WO2021068496A1 - 基于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法 - Google Patents
基于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法 Download PDFInfo
- Publication number
- WO2021068496A1 WO2021068496A1 PCT/CN2020/088569 CN2020088569W WO2021068496A1 WO 2021068496 A1 WO2021068496 A1 WO 2021068496A1 CN 2020088569 W CN2020088569 W CN 2020088569W WO 2021068496 A1 WO2021068496 A1 WO 2021068496A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- array
- tensor
- virtual domain
- dimensional
- axis
- Prior art date
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S3/00—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received
- G01S3/02—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received using radio waves
- G01S3/14—Systems for determining direction or deviation from predetermined direction
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S3/00—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received
- G01S3/02—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received using radio waves
- G01S3/74—Multi-channel systems specially adapted for direction-finding, i.e. having a single antenna system capable of giving simultaneous indications of the directions of different signals
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S3/00—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received
- G01S3/02—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received using radio waves
- G01S3/04—Details
- G01S3/043—Receivers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S3/00—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received
- G01S3/80—Direction-finders for determining the direction from which infrasonic, sonic, ultrasonic, or electromagnetic waves, or particle emission, not having a directional significance, are being received using ultrasonic, sonic or infrasonic waves
- G01S3/8006—Multi-channel systems specially adapted for direction-finding, i.e. having a single aerial system capable of giving simultaneous indications of the directions of different signals
Definitions
- the present invention belongs to the field of array signal processing technology, in particular to a statistical signal processing technology based on second-order statistics of a sparse area array virtual domain, in particular to a coprime area array two-dimensional direction of arrival based on tensor signal processing in a structured virtual domain
- the estimation method can be used for multi-target positioning.
- the coprime array can break through the bottleneck of the limited freedom of the traditional uniform array.
- a common method is to derive the received signal of the coprime array to the virtual domain to realize the augmentation of the array, and use its corresponding second-order virtual domain equivalent received signal for statistical processing.
- the coprime array and its corresponding two-dimensional virtual domain signal processing have begun to receive widespread attention.
- the usual method is to average the relevant statistics of the received signal with multi-dimensional spatial structure information, and derive the second-order virtual domain equivalent received signal through vectorization , And extend the one-dimensional direction of arrival estimation method to two-dimensional/high-dimensional signal scenes, and realize the direction of arrival estimation through further statistical processing.
- the above method not only destroys the multi-dimensional spatial information structure of the original received signal of the coprime array, but also the virtual domain model derived by vectorization has problems such as large linear scale and loss of structural information in the virtual domain.
- Tensor is a multi-dimensional data type that can be used to store complex multi-dimensional signal information; for the feature analysis of multi-dimensional signals, high-order singular value decomposition and tensor decomposition methods provide rich mathematics for tensor-oriented signal processing. tool.
- tensor models have been widely used in many fields such as array signal processing, image signal processing, and statistics. Therefore, the use of tensors to construct a coprime area array received signal and its virtual domain equivalent signal can effectively retain the multi-dimensional structural information of the signal, and provide an important theoretical tool for improving the performance of the direction of arrival estimation.
- the high-order singular value decomposition and tensor decomposition methods are extended to the virtual domain, which is expected to achieve a breakthrough in the comprehensive performance of the direction of arrival estimation in terms of resolution, estimation accuracy, and degree of freedom.
- the existing methods generally do not involve the discussion of the virtual domain tensor space of the coprime area array, and do not use the two-dimensional virtual domain characteristics of the coprime area array. Therefore, designing a two-dimensional DOA estimation method with increased degree of freedom based on a coprime array tensor signal model to achieve accurate DOA estimation under underdetermined conditions is an important issue that needs to be solved urgently.
- the purpose of the present invention is to solve the problem of the loss of degrees of freedom existing in the existing method, and propose a method for estimating the two-dimensional direction of arrival of a coprime area array based on tensor signal processing in a structured virtual domain.
- the domain is associated with the tensor space, fully mining the structural information of the two-dimensional virtual domain, and using the virtual domain tensor structured structure and virtual domain tensor decomposition to realize the two-dimensional direction of arrival estimation under underdetermined conditions provides feasible Ideas and effective solutions.
- a method for estimating the direction of arrival of a coprime array based on structured virtual domain tensor signal processing includes the following steps:
- the receiving end uses 4M x M y + N x N y -1 physical antenna elements, which are structured according to the structure of a coprime array; among them, M x , N x and My , N y are a pair respectively Coprime integers, and M x ⁇ N x , My y ⁇ N y ; the coprime array can be decomposed into two sparse uniform sub-arrays with
- s k [s k,1 ,s k,2 ,...,s k,L ] T is the multi-shot sampling signal waveform corresponding to the k-th incident signal source
- [ ⁇ ] T represents the transposition operation
- ⁇ represents the outer product of the vector
- Is a noise tensor independent of each signal source
- the steering vector in the x-axis and y-axis directions corresponds to the direction of the incoming wave as
- the signal source is expressed as:
- the received signal uses another three-dimensional tensor Means:
- x 1 (l) and x 2 (l) respectively represent the l-th slice of x 1 and x 2 in the third dimension (ie snapshot dimension) direction, and ( ⁇ ) * represents the conjugation operation;
- One of them contains (M x N x +M x +N x -1) ⁇ (M y N y +M y +N y -1) virtual array elements, and the x-axis distribution is (-N x +1)d To (M x N x +M x -1)d, the y-axis distribution is (-N y +1)d to (M y N y +M y -1)d virtual domain uniform area array Expressed as:
- Cross-correlation tensor in It is equivalent to an augmented virtual domain along the x-axis, It is equivalent to an augmented virtual domain along the y-axis, resulting in an augmented non-uniform virtual domain array
- the virtual domain uniform area array described in step (4) Mirror part of The corresponding equivalent received signal can pass through the virtual domain uniform area array Equivalent received signal Is obtained by transforming the space of, the specific operation is: Take the conjugate operation to get Correct The elements in are flipped left and right and up and down in turn to get a uniform area array corresponding to the mirrored virtual domain Equivalent received signal
- step (4) the uniform area array of the virtual domain Equivalent received signal And mirror virtual domain uniform area array Equivalent received signal Superimpose on the third dimension to get a virtual domain 3D tensor signal
- the pair can be decomposed by CANDECOMP/PARACFAC Carry out feature extraction to realize two-dimensional DOA estimation under non-underdetermined conditions.
