Method and device for estimating direction of incoming wave based on signal subspace correlation

Abstract

The present invention provides a method and device for estimating the direction of arrival of waves based on subspace correlation, wherein the method comprises: constructing a full-space signal subspace according to a database storing data of incident signals at different angles in space; obtaining an unknown signal feature vector and a feature value corresponding to the feature vector by receiving data of a specific number of signals incident at different angles; performing correlation operations on the unknown signal feature vector and the feature vector in the full-space signal subspace to obtain a correlation value set; obtaining the direction of arrival of waves corresponding to the unknown signal feature vector by finding the maximum correlation value in the correlation value set, and obtaining the energy intensity of the position signal by the feature value corresponding to the feature vector. The present invention can accurately estimate the angle of the direction of arrival of waves, distinguish the energy intensity of each incoming wave signal, and effectively suppress the influence of the amplitude and phase errors between antenna channels and the coupling effect of the antenna array on the estimation of the direction of arrival of waves.

Description

Technical Field

[0001] This document relates to the field of satellite navigation technology, and in particular to a method and device for estimating incoming waves based on signal subspace correlation. Background Art

[0002] Beidou Navigation Satellite Syetem (BDS) is a satellite navigation system independently developed and built by my country. Together with the United States' GPS, Russia's GLONASS and Europe's Galileo, it constitutes one of the four major global navigation satellite systems (GNSS).

[0003] However, the complex and ever-changing electromagnetic environment in space has threatened the service performance of the Beidou satellite navigation system, and the BDS interference monitoring problem has gradually become a research focus in the industry. GNSS interference monitoring technology can be divided into four major modules according to its functions, namely interference detection, interference identification, interference direction finding and interference source positioning. In terms of GNSS interference monitoring, foreign countries have a large number of mature research results and built a complete monitoring system, but the deployment of my country's BDS interference monitoring system has not yet started. Today, the service scope of the Beidou satellite navigation system is gradually transitioning to the world, and a new development pattern is about to begin. Therefore, the interference monitoring problem will definitely attract attention to ensure the service quality of the Beidou satellite navigation system.

[0004] In my country's military applications, the reliability of satellite navigation systems is particularly important, and higher standards are also required for the quality of Beidou signals. Today, my country's military aircraft are growing exponentially, and aviation safety issues have received attention at all levels, becoming the basic guarantee for national defense development. Once the navigation signal is severely interfered with, it may pose a threat to national security. It can be seen that the Beidou satellite navigation system used in the military must have the strong support of the BDS interference monitoring system.

[0005] At present, there are three main means to resist interference with satellite navigation signals:

[0006] (1) Anti-interference measures for satellite signals include increasing the transmission power of satellite signals and adopting new navigation signals.

[0007] (2) Receiver anti-interference. The main measures taken include the use of integrated navigation technology, such as the combination of GPS and inertial navigation system (INS), adaptive nulling antenna, military code direct capture technology, etc.

[0008] (3) Eliminate interference sources. The main measures taken are to detect and locate artificial or non-artificial radio frequency signals that block or interfere with satellite navigation systems, and ultimately shut down, destroy or circumvent them.

[0009] The first method can only target weak interference; the second method is the most widely used anti-interference method in the field of satellite navigation. It has strong anti-interference ability and is simple to implement, but it is limited to the level of digital-to-analog conversion chips and is powerless against interference with energy intensity beyond the range. The third method is the interference source direction-finding system, which can interfere with positioning or take avoidance measures to avoid interference from affecting navigation positioning. MUSIC, CAPON, interferometer and other algorithms are usually used in interference source direction-finding systems. The performance of the system depends to a large extent on the accuracy of the array manifold. In practical applications, due to the amplitude and phase errors and mutual coupling effects of the antenna, the array manifold distortion does not match the signal subspace, and the system performance will be greatly reduced. Summary of the invention

[0010] The object of the present invention is to provide a method and device for estimating incoming waves based on signal subspace correlation, aiming to solve the above-mentioned problems in the prior art.

