Interference incoming direction estimation method and device, equipment and storage medium

Abstract

The invention discloses an interference incoming direction estimation method and device, equipment and a storage medium, and is applied to the technical field of satellite navigation. The method comprises the following steps: constructing a spherical coordinate system by taking the center of a uniform circular array as a coordinate origin; obtaining a space steering vector of the array according to a phase relation of each array element in the uniform circular array relative to the origin of coordinates; constructing a unitary matrix to transform the array manifold of the uniform circular array to obtain a received data covariance matrix; obtaining an interference signal subspace through eigenvalue decomposition; constructing a generalized eigenvector reconstruction covariance matrix based on the interference signal subspace; defining a new interference signal subspace; and estimating the interference direction by using the new interference signal subspace and the space steering vector. According to the method, unitary matrix transformation array flow patterns are constructed, decorrelation processing is carried out on a covariance matrix after transformation based on the corresponding relation of feature vectors before and after transformation, generalized feature vectors and steering vector space, and estimation of the interference direction is achieved through a subspace algorithm.

Description

Technical Field

[0001] The present application relates to the field of satellite navigation technology, and in particular to an interference direction estimation method, device, equipment and storage medium. Background Art

[0002] With the rapid development of global satellite navigation systems and navigation and positioning technologies, the accuracy of satellite navigation positioning is becoming more and more precise, and satellite navigation services will play a more important role in society and life. However, it is inevitable that during the transmission of satellite navigation signals, the navigation signals received by the receiver will always receive intentional or unintentional interference, resulting in incorrect positioning results of the receiver. Therefore, the safe application of satellite navigation systems and their interference perception capabilities are valued by the majority of users.

[0003] In the process of satellite navigation signal propagation in the atmosphere, in addition to being susceptible to self-interference and electromagnetic interference under complex environmental conditions, the receiver may also be interfered by human factors.

[0004] Therefore, there is an urgent need for a method to obtain the direction of interference so as to estimate the direction of the interference source and take corresponding measures to deal with it. Summary of the invention

[0005] Based on the above problems, the present application provides an interference direction estimation method, apparatus, device and storage medium, which can estimate the direction of the interference source so as to take corresponding measures to deal with it.

[0006] The embodiments of the present application disclose the following technical solutions:

[0007] In a first aspect, the present application provides an interference direction estimation method, which is applied to a uniform circular array, and the method comprises:

[0008] Construct a spherical coordinate system with the center of the uniform circular array as the coordinate origin;

[0009] Acquire the spatial steering vector of the array according to the phase relationship of each array element in the uniform circular array relative to the coordinate origin;

[0010] Constructing a unitary matrix to transform the array manifold of the uniform circular array to obtain a transformed covariance matrix of the uniform circular array received data;

[0011] Obtaining an interference signal subspace by performing eigenvalue decomposition on the data covariance matrix;

[0012] Constructing a generalized eigenvector based on the interference signal subspace to reconstruct a covariance matrix;

[0013] By performing eigenvalue decomposition on the generalized eigenvector reconstruction covariance matrix, a new interference signal subspace is obtained;

[0014] The interference direction is estimated using the new interference signal subspace and the spatial steering vector.

[0015] Optionally, the constructing of a unitary matrix to transform the array manifold of the uniform circular array to obtain a transformed uniform circular array received data covariance matrix includes:

[0016] Construct a diagonal matrix based on the z-order first-kind Bessel function;

[0017] constructing a unitary matrix based on the diagonal matrix;

[0018] Transforming the array manifold of the uniform circular array using the unitary matrix;

[0019] Obtaining data vectors received by the uniform circular array after array manifold transformation;

[0020] A data covariance matrix is ​​obtained based on the data vector.

[0021] Optionally, constructing a generalized eigenvector based on the interference signal subspace to reconstruct a covariance matrix includes:

[0022] Construct a matrix based on the elements in the interference signal subspace

[0023] Using the diagonal matrix and the matrix As well as the diagonal matrix and the matrix The conjugate transpose of is constructed to construct the generalized eigenvector and reconstruct the covariance matrix.

