稀疏陣列測(cè)向技術(shù)研究
發(fā)布時(shí)間：2019-04-10 12:24
【摘要】：稀疏陣是指陣元間距大于半波長(zhǎng)的陣列系統(tǒng)。與常規(guī)滿陣相比,稀疏陣用相同數(shù)量的陣元能獲得更大的陣列孔徑,甚至形成更多的虛擬陣元,因而它具有許多優(yōu)越的測(cè)向性能,如較強(qiáng)的分辨能力、較高的估計(jì)精度和較大的信源處理能力,這些優(yōu)勢(shì)使之成為當(dāng)前陣列測(cè)向方面的研究熱點(diǎn)。論文以稀疏陣列為研究對(duì)象,研究稀疏陣列結(jié)構(gòu)設(shè)計(jì)相應(yīng)的波達(dá)角(DOA)估計(jì)算法及其統(tǒng)計(jì)性能,從理論和仿真上定量地證明稀疏陣列在測(cè)向方面的優(yōu)勢(shì)和潛能。論文主要工作如下： 1.研究了多級(jí)互質(zhì)陣列的解模糊容差及其擴(kuò)展孔徑測(cè)向算法。首先推導(dǎo)了三種解模糊算法的解模糊容差表達(dá)式,定量地證明了多級(jí)互質(zhì)陣列具有形成較大解模糊容差的能力,并通過外場(chǎng)實(shí)驗(yàn)驗(yàn)證了理論的正確性。然后根據(jù)這種陣列的幾何結(jié)構(gòu),分別提出一種模轉(zhuǎn)換虛擬旋轉(zhuǎn)不變因子(MC-VESPRIT)法和一種模轉(zhuǎn)換虛擬傳播因子(MC-VPM)法。這兩種算法都利用陣元間距之間的多級(jí)互質(zhì)關(guān)系,以及四階累積量的陣列擴(kuò)展特性,以突破傳統(tǒng)算法對(duì)參考陣元間距的半波長(zhǎng)限制,從而有效地提高了測(cè)向精度。更為重要地是,MC-VPM算法還采用雙L型結(jié)構(gòu)避免了角度估計(jì)失敗,并利用VPM算法獲得了自動(dòng)配對(duì)的二維(2-D) DOA估計(jì)。 2.研究了兩種嵌套陣的設(shè)計(jì)及其2-D DOA估計(jì)算法。首先,設(shè)計(jì)了一種包含多尺度陣元間距的雙十字型嵌套陣,以期獲得自由度的增加和兩次陣列孔徑擴(kuò)展。為了實(shí)現(xiàn)這一目的,提出一種虛擬矩陣束(VMPM)算法用于2-D DOA估計(jì)。VMPM算法利用多尺度陣元間距,構(gòu)造了由更多虛擬傳感器組成的雙平行陣。與傳播因子(PM)改進(jìn)算法相比,它也使用二階統(tǒng)計(jì)量,卻擁有更高的測(cè)角精度和處理O(P2/32)個(gè)信號(hào)源的能力,其中P為傳感器個(gè)數(shù)。另外,還分析了VMPM算法和PM改進(jìn)算法的統(tǒng)計(jì)性能,推導(dǎo)了它們估計(jì)誤差的漸近方差表達(dá)式和克拉美羅下界(CRLB),并將這些表達(dá)式簡(jiǎn)化為單信號(hào)情況,以便定量地證明VMPM算法的性能優(yōu)勢(shì)。接著,將上述嵌套思想拓展到多級(jí),設(shè)計(jì)了一種多級(jí)嵌套L型陣,并提出一種基于二維空間平滑的2q多重信號(hào)分類(2q-MUSIC)算法。該算法利用2q階統(tǒng)計(jì)量和M+N個(gè)物理陣元,系統(tǒng)地形成了有O(Mq×Nq)個(gè)虛擬陣元的均勻矩形陣(URA),進(jìn)一步提高了測(cè)向性能。此外,還推導(dǎo)了多級(jí)嵌套L型陣的最優(yōu)和次優(yōu)配置表達(dá)式,以便最大化虛擬URA的陣元數(shù)。 3.研究了三種電磁矢量稀疏陣的設(shè)計(jì)及其2-D DOA估計(jì)算法。首先,對(duì)互質(zhì)／嵌套標(biāo)量陣在二維空間-極化聯(lián)合域進(jìn)行推廣,設(shè)計(jì)了電磁矢量互質(zhì)／嵌套面陣。這兩種面陣除了在空域蘊(yùn)含二維互質(zhì)／嵌套特性外,還在極化域蘊(yùn)含多樣性。通過充分挖掘這三維特性,形成包含更多自由度的差合成電磁矢量URA,從而獲得自由度的增加和兩次孔徑擴(kuò)展。為了將這些優(yōu)勢(shì)用于2-D DOA估計(jì),以及將傳感器恢復(fù)成6分量結(jié)構(gòu),提出一種基于三維平滑的極化多重信號(hào)分類(3DS-PMUSIC)算法。相比于PMUSIC算法,該算法在測(cè)向精度、分辨率和最大可處理信源數(shù)等方面都有較大地改善,其中以最大可處理信源數(shù)方面最為突出。理論和仿真結(jié)果都表明其最大可處理信源數(shù)可以從O(P)提高到O(3P2)。之后,針對(duì)空間相關(guān)噪聲背景下2-D DOA和極化參數(shù)估計(jì)性能下降的問題,提出一種基于三尺度平行矢量陣的矩陣重構(gòu)算法。該算法通過構(gòu)造特殊的互相關(guān)矩陣,不僅去除了噪聲項(xiàng),而且充分挖掘了陣列的空間-極化特性,因而獲得估計(jì)性能的提高。另外,該算法無需矩陣開方和高階統(tǒng)計(jì)量,具有低運(yùn)算量。 4.針對(duì)分布式電磁矢量稀疏線陣,提出一種基于增強(qiáng)矩陣的測(cè)向算法和另一種基于PM-矩陣重構(gòu)的參數(shù)估計(jì)算法。其中,前者用于估計(jì)相干信號(hào)的2-D DOA,后者用于估計(jì)混合信號(hào)(相干信號(hào)和獨(dú)立信號(hào)共存)的2-D DOA和極化參數(shù)。兩者不但都保留了傳感器矢量特性,而且允許傳感器內(nèi)部分量的間隔和傳感器之間的間隔可以超過半波長(zhǎng),因而測(cè)向精度較高。尤其是后者,它還將獨(dú)立信號(hào)和相干信號(hào)分開處理,使得陣列孔徑得到更加充分地利用,進(jìn)而改善測(cè)向精度和最大可處理信源數(shù),甚至避免了獨(dú)立信號(hào)和相干信號(hào)因入射角相近時(shí)而導(dǎo)致的估計(jì)性能下降問題。另外,針對(duì)上述陣列,還推導(dǎo)了混合信號(hào)2-D DOA和極化參數(shù)聯(lián)合估計(jì)的CRLB。
[Abstract]:Sparse array is an array system with array element spacing greater than half wavelength. Compared with the conventional full array, the sparse array can obtain larger array aperture and even more virtual array elements by the same number of elements, so that it has many superior direction finding performance, such as higher resolution capability, higher estimation precision and larger source processing capability, These advantages make it a hot spot in current array direction finding. In this paper, a sparse array is used as the research object to study the DOA estimation algorithm and its statistical performance of the sparse array structure design, and the advantages and potential of the sparse array in direction finding are quantitatively proved from the theory and the simulation. The main work of the thesis is as follows: 1. The solution fuzzy tolerance of multi-stage inter-quality array and its extension aperture direction-finding calculation are studied. In this paper, we first derive the solution-fuzzy tolerance expression of three de-fuzzy algorithms, and quantitatively prove that the multi-level mutual-quality array has the capability of forming a large solution-fuzzy tolerance, and the correctness of the theory