WO2021068494A1 - 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 - Google Patents
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 Download PDFInfo
- Publication number
- WO2021068494A1 WO2021068494A1 PCT/CN2020/088567 CN2020088567W WO2021068494A1 WO 2021068494 A1 WO2021068494 A1 WO 2021068494A1 CN 2020088567 W CN2020088567 W CN 2020088567W WO 2021068494 A1 WO2021068494 A1 WO 2021068494A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- array
- tensor
- virtual domain
- signal
- 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
- G01S3/143—Systems for determining direction or deviation from predetermined direction by vectorial combination of signals derived from differently oriented antennae
-
- 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
- G01S3/023—Monitoring or calibrating
-
- 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
- G01S3/02—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
- G01S3/046—Displays or indicators
-
- 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
- G01S3/46—Systems for determining direction or deviation from predetermined direction using antennas spaced apart and measuring phase or time difference between signals therefrom, i.e. path-difference systems
-
- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01Q—ANTENNAS, i.e. RADIO AERIALS
- H01Q21/00—Antenna arrays or systems
- H01Q21/06—Arrays of individually energised antenna units similarly polarised and spaced apart
- H01Q21/061—Two dimensional planar arrays
Definitions
- the present invention belongs to the field of array signal processing technology, and in particular relates to a statistical signal processing technology based on planar coprime array tensor signals, in particular to a high-resolution accurate two-dimensional direction of arrival based on a planar coprime array virtual domain tensor space spectrum search
- the estimation method can be used for passive detection and spatial positioning.
- the planar coprime array As a two-dimensional sparse array with a systematic architecture, the planar coprime array has the characteristics of large aperture and high resolution. Compared with the traditional uniform array, it can achieve the comprehensive performance of the two-dimensional direction of arrival estimation in terms of estimation accuracy and resolution.
- the planar coprime array by constructing a two-dimensional virtual domain, it is possible to perform signal processing that meets the Nyquist matching condition on a uniform surface array of the virtual domain, thereby solving the signal mismatch problem of the planar coprime array.
- the spatial spectrum of the planar coprime array is constructed based on the virtual domain signal, and then through the two-dimensional peak search, an accurate two-dimensional direction of arrival estimation can be obtained.
- the traditional method usually expresses the incident signal with two-dimensional spatial structure information as a vector, and calculates the second-order statistics of the multi-sampled signal in a time-averaged manner, and then derives the second-order virtual domain through vectorization, etc. Price signal.
- the planar coprime array received signal and its virtual domain equivalent signal represented by a vector not only lose the multi-dimensional spatial structure information of the original signal, but also easily cause dimensional disasters as the amount of data increases, so this is the basic structure
- the spatial spectrum and the two-dimensional direction of arrival estimation still have defects in accuracy and resolution.
- the two-dimensional direction of arrival estimation method of planar coprime array based on tensor space spectrum search has begun to attract attention.
- tensors can store the original multi-dimensional information of signals.
- multi-dimensional algebraic theories such as high-order singular value decomposition and tensor decomposition also provide rich analysis tools for multi-dimensional feature extraction of tensor signals. Therefore, the tensor signal model can make full use of the multi-dimensional spatial structure information of the incident signal of the planar coprime array.
- the existing method is still based on the actual received tensor signal for processing, and does not use the two-dimensional virtual domain of the planar coprime array to construct the tensor space spectrum, and does not solve the problem of signal mismatch of the planar coprime array, resulting in accuracy. Damaged; and the resolution of the generated spectral peaks is low, which is easy to cause mutual aliasing. Therefore, the existing methods still have much room for improvement in accuracy and resolution performance.
- the purpose of the present invention is to solve the problems of loss of signal multi-dimensional spatial structure information and limited spatial spectrum resolution and accuracy performance in the above method, and propose a high-resolution accurate two-dimensional space spectrum search based on the virtual domain tensor spatial spectrum of the planar coprime array
- the direction of arrival estimation method provides a feasible way to establish the connection between the planar coprime array tensor signal statistics and the virtual domain spatial spectrum, build a virtual domain tensor spatial spectrum search architecture, and achieve high-resolution, high-precision two-dimensional direction of arrival estimation.