[0024] Optionally, the obtaining of the interference signal subspace by performing eigenvalue decomposition on the data covariance matrix includes:

[0025] Performing eigenvalue decomposition on the data covariance matrix to obtain multiple eigenvalues ​​of the data covariance matrix;

[0026] The space spanned by the eigenvectors corresponding to the larger Q eigenvalues ​​among the multiple eigenvalues ​​is used as the interference signal subspace, and the value of Q is determined by the number of non-correlated signals in the interference signal source.

[0027] Optionally, the obtaining a new interference signal subspace by performing eigenvalue decomposition on the generalized eigenvector reconstructed covariance matrix includes:

[0028] Performing eigenvalue decomposition on the generalized eigenvector reconstruction covariance matrix to obtain multiple eigenvalues ​​of the generalized eigenvector reconstruction covariance matrix;

[0029] A new interference signal subspace is obtained based on the larger P eigenvalues ​​among the multiple eigenvalues, the eigenvectors corresponding to the larger P eigenvalues ​​among the multiple eigenvalues, and the conjugate transpose of the eigenvectors corresponding to the larger P eigenvalues ​​among the multiple eigenvalues; the value of P is determined by the number of interference signal sources.

[0030] Optionally, the estimating the interference direction by using the new interference signal subspace and the spatial steering vector includes:

[0031] Based on the generalized inverse of the new interference signal subspace, the spatial steering vector and the conjugate transpose of the control steering vector, the interference direction is estimated using a spatial spectrum function.

[0032] In a second aspect, an embodiment of the present application provides an interference source estimation device, which is applied to a uniform circular array, and the device includes:

[0033] A spherical coordinate system construction module is used to construct a spherical coordinate system with the center of the uniform circular array as the coordinate origin;

[0034] A spatial steering vector acquisition module, used to acquire the spatial steering vector of the array according to the phase relationship of each array element in the uniform circular array relative to the coordinate origin;

[0035] An array manifold transformation module is used to construct a unitary matrix to transform the array manifold of the uniform circular array to obtain a transformed uniform circular array received data covariance matrix;

[0036] An eigenvalue decomposition module, used for obtaining an interference signal subspace by performing eigenvalue decomposition on the data covariance matrix;

[0037] A generalized eigenvector reconstruction covariance matrix construction module, used to construct a generalized eigenvector reconstruction covariance matrix based on the interference signal subspace;

[0038] An interference signal subspace acquisition module is used to obtain a new interference signal subspace by performing eigenvalue decomposition on the generalized eigenvector reconstructed covariance matrix;

[0039] The interference direction estimation module is used to estimate the interference direction by using the new interference signal subspace and the space steering vector.

[0040] Optionally, the array manifold transformation module includes:

[0041] A diagonal matrix construction submodule, used to construct a diagonal matrix based on the z-order first-kind Bessel function;

[0042] A unitary matrix construction submodule, used for constructing a unitary matrix based on the diagonal matrix;

[0043] An array manifold transformation submodule, used to transform the array manifold of the uniform circular array using the unitary matrix;

[0044] A data vector acquisition submodule is used to acquire data vectors received by the uniform circular array after array manifold transformation;

[0045] The data covariance matrix acquisition submodule is used to acquire the data covariance matrix based on the data vector.

[0046] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0047] Memory for storing computer programs;

[0048] A processor is used to implement the steps of the interference source estimation method when executing the computer program.

[0049] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned interference source estimation method are implemented.

