WO2022126408A1 - 面向电磁矢量互质面阵的合成张量波束成形方法 - Google Patents
面向电磁矢量互质面阵的合成张量波束成形方法 Download PDFInfo
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- 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
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- 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
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01Q—ANTENNAS, i.e. RADIO AERIALS
- H01Q3/00—Arrangements for changing or varying the orientation or the shape of the directional pattern of the waves radiated from an antenna or antenna system
- H01Q3/26—Arrangements for changing or varying the orientation or the shape of the directional pattern of the waves radiated from an antenna or antenna system varying the relative phase or relative amplitude of energisation between two or more active radiating elements; varying the distribution of energy across a radiating aperture
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
- H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
- H04B7/0842—Weighted combining
- H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/10—Polarisation diversity; Directional diversity
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- 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
Definitions
- the invention belongs to the field of array signal processing, and relates to a spatial filtering technology for multi-dimensional sparse array received signals, in particular to a synthetic tensor beamforming method oriented to electromagnetic vector coprime area arrays.
- sparse arrays have larger array apertures and higher spatial resolution than traditional uniform arrays, and can form more precise beam directivity; among them, coprime arrays are used as A typical systematic sparse array architecture is currently a frontier research hotspot in academia.
- electromagnetic vector sensors can sense the direction of arrival and polarization state information of desired signals at the same time, so that the corresponding The spatial filtering is achieved simultaneously on the direction of arrival and the polarization state of the desired signal.
- tensors As a multi-dimensional data type, tensors have been widely used in array signal processing, image processing, machine learning and other fields in recent years to model and analyze multi-dimensional signals, thereby effectively preserving the original structure of multi-dimensional signals. information, and excavate its multi-dimensional spatial features.
- the traditional beamforming method based on vectorized signal processing is generalized in tensor space, which is expected to realize efficient spatial filtering of multi-dimensional received signals.
- the design of tensor beamforming methods for electromagnetic vector coprime area arrays faces the following difficulties: on the one hand, since the multi-dimensional received signals of electromagnetic vector coprime area arrays cover both direction of arrival and polarization state information, it is necessary to match their Complex spatial information structure, design suitable high-dimensional tensor beamforming weights; The output performance of beamforming causes serious losses, so it is necessary to effectively suppress virtual peaks to improve the output performance of beamforming. Therefore, how to simultaneously match the multi-dimensional received signal structure of the electromagnetic vector coprime array and the sparse array layout characteristics to achieve tensor beamforming with the ability to suppress virtual peaks is still a hot and difficult problem to be solved.
- a synthetic tensor beamforming method for electromagnetic vector coprime area arrays including:
- Step 2 The tensor modeling of the received signal of the electromagnetic vector coprime area array
- Step 4 form a tensor beam power pattern of a coprime sparse uniform sub-area array
- step 1 specifically includes:
- the three-dimensional spatial information of the signal received at time t that is, the direction of arrival information in the x-axis direction, the y-axis direction, and the spatial electromagnetic response information, is represented by a three-dimensional tensor, and the three-dimensional data of the collected T sampling snapshots are The signal tensors are superimposed in the fourth dimension as the time dimension, forming a sparse uniform sub-area matrix corresponding to The received signal tensor of Expressed as:
- the output signal at time t can be expressed as:
- ⁇ r represents the inner product of the tensor and the matrix along the rth dimension
- the weight tensor of The weighted equivalent is expressed as the above three beamforming weight vectors right
- the multi-dimensional weighting of , the corresponding optimization problem is expressed as:
- the output signal in the rth dimension is obtained by using the beamforming weight vector pair of the remaining two dimensions except the rth dimension After weighting, it is obtained as:
- step 4 specifically includes:
- the power value of the tensor beam is the largest, which is regarded as the main lobe; on the two-dimensional direction of arrival plane, the sparse uniform sub-area array and The tensor beam power pattern of and There are virtual peaks in all and their corresponding virtual peak positions and do not overlap each other, i.e.
- step 5 specifically includes:
- Coprime synthesis processing is performed on the output signals of the two sparse uniform sub-surface arrays whose virtual peak positions do not overlap each other, so as to realize electromagnetic vector coprime surface array tensor beamforming with virtual peak suppression; wherein, the coprime synthesis
- the processing includes: co-prime synthesis processing based on the multiplicative criterion and co-prime synthesis processing based on the minimization power criterion.