- step (7) the three-dimensional structured virtual domain tensor Perform CANDECOMP/PARAFAC decomposition to obtain three factor matrices, among them, For each incident angle The estimated value of; divide the element in the second row of the factor matrix G by the element in the first row to get Divide the element in the P x +1 row of the factor matrix G by the element in the first row to get After performing similar parameter extraction operations on the factor matrix F, the parameters extracted from G and F are averaged and logarithmically processed to obtain Two-dimensional direction of arrival estimation
- the closed-form solution of is:
- CANDECOMP/PARAFAC decomposition complies with the following uniqueness conditions:
- the optimal values of P x and P y can be obtained, and thus the theoretical maximum value of K can be obtained, that is, the theoretical upper limit of the number of distinguishable information sources K can be obtained under the condition of ensuring that the uniqueness decomposition is satisfied; here, The value of K exceeds the total number of actual physical elements of the coprime array by 4M x M y +N x N y -1.
- the present invention has the following advantages:
- the present invention uses a tensor to represent the actual received signal of the coprime area array, which is different from the traditional matrix method, which uses vectorized representation of two-dimensional spatial information, and averages the snapshot information to obtain relevant statistics for signal processing. .
- the present invention superimposes the snapshot information in the third dimension, and obtains the cross-correlation tensor containing the four-dimensional spatial information through the cross-correlation statistical analysis of the tensor signal, and saves the spatial structure information of the original multi-dimensional signal;
- the present invention derives virtual domain statistics based on the four-dimensional cross-correlation tensor, and merges the dimensions representing the virtual domain information in the same direction in the cross-correlation tensor to derive the equivalent received signal of the virtual domain, which overcomes the traditional matrix method
- the derived virtual domain equivalent signal has problems such as loss of spatial structure information and excessive linear scale;
- the present invention further constructs a three-dimensional tensor signal in the virtual domain, thereby establishing a connection between the two-dimensional virtual domain and the tensor space, in order to obtain a two-dimensional wave using tensor decomposition.
- the closed-form solution of the direction-of-arrival estimation provides a theoretical premise, and at the same time lays a foundation for the construction of the structured virtual domain tensor and the improvement of the degree of freedom;
- the present invention effectively improves the degree of freedom performance of the tensor signal processing method through the dimensional expansion of the virtual domain tensor signal and the structure of the structured virtual domain tensor, and realizes the two-dimensional direction of arrival estimation under underdetermined conditions.
- Figure 1 is a block diagram of the overall flow of the present invention.
- Figure 2 is a schematic diagram of the structure of the coprime area array in the present invention.
- FIG. 3 is a schematic diagram of the structure of the augmented virtual domain area array derived by the present invention.
- Fig. 4 is a schematic diagram of the dimension expansion process of the virtual domain tensor signal of the coprime area array provided by the present invention.
- Fig. 5 is an effect diagram of the multi-source DOA estimation effect of the method proposed in the present invention.
- the present invention proposes a method for estimating the direction of arrival of a coprime array based on the signal processing of the structured virtual domain tensor. , Virtual domain tensor signal construction, virtual domain tensor decomposition, etc., to establish the relationship between the virtual domain of the coprime array and the second-order statistics of the tensor signal to realize the two-dimensional direction of arrival estimation under underdetermined conditions .
- the implementation steps of the present invention are as follows:
- Step 1 Build a relatively prime area array.
- 4M x M y +N x N y -1 physical antenna array elements are used to construct a coprime array, as shown in Figure 2:
- Contains N x ⁇ N y antenna array elements the distance between the elements in the x-axis direction and the y-axis direction is M x d and My y d respectively, and the position coordinates on
- Step 2 Tensor modeling of the received signal of the coprime array.
- K from Oriented far-field narrow-band incoherent signal source, sparse uniform sub-arrays in the coprime array
- L is the number of sampled snapshots
- s k [s k,1 ,s k,2 ,...,s k,L ] T is the multi-shot sampling signal waveform corresponding to the k-th incident signal source
- [ ⁇ ] T represents the transposition operation
- ⁇ represents the outer product of the vector
- Is a noise tensor independent of each signal source
- the steering vector in the x-axis and y-axis directions corresponds to the direction of the incoming wave as
- the signal source is expressed as:
- the received signal can be another three-dimensional tensor Means:
- x 1 (l) and x 2 (l) respectively represent the l-th slice of x 1 and x 2 in the third dimension (ie snapshot dimension) direction, and ( ⁇ ) * represents the conjugation operation;
- Step 3 Derive the second-order equivalent signal of the virtual domain of the coprime area array based on the transformation of the tensor signal cross-correlation statistics.
- the cross-correlation tensor of the two sub-arrays of the coprime area array receiving the tensor signal The ideal modeling (no noise scene) is:
- each virtual array element Represents the power of the k-th incident signal source; at this time, the cross-correlation tensor in It is equivalent to an augmented virtual domain along the x-axis, It is equivalent to an augmented virtual domain along the y-axis, resulting in an augmented non-uniform virtual domain area array
- the position of each virtual array element is expressed as:
- the equivalent received signal requires the cross-correlation tensor
- the first and third dimensions representing the spatial information in the x-axis direction are merged into one dimension, and the second and fourth dimensions representing the spatial information in the y-axis direction are merged into another dimension.
- the dimensional merging of tensors can be achieved through the modular expansion operation of its PARAFAC decomposition, with a four-dimensional tensor For example, define a collection of dimensions with then PARAFAC decomposition model The expansion operation is as follows:
- the tensor subscript represents the modular expansion operation of the tensor PARAFAC decomposition; with Respectively represent the factor vectors of the two dimensions after expansion; here, Represents Kronecker product. Therefore, define a collection of dimensions with Cross-correlation tensor Model for PARAFAC decomposition Expand to get an augmented virtual domain area array Equivalent received signal
- Step 4 Construct a three-dimensional tensor signal of the virtual domain of the coprime array.
- the equivalent received signal can use the virtual domain uniform area array Equivalent received signal
- the specific operation is: change Take the conjugate operation to get Correct The elements in are flipped left and right and up and down in turn to get a uniform area array corresponding to the mirrored virtual domain Equivalent received signal Expressed as:
- Step 5 Construct a five-dimensional virtual domain tensor based on the virtual domain tensor dimension expansion strategy.