is verified by the field experiment. And then, according to the geometrical structure of the array, a mode conversion virtual rotation invariant factor (MC-VESPRIT) method and a mode conversion virtual propagation factor (MC-VPM) are respectively provided. The method comprises the following steps: using the multi-level mutual-quality relation between the array element spacing and the array extension characteristic of the fourth-order cumulant, so as to break through the half-wavelength limitation of the distance between the reference array elements by the traditional algorithm, thereby effectively improving the direction-finding precision; Degree. More importantly, the MC-VPM algorithm avoids the failure of the angle estimation by adopting the double-L type structure, and obtains the two-dimensional (2-D) DOA estimation of the automatic pairing by using the VPM algorithm. 2. The design of two nested matrices and its 2-D DOA estimation are studied. In this paper, a double-cross-type nested matrix with multi-scale array elements is designed, with a view to obtaining an increase in freedom and two array holes. To achieve this, a virtual matrix bundle (VMPM) algorithm is proposed for 2-D DO A. The VMPM algorithm uses the multi-scale array element spacing to construct a double-pair of more virtual sensors. Parallel matrix. Compared with the propagation factor (PM) improvement algorithm, it also uses the second order statistics, but has higher measurement accuracy and the ability to process the O (P2/32) signal source, where P is the sensor. In addition, the statistical properties of the VMPM algorithm and the PM improved algorithm are also analyzed. The asymptotic variance and the lower bound (CRLB) of the estimation errors are derived, and these expressions are simplified to a single signal, so as to quantitatively prove the performance of the VMPM algorithm. And then, expanding the nesting thought to a plurality of stages, designing a multi-level nested L-shaped array, and proposing a 2-q multi-signal classification (2q-MUSIC) based on a two-dimensional space smoothing. The algorithm uses the 2 q-order statistic and the M + N physical array elements to form a uniform rectangular array (URA) with O (Mq, Nq) virtual array elements. Performance. In addition, the optimal and sub-optimal configuration expressions for multi-level nested L-matrices are derived in order to maximize the virtual URA 3. The design of three kinds of electromagnetic vector sparse array and its 2-D DO are studied. A. First, the mutual quality/ nested scalar array is generalized in the two-dimensional space-polarization joint domain, and the mutual quality of the electromagnetic vector is designed. In addition to that two-dimensional intertexture/ nesting feature in the spatial domain, the planar area array is also in the polarization domain. with diversity. By fully excavating the three-dimensional characteristics, a difference synthesis electromagnetic vector URA with more degrees of freedom is formed, thereby obtaining an increase in the degree of freedom and two Sub-aperture expansion. In order to use these advantages for 2-D DOA estimation, and to restore the sensor to a 6-component structure, a three-dimensional smooth-based polarization multiple signal classification (3DS-PMUS) is proposed Compared with the PMUSIC algorithm, the algorithm has a great improvement in the direction-finding precision, resolution and the maximum number of processing information sources, in which the maximum number of processing sources The surface is the most prominent. Both the theoretical and the simulation results show that the maximum number of processing sources can be increased from O (P) to O (3). After that, a three-scale parallel vector matrix moment is proposed for the degradation of 2-D DOA and polarization parameter estimation in the background of space-related noise. This algorithm not only