- a high-resolution accurate two-dimensional direction of arrival estimation method based on the virtual domain tensor space spectrum search of a planar coprime array including the following steps:
- the receiving end uses 4M x M y + N x N y -1 physical antenna array elements, which are structured according to the structure of a planar 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 planar 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 can be another three-dimensional tensor Expressed as:
- 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;
- the steering vector in the x-axis and y-axis directions corresponds to the direction of the incoming wave as Signal source; Represents the power of the k-th incident signal source; here, Represents the Kronecker product; the tensor subscript represents the modulus expansion operation of the PARAFAC decomposition of the tensor;
- the steering vector in the x-axis and y-axis directions corresponds to the direction of the incoming wave as Signal source;
- the orthogonal complement of the factor matrices C x and Cy is calculated; the orthogonal complement of C x is denoted as The orthogonal complement of C y is Among them, min( ⁇ ) represents the operation of taking the minimum value; then take As the noise subspace, use a tensor Represents the noise subspace, Means The h-th slice along the third dimension is expressed as:
- ⁇ ⁇ Q ⁇ > represents the modulo ⁇ Q ⁇ shrinking operation of two tensors along the Q-th dimension, requiring the same size of the Q-th dimension of the two tensors;
- ⁇ F represents the Frobenius norm; with A vector is obtained by condensing modulo ⁇ 1,2 ⁇ along the first and second dimensions Get the spatial spectrum function After that, the spatial spectrum corresponding to the two-dimensional search direction of arrival can be constructed, and then the two-dimensional direction of arrival corresponding to the position of the search spectrum peak is estimated to be the two-dimensional direction of arrival of the incident source.
- the ideal (no noise scene) can be modeled as:
- the uniform area array of the virtual domain described in step (5) Equivalent signal
- the spatial structure information of the virtual domain array is saved in the, however, due to It can be regarded as a single snapshot of the virtual domain signal, and its statistics often have a rank deficit problem. Therefore, based on the idea of two-dimensional spatial smoothing, the virtual domain signal After processing, construct multiple equivalent snapshot virtual domain sub-array signals, and after summing and averaging these virtual domain sub-array signals, the fourth-order autocorrelation tensor is calculated. Subarray The position of the middle element is expressed as:
- step (6) extraction of the multi-dimensional features of the fourth-order autocorrelation tensor of the virtual domain described in step (6) to realize the signal and noise subspace classification can be achieved by high-order singular value decomposition in addition to CANDECOMP/PARACFAC decomposition, which specifically represents for:
- ⁇ Q represents the modulo Q inner product of the tensor and the matrix along the Q-th dimension; Represents a kernel tensor containing high-order singular values, with Represents the singular matrix corresponding to the four dimensions of v.
- the spatial spectrum function is obtained in step (7)
- the specific steps for searching for two-dimensional peaks are as follows: gradually increase with a° as the step size.
- the value of the two-dimensional direction of arrival The search start point is (-90°,0°), and the end point is (90°,180°); each You can calculate a corresponding The spatial spectrum value of, thus can construct a corresponding to The space spectrum.
- step (7) the construction of the tensor space spectrum described in step (7) can also be implemented using a noise subspace obtained based on high-order singular value decomposition, which is expressed as
- ( ⁇ ) H represents the conjugate transpose operation.
- ( ⁇ ) H represents the conjugate transpose operation.
- the present invention has the following advantages:
- the present invention uses a tensor to represent the actual received signal of the plane coprime, which is different from the traditional method of vectorizing the two-dimensional spatial information and averaging the snapshot information to obtain the second-order statistics.
- the present invention takes the snapshots of each sample The signal is superimposed in the third dimension, and the second-order cross-correlation tensor containing the four-dimensional spatial information is used to estimate the spatial spectrum, and the multi-dimensional spatial structure information of the actual incident signal of the planar coprime array is retained;
- the present invention constructs the subspace classification idea of the virtual domain signal through the tensor statistics analysis of the virtual domain equivalent signal, which provides a theoretical basis for the construction of the tensor space spectrum, thereby solving the problem of the signal mismatch of the planar coprime array. Problem, the construction of the virtual domain tensor space spectrum conforming to the Nyquist matching condition is realized;
- the present invention uses tensor CANDECOMP/PARACFAC decomposition and high-order singular value decomposition to extract the multi-dimensional features of the fourth-order autocorrelation tensor of the virtual domain signal, thereby establishing the relationship between the virtual domain model and the signal and noise subspaces.
- the connection of provides a basis for realizing high-precision, high-resolution tensor space spectrum.
- Figure 1 is a block diagram of the overall flow of the present invention.
- Fig. 2 is a schematic diagram of the structure of the planar coprime 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.
- Figure 4 is a schematic diagram of the tensor space spectrum constructed by the present invention.
- the present invention provides a high-resolution accurate two-dimensional wave based on a planar coprime array virtual domain tensor spatial spectrum search.