[0050] Compared with the prior art, this application has the following beneficial effects:

[0051] The embodiment of the present application provides a method for estimating the direction of interference, including: constructing a spherical coordinate system with the center of a uniform circular array as the origin of coordinates; obtaining the spatial steering vector of the array according to the phase relationship of each array element in the uniform circular array relative to the origin of coordinates; constructing a unitary matrix to transform the array manifold of the uniform circular array to obtain the transformed uniform circular array receiving data covariance matrix; obtaining the interference signal subspace by performing eigenvalue decomposition on the data covariance matrix; constructing a generalized eigenvector reconstruction covariance matrix based on the interference signal subspace; obtaining a new interference signal subspace by performing eigenvalue decomposition on the generalized eigenvector reconstruction covariance matrix; and estimating the direction of interference using the new interference signal subspace and the spatial steering vector. By constructing a unitary matrix to transform the array manifold, introducing eigenvector reconstruction through the correspondence between the eigenvectors before and after the transformation, the generalized eigenvectors and the steering vector space, decorrelation processing is performed on the transformed covariance matrix, and reverse eigenvector correction is performed on the basis of the constructed decorrelation signal eigenvector matrix, and it is used in the subspace algorithm to achieve estimation of the direction of the interference source.

[0052] An interference direction estimation device, an electronic device, and a computer-readable storage medium provided in the embodiments of the present application can implement the steps of the above-mentioned interference direction estimation method, and thus also have the above-mentioned beneficial effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0054] Figure 1 A flowchart of a method for estimating interference direction provided in an embodiment of the present application;

[0055] Figure 2 A schematic diagram of a circular array layout provided in an embodiment of the present application;

[0056] Figure 3 A schematic diagram of the structure of an interference source estimation device provided in an embodiment of the present application;

[0057] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] As described above, during the propagation of satellite navigation signals in the atmosphere, in addition to being susceptible to self-interference and electromagnetic interference under complex environmental conditions, the receiver may also be interfered with by human factors.

[0059] After research, the inventor invented a method, device, equipment and storage medium for estimating the direction of interference, which can estimate the direction of the interference source so as to take corresponding measures to deal with it.

[0060] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0061] Method Embodiment

[0062] See also Figure 1 , which is a flow chart of an interference estimation method provided by an embodiment of the present application, comprising the following steps:

[0063] S101, constructing a spherical coordinate system with the center of the uniform circular array as the coordinate origin.

[0064] It should be noted that the uniform circular array is a form of antenna array, which is widely used in the fields of communication, radar, etc. The uniform circular array consists of several isomorphic antennas arranged at equal distances, where the electrical phase and amplitude of each antenna are the same. The steering vector of the uniform circular array refers to the transmission or reception direction of the array in different directions. It is a complex number, indicating the radiation or reception capability in that direction.

[0065] S102 obtains a spatial steering vector of the array according to a phase relationship of each array element in the uniform circular array relative to a coordinate origin.

[0066] Specifically, the signal source pitch angle θ∈[0,π / 2] is the angle between the line from the origin to the signal source and the Z axis, and the azimuth angle The angle r between the projection of the line from the origin to the signal source on the xy plane and the positive half axis of the X axis (counterclockwise). m According to the phase relationship of each array element relative to the coordinate origin, the spatial steering vector of the array can be obtained as:

[0067]

[0068] Among them, ξ=2πRsinθ / λ, r m =2πm / M, m=1,2,…,M, λ is the wavelength of the carrier signal, the incident signal is denoted as s(t), the radius of the uniform circular array is R=λ / 2, then the received array signal can be expressed as

[0069] It should be noted that, as an example, the array layout of the uniform circular array can be found in Figure 2 , which is a schematic diagram of a circular array layout provided in an embodiment of the present application.

[0070] S103, constructing a unitary matrix to transform the array manifold of the uniform circular array to obtain a transformed covariance matrix of the uniform circular array received data.

[0071] Specifically, as an example, a diagonal matrix can be constructed based on the z-order first-kind Bessel function, a unitary matrix can be constructed based on the diagonal matrix, the array manifold of the uniform circular array can be transformed using the unitary matrix, the data vector received by the uniform circular array after the array manifold transformation can be obtained, and finally the data covariance matrix can be obtained based on the data vector.

[0072] S104, obtaining an interference signal subspace by performing eigenvalue decomposition on the data covariance matrix.