[0011] The present invention provides an incoming wave estimation method based on signal subspace correlation, comprising:

[0012] The full-space signal subspace is constructed according to a database storing data of incident signals at different angles in space;

[0013] By receiving data of a specific number of incident signals at different angles, a feature vector of an unknown signal and a feature value corresponding to the feature vector are obtained;

[0014] Perform correlation operation on the unknown signal feature vector and the feature vector in the full-space signal subspace to obtain a set of correlation values;

[0015] The incoming wave direction corresponding to the unknown signal feature vector is obtained by finding the maximum correlation value in the correlation value set, and the energy intensity of the position signal is obtained through the feature value corresponding to the feature vector.

[0016] The present invention provides an incoming wave estimation device based on signal subspace correlation, comprising:

[0017] A full-space signal subspace construction module is used to construct a full-space signal subspace according to a database storing incident signal data at different angles in space;

[0018] The eigenvalue and eigenvector construction module is used to obtain the unknown signal eigenvector and the eigenvalue corresponding to the eigenvector by receiving data of a specific number of incident signals at different angles;

[0019] A correlation operation module is used to perform a correlation operation on the unknown signal feature vector and the feature vector in the full-space signal subspace to obtain a set of correlation values;

[0020] The incoming wave prediction module is used to obtain the incoming wave direction corresponding to the unknown signal feature vector by finding the maximum correlation value in the correlation value set, and obtain the energy intensity of the position signal through the feature value corresponding to the feature vector.

[0021] An embodiment of the present invention also provides an incoming wave estimation device based on signal subspace correlation, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the above-mentioned incoming wave estimation method based on signal subspace correlation when executed by the processor.

[0022] An embodiment of the present invention further provides a computer-readable storage medium, on which a program for implementing information transmission is stored. When the program is executed by a processor, the steps of the above-mentioned method for estimating incoming waves based on signal subspace correlation are implemented.

[0023] By adopting the embodiment of the present invention, a wave direction estimation algorithm for selectively correlating unknown wave direction signals is proposed under the characteristic subspace decomposition model, which can accurately estimate the wave direction angle, distinguish the energy intensity of each wave signal, and effectively suppress the amplitude and phase errors between antenna channels and the influence of antenna array coupling effect on wave direction estimation.

[0024] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate one or more embodiments of this specification 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 recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0026] Figure 1 This is a flow chart of a method for estimating incoming waves based on signal subspace correlation according to an embodiment of the present invention;

[0027] Figure 2 A schematic diagram of the definition of the pitch angle and azimuth angle according to an embodiment of the present invention;

[0028] Figure 3 A schematic diagram of a construction environment of a full-space signal subspace according to an embodiment of the present invention;

[0029] Figure 4A block diagram showing the construction principle of the full-space signal subspace according to an embodiment of the present invention;

[0030] Figure 5 Schematic diagram of an incoming wave estimation device based on signal subspace correlation according to an embodiment of the present invention;

[0031] Figure 6 This is a schematic diagram of a second embodiment of the device of the embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0033] Method Embodiment

[0034] According to an embodiment of the present invention, a method for estimating incoming waves based on signal subspace correlation is provided. Figure 1 FIG. 1 is a flow chart of a method for estimating incoming waves based on signal subspace correlation according to an embodiment of the present invention. Figure 1 As shown, a method for estimating incoming waves based on signal subspace correlation according to an embodiment of the present invention specifically includes:

[0035] Step S101, constructing a full-space signal subspace according to a database storing incident signal data at different angles in space; specifically includes:

[0036] Figure 2 This is a schematic diagram of the definition of the elevation angle and azimuth angle of an embodiment of the present invention. The full-space signal subspace is constructed for the database of incident signals at different angles in space. The signal incident angle is defined as the elevation angle, and the angle with the normal of the antenna array is the azimuth angle. By collecting signal data incident at different angles, constructing a covariance matrix, and performing eigenvalue decomposition, the construction of the full-space signal subspace is completed.

[0037] Using an array antenna containing M array elements and N sampling points, the signal data matrix X can be expressed as

[0038]

[0039] Where x m,n,n=1,,2,…,N,m=1,,2,…,M, represents the sampling value of the mth array element at time n. According to the signal data matrix, the covariance matrix Rx can be obtained.

[0040] Rx=E(XX H ) / N

[0041] in(·) H Denotes conjugate transpose. Decomposing the covariance matrix yields the characteristic subspace,

[0042]

[0043] Where U s , U n denote the signal eigenvector and the noise eigenvector respectively, Λ s ,Λ n represent the signal and noise eigenvalues ​​respectively.