- Direction of arrival estimation method Through the statistical analysis of the tensor signal received by the planar coprime array, the virtual domain equivalent signal with the spatial structure information of the virtual domain array is constructed; based on the multi-dimensional feature analysis method of the virtual domain signal tensor statistics, the virtual domain model and Zhang are established.
- the implementation steps of the present invention are as follows:
- Step 1 Construct a planar coprime array.
- 4M x M y +N x N y -1 physical antenna elements are used to construct a planar coprime array, as shown in Figure 2:
- Contains N x ⁇ N u antenna array elements the distance between the array elements in the x-axis direction and the y-axis direction is M x d and My y d, and its
- Step 2 Modeling the received signal tensor of the planar coprime array.
- K from Direction of the far-field narrow-band incoherent signal source, the sparse sub-array of the planar coprime array
- the snapshot signals of each sample are superimposed in the third dimension to obtain a three-dimensional tensor signal (L is the number of sampled snapshots), expressed as:
- 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 Expressed as:
- Compute subarray with Receive the cross-correlation statistics of the tensor signal x 1 and x 2 to obtain a second-order cross-correlation tensor with four-dimensional spatial information Expressed as:
- 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 equivalent signal of the virtual domain based on the second-order cross-correlation tensor of the planar coprime array.
- the second-order cross-correlation tensor of the tensor signal received by the two sub-arrays of the planar coprime array The ideal modeling (no noise scene) is:
- the equivalent of the received signal will be 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 realized through the modulus expansion operation of its PARAFAC decomposition. Specifically, the dimension set is defined with Cross-correlation tensor Model for PARAFAC decomposition Expand to get an augmented virtual domain area array Equivalent received signal Expressed as:
- Step 4 Construct the equivalent received signal of the uniform area array in the virtual domain.
- the structure of is shown in the dashed box in Figure 3, expressed as:
- the steering vector in the x-axis and y-axis directions corresponds to the direction of the incoming wave as Signal source;
- Step 5 Derive the fourth-order autocorrelation tensor of the smooth signal in the virtual domain.
- a uniform area array of the virtual domain is obtained by the above steps Equivalent signal Virtual domain signal
- the spatial structure information of the virtual domain array is saved in the, however, due to It can be regarded as a single snapshot of the virtual domain signal, and its statistics often have a rank deficit problem. Therefore, based on the idea of two-dimensional spatial smoothing, the virtual domain signal After processing, construct multiple equivalent snapshot virtual domain sub-array signals, and after summing and averaging these virtual domain sub-array signals, the fourth-order autocorrelation tensor is calculated.
- the specific method is to uniform area array in the virtual domain In the x-axis and y-axis directions, take a sub-array with a size of Y 1 ⁇ Y 2 for every other element in the x-axis and y-axis directions. Divide into L 1 ⁇ L 2 uniform sub-arrays partially overlapping each other, and L 1 , L 2 , Y 1 , and Y 2 satisfy the following relationship:
- Y 2 +L 2 -1 M y N y +M y +N y -1.
- Step 6 Multi-dimensional feature extraction based on the fourth-order autocorrelation tensor of the virtual domain realizes signal and noise subspace classification.
- the fourth-order autocorrelation tensor v is subjected to CANDECOMP/PARACFAC decomposition to extract multi-dimensional features. The obtained results are expressed as follows:
- ⁇ Q represents the modulo Q inner product of the tensor and the matrix along the Q-th dimension; Represents a kernel tensor containing high-order singular values, with Represents the singular matrix corresponding to the four dimensions of v.
- Step 7 High-resolution accurate two-dimensional direction of arrival estimation based on virtual domain tensor space spectrum search. Define the two-dimensional direction of arrival for spectral peak search Construct a uniform area array corresponding to the virtual domain Guide information Expressed as:
- ⁇ ⁇ Q ⁇ > represents the modulo ⁇ Q ⁇ shrinking operation of two tensors along the Q-th dimension, requiring the same size of the Q-th dimension of the two tensors;
- ⁇ F represents the Frobenius norm; with A vector is obtained by condensing modulo ⁇ 1,2 ⁇ along the first and second dimensions
- the two-dimensional direction of arrival estimation result is obtained through the two-dimensional spectral peak search, the specific steps are: gradually increase the search step size a° The value of the two-dimensional direction of arrival
- the search start point is (-90°,0°), and the end point is (90°,180°); each You can calculate a corresponding The spatial spectrum value of, thus can construct a corresponding to The space spectrum.
- ( ⁇ ) H represents the conjugate transpose operation.
- ( ⁇ ) H represents the conjugate transpose operation.