- the processing principle of the coprime synthesis based on the multiplicative criterion is: in the two-dimensional direction of arrival superior, The tensor beam power pattern of Corresponding to the virtual peak, The tensor beam power pattern of does not correspond to the virtual peak, so in location will be and Multiplying the tensor beam power of , the virtual peak will be suppressed; similarly, in the two-dimensional direction of arrival superior, The tensor beam power pattern of Corresponding to the virtual peak, The tensor beam power pattern of does not correspond to the virtual peak, then the and By multiplying the tensor beam power of , the virtual peak corresponding to this position can also be suppressed. and The output signal at time t and Multiply to get, expressed as:
- the processing principle of the coprime synthesis based on the minimization power criterion is: in the two-dimensional direction of arrival superior, The virtual peak response value of more than the The non-imaginary peak position of the corresponding response value By selecting the minimum value among them, the suppression of virtual peaks is realized; similarly, in superior, The virtual peak response value of more than the The non-virtual peak position response value of By selecting the minimum value among them, the suppression of virtual peaks is also achieved;
- the output signal y min (t) of the electromagnetic vector coprime area array based on the minimization power criterion is the sparse uniform sub-area array and The output signal at time t and The power of the minimization process is obtained:
- the present invention has the following advantages:
- the present invention matches the multi-dimensional received signal structure of the electromagnetic vector coprime area array, and at the same time retains its original structural information by constructing the tensorized signal, the spatial filtering principle of the received signal tensor of the coprime sparse uniform sub-area array is formed, It lays a foundation for realizing electromagnetic vector coprime array tensor beamforming with virtual peak suppression capability;
- the present invention matches the co-prime layout characteristics of two sparse uniform sub-arrays, and obtains the mutual non-overlapping characteristics of the virtual peaks of the two sparse uniform sub-arrays, and based on this, constructs a sparse uniform sub-array based on
- the two coprime synthesis processing methods proposed under this framework can effectively achieve virtual peak suppression;
- the present invention fully combines the multi-dimensional received signal structure of the electromagnetic vector coprime area array and the sparse arrangement of the array, and establishes the relationship between the multidimensional received signal structure of the electromagnetic vector coprime area array and the tensor space filtering principle, as well as the sparse homogenizer
- the correlation between the co-prime layout characteristics of the area array and the distribution of virtual peaks forms the technical route of electromagnetic vector co-prime area array tensor beamforming based on the co-prime synthesis of sparse uniform sub-area arrays.
- Fig. 1 is the overall flow chart of the present invention
- Fig. 2 is the structural representation of the electromagnetic vector coprime array in the present invention
- FIG. 4 is a block diagram of a coprime synthesis process based on the minimization power criterion proposed by the present invention
- Fig. 6a is the performance comparison diagram of the output SINR of the present invention with the signal-to-noise ratio SNR change;
- the present invention adopts the spatial filtering of the received signal tensor by the coprime sparse uniform sub-array, and the co-prime synthesis of the output signal of the sub-area array matching the characteristic that the virtual peaks corresponding to the co-prime sparse uniform sub-array do not overlap each other.
- processing to realize electromagnetic vector coprime surface array tensor beamforming with virtual peak suppression capability and improved output performance include:
- Step 1 Construct the electromagnetic vector coprime area array
- Each antenna element uses three mutually orthogonal electric dipoles and three mutually orthogonal magnetic dipoles to realize the perception of the electromagnetic field, with six outputs ;
- a pair of sparse uniform sub-area arrays are constructed on the plane coordinate system xoy and and respectively contain and antenna elements, as well as respectively a pair of coprime integers; sparse uniform subarea array
- the spacing of the antenna elements in the x-axis and y-axis directions are respectively and B The positions of the antenna elements in the x-axis and y-axis directions are and in, but B The positions of the antenna elements in the x-axis and y-axis directions are and in, Will and According to the array element at the origin of the coordinate system Combine subarrays in an overlapping manner to obtain the actual containing The electromagnetic vector coprime array of antenna elements;
- Step 2 The tensor modeling of the received signal of the electromagnetic vector coprime area array
- each array element in the electromagnetic vector coprime array also contain the direction of arrival information and polarization state information where ⁇ [0,2 ⁇ ] and ⁇ [- ⁇ , ⁇ ] represent the polarization auxiliary angle and polarization phase difference, respectively, and the direction of arrival matrix
- the polarization state vector g( ⁇ , ⁇ ) can be specifically defined as:
- each array element in the electromagnetic vector coprime array can use a space electromagnetic response vector Expressed as:
- the three-dimensional spatial information of the signal received at time t that is, the direction of arrival information in the x-axis direction, the y-axis direction, and the spatial electromagnetic response information, is represented by a three-dimensional tensor, and the three-dimensional data of the collected T sampling snapshots are The signal tensors are superimposed in the fourth dimension as the time dimension, forming a sparse uniform sub-area matrix corresponding to The received signal tensor of Expressed as:
- ⁇ > represents the tensor inner product
- ( ⁇ ) * represents the conjugate operation, in order to obtain the tensor beamformer corresponding to the two sparse uniform sub-area arrays Minimize the average output power of the tensor beamformer, and ensure that the direction of arrival of the desired signal and its corresponding polarization state response are free of distortion, and perform optimization processing, the expression is:
- ( ⁇ ) H represents the conjugate transpose operation, and the corresponding sparse uniform sub-area matrix is solved sequentially by the Lagrange multiplier method. and Three beamforming weight vectors each The six sub-optimization problems of , whose closed-form solutions are:
- Step 4 form a tensor beam power pattern of a coprime sparse uniform sub-area array
- the output signals of the co-prime sparse uniform sub-array are synthesized and processed to realize electromagnetic vector co-prime array tensor beamforming with virtual peak suppression.