removes the noise, but also fully excavates the spatial-polarization characteristics of the array, thus obtaining the estimation. In addition, the algorithm does not need matrix and higher order statistics, 4. Aiming at the sparse linear array of the distributed electromagnetic vector, a direction finding algorithm based on the enhancement matrix and another based on the PM-matrix reconstruction are proposed. The former is used to estimate the 2-D DOA of the coherent signal, which is used to estimate the 2-D D of the mixed signal (the coherent signal and the independent signal co-exist). both the oa and the polarization parameters. both retain the sensor vector characteristics and allow the spacing of the sensor's internal components and the spacing between the sensors to exceed the half-wavelength, in particular the latter, the independent signal and the coherent signal are separately processed, so that the array aperture is more fully utilized, large number of processing sources, even avoiding the estimation of the independent signal and the coherent signal due to the close angle of incidence In addition, for that above-mentioned array, the combination signal 2-D DOA and the polarization parameter combination are also derived.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7
本文編號(hào)：2455797
[Abstract]:Sparse array is an array system with array element spacing greater than half wavelength. Compared with the conventional full array, the sparse array can obtain larger array aperture and even more virtual array elements by the same number of elements, so that it has many superior direction finding performance, such as higher resolution capability, higher estimation precision and larger source processing capability, These advantages make it a hot spot in current array direction finding. In this paper, a sparse array is used as the research object to study the DOA estimation algorithm and its statistical performance of the sparse array structure design, and the advantages and potential of the sparse array in direction finding are quantitatively proved from the theory and the simulation. The main work of the thesis is as follows: 1. The solution fuzzy tolerance of multi-stage inter-quality array and its extension aperture direction-finding calculation are studied. In this paper, we first derive the solution-fuzzy tolerance expression of three de-fuzzy algorithms, and quantitatively prove that the multi-level mutual-quality array has the capability of forming a large solution-fuzzy tolerance, and the correctness of the theory is verified by the field experiment. And then, according to the geometrical structure of the array, a mode conversion virtual rotation invariant factor (MC-VESPRIT) method and a mode conversion virtual propagation factor (MC-VPM) are respectively provided. The method comprises the following steps: using the multi-level mutual-quality relation between the array element spacing and the array extension characteristic of the fourth-order cumulant, so as to break through the half-wavelength limitation of the distance between the reference array elements by the traditional algorithm, thereby effectively improving the direction-finding precision; Degree. More importantly, the MC-VPM algorithm avoids the failure of the angle estimation by adopting the double-L type structure, and obtains the two-dimensional (2-D) DOA estimation of the automatic pairing by using the VPM algorithm. 