Signal arrival angle estimation method, electronic equipment and readable storage medium

Abstract

The invention discloses a signal arrival angle estimation method, electronic equipment and a readable storage medium, and the method comprises the steps: constructing a data covariance matrix corresponding to a to-be-estimated signal according to the received signal data of the to-be-estimated signal; obtaining a plurality of feature values of the data covariance matrix and feature vectors corresponding to the feature values; based on the multiple feature values and feature vectors corresponding to the feature values, a target space matrix is obtained, the target space matrix is composed of a maximum signal feature vector and a noise feature vector, the maximum signal feature vector is the feature vector corresponding to the maximum feature value in the multiple feature values, and the noise feature vector is the feature vector corresponding to the maximum feature value in the multiple feature values; the noise feature vector is a feature vector corresponding to a noise feature value in the plurality of feature values, and the noise feature value is smaller than a preset threshold value; based on the target space matrix, obtaining a target corresponding relation between the target space spectrum and an incident angle of the to-be-estimated signal; and estimating a signal arrival angle corresponding to the to-be-estimated signal based on the target corresponding relation.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of wireless communication technologies, and in particular, to a method for estimating the angle of arrival of a signal, an electronic device, and a readable storage medium. Background Art

[0002] With the increase in 5G communication frequencies, the directivity of electromagnetic field propagation is enhanced, and the importance of signal direction of arrival (DOA) estimation has become increasingly prominent. The main purpose of signal DOA estimation is to estimate the incoming wave direction of array signals as accurately as possible. Among them, the multiple signal classification algorithm is one of the most widely used DOA estimation algorithms.

[0003] However, the multiple signal classification algorithm (MUSIC) that utilizes noise subspace information has large estimation biases and low resolution capabilities in non-ideal situations, such as low signal-to-noise ratios, small numbers of snapshots, and extremely close signal incident angles. Summary of the Invention

[0004] Embodiments of the present application provide a method for estimating the angle of arrival of a signal, an electronic device, and a readable storage medium, which can solve the problems of large estimation biases and low resolution capabilities of the multiple signal classification algorithm in non-ideal situations.

[0005] To solve the above technical problems, the present application is implemented as follows:

[0006] In a first aspect, a method for estimating the angle of arrival of a signal is provided. The method includes: constructing a data covariance matrix corresponding to the signal to be estimated according to the signal data of the received signal to be estimated; obtaining a plurality of eigenvalues of the data covariance matrix and eigenvectors corresponding to each of the eigenvalues; obtaining a target space matrix based on the plurality of eigenvalues and the eigenvectors corresponding to each of the eigenvalues, where the target space matrix is composed of a maximum signal eigenvector and a noise eigenvector, the maximum signal eigenvector is the eigenvector corresponding to the largest eigenvalue among the plurality of eigenvalues, the noise eigenvector is the eigenvector corresponding to the noise eigenvalue among the plurality of eigenvalues, and the noise eigenvalue is less than a preset threshold; obtaining a target correspondence between a target space spectrum and the incident angle of the signal to be estimated based on the target space matrix; and estimating the angle of arrival of the signal corresponding to the signal to be estimated based on the target correspondence.

[0007] In a second aspect, an electronic device is provided, which includes a processor and a memory. The memory stores a program or instructions that can run on the processor. When the program or instructions are executed by the processor, the steps of the method for estimating the angle of arrival of a signal as described in the first aspect are implemented.

[0008] In a third aspect, a readable storage medium is provided, which is characterized in that a program or instructions are stored on the readable storage medium. When the program or instructions are executed by a processor, the steps of the method for estimating the angle of arrival of a signal as described in the first aspect are implemented.

[0009] In the embodiments of the present application, according to the signal data of the signal to be estimated received, a data covariance matrix corresponding to the signal to be estimated is constructed; a plurality of eigenvalues of the data covariance matrix and the eigenvectors corresponding to each of the eigenvalues are obtained; based on the plurality of eigenvalues and the eigenvectors corresponding to each of the eigenvalues, a target space matrix is obtained, where the target space matrix is composed of a maximum signal eigenvector and a noise eigenvector, the maximum signal eigenvector is the eigenvector corresponding to the largest eigenvalue among the plurality of eigenvalues, the noise eigenvector is the eigenvector corresponding to the noise eigenvalue among the plurality of eigenvalues, and the noise eigenvalue is less than a preset threshold; based on the target space matrix, a target correspondence between the target space spectrum and the incident angle of the signal to be estimated is obtained; based on the target correspondence, the angle of arrival of the signal corresponding to the signal to be estimated is estimated. It can make full use of the signal data of the signal to be estimated, and obtain the target space matrix through the eigenvector corresponding to the largest eigenvalue and the noise eigenvector, which can strengthen the effective information of the target space matrix. Therefore, the target correspondence between the target space spectrum obtained according to this target space matrix and the incident angle of the signal to be estimated can more accurately estimate the angle of arrival of the signal, solve the estimation deviation existing in non-ideal situations, and improve the resolution ability of the angle of arrival of the signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application.