[0044] Figure 3 Schematic diagram of the construction environment of the full-space signal subspace according to an embodiment of the present invention. By changing the signal incident angle, signal feature vectors of different incident directions are obtained, thereby constructing the full-space signal subspace.

[0045] Figure 4 This is a block diagram of the construction principle of the full-space signal subspace according to an embodiment of the present invention.

[0046] Step S102, by receiving data of a specific number of incident signals from different angles, obtain the unknown signal feature vector and the feature value corresponding to the feature vector; Step S102 specifically includes: using the method of step S101, by receiving data of incident signals from P different directions, calculate the feature vector U of the unknown signal u and the eigenvalue Λ u , and the eigenvectors correspond one to one to the eigenvalues:

[0047] Step S103, performing correlation operation on the unknown signal feature vector and the feature vector in the full-space signal subspace to obtain a correlation value set;

[0048] Step S104, by finding the maximum correlation value in the correlation value set to obtain the wave direction corresponding to the unknown signal feature vector, and by the eigenvalue corresponding to the eigenvector to obtain the energy intensity of the position signal, step S104 specifically includes: the wave direction corresponding to the unknown signal feature vector can be obtained by finding the maximum correlation value, so that the wave directions of P unknown signals can be estimated. Due to the one-to-one correspondence between the eigenvector and the eigenvalue, and the eigenvalue can roughly represent the signal energy intensity, it is possible to make a judgment on the intensity of the incident signal at different angles.

[0049] The following is a specific example to illustrate the process of the incoming wave estimation method based on signal subspace correlation:

[0050] Step 1: Construction of the full-space signal subspace

[0051] The full-space signal subspace was constructed for the database of signals incident at different angles in space. The signal incident angle is defined as the angle between the signal incident angle and the normal of the antenna array as the elevation angle, and the angle between the signal incident angle and the positive direction of the X-axis as the azimuth angle. The elevation angle [0°~90°] is divided into 91 grids at equal intervals, and the azimuth angle [0°~360°] is divided into 360 grids at equal intervals. By collecting signal data incident at different azimuth and elevation angles, the covariance matrix is ​​constructed, and the eigenvalue decomposition is performed to complete the construction of the full-space signal subspace.

[0052] Using an array antenna with 7 elements and 1024 sampling points, the signal data matrix X can be expressed as

[0053]

[0054] Where x m,n ,n=1,,2,…,1024,m=1,,2,…,7, represents the sampling value of the mth array element at time n. According to the signal data matrix, the covariance matrix Rx can be obtained.

[0055] Rx=E(XX H ) / 1024

[0056] Where (·u H Denotes conjugate transpose. Decomposing the covariance matrix yields the characteristic subspace,

[0057]

[0058] Where U s , U n denote the signal eigenvector and the noise eigenvector respectively, Λ s ,Λ n represent the signal and noise eigenvalues ​​respectively.

[0059] By changing the signal incident angle, the signal feature vectors of different incident directions are obtained, thereby constructing the full-space signal subspace;

[0060] Step 2: Unknown signal feature extraction

[0061] Using the method in step 1, by receiving data of incident signals from three different directions, the characteristic vector U of the unknown signal is calculated. u and the eigenvalue Λ u , and the eigenvectors correspond to the eigenvalues ​​one by one.

[0062] Step 3: Feature-related selection

[0063] The unknown signal eigenvector is correlated with the full-space signal subspace, and the direction of arrival corresponding to the unknown signal eigenvector can be obtained by finding the maximum correlation value, thereby estimating the direction of arrival of the three unknown signals. Since the eigenvector and the eigenvalue have a one-to-one correspondence, and the eigenvalue can roughly represent the signal energy intensity, it is possible to judge the intensity of the incident signal at different angles.