2. The design of two nested matrices and its 2-D DOA estimation are studied. In this paper, a double-cross-type nested matrix with multi-scale array elements is designed, with a view to obtaining an increase in freedom and two array holes. To achieve this, a virtual matrix bundle (VMPM) algorithm is proposed for 2-D DO A. The VMPM algorithm uses the multi-scale array element spacing to construct a double-pair of more virtual sensors. Parallel matrix. Compared with the propagation factor (PM) improvement algorithm, it also uses the second order statistics, but has higher measurement accuracy and the ability to process the O (P2/32) signal source, where P is the sensor. In addition, the statistical properties of the VMPM algorithm and the PM improved algorithm are also analyzed. The asymptotic variance and the lower bound (CRLB) of the estimation errors are derived, and these expressions are simplified to a single signal, so as to quantitatively prove the performance of the VMPM algorithm. And then, expanding the nesting thought to a plurality of stages, designing a multi-level nested L-shaped array, and proposing a 2-q multi-signal classification (2q-MUSIC) based on a two-dimensional space smoothing. The algorithm uses the 2 q-order statistic and the M + N physical array elements to form a uniform rectangular array (URA) with O (Mq, Nq) virtual array elements. Performance. In addition, the optimal and sub-optimal configuration expressions for multi-level nested L-matrices are derived in order to maximize the virtual URA 3. The design of three kinds of electromagnetic vector sparse array and its 2-D DO are studied. A. First, the mutual quality/ nested scalar array is generalized in the two-dimensional space-polarization joint domain, and the mutual quality of the electromagnetic vector is designed. In addition to that two-dimensional intertexture/ nesting feature in the spatial domain, the planar area array is also in the polarization domain. with diversity. By fully excavating the three-dimensional characteristics, a difference synthesis electromagnetic vector URA with more degrees of freedom is formed, thereby obtaining an increase in the degree of freedom and two Sub-aperture expansion. In order to use these advantages for 2-D DOA estimation, and to restore the sensor to a 6-component structure, a three-dimensional smooth-based polarization multiple signal classification (3DS-PMUS) is proposed Compared with the PMUSIC algorithm, the algorithm has a great improvement in the direction-finding precision, resolution and the maximum number of processing information sources, in which the maximum number of processing sources The surface is the most prominent. Both the theoretical and the simulation results show that the maximum number of processing sources can be increased from O (P) to O (3). After that, a three-scale parallel vector matrix moment is proposed for the degradation of 2-D DOA and polarization parameter estimation in the background of space-related noise. This algorithm not only removes the noise, but also fully excavates the spatial-polarization characteristics of the array, thus obtaining the estimation. In addition, the algorithm does not need matrix and higher order statistics, 4. Aiming at the sparse linear array of the distributed electromagnetic vector, a direction finding algorithm based on the enhancement matrix and another based on the PM-matrix reconstruction are proposed. The former is used to estimate the 2-D DOA of the coherent signal, which is used to estimate the 2-D D of the mixed signal (the coherent signal and the independent signal co-exist). both the oa and the polarization parameters. both retain the sensor vector characteristics and allow the spacing of the sensor's internal components and the spacing between the sensors to exceed the half-wavelength, in particular the latter, the independent signal and the coherent signal are separately processed, so that the array aperture is more fully utilized, large number of processing sources, even avoiding the estimation of the independent signal and the coherent signal due to the close angle of incidence In addition, for that above-mentioned array, the combination signal 2-D DOA and the polarization parameter combination are also derived.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7
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