- the uniform area array in the virtual domain In the x-axis and y-axis directions, take every other array element with a size of P x ⁇ P y sub-array, then the virtual domain can be uniformly arrayed Divide into L x ⁇ L y uniform sub-arrays partially overlapping each other, and L x , Ly , P x , P y satisfy the following relationship:
- Step 6 Form a structured virtual domain tensor containing 3D spatial information.
- a structured virtual domain tensor a five-dimensional virtual domain tensor with dimensional expansion Merging along the first and second dimensions representing the spatial angle domain information, and at the same time merging along the fourth and fifth dimensions representing the spatial translation factor information, and retaining the third dimension representing the spatial mirror transformation information; the specific operation is: definition Dimension set Then pass PARAFAC decomposition model Expand to get the three-dimensional structured virtual domain tensor
- Structured virtual domain tensor The three dimensions represent spatial angle domain information, spatial mirror transformation information, and spatial translation factor information;
- Step 7 Obtain the two-dimensional direction of arrival estimation through CANDECOMP/PARAFAC decomposition of the structured virtual domain tensor.
- Tensor of 3D structured virtual domain Perform CANDECOMP/PARAFAC decomposition to obtain three factor matrices, among them, For each incident angle The estimated value of; divide the element in the second row of the factor matrix G by the element in the first row to get Divide the element in the P x +1 row of the factor matrix G by the element in the first row to get After performing similar parameter extraction operations on the factor matrix F, the parameters extracted from G and F are averaged and logarithmically processed to obtain Two-dimensional direction of arrival estimation
- the closed-form solution of is:
- CANDECOMP/PARAFAC decomposition follows the following uniqueness conditions:
- the optimal values of P x and P y can be obtained, and thus the theoretical maximum value of K can be obtained, that is, the theoretical upper limit of the number of distinguishable information sources K can be obtained under the condition of ensuring that the uniqueness decomposition is satisfied; here, Due to the construction and processing of the structured virtual domain tensor, the value of K exceeds the total number of actual physical elements of the coprime array 4M x M y +N x N y -1, indicating that the degree of freedom performance of the direction of arrival estimation is obtained ⁇ lifted.
- the number of incident narrowband signals is 50, and the azimuth angle of the incident direction is evenly distributed in [-65°,-5°] ⁇ [5°,65°], and the pitch angle is evenly distributed in the space of [5°,65°] Within the angle domain; 500 noise-free sampling snapshots are used for simulation experiments.
- the estimation result of the two-dimensional direction of arrival estimation method of the coprime array based on the structured virtual domain tensor signal processing proposed by the present invention is shown in Fig. 5, where the x-axis and the y-axis represent the elevation angle and azimuth of the incident signal source, respectively angle. It can be seen that the method proposed in the present invention can effectively distinguish the 50 incident signal sources. As for the traditional direction of arrival estimation method using a uniform area array, 35 physical antenna array elements can only distinguish 34 incident signals at most. The above results show that the method provided by the present invention has achieved an increase in the degree of freedom.