- the co-prime synthesis processing of the output signal of the sparse uniform sub-area array includes: co-prime synthesis processing based on the multiplicative criterion and co-prime synthesis processing based on the minimization power criterion;
- the processing principle of the coprime synthesis based on the multiplicative criterion is: because in the two-dimensional direction of arrival, the superior, The tensor beam power pattern of corresponds to the virtual peak, while The tensor beam power pattern of does not correspond to the virtual peak, so in location will be and Multiplying the tensor beam power of , the virtual peak will be suppressed; similarly, in the two-dimensional direction of arrival superior, The tensor beam power pattern of corresponds to the virtual peak, while The tensor beam power pattern of does not correspond to the virtual peak, then the and By multiplying the tensor beam power of , the virtual peak corresponding to this position can also be suppressed.
- the output signal y mul (t) of the electromagnetic vector coprime area array based on the multiplicative criterion is obtained by dividing the sparse uniform sub-area array into and The output signal at time t and Multiply to get, expressed as:
- its tensor beam power pattern is the arithmetic square root of the product of two sparse uniform sub-area tensor beam power patterns:
- the processing principle of the coprime synthesis based on the minimization power criterion is: in the two-dimensional direction of arrival on, because The virtual peak response value of more than the The non-imaginary peak position of the corresponding response value By selecting the minimum value among them, the suppression of virtual peaks is realized; similarly, in on, because The virtual peak response value of more than the The non-virtual peak position response value of By selecting the minimum value among them, the suppression of virtual peaks will also be achieved; as shown in Figure 4, the output signal under this criterion is a sparse uniform sub-area array and The output signal at time t and The power of the minimization process is obtained:
- min( ) represents the operation of taking the minimum value; correspondingly, its tensor beam power pattern is formed by selecting the minimum value for the tensor beam power comparison of two sparse uniform sub-arrays in each two-dimensional direction of arrival:
- the minimization power criterion constrains the response of the virtual peak to the greatest extent on the tensor beam power pattern, the corresponding electromagnetic coprime array tensor beamforming is better than the electromagnetic vector based on the multiplicative criterion in performance.
- Coprime Array Tensor Beamforming since the minimization power criterion constrains the response of the virtual peak to the greatest extent on the tensor beam power pattern, the corresponding electromagnetic coprime array tensor beamforming is better than the electromagnetic vector based on the multiplicative criterion in performance.