[0064] Device Example 1

[0065] According to an embodiment of the present invention, a device for estimating incoming waves based on signal subspace correlation is provided. Figure 5 is a schematic diagram of an incoming wave estimation device based on signal subspace correlation according to an embodiment of the present invention. Figure 5 As shown, a signal subspace correlation-based incoming wave estimation device according to an embodiment of the present invention specifically includes:

[0066] The full-space signal subspace construction module 50 is used to construct the full-space signal subspace according to a database storing data of incident signals at different angles in space. The full-space signal subspace construction module 50 includes:

[0067] A signal incident angle definition unit is used to define the signal incident angle, wherein the angle with the normal line of the antenna array is the pitch angle, and the angle with the positive direction of the X-axis is the azimuth angle. The pitch angle is equally spaced into a first specific number of grids, and the azimuth angle is equally spaced into a second specific number of grids.

[0068] The subspace construction unit collects data of incident signals at different angles, constructs a covariance matrix, and performs eigenvalue decomposition to complete the construction of the full-space signal subspace, which specifically includes:

[0069] Using an array antenna containing M array elements and N sampling points, the signal data matrix X can be expressed as:

[0070]

[0071] Where x m,n ,n=1,,2,…,N,m=1,,2,…,M, represents the sampling value of the mth array element at time n. According to the signal data matrix, the covariance matrix Rx can be obtained.

[0072] Rx=E(XX H ) / N

[0073] in(·) H Denotes conjugate transpose. Decomposing the covariance matrix yields the characteristic subspace,

[0074]

[0075] Where U s , U n denote the signal eigenvector and the noise eigenvector respectively, Λ s ,Λ n represent the signal and noise eigenvalues ​​respectively.

[0076] By changing the signal incident angle, the signal feature vectors of different incident directions are obtained, thereby constructing the full-space signal subspace.

[0077] The eigenvalue and eigenvector construction module 52 is used to obtain the unknown signal eigenvector and the eigenvalue corresponding to the eigenvector by receiving data of a specific number of incident signals at different angles;

[0078] A correlation operation module 54 is used to perform a correlation operation on the unknown signal feature vector and the feature vector in the full-space signal subspace to obtain a correlation value set;

[0079] The wave prediction module 56 is used to obtain the wave direction corresponding to the unknown signal feature vector by finding the maximum correlation value in the correlation value set, and obtain the energy intensity of the position signal through the feature value corresponding to the feature vector. The wave prediction module 56 specifically includes:

[0080] The unknown signal eigenvector is correlated with the full-space signal subspace, and the direction of arrival corresponding to the unknown signal eigenvector can be obtained by finding the maximum correlation value, thereby estimating the direction of arrival of P unknown signals. Due to the one-to-one correspondence between eigenvectors and eigenvalues, and the eigenvalue can roughly represent the signal energy intensity, it is possible to judge the intensity of incident signals at different angles.

[0081] Device Example 2

[0082] The embodiment of the present invention provides a device for estimating incoming waves based on signal subspace correlation, such as Figure 6 As shown, it includes: a memory 60, a processor 62, and a computer program stored in the memory 60 and executable on the processor 62. When the computer program is executed by the processor 62, the steps described in the method embodiment are implemented.

[0083] Device Example 3

[0084] An embodiment of the present invention provides a computer-readable storage medium, on which a program for implementing information transmission is stored. When the program is executed by the processor 62, the steps described in the method embodiment are implemented.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating the direction of arrival of a wave based on signal subspace correlation, characterized in that: include: The full-space signal subspace is constructed according to a database storing data of incident signals at different angles in space; By receiving data of a specific number of incident signals at different angles, a feature vector of an unknown signal and a feature value corresponding to the feature vector are obtained; Performing correlation operation on the unknown signal feature vector and the feature vector in the full-space signal subspace to obtain a correlation value set; The incoming wave direction corresponding to the unknown signal feature vector is obtained by finding the maximum correlation value in the correlation value set, and the energy intensity of the unknown signal is obtained by the feature value corresponding to the unknown signal feature vector.

2. The method according to claim 1, characterized in that The construction of the full-space signal subspace according to the database storing the incident signal data at different angles in the space specifically includes: The signal incident angle is defined as follows: the angle with the normal line of the antenna array is the elevation angle, and the angle with the positive direction of the X-axis is the azimuth angle. The elevation angle is equally spaced into a first specific number of grids, and the azimuth angle is equally spaced into a second specific number of grids. By collecting data of incident signals at different angles, constructing the covariance matrix and performing eigenvalue decomposition, the construction of the full-space signal subspace is completed.