- the present invention fully considers the relationship between the two-dimensional virtual domain of the coprime area array and the tensor signal, and obtains the equivalent signal of the virtual domain through the second-order statistics analysis of the tensor signal, and retains the original received signal and the tensor signal.
- the spatial structure information of the virtual domain in addition, the construction mechanism of virtual domain tensor dimension expansion and structured virtual domain tensor is established, which lays a theoretical foundation for maximizing the number of identifiable information sources; finally, the present invention adopts The multi-dimensional feature extraction of the structured virtual domain tensor forms a closed-form solution of the two-dimensional direction of arrival estimation, and achieves a breakthrough in the performance of degrees of freedom.
Abstract
一种基于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法,主要解决现有方法中多维空间结构信息丢失和自由度损失的问题,其实现步骤是:构建互质面阵;互质面阵接收信号的张量建模;推导基于张量信号互相关统计量变换的互质面阵虚拟域二阶等价信号;构造互质面阵虚拟域三维张量信号;基于虚拟域张量维度扩展策略构造五维虚拟域张量;形成包含三维空间信息的结构化虚拟域张量;通过结构化虚拟域张量CANDECOMP/PARACFAC分解得到二维波达方向估计;该方法基于互质面阵张量信号的统计分析,构建结构化虚拟域张量信号处理框架,在保证分辨率和估计精度等性能的基础上,实现欠定条件下的多信源二维波达方向估计,可用于多目标定位。
Description
本发明属于阵列信号处理技术领域,尤其涉及基于稀疏面阵虚拟域二阶统计量的统计信号处理技术,具体是一种基于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法,可用于多目标定位。
互质阵列作为一种典型的系统化稀疏阵列架构,能够突破传统均匀阵列自由度受限的瓶颈。为了增加自由度,常用做法是将互质阵列接收信号推导至虚拟域以实现阵列的增广,并利用其对应的二阶虚拟域等价接收信号进行统计处理。为了提升二维波达方向估计的自由度,互质面阵及其对应的二维虚拟域信号处理开始受到广泛关注。在传统基于互质面阵的二维波达方向估计方法中,通常的做法是将具有多维空间结构信息的接收信号进行相关统计量平均化处理,通过矢量化推导二阶虚拟域等价接收信号,并将一维波达方向估计方法推广至二维/高维信号场景,通过进一步的统计处理实现波达方向估计。上述做法不仅破坏了互质面阵原始接收信号的多维空间信息结构,且由矢量化推导得到的虚拟域模型存在线性尺度大、虚拟域结构化信息丢失等问题。
张量是一种多维的数据类型,可以用来保存复杂的多维信号信息;针对多维信号的特征分析,高阶奇异值分解、张量分解类方法为面向张量的信号处理提供了丰富的数学工具。近年来,张量模型已被广泛应用于阵列信号处理、图像信号处理、统计学等多个领域。因此,采用张量构造互质面阵接收信号及其虚拟域等价信号,能够有效保留信号的多维结构信息,为提升波达方向估计的性能提供了重要的理论工具。与此同时,将高阶奇异值分解和张量分解等方法推广至虚拟域,有望实现波达方向估计在分辨率、估计精度和自由度等综合性能上的突破。然而,现有方法普遍还没有涉及到互质面阵虚拟域张量空间的讨 论,且没有利用互质面阵的二维虚拟域特性。因此,基于互质面阵张量信号模型设计自由度提升的二维波达方向估计方法,以实现欠定条件下的精确波达方向估计,是当前亟待解决的一个重要问题。
发明内容
本发明的目的在于针对现有方法存在的自由度损失问题,提出一种基于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法,为构建互质面阵二维虚拟域与张量空间关联,充分挖掘二维虚拟域的结构信息,并利用虚拟域张量结构化构造和虚拟域张量分解等手段实现欠定条件下的二维波达方向估计提供了可行的思路和有效的解决方案。
本发明的目的是通过以下技术方案来实现的:一种基于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法,该方法包含以下步骤:
(1)接收端使用4M
xM
y+N
xN
y-1个物理天线阵元,按照互质面阵的结构进行架构;其中,M
x、N
x以及M
y、N
y分别为一对互质整数,且M
x<N
x,M
y<N
y;该互质面阵可分解为两个稀疏均匀子阵列
和
其中,s
k=[s
k,1,s
k,2,…,s
k,L]
T为对应第k个入射信源的多快拍采样信号波形,[·]
T表示转置操作,ο表示矢量外积,
为与各信号源相互独立的噪声张量,
和
分别为
在x轴和y轴方向上的导引矢量,对应于来波方向为
的信号源,表示为:
这里,x
1(l)和x
2(l)分别表示x
1和x
2在第三维度(即快拍维度)方向上的第l个切片,(·)
*表示共轭操作;
其中,单位间隔d取为入射窄带信号波长λ的一半,即d=λ/2。
中有一个包含(M
xN
x+M
x+N
x-1)×(M
yN
y+M
y+N
y-1)个虚拟阵元、x轴分布为(-N
x+1)d到(M
xN
x+M
x-1)d、y轴分布为(-N
y+1)d到(M
yN
y+M
y-1)d的虚拟域均匀面阵
表示为:
其中,
是对应于
方向的增广虚拟域面阵
在x轴和y轴上的导引矢量,
表示第k个入射信号源的功率,这里,
表示克罗内克积。通过选取U中与
中各虚拟阵元位置相对应的元素,可获得虚拟域均匀面阵
的等价接收信号
可建模为:
(5)在虚拟域均匀面阵
中,分别沿x轴和y轴方向每隔一个阵元取一个大小为P
x×P
y子阵列,则可以将虚拟域均匀面阵
分割成L
x×L
y个互相部分重叠的均匀子阵列;将上述子阵列表示为
s
x=1,2,…,L
x,s
y=1,2,…,L
y,则
中阵元的位置表示为:
其中,
和
为对应于
方向的虚拟域子阵列
在x轴和y轴上的导引矢量。经过上述操作,一共得到L
x×L
y个维度均为P
x×P
y×2的三维张量
将这些三维张量
中具有相同s
y索引下标的张量在第四维度进行扩展叠加,得到L
y个维度为P
x×P
y×2×L
x的四维张量;进一步地,将这L