[0073] In the embodiment provided in the present application, the data covariance matrix can be subjected to eigenvalue decomposition to obtain multiple eigenvalues ​​of the data covariance matrix, and the space spanned by the eigenvectors corresponding to the larger Q eigenvalues ​​among the multiple eigenvalues ​​is used as the interference signal subspace, and the value of Q is determined by the number of non-correlated signals in the interference signal source. Specifically, if the number of interference signal sources is P, which includes K coherent incident signals and PK non-correlated signals, then the value of Q can be P-K+1 at this time.

[0074] S105: construct a generalized eigenvector based on the interference signal subspace and reconstruct a covariance matrix.

[0075] Specifically, in the embodiment provided in the present application, as an example, a matrix can be constructed based on the elements in the interference signal subspace: Using the diagonal matrix and the matrix As well as the diagonal matrix and the matrix The conjugate transpose of is constructed to construct the generalized eigenvector and reconstruct the covariance matrix.

[0076] For example, if the diagonal matrix is ​​D, the constructed matrix for:

[0077]

[0078] Among them, v′ B,1 Refers to the interference signal subspace element in the Bth row and the first column. At this time, the constructed generalized eigenvector reconstructed covariance matrix is

[0079] S106, obtaining a new interference signal subspace by performing eigenvalue decomposition on the generalized eigenvector reconstructed covariance matrix.

[0080] Specifically, the generalized eigenvector reconstruction covariance matrix can be subjected to eigenvalue decomposition to obtain multiple eigenvalues ​​of the generalized eigenvector reconstruction covariance matrix; a new interference signal subspace is obtained based on the larger P eigenvalues ​​among the multiple eigenvalues, the eigenvectors corresponding to the larger P eigenvalues ​​among the multiple eigenvalues, and the conjugate transpose of the eigenvectors corresponding to the larger P eigenvalues ​​among the multiple eigenvalues; the value of P is determined by the number of interference signal sources. For example, the value of P can be the same as the number of interference signal sources.

[0081] S107: Estimate the interference direction by using the new interference signal subspace and the spatial steering vector.

[0082] Specifically, the interference direction can be estimated using a spatial spectrum function based on the generalized inverse of the new interference signal subspace, the spatial steering vector, and the conjugate transpose of the control steering vector. Finally, the direction of arrival can be obtained by seeking the peak value.

[0083] It should be noted that the spatial spectrum function can be:

[0084]

[0085] in, is the conjugate transpose of the spatial orientation vector, is the generalized inverse of the new interference signal subspace, is the spatial orientation vector.

[0086] The embodiment of the present application provides a method for estimating the direction of interference. The method comprises the following steps: constructing a spherical coordinate system with the center of a uniform circular array as the origin of coordinates; obtaining the spatial steering vector of the array according to the phase relationship of each array element in the uniform circular array relative to the origin of coordinates; constructing a unitary matrix to transform the array manifold of the uniform circular array to obtain the transformed uniform circular array receiving data covariance matrix; obtaining the interference signal subspace by performing eigenvalue decomposition on the data covariance matrix; constructing a generalized eigenvector reconstruction covariance matrix based on the interference signal subspace; obtaining a new interference signal subspace by performing eigenvalue decomposition on the generalized eigenvector reconstruction covariance matrix; and estimating the direction of interference using the new interference signal subspace and the spatial steering vector. By constructing a unitary matrix to transform the array manifold, introducing eigenvector reconstruction through the correspondence between the eigenvectors before and after the transformation, the generalized eigenvectors and the steering vector space, decorrelating the transformed covariance matrix, and performing reverse eigenvector correction on the basis of the constructed decorrelated signal eigenvector matrix, the method is used in the subspace algorithm to estimate the direction of the interference source.