- the spatial spectrum of the high-resolution accurate two-dimensional direction of arrival estimation method based on the virtual domain tensor spatial spectrum search of the planar coprime array proposed by the present invention is shown in FIG. 4. It can be seen that the method proposed in the present invention can effectively construct a two-dimensional spatial spectrum, in which there is a sharp peak corresponding to the two-dimensional direction of arrival of the incident signal source, and the x-axis and y-axis corresponding to the peak The value of is the elevation angle and azimuth angle of the incident source.
- the present invention fully considers the multi-dimensional structure information of the planar coprime array signal, uses tensor signal modeling, constructs virtual domain equivalent signals with virtual domain area array spatial structure information, and analyzes its tensor statistics Features, constructs a subspace classification idea based on multi-dimensional feature extraction of virtual domain autocorrelation tensor, establishes the connection between the virtual domain model of the planar coprime array and the tensor space spectrum, and solves the signal mismatch problem of the planar coprime array
- the present invention proposes a high-precision, high-resolution tensor spatial spectrum construction mechanism by using two tensor feature extraction methods, namely tensor decomposition and high-order singular value decomposition. Compared with the existing methods, in the spatial spectrum A breakthrough has been made in the resolution and accuracy of the two-dimensional direction of arrival estimation.
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
- Radar Systems Or Details Thereof (AREA)
Abstract
一种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,主要解决现有方法中信号多维信息丢失和空间谱分辨度、精确度受限的问题,其实现步骤是:构建平面互质阵列;平面互质阵列接收信号张量建模;推导基于平面互质阵列二阶互相关张量的虚拟域等价信号;构造虚拟域均匀面阵的等价接收信号;推导虚拟域平滑信号的四阶自相关张量;基于虚拟域自相关张量的多维特征提取实现信号与噪声子空间分类;基于虚拟域张量空间谱搜索的高分辨精确二维波达方向估计。该方法基于平面互质阵列虚拟域张量统计量的多维特征提取,实现基于张量空间谱搜索的高分辨精确二维波达方向估计,可用于无源探测和目标定位。
Description
本发明属于阵列信号处理技术领域,尤其涉及基于平面互质阵列张量信号的统计信号处理技术,具体是一种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,可用于无源探测和空间定位。
平面互质阵列作为一种具有系统化架构的二维稀疏阵列,具有大孔径、高分辨的特点,相较于传统均匀阵列,能够实现二维波达方向估计在估计精度、分辨度等综合性能上的突破;与此同时,通过构造二维虚拟域,能够在虚拟域均匀面阵上进行符合奈奎斯特匹配条件的信号处理,从而解决平面互质阵列的信号失配问题。基于虚拟域信号构造平面互质阵列空间谱,进而通过二维谱峰搜索,可以得到精确的二维波达方向估计。以此为思路,传统的方法通常将具有二维空间结构信息的入射信号用矢量进行表示,并以时间平均的方式计算多采样信号的二阶统计量,进而通过矢量化推导虚拟域二阶等价信号。然而,以矢量方式表示的平面互质阵列接收信号及其虚拟域等价信号不仅丢失了原始信号的多维空间结构信息,且随着数据量增大,容易造成维度灾难,因此以此为基础构造空间谱并得到二维波达方向估计在精确度、分辨度等性能上仍存在缺陷。
为了解决上述问题,基于张量空间谱搜索的平面互质阵列二维波达方向估计方法开始受到关注。张量作为一种高维的数据结构,可以保存信号的原始多维信息;同时,高阶奇异值分解、张量分解等多维代数理论也为张量信号的多维特征提取提供了丰富的分析工具。因此,张量信号模型能够充分利用平面互质阵列入射信号的多维空间结构信息。然而,现有方法仍然是基于实际接收张量信号进行处理,并没有利用平面互质阵列的二维虚拟域进行张量空间谱构造, 没有解决平面互质阵列信号失配的问题,导致精确度受损;且生成的谱峰分辨度低,易产生相互混叠。因此,现有方法在精确度和分辨度性能上仍存在较大的提升空间。
发明内容
本发明的目的在于针对上述方法中存在的信号多维空间结构信息丢失和空间谱分辨度、精度性能受限问题,提出一种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,为建立平面互质阵列张量信号统计量和虚拟域空间谱联系,搭建虚拟域张量空间谱搜索架构,实现高分辨、高精度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实现的:一种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,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,按照平面互质阵列的结构进行架构;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;该平面互质阵列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。定义维度集合