y个四维张量在第五维度进行扩展叠加,得到一个五维的虚拟域张量
表示为:
(6)定义维度集合
通过五维虚拟域张量
的PARAFAC分解的模
展开,将五维虚拟域张量
的第1、2维度合并成一个维度,同时将其第4、5维度合并成一个维度,并保留第3维度,从而得到三维结构化虚拟域张量
进一步地,步骤(1)所述的互质面阵结构可具体描述为:在平面坐标系xoy上构造一对稀疏均匀平面子阵列
和
其中
包含2M
x×2M
y个天线阵元,在x轴方向上和y轴方向上的阵元间距分别为N
xd和N
yd,其在xoy上的位置坐标为{(N
xdm
x,N
ydm
y),m
x=0,1,...,2M
x-1,m
y=0,1,...,2M
y-1};
包含N
x×N
y个天线阵元,在x轴方向上和y轴方向上的阵元间距分别为M
xd和M
yd,其在xoy上的位置坐标为{(M
xdn
x,M
ydn
y),n
x=0,1,...,N
x-1,n
y=0,1,...,N
y-1};这里,M
x、N
x以及M
y、N
y分别为一对互质整数,且M
x<N
x,M
y<N
y;将
和
按照坐标系(0,0)位置处阵元重叠的方式进行子阵列组合,获得实际包含4M
xM
y+N
xN
y-1个物理天线阵元的互质面阵。
进一步地,步骤(4)所述的虚拟域均匀面阵
的镜像部分
对应的等价接收 信号,可通过虚拟域均匀面阵
的等价接收信号
的空间变换得到,具体操作为:将
取共轭操作得到
对
中的元素依次进行左右翻转和上下翻转,即可得到对应镜像虚拟域均匀面阵
的等价接收信号
进一步地,步骤(4)所述通过将虚拟域均匀面阵
的等价接收信号
和镜像虚拟域均匀面阵
的等价接收信号
在第三维度上进行叠加,得到一个虚拟域三维张量信号
可通过CANDECOMP/PARACFAC分解对
进行特征提取,在非欠定条件下实现二维波达方向估计。
进一步地,步骤(7)中,通过对三维结构化虚拟域张量
进行CANDECOMP/PARAFAC分解,得到三个因子矩阵,
其中,
为各入射角度
的估计值;将因子矩阵G中的第2行元素除以第1行元素,得到
将因子矩阵G中的第P
x+1行元素除以第1行元素,得到
对因子矩阵F也进行类似的参数提取操作后,将从G和F中分别提取的参数进行平均和取对数处理后,从而得到
则二维波达方向估计
的闭式解为:
上述步骤中,CANDECOMP/PARAFAC分解遵循以下唯一性条件:
根据上述不等式,可以获得最优的P
x和P
y值,从而得到K的理论最大值,即在保证满足唯一性分解条件下求得可分辨信源个数K的理论上限值;这里,K的 值超过互质面阵的实际物理阵元总个数4M
xM
y+N
xN
y-1。
本发明与现有技术相比具有以下优点:
(1)本发明通过张量表示互质面阵实际接收信号,不同于传统矩阵类方法将二维空间信息进行矢量化表征,并将快拍信息进行平均得到相关统计量进行信号处理的技术路线。本发明将快拍信息在第三维度上进行叠加,并通过张量信号的互相关统计分析得到包含四维空间信息的互相关张量,保存了原始多维信号的空间结构信息;
(2)本发明基于四维互相关张量推导虚拟域统计量,并通过将互相关张量中表征相同方向虚拟域信息的维度进行合并,从而推导得到虚拟域等价接收信号,克服了传统矩阵类方法所推导的虚拟域等价信号存在空间结构信息丢失、线性尺度过大等问题;
(3)本发明在虚拟域等价接收信号构建的基础上,在虚拟域进一步构建三维张量信号,从而建立起二维虚拟域与张量空间的联系,为利用张量分解获得二维波达方向估计闭式解提供了理论前提,同时也为结构化虚拟域张量的构造及自由度的提升奠定了基础;
(4)本发明通过虚拟域张量信号的维度扩展及结构化虚拟域张量构造,有效提升了张量信号处理方法的自由度性能,实现了欠定条件下的二维波达方向估计。
图1是本发明的总体流程框图。
图2是本发明中互质面阵的结构示意图。
图3是本发明所推导增广虚拟域面阵结构示意图。
图4是本发明所提互质面阵虚拟域张量信号的维度扩展过程示意图。
图5是本发明所提方法的多信源波达方向估计效果图。
以下参照附图,对本发明的技术方案作进一步的详细说明。
为了解决现有张量方法存在的自由度性能损失问题,本发明提出了一种基 于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法,通过结合张量统计特性分析、虚拟域张量信号构造、虚拟域张量分解等手段,建立起互质面阵虚拟域与张量信号二阶统计量之间的联系,以实现欠定条件下的二维波达方向估计。参照图1,本发明的实现步骤如下:
步骤1:构建互质面阵。在接收端使用4M
xM
y+N
xN
y-1个物理天线阵元构建互质面阵,如图2所示:在平面坐标系xoy上构造一对稀疏均匀平面子阵列
和
其中
包含2M
x×2M
y个天线阵元,在x轴方向上和y轴方向上的阵元间距分别为N
xd和N
yd,其在xoy上的位置坐标为{(N
xdm
x,N
ydm
y),m
x=0,1,...,2M
x-1,m
y=0,1,...,2M
y-1};
包含N
x×N
y个天线阵元,在x轴方向上和y轴方向上的阵元间距分别为M
xd和M
yd,其在xoy上的位置坐标为{(M
xdn
x,M
ydn
y),n
x=0,1,...,N
x-1,n
y=0,1,...,N
y-1};M
x、N
x以及M
y、N
y分别为一对互质整数,且M
x<N
x,M
y<N
y;单位间隔d取为入射窄带信号波长λ的一半,即d=λ/2;将
和
按照坐标系(0,0)位置处阵元重叠的方式进行子阵列组合,获得实际包含4M
xM
y+N
xN
y-1个物理天线阵元的互质面阵;
步骤2:互质面阵接收信号的张量建模。假设有K个来自
方向的远场窄带非相干信号源,将互质面阵中稀疏均匀子阵列
的各采样快拍信号在第三个维度进行叠加后,可以得到一个三维张量信号
(L为采样快拍个数),可建模为:
其中,s
k=[s
k,1,s
k,2,…,s
k,L]
T为对应第k个入射信源的多快拍采样信号波形,[·]
T表示转置操作,ο表示矢量外积,
为与各信号源相互独立的噪声张量,
和
分别为
在x轴和y轴方向上的导引矢量,对应于来波方向为
的信号源,表示为:
这里,x
1(l)和x
2(l)分别表示x
1和x
2在第三维度(即快拍维度)方向上的第l个切片,(·)
*表示共轭操作;
其中,
表示第k个入射信号源的功率;此时,互相关张量
中
等价于沿着x轴的一个增广虚拟域,
等价于 沿着y轴的一个增广虚拟域,从而得到一个增广的非均匀虚拟域面阵
如图3所示,其中各虚拟阵元的位置表示为:
中有一个包含(M
xN
x+M
x+N
x-1)×(M
yN
y+M
y+N
y-1)个虚拟阵元,且x轴分布为(-N
x+1)d到(M
xN
x+M
x-1)d、y轴分布为(-N
y+1)d到(M
yN
y+M
y-1)d的均匀面阵
如图3虚线框中所示,具体表示为:
为了得到对应于增广虚拟域面阵
的等价接收信号,需要将互相关张量
中表征x轴方向空间信息的第1、3维度合并为一个维度,将表征y轴方向空间信息的第2、4维度合并为另一个维度。张量的维度合并可通过其PARAFAC分解的模展开操作实现,以一个四维张量
为例,定义维度集合
和
则
的PARAFAC分解的模
展开操作如下:
其中,张量下标表示张量PARAFAC分解的模展开操作;
和
分别表示展开后两个维度的因子矢量;这里,
表示克罗内克积。因此,定义维度集合
和
则通过对互相关张量
进行PARAFAC分解的模
展开,可获得增广虚拟域面阵
的等价接收信号
为了得到镜像虚拟域均匀面阵
的等价接收信号,可利用虚拟域均匀面阵
的等价接收信号
进行变换,具体操作为:将
取共轭操作得到
对
中的元素依次进行左右翻转和上下翻转,即可得到对应镜像虚拟域均匀面阵
的等价接收信号
表示为:
步骤5:基于虚拟域张量维度扩展策略构造五维虚拟域张量。如图4所示,在虚拟域均匀面阵
中,分别沿x轴和y轴方向每隔一个阵元取一个大小为P
x×P
y子阵列,则可以将虚拟域均匀面阵
分割成L
x×L
y个互相部分重叠的均匀子阵列,L
x、L
y、P
x、P
y之间满足以下关系:
P
x+L
x-1=M
xN
x+M
x+N
x-1,
P
y+L
y-1=M
yN
y+M
y+N
y-1.