[0087] The embodiment of the present application also provides a method for estimating interference, comprising the following steps:

[0088] First, assume that there is an M-element uniform circular array with its center at the origin of the coordinate system. The radius of the uniform circular array is R = λ / 2, with λ being the wavelength of the carrier signal. The spherical coordinate system is used to represent the direction of arrival of the incident plane wave. The origin O of the coordinate system is located at the center of the array, i.e., the center of the circle. The signal source elevation angle θ∈[0,π / 2] is the angle between the line from the origin to the signal source and the Z axis, and the azimuth angle The angle r between the projection of the line from the origin to the signal source on the xy plane and the positive half axis of the X axis (counterclockwise). m According to the phase relationship of each array element relative to the coordinate origin, the spatial steering vector of the array can be obtained as:

[0089]

[0090] where ξ=2πRsinθ / λ, r m =2πm / M, m=1,2,…,M, the incident signal is denoted as s(t), then the received array signal is denoted as

[0091] In this embodiment, the direction of the suppression interference signal is estimated. Therefore, it can be assumed that there are a total of P interference signal sources incident on the array in the system. It is assumed that the first K are coherent incident signals and the last PK are uncorrelated incident signals, which are independent of the first K signal sources.

[0092] The set of interference signal wave directions is Without loss of generality, the angle of the interference signal source is The interference signal is denoted as s i (t). In addition to receiving the interference signal s(t), the M-element uniform circular array also receives the noise n(t). Thus, the data vector received by the M×1 array at time t is x(t)=As(t)+n(t), where the interference source vector s(t)=[s1(t),...,s P (t)] T , array flow matrix Let the received signals of M array elements be represented as x1(t),...,x M (t), and X represents the input signal matrix as X = [x1, x2, ..., x M ] T .

[0093] The covariance matrix of the M-element uniform circular array received data can be expressed as: Where R S =E[s(t)s H (t)] is the interference source covariance matrix, rank(R S )=P-K+1=Q, represents the noise correlation matrix, represents the noise power, and I represents the identity matrix of M*M order.

[0094] Perform eigenvalue decomposition on R, are all eigenvalues ​​of R. The interference signal subspace U spanned by the eigenvectors corresponding to the Q large eigenvalues S =[u1,u2,...,u Q ], and the eigenvectors corresponding to the MQ smaller eigenvalues ​​form the noise subspace U n =[u Q+1 ,u Q+2,...,u M ].

[0095] Then, define a diagonal matrix Among them, D z (β0) represents the z-order first-kind Bessel function, defining the maximum number of excited modes Then construct a unitary matrix based on the diagonal matrix D The unitary matrix T is used for array manifold mode transformation of uniform circular array.

[0096] After the array manifold of the uniform circular array is transformed into TA, the data vector received by the corresponding mode transformation is expressed as Tx(t)=TAs(t)+Tn(t), then the data covariance matrix is Its generalized eigenvalues ​​λ1,λ2...λ 2Z+1 Arranged from large to small, the eigenvectors corresponding to the first Q large eigenvalues ​​form the interference signal subspace After the mode transformation, the interference signal subspace formed by the steering vector is expressed as

[0097] Then, construct the matrix Among them, v′ B,1 It refers to the interference signal subspace element in the Bth row and the first column. B=2Z-n+2>P,n>P. Thus, the B×B-dimensional generalized eigenvector reconstruction covariance matrix is ​​obtained

[0098] Arrange all eigenvalues ​​of R′ in descending order as λ1≥λ2≥…≥λ P ≥λ P+1 =…=λ B It should be noted that The corresponding eigenvector is Then the interference signal subspace is redefined to obtain a new interference signal subspace Its generalized inverse is

[0099] Finally, based on the generalized inverse of the new interference signal subspace, the spatial steering vector and the conjugate transpose of the control steering vector, the interference direction is estimated using the spatial spectrum function, which is: in, is the conjugate transpose of the spatial orientation vector, is the generalized inverse of the new interference signal subspace, is the spatial orientation vector.

[0100] Another interference direction estimation method provided by an embodiment of the present application constructs a unitary matrix transformation array flow type, introduces eigenvector reconstruction through the correspondence between the eigenvectors before and after the transformation, the generalized eigenvectors and the steering vector space, and decorrelates the transformed covariance matrix. At the same time, a reverse eigenvector correction is performed based on the constructed decorrelated signal eigenvector matrix, which is used in a subspace algorithm to realize the uniform circular array to estimate the direction of the interference source.

