特殊陣列下的快速波達(dá)方向估計(jì)
發(fā)布時(shí)間：2018-11-07 17:14
【摘要】：作為陣列信號(hào)處理的重要分支之一,波達(dá)方向(Direction of Arrival,DOA)估計(jì)技術(shù)具有廣泛的應(yīng)用背景,其相關(guān)研究也一直是陣列信號(hào)處理領(lǐng)域的熱點(diǎn)。從問(wèn)世至今,DOA估計(jì)經(jīng)歷了從高分辨到超分辨的發(fā)展歷程,在估計(jì)性能日益完善的同時(shí),如何降低計(jì)算量以便超分辨估計(jì)算法由理論走向工程應(yīng)用逐漸成為另一個(gè)熱點(diǎn)問(wèn)題。本文以均勻線陣(Uniform Linear Array,ULA)和互質(zhì)陣為依托,針對(duì)常規(guī)波達(dá)方向估計(jì)計(jì)算量大的問(wèn)題,提出三種降低計(jì)算量的快速算法,為超分辨估計(jì)算法工程化提供理論參考。本文的主要研究?jī)?nèi)容如下:首先,在充分研究基于L陣二維DOA估計(jì)的CESA算法后,提出了一種新的基于ULA的一維快速DOA估計(jì)算法。新算法在陣型上將ULA劃分成兩個(gè)陣元數(shù)相等的子陣,然后求子陣的前、后向互協(xié)方差矩陣并構(gòu)造聯(lián)合互協(xié)方差矩陣,再對(duì)該矩陣的第一列向量線性操作以獲取信號(hào)子空間,最后構(gòu)造多項(xiàng)式求根求得DOA。仿真實(shí)驗(yàn)證明,該算法在保證估計(jì)精度可接受的同時(shí)有效降低了協(xié)方差矩陣計(jì)算和子空間分解的計(jì)算量。其次,在深入研究針對(duì)L陣二維DOA估計(jì)的CODE算法后,提出一種新的基于ULA的子空間快速估計(jì)算法。新算法先將ULA劃分成兩個(gè)子陣,然后將流型矩陣劃分成兩個(gè)存在旋轉(zhuǎn)不變關(guān)系的子矩陣,根據(jù)子陣互協(xié)方差矩陣與流型矩陣的對(duì)應(yīng)關(guān)系,將互協(xié)方差矩陣也劃分成滿足旋轉(zhuǎn)不變性的兩個(gè)子矩陣,最后利用這兩個(gè)子矩陣求解出旋轉(zhuǎn)不變關(guān)系矩陣,繼而求出子空間、構(gòu)造多項(xiàng)式求解DOA。新算法相對(duì)于CODE算法實(shí)現(xiàn)了估計(jì)精度的提高,同時(shí)有效降低了子空間獲取的計(jì)算量。最后,針對(duì)現(xiàn)階段較為熱點(diǎn)的互質(zhì)陣提出了一種新的DOA快速估計(jì)算法,新算法利用互質(zhì)陣部分均勻線性的特點(diǎn)將互質(zhì)陣看成由兩個(gè)ULA子陣構(gòu)成,然后分別根據(jù)root-MUSIC算法構(gòu)造子陣多項(xiàng)式,之后利用子陣多項(xiàng)式構(gòu)造互質(zhì)陣的多項(xiàng)式,最后研究證明該互質(zhì)陣多項(xiàng)式單位圓上的根就是待求的DOA對(duì)應(yīng)的根。仿真實(shí)驗(yàn)證明,新算法的估計(jì)精度和效率均高于求根MUSIC算法及現(xiàn)有的同類算法。
[Abstract]:As an important branch of array signal processing, DOA (Direction of Arrival,DOA) estimation technology has a wide application background, and its related research has been a hot spot in the field of array signal processing. Up to now, DOA estimation has gone through the development from high resolution to super resolution. While the performance of DOA estimation is becoming more and more perfect, how to reduce the computational complexity so that the super-resolution estimation algorithm can be applied from theory to engineering has gradually become another hot issue. In this paper, based on the uniform linear array (Uniform Linear Array,ULA) and the mutual mass matrix, three fast algorithms to reduce the computational complexity are proposed to solve the problem of large computational complexity in the conventional DOA estimation, which provides a theoretical reference for the engineering of the super-resolution estimation algorithm. The main contents of this paper are as follows: firstly, after fully studying the CESA algorithm based on L-matrix two-dimensional DOA estimation, a new one-dimensional fast DOA estimation algorithm based on ULA is proposed. The new algorithm divides ULA into two submatrices with equal number of elements in the matrix, then obtains the forward and backward cross-covariance matrix of the submatrix and constructs the joint cross-covariance matrix. Then the first column vector of the matrix is linearly operated to obtain the signal subspace. Finally, we construct polynomial to find the root of DOA.. Simulation results show that the proposed algorithm can effectively reduce the computational complexity of covariance matrix calculation and subspace decomposition while ensuring acceptable estimation accuracy. Secondly, after deeply studying the CODE algorithm for 2-D DOA estimation of L-matrix, a new fast subspace estimation algorithm based on ULA is proposed. The new algorithm first divides ULA into two submatrices, then divides the flow pattern matrix into two submatrices with rotation-invariant relations. According to the corresponding relationship between the submatrix cross-covariance matrix and the flow pattern matrix, the new algorithm divides the flow pattern matrix into two submatrices. The cross covariance matrix is also divided into two submatrices which satisfy the rotation invariance. Finally, the rotation invariant relation matrix is solved by using these two submatrices, and then the subspace is obtained, and the polynomial is constructed to solve the DOA.. Compared with the CODE algorithm, the new algorithm improves the estimation accuracy and reduces the computational complexity of subspace acquisition. Finally, a new DOA fast estimation algorithm is proposed for the hot mutual-prime matrix. The new algorithm takes advantage of the partial uniform linearity of the mutual-prime matrix to treat the matrix as two ULA subarrays. Then the submatrix polynomials are constructed according to the root-MUSIC algorithm, and then the polynomials of the coprime matrix are constructed by using the submatrix polynomials. Finally, it is proved that the roots on the unit circle of the coprime polynomials are the DOA corresponding roots to be solved. The simulation results show that the estimation accuracy and efficiency of the new algorithm are higher than that of the root seeking MUSIC algorithm and the existing similar algorithms.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN911.7