其中,
和
为对应于
方向的虚拟域子阵列
在x轴和y轴上的导引矢量。经过上述操作,一共得到L
x×L
y个维度均为P
x×P
y×2的三维张量
为了对虚拟域张量进行维度扩展,首先将这些三维张量
中具有相同s
y索引下标的张量在第四维度进行扩展叠加,从而得到L
y个维度为P
x×P
y×2×L
x的四维张量;进一步地,将这L
y个四维张量在第五维度进一步扩展叠加,得到一个五维的虚拟域张量
表示为:
步骤6:形成包含三维空间信息的结构化虚拟域张量。为了得到结构化的虚拟域张量,将经过维度扩展的五维虚拟域张量
沿着表征空间角度域信息的第1、2维度进行合并,同时沿着表征空间平移因子信息的第4、5维度进行合并,并保留表征空间镜像变换信息的第3维度;具体操作为:定义维度集合
则通过
的PARAFAC分解的模
展开,可得到三维结构化虚拟域张量
步骤7:通过结构化虚拟域张量的CANDECOMP/PARAFAC分解得到二维波达方向估计。通过对三维结构化虚拟域张量
进行CANDECOMP/PARAFAC分解,得到三个因子矩阵,
其中,
为各入射角度
的估计值;将因子矩阵G中的第2行元素除以第1行元素,得到
将因子矩阵G中的第P
x+1行元素除以第1行元素,得到
对因子矩阵F也进行类似的参数提取操作后,将从G和F中分别提取的参数进行平均和取对数处理后,从而得到
则二维波达方向估计
的闭式解为:
上述步骤中,CANDECOMP/PARAFAC分解遵循以下的唯一性条件:
根据上述不等式,可以获得最优的P
x和P
y值,从而得到K的理论最大值,即在保证满足唯一性分解条件下求得可分辨信源个数K的理论上限值;这里,由于结构化虚拟域张量的构建与处理,K的值超过互质面阵的实际物理阵元总个数4M
xM
y+N
xN
y-1,说明波达方向估计的自由度性能得到了提升。
下面结合仿真实例对本发明的效果做进一步的描述。
仿真实例:采用互质面阵接收入射信号,其参数选取为M
x=2,M
y=3,N
x=3,N
y=4,即架构的互质面阵共包含4M
xM
y+N
xN
y-1=35个物理阵元。假定入射窄带信号个数为50,且入射方向方位角均匀分布于[-65°,-5°]∪[5°,65°],俯仰角均匀分布于[5°,65°]这一空间角度域范围内;采用500个无噪采样快拍进行仿真实验。
本发明所提出的基于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法估计结果如图5所示,其中x轴和y轴分别表示入射信号源的俯仰角和方位角。可以看出,本发明所提方法能够有效分辨这50个入射信号源。而对于传统采用均匀面阵的波达方向估计方法,利用35个物理天线阵元最多只能分辨34个入射信号,上述结果表明本发明所提方法实现了自由度的增加。
综上所述,本发明充分考虑了互质面阵二维虚拟域与张量信号之间的联系,通过张量信号二阶统计量分析推导得到虚拟域等价信号,保留了原始接收信号和虚拟域的空间结构信息;再者,建立起虚拟域张量维度扩展和结构化虚拟域张量的构造机理,为实现最大化可识别的信源个数奠定了理论基础;最后,本发明通过对结构化虚拟域张量进行多维特征提取,形成了二维波达方向估计的闭式解,并实现了其在自由度性能上的突破。
以上所述仅是本发明的优选实施方式,虽然本发明已以较佳实施例披露如上,然而并非用以限定本发明。任何熟悉本领域的技术人员,在不脱离本发明技术方案范围情况下,都可利用上述揭示的方法和技术内容对本发明技术方案做出许多可能的变动和修饰,或修改为等同变化的等效实施例。因此,凡是未脱离本发明技术方案的内容,依据本发明的技术实质对以上实施例所做的任何的简单修改、等同变化及修饰,均仍属于本发明技术方案保护的范围内。
Claims (6)
- 一种基于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法,其特征在于,包含以下步骤:(1)接收端使用4M xM y+N xN y-1个物理天线阵元,按照互质面阵的结构进行架构;其中,M x、N x以及M y、N y分别为一对互质整数,且M x<N x,M y<N y;该互质面阵可分解为两个稀疏均匀子阵列 和其中,s k=[s k,1,s k,2,…,s k,L] T为对应第k个入射信源的多快拍采样信号波形,[·] T表示转置操作,ο表示矢量外积, 为与各信号源相互独立的噪声张量, 和 分别为 在x轴和y轴方向上的导引矢量,对应于来波方向为 的信号源,表示为:其中,x 1(l)和x 2(l)分别表示x 1和x 2在第三维度(即快拍维度)方向上的第l个切片,(·) *表示共轭操作;其中,单位间隔d取为入射窄带信号波长λ的一半,即d=λ/2; 中有一个包含(M xN x+M x+N x-1)×(M yN y+M y+N y-1)个虚拟阵元、x轴分布为(-N x+1)d到(M xN x+M x-1)d、y轴分布为(-N y+1)d到(M yN y+M y-1)d的虚拟域均匀面阵 表示为:其中, 是对应于 方向的增广虚拟域面阵 在x轴和y轴上的导引矢量, 表示第k个入射信号源的功率, 表示克罗内克积;通过选取U中与 中 各虚拟阵元位置相对应的元素,可获得虚拟域均匀面阵 的等价接收信号 可建模为:(5)在虚拟域均匀面阵 中,分别沿x轴和y轴方向每隔一个阵元取一个大小 为P x×P y子阵列,则可以将虚拟域均匀面阵 分割成L x×L y个互相部分重叠的均匀子阵列;将上述子阵列表示为 则 中阵元的位置表示为:其中, 和 为对应于 方向的虚拟域子阵列 在x轴和y轴上的导引矢量;经过上述操作,一共得到L x×L y个维度均为P x×P y×2的三维张量 将这些三维张量 中具有相同s y索引下标的张量在第四维度进行扩展叠加,得到L y个维度为P x×P y×2×L x的四维张量;将这L y个四维张量在第五维度进行扩展叠加,得到一个五维的虚拟域张量 表示为:(6)定义维度集合 通过五维虚拟域张量 的PARAFAC分解的模 展开,将五维虚拟域张量 的第1、2维度合并成一个维度,同时将其第4、5维度合并成一个维度,并保留第3维度,从而得到三维结构化虚拟域张量
- 根据权利要求1所述的基于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法,其特征在于,步骤(1)所述的互质面阵结构可具体描述为:在平面坐标系xoy上构造一对稀疏均匀平面子阵列 和 其中 包含2M x×2M y个天线阵元,在x轴方向上和y轴方向上的阵元间距分别为N xd和N yd,其在xoy上的位置坐标为{(N xdm x,N ydm y),m x=0,1,...,2M x-1,m y=0,1,...,2M y-1}; 包含N x×N y个天线阵元,在x轴方向上和y轴方向上的阵元间距分别为M xd和M yd,其在xoy上的位置坐标为{(M xdn x,M ydn y),n x=0,1,...,N x-1,n y=0,1,...,N y-1};M x、N x以及M y、N y分别为一对互质整数,且M x<N x,M y<N y;将 和 按照坐标系(0,0)位置处阵元重叠的方式进行子阵列组合,获得实际包含4M xM y+N xN y-1个物理天线阵元的互质面阵。
- 其中, 为各入射角度 的估计值;将因子矩阵G中的第2行元素除以第1行元素,得到 将因子矩阵G中的第P x+1行元素除以第1行元素,得到 对因子矩阵F也进行类似的参数提取操作后,将从G和F中分别提取的参数进行平均和取对数处理后,从而得到 则二维波达方向估计 的闭式解为:上述步骤中,CANDECOMP/PARAFAC分解遵循以下唯一性条件:根据上述不等式,可以获得最优的P x和P y值,从而得到K的理论最大值,即在保证满足唯一性分解条件下求得可分辨信源个数K的理论上限值;这里,K的值超过互质面阵的实际物理阵元总个数4M xM y+N xN y-1。
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP2021541669A JP7044290B2 (ja) | 2020-05-03 | 2020-05-03 | 構造化仮想ドメインのテンソル信号の処理に基づく互いに素なエリアアレイの二次元到来方向の推定方法 |