[0101] Device Embodiment

[0102] See also Figure 3 , this figure is a schematic diagram of the structure of an interference direction estimation device provided in an embodiment of the present application, including: a spherical coordinate system construction module 301, a space steering vector acquisition module 302, an array manifold transformation module 303, an eigenvalue decomposition module 304, a generalized eigenvector reconstruction covariance matrix construction module 305, an interference signal subspace acquisition module 306 and an interference direction estimation module 307.

[0103] The spherical coordinate system construction module 301 is used to construct a spherical coordinate system with the center of the uniform circular array as the coordinate origin;

[0104] A spatial steering vector acquisition module 302, configured to acquire a spatial steering vector of the array according to a phase relationship of each array element in the uniform circular array relative to a coordinate origin;

[0105] An array manifold transformation module 303 is used to construct a unitary matrix to transform the array manifold of the uniform circular array to obtain a transformed uniform circular array received data covariance matrix;

[0106] An eigenvalue decomposition module 304 is used to obtain an interference signal subspace by performing eigenvalue decomposition on the data covariance matrix;

[0107] A generalized eigenvector reconstruction covariance matrix construction module 305 is used to construct a generalized eigenvector reconstruction covariance matrix based on the interference signal subspace;

[0108] An interference signal subspace acquisition module 306 is used to obtain a new interference signal subspace by performing eigenvalue decomposition on the generalized eigenvector reconstructed covariance matrix;

[0109] The interference direction estimation module 307 is used to estimate the interference direction by using the new interference signal subspace and the spatial steering vector.

[0110] Optionally, the array manifold transformation module 303 includes:

[0111] A diagonal matrix construction submodule, used to construct a diagonal matrix based on the z-order first-kind Bessel function;

[0112] A unitary matrix construction submodule, used for constructing a unitary matrix based on the diagonal matrix;

[0113] An array manifold transformation submodule, used to transform the array manifold of the uniform circular array using the unitary matrix;

[0114] A data vector acquisition submodule is used to acquire data vectors received by the uniform circular array after array manifold transformation;

[0115] The data covariance matrix acquisition submodule is used to acquire the data covariance matrix based on the data vector.

[0116] Optionally, the generalized eigenvector reconstruction covariance matrix construction module 305 includes:

[0117] A matrix construction submodule, used to construct a matrix based on the elements in the interference signal subspace

[0118] The generalized eigenvector reconstruction covariance matrix construction submodule is used to use the diagonal matrix and the matrix As well as the diagonal matrix and the matrix The conjugate transpose of is constructed to construct the generalized eigenvector and reconstruct the covariance matrix.

[0119] Optionally, the eigenvalue decomposition module 304 includes:

[0120] A first eigenvalue decomposition submodule, used for performing eigenvalue decomposition on the data covariance matrix to obtain multiple eigenvalues ​​of the data covariance matrix;

[0121] The first interference signal subspace acquisition submodule is used to use the space spanned by the eigenvectors corresponding to the larger Q eigenvalues ​​among the multiple eigenvalues ​​as the interference signal subspace, and the value of Q is determined by the number of non-correlated signals in the interference signal source.

[0122] Optionally, the interference signal subspace acquisition module 306 includes:

[0123] A second eigenvalue decomposition submodule is used to perform eigenvalue decomposition on the generalized eigenvector reconstruction covariance matrix to obtain multiple eigenvalues ​​of the generalized eigenvector reconstruction covariance matrix;

[0124] The second interference signal subspace acquisition submodule is used to acquire a new interference signal subspace based on the larger P eigenvalues ​​among the multiple eigenvalues, the eigenvectors corresponding to the larger P eigenvalues ​​among the multiple eigenvalues, and the conjugate transpose of the eigenvectors corresponding to the larger P eigenvalues ​​among the multiple eigenvalues; the value of P is determined by the number of interference signal sources.

[0125] Optionally, the interference source estimation module 307 is specifically configured to:

[0126] Based on the generalized inverse of the new interference signal subspace, the spatial steering vector and the conjugate transpose of the control steering vector, the interference direction is estimated using a spatial spectrum function.