Abstract
本发明属于阵列信号处理领域,一种面向电磁矢量互质面阵的合成张量波束成形方法,包括:构建电磁矢量互质面阵;电磁矢量互质面阵接收信号的张量建模;对应互质稀疏均匀子面阵的三维权重张量设计;形成互质稀疏均匀子面阵的张量波束功率图样;基于稀疏均匀子面阵互质合成处理的电磁矢量互质面阵张量波束成形。本发明从构成电磁矢量互质面阵两个稀疏均匀子面阵的接收信号张量空域滤波原理出发,形成基于稀疏均匀子面阵输出信号的互质合成处理方法,在有效抑制虚峰的条件下,实现电磁矢量互质面阵张量波束成形输出性能的提升,可用于目标定位跟踪与成像。
Description
本发明属于阵列信号处理领域,涉及多维稀疏阵列接收信号的空域滤波技术,具体为一种面向电磁矢量互质面阵的合成张量波束成形方法。
波束成形作为阵列信号处理的关键技术之一,被广泛应用于雷达、射电天文、医学成像和5G通信等领域。在软硬件资源受限的情况下,稀疏阵列相比于传统的均匀阵列,拥有更大的阵列孔径和更高的空间分辨率,能够形成更加精尖的波束指向性;其中,互质阵列作为一种典型的系统化稀疏阵列架构,是当前学术界的前沿研究热点。另一方面,为了满足复杂信号探测场景对空间信号极化信息的需求,电磁矢量传感器与传统的标量传感器阵列相比,可以同时感知期望信号的波达方向和极化状态信息,从而能够在对应期望信号的波达方向和极化状态上同时实现空域滤波。为此,在融合电磁矢量传感器与互质面阵的新形态阵列架构上探索有效的波束成形手段,有望实现相关应用领域的性能突破。然而,当前面向电磁矢量互质面阵的波束成形方法研究仍处于起步阶段,由于电磁矢量互质面阵的接收信号涵盖多维度的空间信息,传统矢量化接收信号进行处理分析的手段将破坏其原始的结构化信息。
张量作为一种多维的数据类型,近年来被广泛应用于阵列信号处理、图像处理、机器学习等多个领域,用于进行多维信号的建模和分析,从而有效保留多维信号的原始结构化信息,并挖掘其多维度空间特征。在阵列信号处理领域,将传统基于矢量化信号处理的波束成形方法在张量空间中进行推广,有望实现多维接收信号的高效空域滤波。然而,面向电磁矢量互质面阵的张量波束成形方法设计面临着以下困难:一方面,由于电磁矢量互质面阵的多维接收信号同时涵盖了波达方向和极化状态信息,需要匹配其复杂空间信息结构,设计相适应的高维张量波束成形权重;另一方面,由于电磁矢量互质面阵中阵元的稀疏布设不满足奈奎斯特采样速率,所引入的虚峰将对波束成形的输出性能造成严重损失,因此需要对虚峰进行有效抑制,以提升波束成形的输出性能。因此,如何同时匹配电磁矢量互质面阵的多维接收信号结构和阵列稀疏布设特点,实现具有虚峰抑制能力的张量波束成形,仍然是一个亟待解决的热点和难点问题。
发明内容
为了解决现有技术中存在的多维信号结构化信息损失和虚峰干扰技术问题,本发明提出一种面向电磁矢量互质面阵的合成张量波束成形方法,其具体技术方案如下。
面向电磁矢量互质面阵的合成张量波束成形方法,包括:
步骤1:构建电磁矢量互质面阵;
步骤2:电磁矢量互质面阵接收信号的张量建模;
步骤3:对应互质稀疏均匀子面阵的三维权重张量设计;
步骤4:形成互质稀疏均匀子面阵的张量波束功率图样;
步骤5:基于稀疏均匀子面阵互质合成处理的电磁矢量互质面阵张量波束成形。
进一步的,所述步骤1具体包括:
在接收端的平面坐标系xoy上构造一对稀疏均匀子面阵
和
和
分别包含
和
个天线阵元,
以及
分别为一对互质整数;稀疏均匀子面阵
的天线阵元在x轴和y轴方向上的间隔分别为
和
单位间隔d=λ/2,λ表示信号波长;
同理,稀疏均匀子面阵
的天线阵元在x轴和y轴方向上的间隔分别为
和
中第
个天线阵元在x轴和y轴方向上的位置分别为
和
其中,
则
中第
个天线阵元在x轴和y轴方向上的位置分别为
和
其中,
将
和
按照坐标系原点位置处阵元
重叠的方式进行子阵列组合,获得实际包含
个天线阵元的电磁矢量互质面阵,每个天线阵元利用三个相互正交的电偶极子和三个相互正交的磁偶极子来实现电磁场的感知,具备六路输出。