PCT/CN2020/088569 WO2021068496A1 (zh) | 2020-05-03 | 2020-05-03 | 基于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法 |
US17/401,345 US11408960B2 (en) | 2020-05-03 | 2021-08-13 | Two-dimensional direction-of-arrival estimation method for coprime planar array based on structured coarray tensor processing |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/CN2020/088569 WO2021068496A1 (zh) | 2020-05-03 | 2020-05-03 | 基于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法 |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US17/401,345 Continuation US11408960B2 (en) | 2020-05-03 | 2021-08-13 | Two-dimensional direction-of-arrival estimation method for coprime planar array based on structured coarray tensor processing |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2021068496A1 true WO2021068496A1 (zh) | 2021-04-15 |
Family
ID=75437801
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2020/088569 WO2021068496A1 (zh) | 2020-05-03 | 2020-05-03 | 基于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法 |
Country Status (3)
Country | Link |
---|---|
US (1) | US11408960B2 (zh) |
JP (1) | JP7044290B2 (zh) |
WO (1) | WO2021068496A1 (zh) |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113296049A (zh) * | 2021-04-28 | 2021-08-24 | 南京航空航天大学 | 互质阵列脉冲环境下非圆信号的共轭增广doa估计方法 |
CN113359086A (zh) * | 2021-06-25 | 2021-09-07 | 南京航空航天大学 | 基于增广互质阵列的加权子空间数据融合直接定位方法 |
CN113484821A (zh) * | 2021-07-06 | 2021-10-08 | 北京邮电大学 | 一种新型虚拟阵列结构及其doa估计方法 |
CN113673317A (zh) * | 2021-07-12 | 2021-11-19 | 电子科技大学 | 基于原子范数最小化可降维的二维离格doa估计方法 |
CN114624647A (zh) * | 2022-03-18 | 2022-06-14 | 北京航空航天大学 | 一种基于后向选择的虚拟阵列doa估计方法 |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20030058153A1 (en) * | 2001-09-07 | 2003-03-27 | Lockheed Martin Corporation | Adaptive digital beamforming radar method and system for maintaining multiple source angle super-resolution capability in jamming |
CN104749552A (zh) * | 2015-03-21 | 2015-07-01 | 西安电子科技大学 | 基于稀疏重构的互质阵列波达方向角估计方法 |
CN106226729A (zh) * | 2016-07-15 | 2016-12-14 | 西安电子科技大学 | 基于四阶累量的互质阵列波达方向角估计方法 |
CN106896340A (zh) * | 2017-01-20 | 2017-06-27 | 浙江大学 | 一种基于压缩感知的互质阵列高精度波达方向估计方法 |
CN107037392A (zh) * | 2017-03-01 | 2017-08-11 | 浙江大学 | 一种基于压缩感知的自由度增加型互质阵列波达方向估计方法 |
CN107102291A (zh) * | 2017-05-03 | 2017-08-29 | 浙江大学 | 基于虚拟阵列内插的无网格化互质阵列波达方向估计方法 |
CN108344967A (zh) * | 2018-01-20 | 2018-07-31 | 中国人民解放军战略支援部队信息工程大学 | 基于互质面阵的二维波达方向快速估计方法 |
CN109143152A (zh) * | 2018-09-25 | 2019-01-04 | 哈尔滨工业大学 | 基于张量建模的极化阵列波达方向和极化参数估计方法 |
Family Cites Families (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US9912074B2 (en) * | 2014-12-12 | 2018-03-06 | The Boeing Company | Congruent non-uniform antenna arrays |
US11125866B2 (en) * | 2015-06-04 | 2021-09-21 | Chikayoshi Sumi | Measurement and imaging instruments and beamforming method |
JP2017116425A (ja) | 2015-12-24 | 2017-06-29 | 学校法人東京電機大学 | Mimoレーダシステム、および信号処理装置 |
CN109471086B (zh) | 2018-10-18 | 2020-11-24 | 浙江大学 | 基于多采样快拍和集阵列信号离散傅里叶变换的互质mimo雷达波达方向估计方法 |
CN110927661A (zh) | 2019-11-22 | 2020-03-27 | 重庆邮电大学 | 基于music算法的单基地展开互质阵列mimo雷达doa估计方法 |
WO2021068495A1 (zh) * | 2020-05-03 | 2021-04-15 | 浙江大学 | 基于平面互质阵列块采样张量信号构造的自由度增强型空间谱估计方法 |
-
2020
- 2020-05-03 WO PCT/CN2020/088569 patent/WO2021068496A1/zh active Application Filing
- 2020-05-03 JP JP2021541669A patent/JP7044290B2/ja active Active
-
2021
- 2021-08-13 US US17/401,345 patent/US11408960B2/en active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20030058153A1 (en) * | 2001-09-07 | 2003-03-27 | Lockheed Martin Corporation | Adaptive digital beamforming radar method and system for maintaining multiple source angle super-resolution capability in jamming |