和
则通过对互相关张量
的理想值
(无噪声场景)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
的理想表示为:
(4)
中包含一个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
中共有D
x×D
y个虚拟阵元,其中D
x=M
xN
x+M
x+N
x-1,D
y=M
yN
y+M
y+N
y-1,
表示为:
(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Y
1×Y
2的子阵列,则可以将虚拟域均匀面阵
分割成L
1×L
2个互相部分重叠的均匀子阵列。将上述子阵列表示为
g
1=1,2,…,L
1,g
2=1,2,…,L
2,根据子阵列
对应虚拟域信号
中相应位置元素,得到虚拟域子阵列
的等价信号
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
1×L
2个维度均为Y
1×Y
2的虚拟域子阵信号
对这L
1×L
2个虚拟域子阵信号
求平均值,得到一个虚拟域平滑信号
(6)对四阶自相关张量v进行CANDECOMP/PARACFAC分解以提取多维特征,得到结果表示如下:
其中,
和
为CANDECOMP/PARACFAC分解得到的两组正交因子矢量,分别表示x轴和y轴方向上的空间信息,
和
为因子矩阵;取
张成的空间,记作
作为信号子空间,用一个张量
表示该信号子空间,其中
表示
沿着第三维度的第k个切片,表示为:
为了得到噪声子空间,对因子矩阵C
x和C
y求其正交补;C
x的正交补记为
C
y的正交补记为
其中min(·)表示取最小值操作;则取
作为噪声子空间,用张量
表示该噪声子空间,
表示
沿着第三维度的第h个切片,表示为:
其中,<×
{Q}>表示两个张量沿着第Q维度的模{Q}缩并操作,要求两个张量的第Q维度的大小相同;‖·‖
F表示Frobenius范数;
和
沿着第1,2维度的模{1,2}缩并操作得到一个矢量
得到空间谱函数
之后,可以构造出对应二维搜索波达方向的空间谱,随后通过搜索谱峰所在位置对应的二维波达方向,即为入射信源的二维波达方向估计。
进一步地,步骤(1)所述的平面互质阵列结构可具体描述为: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(0,0)坐标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(5)所述的虚拟域均匀面阵
的等价信号
中保存了虚拟域面阵的空间结构信息,然而,由于
可以视作一个单快拍的虚拟域信号,其统计量往往存在秩亏问题。因此,基于二维空间平滑的思想对虚拟域信号
进行处理,构造多个等效快拍虚拟域子阵信号,对这些虚拟域子阵信号进行求和平均后,求其四阶自相关张量。子阵列
中阵元的位置表示为:
进一步地,步骤(6)所述的提取虚拟域四阶自相关张量的多维特征以实现信号与噪声子空间分类,除了通过CANDECOMP/PARACFAC分解,还可以通过高阶奇异值分解实现,具体表示为:
其中,×
Q表示张量与矩阵沿着第Q维度的模Q内积;
表示包含高阶奇异值的核张量,
和
表示对应v四个维度的奇异矩阵。将D
x的前K列和后Y
1-K列分开为信号子空间
和噪声子空间
类似地,将D
y的前K列和后Y
2-K列分开为信号子空间
和噪声子空间
进一步地,步骤(7)中得到空间谱函数
之后进行二维谱峰搜索的具体步骤为:以a°为步长逐渐分别增加
的值,二维波达方向
的搜索起点为(-90°,0°),终点为(90°,180°);每个
可以对应计算出一个
的空间谱值,从而可以构造出一个对应于
的空间谱。空间谱中存在K个峰值,该K个峰值所对应的
的值,即为信源的二维波达方向估计。
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平面互质实际接收信号,不同于传统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二阶统计量,本发明将各采样快拍信号在第三维度上叠加,并利用包含四维空间信息的二阶互相关张量进行空间谱估计,保留了平面互质阵列实际入射信号的多维空间结构信息;
(2)本发明通过虚拟域等价信号的张量统计量分析构建虚拟域信号的子空间分类思路,为张量空间谱的构造提供了理论基础,从而解决了平面互质阵列信号失配的问题,实现了符合奈奎斯特匹配条件的虚拟域张量空间谱构造;
(3)本发明采用张量CANDECOMP/PARACFAC分解和高阶奇异值分解的方式对虚拟域信号的四阶自相关张量进行多维特征提取,从而建立起虚拟域模型与信号、噪声子空间之间的联系,为实现高精度、高分辨度的张量空间谱提供了基础。
图1是本发明的总体流程框图。
图2是本发明中平面互质阵列的结构示意图。
图3是本发明所推导增广虚拟域面阵结构示意图。
图4是本发明所构造张量空间谱示意图。
以下参照附图,对本发明的技术方案作进一步的详细说明。
为了解决现有方法存在的信号多维空间结构信息丢失和空间谱分辨度、精度性能受限问题,本发明提供了一种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。通过对平面互质阵列接收张量信号进行统计分析,构造具有虚拟域面阵空间结构信息的虚拟域等价信号;基于虚拟域 信号张量统计量的多维特征分析手段,建立虚拟域模型与张量空间谱之间的联系,从而在虚拟域上实现符合奈奎斯特匹配条件的基于张量空间谱搜索的高分辨精确二维波达方向估计方法。参照图1,本发明的实现步骤如下:
步骤1:构建平面互质阵列。在接收端使用4M
xM
y+N
xN
y-1个物理天线阵元构建平面互质阵列,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
u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;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(0,0)坐标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个物理天线阵元的平面互质阵列;
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;
为了得到对应于增广虚拟域面阵
的等价接收信号,将互相关张量
中表征x轴方向空间信息的第1、3维度合并成一个维度,将表征y轴方向空间信息的第2、4维度合并成另一个维度。张量的维度合并可通过其PARAFAC分解的模展开操作实现,具体地,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
表示为:
步骤4:构造虚拟域均匀面阵的等价接收信号。增广虚拟域面阵
中包含一个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
中共有D
x×D
y个虚拟阵元,其中D
x=M
xN
x+M
x+N
x-1,D
y=M
yN
y+M
y+N
y-1;虚拟域均匀面阵
的结构如图3中虚线框内所示,表示为:
其中,
步骤5:推导虚拟域平滑信号的四阶自相关张量。由上述步骤得到虚拟域均匀面阵
的等价信号
虚拟域信号
中保存了虚拟域面阵的空间结构信息,然而,由于
可以视作一个单快拍的虚拟域信号,其统计量往往存在秩亏问题。因此,基于二维空间平滑的思想对虚拟域信号
进行处理,构造多个等效快拍虚拟域子阵信号,对这些虚拟域子阵信号进行求和平均后,求其四阶自相关张量。具体做法为,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Y
1×Y
2的子阵列,则可以将虚拟域均匀面阵
分割成L
1×L
2个互相部分重叠的均匀子阵列,L
1、L
2、Y
1、Y
2之间满足以下关系:
Y
1+L
1-1=M
xN
x+M
x+N
x-1,
Y
2+L
2-1=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
1×L
2个维度均为Y
1×Y
2的虚拟域子阵信号
对这L
1×L
2个虚拟域子阵信号
求平均值,得到一个虚拟域平滑信号
步骤6:基于虚拟域四阶自相关张量的多维特征提取实现信号与噪声子空间分类。为了构建基于子空间分类思想的张量空间谱,对四阶自相关张量v进行CANDECOMP/PARACFAC分解以提取多维特征,得到结果表示如下:
其中,
和
为CANDECOMP/PARACFAC分解得到的两组正交因子矢量,分别表示x轴和y轴方向的空间信息,
和
为因子矩阵;取
张成的空间,记作
作为信号子空间,用一个张量
表示该信号子空间,其中
表示
沿着第三维度的第k个切片,表示为:
为了得到噪声子空间,需要对因子矩阵C
x和C
y求其正交补;C
x的正交补记为
C
y的正交补记为
其中min(·)表示取最小值操作;则取
作为噪声子空间,用张量
表示该噪声子空间,
表示
沿着第三维度的第h个切片,表示为:
除了使用张量分解提取虚拟域自相关张量的多维特征,还可以通过高阶奇 异值分解,具体表示为:
其中,×
Q表示张量与矩阵沿着第Q维度的模Q内积;
表示包含高阶奇异值的核张量,
和
表示对应v四个维度的奇异矩阵。将D
x的前K列和后Y
1-K列分开为信号子空间
和噪声子空间
类似地,将D
y的前K列和后Y
2-K列分开为信号子空间
和噪声子空间
得到空间谱函数
之后,通过二维谱峰搜索得到二维波达方向估计结果,具体步骤为:以搜索步长a°逐渐分别增加
的值,二维波达方向
的搜索起点为(-90°,0°),终点为(90°,180°);每个
可以对应计算出一个
的空间谱值,从而可以构造出一个对应于
的空间谱。空间谱中存在K个峰值,该K个峰值位置所对应的
的值,即为信源的二维波达方向估计。
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:采用互质阵列接收入射信号,其参数选取为M
x=2,M
y=2,N
x=3,N
y=3,即架构的互质阵列共包含4M
xM
y+N
xN
y-1=24个物理阵元。假定入射窄带信号个数为1,且入射方向方位角和仰角分别为[45°,50°];采用L=500个采样快拍及10dB的输入信噪比进行仿真实验。
本发明所提出的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的空间谱如图4所示。可以看出,本发明所提方法能够有效地构造出二维空间谱,其中对应入射信源的二维波达方向位置存在一个精尖的谱峰,该谱峰所对应的x轴和y轴的值即为入射信源的俯仰角和方位角。
综上所述,本发明充分考虑了平面互质阵列信号的多维结构信息,利用张量信号建模,构造具有虚拟域面阵空间结构信息的虚拟域等价信号,并通过分析其张量统计特性,构建起基于虚拟域自相关张量多维特征提取的子空间分类思路,建立起平面互质阵列虚拟域模型与张量空间谱之间的联系,解决了平面互质阵列的信号失配问题;同时,本发明通过利用张量分解和高阶奇异值分解两种张量特征提取手段,提出了高精度、高分辨度张量空间谱的构造机理,相较于现有方法,在空间谱的分辨度和二维波达方向估计精度性能上取得了突破。
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 (7)
- 一种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,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,按照平面互质阵列的结构进行架构;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;该平面互质阵列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, 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。定义维度集合 和 则通过对互相关张量 的理想值 (无噪声场景)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 的理想表示为:(4) 中包含一个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 中共有D x×D y个虚拟 阵元,其中D x=M xN x+M x+N x-1,D y=M yN y+M y+N y-1, 表示为:(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Y 1×Y 2的子阵列,则可以将虚拟域均匀面阵 分割成L 1×L 2个互相部分重叠的均匀子阵列;将上述子阵列表示为 根据子阵列 对应虚拟域信号 中相应位置元素,得到虚拟域子阵列 的等价信号其中, 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 1×L 2个维度均为Y 1×Y 2的虚拟域子阵信号 对这L 1×L 2个虚拟域子阵信号 求平均值,得到一个虚拟域平滑信号其中, 和 为CANDECOMP/PARACFAC分解得到的两组正交因子矢量,分别表示x轴和y轴方向上的空间信息, 和 为因子矩阵;取 张成的空间,记作 作为信号子空间,用一个张量 表示该信号子空间,其中 表示 沿着第三维度的第k个切片,表示为:
- 根据权利要求1所述的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,其特征在于,步骤(1)所述的平面互质阵列结构可具体描述为: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(0,0)坐标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。