进一步的,所述步骤2具体包括:
设置一个远场窄带期望信号从
方向入射至所述电磁矢量互质面阵,其中θ和
分别表示所述期望信号的方位角和俯仰角,且θ∈[-π/2,π/2],
电磁矢量互质面阵中各阵元的六路输出同时包含了波达方向信息
和极化状态信息
其中γ∈[0,2π]和η∈[-π,π]分别表示极化辅助角和极化相位差,波达方向矩阵
和极化状态矢量g(γ,η)具体定义为:
保留稀疏均匀子面阵
在t时刻接收信号的三维空间信息,即x轴方向、y轴方向的波达方向信息以及空间电磁响应信息,采用一个三维张量对其进行表示,并将所采集T个采样快拍的三维信号张量在第四维度为时间维度上进行叠加,构成对应于稀疏均匀子面阵
的接收信号张量
表示为:
其中:
分别表示电磁矢量互质面阵在x轴和y轴方向上的期望信号导引矢量,且
为期望信号的信号波形,ο表示矢量外积,(·)
T表示转置操作,
为独立同分布的加性高斯白噪声张量;则
分别表示电磁矢量互质面阵在x轴和y轴方向上的导引矢量,对应于第g个干扰信号,
表示第g个干扰信号的信号波形。
进一步的,所述步骤3具体包括:
其中:<·>表示张量内积,(·)
*表示共轭操作,然后最小化张量波束成形器的平均输出功率,并进行优化处理,使得期望信号的波达方向及其对应极化状态响应无失真,获得两个稀疏均匀子面阵所对应的张量波束成形器
所述优化处理表达式为:
其中:
表示稀疏均匀子面阵
对应于期望信号波达方向
和极化状态(γ,η)的三维空间流形张量,|·|表示复数的求模操作,E[·]表示取期望操作;求解得到分别对应稀疏均匀子面阵
和
的三维权重张量
和
并生成输出信号
和
其中,×
r表示张量和矩阵沿着第r维度的内积;
进一步的,所述步骤4具体包括:
进一步的,所述步骤5具体包括:
对所述的虚峰位置互不重叠的两个稀疏均匀子面阵的输出信号进行互质合成处理,实现虚峰抑制的电磁矢量互质面阵张量波束成形;其中,所述互质合成处理包括:基于乘性准则的互质合成处理和基于最小化功率准则的互质合成处理。
进一步的,所述基于乘性准则的互质合成处理原理为:在二维波达方向上
上,
的张量波束功率图样
对应虚峰,
的张量波束功率图样
并不对应虚峰,因此在
的位置将
和
的张量波束功率相乘,虚峰将被抑制;同理,在二维波达方向
上,
的张量波束功率图样
对应虚峰,
的张量波束功率图样
并不对应虚峰,则通过将
和
的张量波束功率相乘,该位置所对应的虚峰也可被抑制;将基于乘性准则的电磁矢量互质面阵输出信号y
mul(t)通过 将稀疏均匀子面阵
和
在t时刻的输出信号
和
相乘得到,表示为:
相应地,该电磁矢量互质面阵的张量波束功率图样为两个稀疏均匀子面阵张量波束功率图样乘积的算术平方根:
进一步的,所述基于最小化功率准则的互质合成处理原理为:在二维波达方向
上,
的虚峰响应值
大于
的非虚峰位置对应响应值
通过选取它们中的最小值,实现虚峰的抑制;同理,在
上,
的虚峰响应值
大于
的非虚峰位置响应值
通过选取它们中的最小值,也实现虚峰的抑制;将基于最小化功率准则的电磁矢量互质面阵的输出信号y
min(t)是对稀疏均匀子面阵
和
在t时刻的输出信号
和
的功率取最小化处理得到:
其中,min(·)表示取最小值操作;相应地,该电磁矢量互质面阵的张量波束功率图样是对各二维波达方向上两个稀疏均匀子面阵的张量波束功率对比选取最小值构成的:
本发明与现有技术相比具有以下优点:
(1)本发明匹配电磁矢量互质面阵的多维接收信号结构,在通过构造张量化信号保留其原始结构化信息的同时,形成互质稀疏均匀子面阵接收信号张量的空域滤波原理,为实现具有虚峰抑制能力的电磁矢量互质面阵张量波束成形奠定了基础;
(2)本发明匹配两个稀疏均匀子面阵的互质布设特点,得出这两个稀疏均匀子面阵虚峰的互不重叠特点,并以此为基础,构建基于稀疏均匀子面阵的互质合成处理技术框架,在该框架下提出的两种互质合成处理手段均有效地实现了虚峰抑制;