本文編號(hào)：2317024
[Abstract]:As an important branch of array signal processing, DOA (Direction of Arrival,DOA) estimation technology has a wide application background, and its related research has been a hot spot in the field of array signal processing. Up to now, DOA estimation has gone through the development from high resolution to super resolution. While the performance of DOA estimation is becoming more and more perfect, how to reduce the computational complexity so that the super-resolution estimation algorithm can be applied from theory to engineering has gradually become another hot issue. In this paper, based on the uniform linear array (Uniform Linear Array,ULA) and the mutual mass matrix, three fast algorithms to reduce the computational complexity are proposed to solve the problem of large computational complexity in the conventional DOA estimation, which provides a theoretical reference for the engineering of the super-resolution estimation algorithm. The main contents of this paper are as follows: firstly, after fully studying the CESA algorithm based on L-matrix two-dimensional DOA estimation, a new one-dimensional fast DOA estimation algorithm based on ULA is proposed. The new algorithm divides ULA into two submatrices with equal number of elements in the matrix, then obtains the forward and backward cross-covariance matrix of the submatrix and constructs the joint cross-covariance matrix. Then the first column vector of the matrix is linearly operated to obtain the signal subspace. Finally, we construct polynomial to find the root of DOA.. Simulation results show that the proposed algorithm can effectively reduce the computational complexity of covariance matrix calculation and subspace decomposition while ensuring acceptable estimation accuracy. Secondly, after deeply studying the CODE algorithm for 2-D DOA estimation of L-matrix, a new fast subspace estimation algorithm based on ULA is proposed. The new algorithm first divides ULA into two submatrices, then divides the flow pattern matrix into two submatrices with rotation-invariant relations. According to the corresponding relationship between the submatrix cross-covariance matrix and the flow pattern matrix, the new algorithm divides the flow pattern matrix into two submatrices. The cross covariance matrix is also divided into two submatrices which satisfy the rotation invariance. Finally, the rotation invariant relation matrix is solved by using these two submatrices, and then the subspace is obtained, and the polynomial is constructed to solve the DOA.. Compared with the CODE algorithm, the new algorithm improves the estimation accuracy and reduces the computational complexity of subspace acquisition. Finally, a new DOA fast estimation algorithm is proposed for the hot mutual-prime matrix. The new algorithm takes advantage of the partial uniform linearity of the mutual-prime matrix to treat the matrix as two ULA subarrays. Then the submatrix polynomials are constructed according to the root-MUSIC algorithm, and then the polynomials of the coprime matrix are constructed by using the submatrix polynomials. Finally, it is proved that the roots on the unit circle of the coprime polynomials are the DOA corresponding roots to be solved. The simulation results show that the estimation accuracy and efficiency of the new algorithm are higher than that of the root seeking MUSIC algorithm and the existing similar algorithms.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN911.7
【引證文獻(xiàn)】
相關(guān)期刊論文 前1條
1 閆鋒剛;榮加加;劉帥;沈毅;金銘;;聯(lián)合互協(xié)方差矩陣的快速波達(dá)方向估計(jì)[J];系統(tǒng)工程與電子技術(shù);2018年04期
，本文編號(hào)：2317024
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