[0127] An interference direction estimation device provided by an embodiment of the present application utilizes a spherical coordinate system construction module, a spatial steering vector acquisition module, an array manifold transformation module, an eigenvalue decomposition module, a generalized eigenvector reconstruction covariance matrix construction module, an interference signal subspace acquisition module, and an interference direction estimation module. A spherical coordinate system is constructed by taking the center of a uniform circular array as the coordinate origin; the spatial steering vector of the array is acquired according to the phase relationship of each array element in the uniform circular array relative to the coordinate origin; a unitary matrix is ​​constructed to transform the array manifold of the uniform circular array to obtain a transformed uniform circular array received data covariance matrix; an interference signal subspace is obtained by performing eigenvalue decomposition on the data covariance matrix; a generalized eigenvector reconstruction covariance matrix is ​​constructed based on the interference signal subspace; a new interference signal subspace is obtained by performing eigenvalue decomposition on the generalized eigenvector reconstruction covariance matrix; and the interference direction is estimated using the new interference signal subspace and the spatial steering vector. By constructing a unitary matrix transformation array manifold, introducing the correspondence between the eigenvectors before and after the transformation, the generalized eigenvectors and the steering vector space, the eigenvector reconstruction is introduced, and the transformed covariance matrix is ​​decorrelated. At the same time, the reverse eigenvector correction is performed based on the constructed decorrelated signal eigenvector matrix, which is used in the subspace algorithm to estimate the direction of the interference source.

[0128] Electronic device embodiment

[0129] See also Figure 4 , which is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, including:

[0130] A memory 11, used for storing computer programs;

[0131] The processor 12 is configured to implement the steps of the interference source estimation method described in any of the above method embodiments when executing the computer program.

[0132] In this embodiment, the device may be a vehicle-mounted computer, a PC (Personal Computer), or a terminal device such as a smart phone, a tablet computer, a PDA, or a portable computer.

[0133] The device may include a memory 11 , a processor 12 , and a bus 13 .

[0134] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the device, such as a hard disk of the device. In other embodiments, the memory 11 can also be an external storage device of the device, such as a plug-in hard disk equipped on the device, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Further, the memory 11 can also include both an internal storage unit of the device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the device, such as program code for executing the interference estimation method, but also can be used to temporarily store data that has been output or is to be output.

[0135] In some embodiments, the processor 12 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 11, such as executing program codes of interference estimation methods.

[0136] The bus 13 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0137] Furthermore, the device may also include a network interface 14, which may optionally include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the device and other electronic devices.

[0138] Optionally, the device may further include a user interface 15, which may include a display (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 15 may also include a standard wired interface and a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device, etc. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the device and to display a visual user interface.

[0139] Figure 4 Only the device with components 11-15 is shown, and it can be understood by those skilled in the art that Figure 4 The structure shown does not constitute a limitation of the device, and may include fewer or more components than shown, or combine certain components, or arrange the components differently.

[0140] Readable storage medium embodiment

[0141] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the interference source estimation method described in any of the above method embodiments are implemented.

[0142] The storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0143] It should be noted that each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, electronic device and readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device, electronic device and readable storage medium embodiments described above are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components indicated as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0144] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for estimating the direction of interference, characterized in that: Applied to a uniform circular array, the method comprises: Construct a spherical coordinate system with the center of the uniform circular array as the coordinate origin; Acquire the spatial steering vector of the array according to the phase relationship of each array element in the uniform circular array relative to the coordinate origin; Constructing a unitary matrix to transform the array manifold of the uniform circular array to obtain a transformed covariance matrix of the uniform circular array received data; Obtaining an interference signal subspace by performing eigenvalue decomposition on the data covariance matrix; Constructing a generalized eigenvector based on the interference signal subspace to reconstruct a covariance matrix; By performing eigenvalue decomposition on the generalized eigenvector reconstruction covariance matrix, a new interference signal subspace is obtained; The interference direction is estimated using the new interference signal subspace and the spatial steering vector.