(3)本发明充分结合了电磁矢量互质面阵的多维接收信号结构和阵列稀疏布设特点,建立起电磁矢量互质面阵多维接收信号结构与张量空域滤波原理之间、以及稀疏均匀子面阵互质布设特点与虚峰分布之间的关联性,形成了基于稀疏均匀子面阵互质合成处理的电磁矢量互质面阵张量波束成形技术路线。
图1是本发明的总体流程框图;
图2是本发明中电磁矢量互质面阵的结构示意图;
图3是本发明所提基于乘性准则的互质合成处理流程框图;
图4是本发明所提基于最小化功率准则的互质合成处理流程框图;
图5a是本发明的基于乘性规准则的张量波束功率图样效果示意图;
图5b是本发明的基于最小化功率准则的张量波束功率图样效果示意图;
图6a是本发明的输出SINR随信噪比SNR变化的性能对比图;
图6b是本发明的输出SINR随采样快拍数T变化的性能对比图。
为了使本发明的目的、技术方案和技术效果更加清楚明白,以下结合说明书附图和实施例,对本发明作进一步详细说明。
如图1所示,本发明通过互质稀疏均匀子面阵接收信号张量的空域滤波,以及匹配互质稀疏均匀子面阵所对应虚峰互不重叠特点的子面阵输出信号互质合成处理,实现具有虚峰抑制能力且输出性能提升的电磁矢量互质面阵张量波束成形,具体实现步骤包括:
步骤1:构建电磁矢量互质面阵;
如图2所示,在平面坐标系xoy上构造一对稀疏均匀子面阵
和
和
分别包含
和
个天线阵元,
以及
分别为一对互质整数;稀疏均匀子面阵
的天线阵元在x轴和y轴方向上的间隔分别为
和
单位间隔d=λ/2,λ表示信号波长;同理,稀疏均匀子面阵
的天线阵元在x轴和y轴方向上的间隔分别为
和
中第
个天线阵元在x轴和y轴方向上的位置分别为
和
其中,
则
中第
个天线阵元在x轴和y轴方向上的位置分别为
和
其中,
将
和
按照坐标系原点位置处阵元
重叠的方式进行子阵列组合,获得实际包含
个天线阵元的电磁矢量互质面阵;
步骤2:电磁矢量互质面阵接收信号的张量建模;
设置一个远场窄带期望信号从
方向入射至电磁矢量互质面阵,其中θ和
分别表示所述期望信号的方位角和俯仰角,且θ∈[-π/2,π/2],
电磁矢量互质面阵中各阵元的六路输出同时包含了波达方向信息
和极化状态信息
其中γ∈[0,2π]和η∈[-π,π]分别表示极化辅助角和极化相位差,波达方向矩阵
和极化状态矢量g(γ,η)可具体定义为:
保留稀疏均匀子面阵
在t时刻接收信号的三维空间信息,即x轴方向、y轴方向的波达方向信息以及空间电磁响应信息,采用一个三维张量对其进行表示,并将所采集T个采样快拍的三维信号张量在第四维度为时间维度上进行叠加,构成对应于稀疏均匀子面阵
的接收信号张量
表示为:
其中,
和
分别表示电磁矢量互质面阵在x轴和y轴方向上的期望信号导引矢量,且
为期望信号的信号波形,ο表示矢量外积,(·)
T表示转置操作,
为独立同分布的加性高斯白噪声张量;则
分别表示电磁矢量互质面阵在x轴和y轴 方向上的导引矢量,对应于第g个干扰信号,
表示第g个干扰信号的信号波形;
步骤3:对应互质稀疏均匀子面阵的三维权重张量设计;
其中,<·>表示张量内积,(·)
*表示共轭操作,为了获得两个稀疏均匀子面阵所对应的张量波束成形器
最小化张量波束成形器的平均输出功率,并保证期望信号的波达方向及其对应极化状态响应无失真,进行优化处理,表达式为:
其中,
表示稀疏均匀子面阵
对应于期望信号波达方向
和极化状态(γ,η)的三维空间流形张量,|·|表示复数的求模操作,E[·]表示取期望操作;求解得到分别对应稀疏均匀子面阵
和
的三维权重张量
和
并生成输出信号
和
步骤4:形成互质稀疏均匀子面阵的张量波束功率图样;
其中,
当波达方向在期望信号方向上,即
时,
的张量波束功率值最大,视为主瓣。然而,由于稀疏均匀子面阵中的阵元间距大于半波长,不满足奈奎斯特采样速率,导致当
时,
存在虚峰,而当
时,
存在虚峰;由于稀疏均匀子面阵
和
沿着x轴方向和y轴方向的阵元排布都满足互质特性,因此在二维波达方向平面上, 稀疏均匀子面阵
和
分别对应的虚峰位置
和
互不重叠,即
步骤5:基于稀疏均匀子面阵互质合成处理的电磁矢量互质面阵张量波束成形;