Priority Applications (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/CN2020/088567 WO2021068494A1 (zh) | 2020-05-03 | 2020-05-03 | 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 |
JP2021541668A JP7044289B2 (ja) | 2020-05-03 | 2020-05-03 | コプライム平面アレーのバーチャルドメインテンソル空間スペクトル検索に基づく高解像の正確な二次元到来方向推定方法 |
US17/395,478 US11300648B2 (en) | 2020-05-03 | 2021-08-06 | High-resolution, accurate, two-dimensional direction-of-arrival estimation method based on coarray tensor spatial spectrum searching with co-prime planar array |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/CN2020/088567 WO2021068494A1 (zh) | 2020-05-03 | 2020-05-03 | 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 |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US17/395,478 Continuation US11300648B2 (en) | 2020-05-03 | 2021-08-06 | High-resolution, accurate, two-dimensional direction-of-arrival estimation method based on coarray tensor spatial spectrum searching with co-prime planar array |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2021068494A1 true WO2021068494A1 (zh) | 2021-04-15 |
Family
ID=75437799
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2020/088567 WO2021068494A1 (zh) | 2020-05-03 | 2020-05-03 | 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 |
Country Status (3)
Country | Link |
---|---|
US (1) | US11300648B2 (zh) |
JP (1) | JP7044289B2 (zh) |
WO (1) | WO2021068494A1 (zh) |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113552532B (zh) * | 2021-07-09 | 2022-03-22 | 浙江大学 | 基于耦合张量分解的l型互质阵列波达方向估计方法 |
CN114844544B (zh) * | 2022-04-28 | 2024-05-14 | 中国人民解放军国防科技大学 | 一种基于低管秩张量分解的互质阵列波束成形方法、系统及介质 |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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 |
Family Cites Families (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
FR2883980B1 (fr) * | 2005-04-01 | 2009-04-17 | Thales Sa | Procede et dispositif de goniometrie a haute resolution a un ordre pair arbitraire |
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 |
CN107037392B (zh) * | 2017-03-01 | 2020-08-07 | 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 |
US11315032B2 (en) * | 2017-04-05 | 2022-04-26 | Yahoo Assets Llc | Method and system for recommending content items to a user based on tensor factorization |
JP7362727B2 (ja) * | 2018-05-15 | 2023-10-17 | ライトマター インコーポレイテッド | フォトニック処理デバイス及び方法 |
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 |
-
2020
- 2020-05-03 JP JP2021541668A patent/JP7044289B2/ja active Active
- 2020-05-03 WO PCT/CN2020/088567 patent/WO2021068494A1/zh active Application Filing
-
2021
- 2021-08-06 US US17/395,478 patent/US11300648B2/en active Active
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
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 |
Non-Patent Citations (5)
Title |
---|
FAN JIN-YU, GU HONG, SU WEI-MIN, CHEN JIN-LI: "Co-prime MIMO Radar Multi-parameter Estimation Based on Tensor Decomposition", JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, KEXUE CHUBANSHE, vol. 37, no. 4, 1 April 2015 (2015-04-01), Kexue Chubanshe, pages 933 - 938, XP055800308, ISSN: 1009-5896, DOI: 10.11999/JEIT140826 * |