CN104749552A (zh) * | 2015-03-21 | 2015-07-01 | 西安电子科技大学 | 基于稀疏重构的互质阵列波达方向角估计方法 |
CN106226729A (zh) * | 2016-07-15 | 2016-12-14 | 西安电子科技大学 | 基于四阶累量的互质阵列波达方向角估计方法 |
CN106896340A (zh) * | 2017-01-20 | 2017-06-27 | 浙江大学 | 一种基于压缩感知的互质阵列高精度波达方向估计方法 |
CN107037392A (zh) * | 2017-03-01 | 2017-08-11 | 浙江大学 | 一种基于压缩感知的自由度增加型互质阵列波达方向估计方法 |
CN107102291A (zh) * | 2017-05-03 | 2017-08-29 | 浙江大学 | 基于虚拟阵列内插的无网格化互质阵列波达方向估计方法 |
CN108344967A (zh) * | 2018-01-20 | 2018-07-31 | 中国人民解放军战略支援部队信息工程大学 | 基于互质面阵的二维波达方向快速估计方法 |
CN109143152A (zh) * | 2018-09-25 | 2019-01-04 | 哈尔滨工业大学 | 基于张量建模的极化阵列波达方向和极化参数估计方法 |
Cited By (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113296049A (zh) * | 2021-04-28 | 2021-08-24 | 南京航空航天大学 | 互质阵列脉冲环境下非圆信号的共轭增广doa估计方法 |
CN113296049B (zh) * | 2021-04-28 | 2024-02-20 | 南京航空航天大学 | 互质阵列脉冲环境下非圆信号的共轭增广doa估计方法 |
CN113359086A (zh) * | 2021-06-25 | 2021-09-07 | 南京航空航天大学 | 基于增广互质阵列的加权子空间数据融合直接定位方法 |
CN113359086B (zh) * | 2021-06-25 | 2023-05-12 | 南京航空航天大学 | 基于增广互质阵列的加权子空间数据融合直接定位方法 |
CN113484821A (zh) * | 2021-07-06 | 2021-10-08 | 北京邮电大学 | 一种新型虚拟阵列结构及其doa估计方法 |
CN113484821B (zh) * | 2021-07-06 | 2024-04-12 | 北京邮电大学 | 一种新型虚拟阵列结构及其doa估计方法 |
CN113673317A (zh) * | 2021-07-12 | 2021-11-19 | 电子科技大学 | 基于原子范数最小化可降维的二维离格doa估计方法 |
CN113673317B (zh) * | 2021-07-12 | 2023-04-07 | 电子科技大学 | 基于原子范数最小化可降维的二维离格doa估计方法 |
CN114624647A (zh) * | 2022-03-18 | 2022-06-14 | 北京航空航天大学 | 一种基于后向选择的虚拟阵列doa估计方法 |
Also Published As
Publication number | Publication date |
---|---|
US11408960B2 (en) | 2022-08-09 |
JP2022508505A (ja) | 2022-01-19 |
JP7044290B2 (ja) | 2022-03-30 |
US20210373113A1 (en) | 2021-12-02 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2021068496A1 (zh) | 基于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法 | |
CN111624545B (zh) | 基于结构化虚拟域张量信号处理的互质面阵二维波达方向估计方法 | |
CN108872929B (zh) | 基于内插虚拟阵列协方差矩阵子空间旋转不变性的互质阵列波达方向估计方法 | |
CN109655799B (zh) | 基于iaa的协方差矩阵向量化的非均匀稀疏阵列测向方法 | |
CN107037392B (zh) | 一种基于压缩感知的自由度增加型互质阵列波达方向估计方法 | |
CN107092004B (zh) | 基于信号子空间旋转不变性的互质阵列波达方向估计方法 | |
WO2021068495A1 (zh) | 基于平面互质阵列块采样张量信号构造的自由度增强型空间谱估计方法 | |
CN107589399B (zh) | 基于多采样虚拟信号奇异值分解的互质阵列波达方向估计方法 | |
CN111610486B (zh) | 基于平面互质阵列虚拟域张量空间谱搜索的高分辨精确二维波达方向估计方法 | |
WO2022151511A1 (zh) | 基于互相关张量的三维互质立方阵列波达方向估计方法 | |
CN112731278B (zh) | 一种部分极化信号的角度与极化参数欠定联合估计方法 | |
CN111610485B (zh) | 基于平面互质阵列块采样张量信号构造的自由度增强型空间谱估计方法 | |
CN113552532B (zh) | 基于耦合张量分解的l型互质阵列波达方向估计方法 | |
CN112731275B (zh) | 一种基于零化插值的互质阵部分极化信号参数估计方法 | |
CN109143151B (zh) | 部分阵元损坏的均匀面阵张量重构方法及信源定位方法 | |
CN109270483B (zh) | 三维阵虚拟扩展相干源二维波达方向估计方法 | |
CN112711000B (zh) | 基于最小化准则的电磁矢量互质面阵张量功率谱估计方法 | |
CN116299150B (zh) | 一种均匀面阵中降维传播算子的二维doa估计方法 | |
CN109507634B (zh) | 一种任意传感器阵列下的基于传播算子的盲远场信号波达方向估计方法 | |
CN112016037A (zh) | 一种互质面阵中基于降维Capon求根的二维测向估计方法 | |
WO2023137813A1 (zh) | 基于最优结构化虚拟域张量填充的超分辨互质面阵空间谱估计方法 | |
CN112710983B (zh) | 基于乘性张量波束扫描的电磁矢量互质面阵多维参数估计方法 | |
CN114397619A (zh) | 基于非均匀稀疏阵列二维定位算法 | |
CN113325364A (zh) | 一种基于数据压缩的空时联合测向方法 | |
WO2021068494A1 (zh) | 基于平面互质阵列虚拟域张量空间谱搜索的高分辨精确二维波达方向估计方法 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 20873615 Country of ref document: EP Kind code of ref document: A1 |
|
ENP | Entry into the national phase |
Ref document number: 2021541669 Country of ref document: JP Kind code of ref document: A |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 20873615 Country of ref document: EP Kind code of ref document: A1 |