2. The method according to claim 1, characterized in that The step of constructing a unitary matrix to transform the array manifold of the uniform circular array to obtain a transformed uniform circular array received data covariance matrix includes: Construct a diagonal matrix based on the z-order first-kind Bessel function; constructing a unitary matrix based on the diagonal matrix; Transforming the array manifold of the uniform circular array using the unitary matrix; Obtaining the data vector received by the uniform circular array after array manifold transformation; A data covariance matrix is ​​obtained based on the data vector.

3. The method according to claim 2, characterized in that The constructing a generalized eigenvector based on the interference signal subspace to reconstruct a covariance matrix includes: Construct a matrix based on the elements in the interference signal subspace Using the diagonal matrix and the matrix And the diagonal matrix and the matrix The conjugate transpose of is constructed to construct the generalized eigenvector and reconstruct the covariance matrix.

4. The method according to claim 1, characterized in that: The interfering signal subspace is obtained by performing eigenvalue decomposition on the data covariance matrix, including: Performing eigenvalue decomposition on the data covariance matrix to obtain multiple eigenvalues ​​of the data covariance matrix; The space spanned by the eigenvectors corresponding to the larger Q eigenvalues ​​among the multiple eigenvalues ​​is used as the interference signal subspace, and the value of Q is determined by the number of non-correlated signals in the interference signal source.

5. The method according to claim 1, characterized in that The new interference signal subspace is obtained by performing eigenvalue decomposition on the generalized eigenvector reconstructed covariance matrix, including: Performing eigenvalue decomposition on the generalized eigenvector reconstruction covariance matrix to obtain multiple eigenvalues ​​of the generalized eigenvector reconstruction covariance matrix; A new interference signal subspace is obtained based on the larger P eigenvalues ​​among the multiple eigenvalues, the eigenvectors corresponding to the larger P eigenvalues ​​among the multiple eigenvalues, and the conjugate transpose of the eigenvectors corresponding to the larger P eigenvalues ​​among the multiple eigenvalues; the value of P is determined by the number of interference signal sources.

6. The method according to claim 1, characterized in that The estimating the interference direction by using the new interference signal subspace and the spatial steering vector comprises: Based on the generalized inverse of the new interference signal subspace, the spatial steering vector and the conjugate transpose of the control steering vector, the interference direction is estimated using a spatial spectrum function.

7. An interference source estimation device, characterized in that: Applied to a uniform circular array, the device comprises: A spherical coordinate system construction module is used to construct a spherical coordinate system with the center of the uniform circular array as the coordinate origin; A spatial steering vector acquisition module, used to acquire the spatial steering vector of the array according to the phase relationship of each array element in the uniform circular array relative to the coordinate origin; An array manifold transformation module is used to construct a unitary matrix to transform the array manifold of the uniform circular array to obtain a transformed uniform circular array received data covariance matrix; An eigenvalue decomposition module, used for obtaining an interference signal subspace by performing eigenvalue decomposition on the data covariance matrix; A generalized eigenvector reconstruction covariance matrix construction module, used to construct a generalized eigenvector reconstruction covariance matrix based on the interference signal subspace; An interference signal subspace acquisition module is used to obtain a new interference signal subspace by performing eigenvalue decomposition on the generalized eigenvector reconstructed covariance matrix; The interference direction estimation module is used to estimate the interference direction by using the new interference signal subspace and the space steering vector.

8. The device according to claim 7, characterized in that The array manifold transformation module comprises: A diagonal matrix construction submodule, used to construct a diagonal matrix based on the z-order first-kind Bessel function; A unitary matrix construction submodule, used for constructing a unitary matrix based on the diagonal matrix; An array manifold transformation submodule, used to transform the array manifold of the uniform circular array using the unitary matrix; A data vector acquisition submodule is used to acquire data vectors received by the uniform circular array after array manifold transformation; The data covariance matrix acquisition submodule is used to acquire the data covariance matrix based on the data vector.

9. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the interference direction estimation method according to any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the interference direction estimation method according to any one of claims 1 to 6 are implemented.

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