GUI YUFENG, RAO WEI: "Two-dimensional coprime vector-sensor array signal processing based on tensor decompositions", JOURNAL OF NANCHANG INSTITUTE OF TECHNOLOGY, vol. 38, no. 4, 1 August 2019 (2019-08-01), pages 83 - 91, XP055800302 * |
LIU CHUN-LIN; VAIDYANATHAN P. P.: "Tensor MUSIC in multidimensional sparse arrays", 2015 49TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS, IEEE, 8 November 2015 (2015-11-08), pages 1783 - 1787, XP032874535, DOI: 10.1109/ACSSC.2015.7421458 * |
RAO WEI, LI DAN, ZHANG JIAN QIU: "A Tensor-Based Approach to L-Shaped Arrays Processing With Enhanced Degrees of Freedom", IEEE SIGNAL PROCESSING LETTERS., IEEE SERVICE CENTER, PISCATAWAY, NJ., US, vol. 25, no. 2, 1 February 2018 (2018-02-01), US, pages 1 - 5, XP055800309, ISSN: 1070-9908, DOI: 10.1109/LSP.2017.2783370 * |
SHI JUNPENG, HU GUOPING, ZHANG XIAOFEI, SUN FENGGANG, ZHOU HAO: "Sparsity-Based Two-Dimensional DOA Estimation for Coprime Array: From Sum–Difference Coarray Viewpoint", IEEE TRANSACTIONS ON SIGNAL PROCESSING., IEEE SERVICE CENTER, NEW YORK, NY., US, vol. 65, no. 21, 1 November 2017 (2017-11-01), US, pages 5591 - 5604, XP055800311, ISSN: 1053-587X, DOI: 10.1109/TSP.2017.2739105 * |
Also Published As
Publication number | Publication date |
---|---|
JP7044289B2 (ja) | 2022-03-30 |
JP2022511994A (ja) | 2022-02-01 |
US11300648B2 (en) | 2022-04-12 |
US20210364591A1 (en) | 2021-11-25 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
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 | |
CN109932680B (zh) | 一种基于平移互质阵列的非圆信号波达方向估计方法 | |
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 | |
CN104898085B (zh) | 一种极化敏感阵列参数估计的降维music算法 | |
Chen et al. | ESPRIT-like two-dimensional direction finding for mixed circular and strictly noncircular sources based on joint diagonalization | |
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 | |
CN102608565B (zh) | 一种基于均匀圆阵列的波达方向估计方法 | |
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 | |
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 | |
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 | |
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 | |
CN113673317B (zh) | 基于原子范数最小化可降维的二维离格doa估计方法 | |
CN107907855A (zh) | 一种互素阵列转化为均匀线阵的doa估计方法及装置 | |
CN107092004A (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 | |
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 | |
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 | |
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 | |
CN104515969A (zh) | 一种基于六角形阵列的相干信号二维doa估计方法 | |
CN111965591A (zh) | 一种基于四阶累积量矢量化dft的测向估计方法 | |
CN109521393A (zh) | 一种基于信号子空间旋转特性的波达方向估计算法 | |
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 | |
CN109946663B (zh) | 一种线性复杂度的Massive MIMO目标空间方位估计方法和装置 | |
CN114200388A (zh) | 基于四阶采样协方差张量去噪的子阵分置式l型互质阵列波达方向估计方法 | |
CN109490821A (zh) | 一种基于music算法的降维圆和非圆混合信号doa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: 20874553 Country of ref document: EP Kind code of ref document: A1 |
|
ENP | Entry into the national phase |
Ref document number: 2021541668 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: 20874553 Country of ref document: EP Kind code of ref document: A1 |