利用所述两个稀疏均匀子面阵虚峰位置互不重叠的特点,对互质稀疏均匀子面阵的输出信号进行合成处理,实现虚峰抑制的电磁矢量互质面阵张量波束成形。
所述稀疏均匀子面阵输出信号的互质合成处理包括:基于乘性准则的互质合成处理和基于最小化功率准则的互质合成处理;
所述基于乘性准则的互质合成处理原理为:由于在二维波达方向上
上,
的张量波束功率图样
对应虚峰,而
的张量波束功率图样
并不对应虚峰,因此在
的位置将
和
的张量波束功率相乘,虚峰将被抑制;同理,在二维波达方向
上,
的张量波束功率图样
对应虚峰,而
的张量波束功率图样
并不对应虚峰,则通过将
和
的张量波束功率相乘,该位置所对应的虚峰也可被抑制。如图3所示,基于乘性准则的电磁矢量互质面阵输出信号y
mul(t)通过将稀疏均匀子面阵
和
在t时刻的输出信号
和
相乘得到,表示为:
相应地,其张量波束功率图样为两个稀疏均匀子面阵张量波束功率图样乘积的算术平方根:
所述基于最小化功率准则的互质合成处理原理为:在二维波达方向
上,由于
的虚峰响应值
大于
的非虚峰位置对应响应值
通过选取它们中的最小值,实现虚峰的抑制;同理,在
上,由于
的虚峰响应值
大于
的非虚峰位置响应值
通过选取它们中的最小值,也将实现虚峰的抑制;如图4所示,该准则下的输出信号是对稀疏均匀子面阵
和
在t时刻的输出信号
和
的功率取最小化处理得到:
其中,min(·)表示取最小值操作;相应地,其张量波束功率图样是对各二维波达方向上两个稀疏均匀子面阵的张量波束功率对比选取最小值构成的:
下面结合实施例对本发明的效果做进一步的描述。
在期望信号的信噪比(Signal-to-Noise Ratio,SNR)为0dB,采样快拍数T=300的条件下,绘制基于乘性准则和基于最小化功率准则的电磁矢量互质面阵张量波束功率图样
和
如图图5a和图5b所示,电磁矢量互质面阵的张量波束功率图样在期望信号波达方向的位置对应一个主瓣,而其他的位置不存在虚峰,由此可见,所提电磁矢量互质面阵合成张量波束成形方法有效地抑制了虚峰。
实施例2:进一步地,对比所提电磁矢量互质面阵合成张量波束成形方法与基于电磁矢量均匀面阵的张量信号处理方法的输出信干噪比(Signal-to-Interference-plus-Noise Ratio,SINR)性能;为了保证仿真对比的公平性,电磁矢量均匀面阵按照5行8列的结构排布40个阵元;在采样快拍数T=300条件下,绘制输出SINR随信噪比SNR变化的性能对比曲线,如图6a所示;在SNR=0dB条件下,绘制输出SINR随采样快拍数T变化的性能对比曲线,如图6b所示。从图6a和6b的对比结果可以看出,无论是在不同的期望信号信噪比SNR场景,还是在不同的采样快拍数T场景下,所提基于乘性准则和最小化功率准则的电磁矢量互质面阵合成张量波束成形方法的输出SINR性能均优于基于电磁矢量均匀面阵的张量信号处理方法。得益于电磁矢量互质面阵的阵元稀疏排布带来的大孔径优势以及所提方法对虚峰的有效抑制作用,电磁矢量互质面阵相较于均匀面阵具有更高的输出SINR。与此同时,由于最小化功率准则在张量波束功率图样上最大程度地约束虚峰的响应,其所对应的电磁互质面阵张量波束成形在性能上优于基于乘性准则的电磁矢量互质面阵张量波束成形。
综上所述,本发明匹配电磁矢量互质面阵多维接收信号中涵盖的结构化空间信息,形成了面向互质稀疏均匀子面阵接收信号张量的空域滤波原理;再者,匹配两个稀疏均匀子面阵的互质布设特点,利用二者张量波束功率图样中虚峰互不重叠的特点,对稀疏均匀子面阵的 输出信号进行互质合成处理,从而实现具有虚峰抑制能力且输出性能提升的电磁矢量互质面阵张量波束成形。
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 (8)
- 面向电磁矢量互质面阵的合成张量波束成形方法,其特征在于,包括:步骤1:构建电磁矢量互质面阵;步骤2:电磁矢量互质面阵接收信号的张量建模;步骤3:对应互质稀疏均匀子面阵的三维权重张量设计;步骤4:形成互质稀疏均匀子面阵的张量波束功率图样;步骤5:基于稀疏均匀子面阵互质合成处理的电磁矢量互质面阵张量波束成形。