3. The method according to claim 2, characterized in that The method of collecting signal data incident at different angles, constructing a covariance matrix, and performing eigenvalue decomposition to complete the construction of the full-space signal subspace specifically includes: Using an array antenna with M array elements and N sampling points, the signal data matrix X is obtained by formula 1: Among them, x m,n ,n=1,,2,…,N,m=1,,2,…,M, represents the sampling value of the mth array element at time n; According to the signal data matrix X, the covariance matrix Rx is obtained by formula 2: Rx=E(XX H ) / N formula 2; in,(·) H represents conjugate transpose; The covariance matrix Rx is decomposed by formula 3 to obtain the characteristic subspace, Among them, U s , U n denote the signal eigenvector and the noise eigenvector respectively, Λ s ,Λ n denote the signal and noise eigenvalues ​​respectively; By changing the signal incident angle, the signal feature vectors of different incident directions are obtained, thereby constructing the full-space signal subspace.

4. The method according to claim 1, characterized in that: The obtaining the energy intensity of the unknown signal by using the eigenvalue corresponding to the eigenvector of the unknown signal specifically includes: According to the one-to-one correspondence between the unknown signal feature vector and the eigenvalue corresponding to the unknown signal feature vector, the signal energy intensity is roughly represented by the eigenvalue corresponding to the unknown signal feature vector, and the roughly represented signal energy intensity is used as the energy intensity of the unknown signal.

5. A device for estimating the direction of arrival of a wave based on signal subspace correlation, characterized in that: include: A full-space signal subspace construction module is used to construct a full-space signal subspace according to a database storing incident signal data at different angles in space; The eigenvalue and eigenvector construction module is used to obtain the unknown signal eigenvector and the eigenvalue corresponding to the eigenvector by receiving data of a specific number of incident signals at different angles; A correlation operation module, used for performing a correlation operation on the unknown signal feature vector and the feature vector in the full-space signal subspace to obtain a correlation value set; The incoming wave prediction module is used to obtain the incoming wave direction corresponding to the unknown signal feature vector by finding the maximum correlation value in the correlation value set, and obtain the energy intensity of the unknown signal through the feature value corresponding to the unknown signal feature vector.

6. The device according to claim 5, characterized in that The full-space signal subspace construction module specifically includes: A signal incident angle definition unit is used to define the signal incident angle as follows: the angle with the normal line of the antenna array is the pitch angle, the angle with the positive direction of the X-axis is the azimuth angle, the pitch angle is equally spaced into a first specific number of grids, and the azimuth angle is equally spaced into a second specific number of grids; The subspace construction unit collects data of incident signals at different angles, constructs a covariance matrix, and performs eigenvalue decomposition, thereby completing the construction of the full-space signal subspace.

7. The device according to claim 6, characterized in that The subspace construction unit specifically includes: Using an array antenna with M array elements and N sampling points, the signal data matrix X is obtained by formula 1: Among them, x m,n ,n=1,,2,…,N,m=1,,2,…,M, represents the sampling value of the mth array element at time n; According to the signal data matrix X, the covariance matrix Rx is obtained by formula 2: Rx=E(XX H ) / N formula 2; in,(·) H represents conjugate transpose; The covariance matrix Rx is decomposed by formula 3 to obtain the characteristic subspace, Among them, U s , U n denote the signal eigenvector and the noise eigenvector respectively, Λ s ,Λ n denote the signal and noise eigenvalues ​​respectively; By changing the signal incident angle, the signal feature vectors of different incident directions are obtained, thereby constructing the full-space signal subspace.

8. The device according to claim 5, characterized in that The energy intensity of the unknown signal obtained in the incoming wave prediction module specifically includes: According to the one-to-one correspondence between the unknown signal feature vector and the eigenvalue corresponding to the unknown signal feature vector, the signal energy intensity is roughly represented by the eigenvalue corresponding to the unknown signal feature vector, and the roughly represented signal energy intensity is used as the energy intensity of the unknown signal.

9. A device for estimating the direction of arrival of a wave based on signal subspace correlation, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method for estimating the direction of arrival of a wave based on signal subspace correlation as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an implementation program for information transmission, and when the program is executed by a processor, the steps of the method for estimating the direction of arrival of a wave based on signal subspace correlation as described in any one of claims 1 to 4 are implemented.

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