪比較低或快拍數(shù)較小時,由于復(fù)用了互相關(guān)矩陣中的數(shù)據(jù),改進(jìn)算法的角度估計精度要好于面陣ESPRIT算法。(2)對陣列孔徑進(jìn)行擴(kuò)展是提高DOA估計性能的一個重要手段。本文在雙平行線陣模型下提出了一種高效的二維測向改進(jìn)算法。主要利用陣列流形矩陣的共軛對稱特性來擴(kuò)展陣列的有效孔徑,并結(jié)合PM算法來實(shí)現(xiàn)自動配對的二維參數(shù)估計。仿真結(jié)果表明,與現(xiàn)有的一些測向算法相比,本文算法的角度估計精度顯著提高,且復(fù)雜度較低。(3)在相干信源DOA估計問題上,本文基于L型陣列提出了一種改進(jìn)的相干信源二維DOA估計算法。改進(jìn)算法提取互相關(guān)矩陣的第一列,利用相關(guān)規(guī)則構(gòu)建一個列秩與信源相關(guān)性無關(guān)的新矩陣,然后對其正交化,構(gòu)造求根多項(xiàng)式來降低計算復(fù)雜度。仿真結(jié)果表明,與現(xiàn)有的一些解相干算法相比,本文算法擴(kuò)展了陣列有效孔徑,提高了角度估計精度。
[Abstract]:Spatial signal DOA estimation is an important branch of array signal processing. Two-dimensional DOA estimation based on subspace analysis can locate the spatial signal more accurately. And make full use of the inherent structure characteristic of array data matrix or its second order statistics, it has the advantages of high resolution, low complexity, good real time and so on. It is one of the key points in the field of DOA estimation. With the further research on the practicability of two-dimensional DOA estimation in engineering practice, the estimation accuracy and resolution of the algorithm have been studied. Based on the existing research results of subspace class estimation algorithms, this paper proposes three improved algorithms for two-dimensional DOA estimation, the main work of which is as follows: (1) in order to improve the performance of two-dimensional DOA estimation algorithm under plane array, In this paper, an improved algorithm based on propagation operator PM) is proposed. Four overlapping subarrays are extracted from the received signals of the array according to certain rules, and a new data matrix is created by the cross-correlation matrix between the received data of the subarray. Then the rotation invariant matrix is obtained by using PM algorithm, and the 2-D angle estimation of automatic matching is obtained. The computational complexity of this algorithm is less than that of the traditional ESPRIT algorithm. The simulation results show that, when the SNR is low or the number of beats is small, Because the data in the cross-correlation matrix is multiplexed, The angle estimation accuracy of the improved algorithm is better than that of the plane array ESPRIT algorithm. The expansion of the array aperture is an important means to improve the performance of the DOA estimation. In this paper, an efficient two-dimensional direction finding improvement based on the dual parallel linear array model is proposed. The conjugate symmetry of the array manifold matrix is used to expand the effective aperture of the array. The simulation results show that compared with some existing directional finding algorithms, the angle estimation accuracy of this algorithm is significantly improved, and the complexity is lower. 3) in the problem of coherent source DOA estimation, the simulation results show that the proposed algorithm can be used to estimate the DOA of coherent source. In this paper, an improved two-dimensional DOA estimation algorithm for coherent sources is proposed based on L-type arrays. The improved algorithm extracts the first column of the cross-correlation matrix, constructs a new matrix with column rank independent of source correlation using correlation rules, and then orthogonalizes it. The simulation results show that the proposed algorithm extends the effective aperture of the array and improves the precision of angle estimation.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN911.7

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本文編號：1584635