- 如权利要求1所述的面向电磁矢量互质面阵的合成张量波束成形方法,其特征在于,所述步骤1具体包括:在接收端的平面坐标系xoy上构造一对稀疏均匀子面阵 和 和 分别包含 个天线阵元, 以及 分别为一对互质整数;稀疏均匀子面阵 的天线阵元在x轴和y轴方向上的间隔分别为 和 单位间隔d=λ/2,λ表示信号波长;
- 如权利要求2所述的面向电磁矢量互质面阵的合成张量波束成形方法,其特征在于,所述步骤2具体包括:设置一个远场窄带期望信号从 方向入射至所述电磁矢量互质面阵,其中θ和 分别表示所述期望信号的方位角和俯仰角,且θ∈[-π/2,π/2], 电磁矢量互质面阵中各阵元的六路输出同时包含了波达方向信息 和极化状态信息 其中γ∈[0,2π]和η∈[-π,π]分别表示极化辅助角和极化相位差,波达方向矩阵 和极化状态矢量g(γ,η)具体定义为:保留稀疏均匀子面阵 在t时刻接收信号的三维空间信息,即x轴方向、y轴方向的波达方向信息以及空间电磁响应信息,采用一个三维张量对其进行表示,并将所采集T个采样快拍的三维信号张量在第四维度为时间维度上进行叠加,构成对应于稀疏均匀子面阵 的接收信号张量 表示为:其中:
- 如权利要求3所述的面向电磁矢量互质面阵的合成张量波束成形方法,其特征在于,所述步骤3具体包括:其中:<·>表示张量内积,(·) *表示共轭操作,然后最小化张量波束成形器的平均输出功率,并进行优化处理,使得期望信号的波达方向及其对应极化状态响应无失真,获得两个稀疏均匀子面阵所对应的张量波束成形器 所述优化处理表达式为:其中: 表示稀疏均匀子面阵 对应于期望信号波达方向 和极化状态(γ,η)的三维空间流形张量,|·|表示复数的求模操作,E[·]表示取期望操作;求解得到分别对应稀疏均匀子面阵 和 的三维权重张量 和 并生成输出信号 和其中:× r表示张量和矩阵沿着第r维度的内积;
- 如权利要求5所述的面向电磁矢量互质面阵的合成张量波束成形方法,其特征在于,所述步骤5具体包括:对所述的虚峰位置互不重叠的两个稀疏均匀子面阵的输出信号进行互质合成处理,实现虚峰抑制的电磁矢量互质面阵张量波束成形;其中,所述互质合成处理包括:基于乘性准则的互质合成处理和基于最小化功率准则的互质合成处理。
- 如权利要求6所述的面向电磁矢量互质面阵的合成张量波束成形方法,其特征在于,所述基于乘性准则的互质合成处理原理为:在二维波达方向上 上, 的张量波束功率图样 对应虚峰, 的张量波束功率图样 并不对应虚峰,因此在 的位置将 和 的张量波束功率相乘,虚峰将被抑制;同理,在二维波达方向 上, 的张量波束功率图样 对应虚峰, 的张量波束功率图样 并不对应虚峰,则通过将 和 的张量波束功率相乘,该位置所对应的虚峰也可被抑制;将基于乘性准则的电磁矢量互质面阵输出信号y mul(t)通过将稀疏均匀子面阵 和 在t时刻的输出信号 和 相乘得到,表示为:相应地,该电磁矢量互质面阵的张量波束功率图样为两个稀疏均匀子面阵张量波束功率图样乘积的算术平方根:
- 如权利要求6所述的面向电磁矢量互质面阵的合成张量波束成形方法,其特征在于,所述基于最小化功率准则的互质合成处理原理为:在二维波达方向 上, 的虚峰响应值 大于 的非虚峰位置对应响应值 通过选取它们中的最小值,实现虚峰的抑制;同理,在 上, 的虚峰响应值 大于 的非虚峰位置响应值 通过选取它们中的最小值,也实现虚峰的抑制;将基于最小化功率准则的电磁矢量互质面阵的输出信号y min(t)是对稀疏均匀子面阵 和 在t时刻的输出信号 和 的功率取最小化处理得到:其中:min(·)表示取最小值操作;相应地,该电磁矢量互质面阵的张量波束功率图样是对各二维波达方向上两个稀疏均匀子面阵的张量波束功率对比选取最小值构成的:
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