[0011] Figure 1 A schematic flowchart of a method for estimating the angle of arrival of a signal provided by an exemplary embodiment of the present application is shown;

[0012] Figure 2 A schematic flowchart of another method for estimating the angle of arrival of a signal provided by an exemplary embodiment of the present application is shown;

[0013] Figure 3 A schematic diagram of an application scenario provided by an exemplary embodiment of the present application is shown;

[0014] Figure 4a Shows an evaluation line graph of the estimation performance in an application scenario provided by an exemplary embodiment of the present application;

[0015] Figure 4b Shows an evaluation line graph of the estimation performance in another application scenario provided by an exemplary embodiment of the present application;

[0016] Figure 4c Shows an evaluation line graph of the estimation performance in another application scenario provided by an exemplary embodiment of the present application;

[0017] Figure 5 Shows a schematic structural diagram of an electronic device provided by an exemplary embodiment of the present application. Detailed implementation manners

[0018] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0019] First, an exemplary application environment applicable to the embodiments of the present application is provided, which is a signal base station or a radar base station equipped with a one-dimensional uniform linear array, and the spacing between the array elements is not greater than half the wavelength of the signal. In a wireless communication system, the signal base station is used to receive and send wireless signals and communicate with terminal devices. In a positioning system, by analyzing the signals of the terminal device received by multiple signal base stations, the location of the terminal device is determined to achieve positioning services.

[0020] Secondly, in view of the existing related technologies for non-ideal situations, such as multiple beams of signals with a small angle interval of the incident angle. Since the incident angles of these multiple beams of signals are extremely close, the spatial spectrum obtained from the signal data will overlap, so the estimated signal arrival angles based on the corresponding relationship between the spatial spectrum and the incident angle are the same, but in fact they are not the same, resulting in a problem of low resolution ability. Another example is in the case of low signal-to-noise ratio and a small number of beats. At this time, the signals received by the base station are less affected by noise interference due to the low signal-to-noise ratio and the small number of beats, and the effective information that can be presented by the noise eigenvalue obtained from the signal data of this signal is less, which will also lead to a low resolution ability for the signal incident angle. Through a signal arrival angle estimation method, an electronic device, and a readable storage medium provided by the embodiments of the present application, at least the above problems are solved.

[0021] Figure 1The flowchart shows a method for estimating the angle of arrival of a signal provided by an exemplary embodiment of the present application. Refer to Figure 1 , the method includes the following steps.

[0022] Step 102: Construct a data covariance matrix corresponding to the signal to be estimated according to the signal data of the signal to be estimated received.

[0023] Among them, by receiving the signal to be estimated through a base station, the signal data of the signal to be estimated can be obtained, such as the incident signal vector, the direction vector of the incident angle, and the noise vector. A received signal matrix is constructed according to the obtained incident signal vector, direction vector of the incident angle, and noise vector of the signal to be estimated, and then the data covariance matrix corresponding to the signal to be estimated is obtained according to the received signal matrix. In addition, in practical applications, the received signal data is sampled by a digital receiver, so that the data sampling covariance matrix of the base station receiving signal data is used to approximate the data covariance matrix of the base station receiving signal data.

[0024] Step 104: Obtain a plurality of eigenvalues of the data covariance matrix and the eigenvectors corresponding to each of the eigenvalues.

[0025] Among them, performing eigenvalue decomposition on the data covariance matrix can obtain a plurality of eigenvalues of the data covariance matrix and the eigenvectors corresponding to each eigenvalue. And due to the mutual independence of the signal characteristics and noise characteristics of the signal to be estimated, the plurality of eigenvalues include signal eigenvalues and noise eigenvalues, and the eigenvectors include signal eigenvectors corresponding to signal eigenvalues and noise eigenvectors corresponding to noise eigenvalues.

[0026] Step 106: Based on the plurality of eigenvalues and the eigenvectors corresponding to each of the eigenvalues, obtain a target space matrix, where the target space matrix is composed of the maximum signal eigenvector and the noise eigenvector, the maximum signal eigenvector is the eigenvector corresponding to the largest eigenvalue among the plurality of eigenvalues, the noise eigenvector is the eigenvector corresponding to the noise eigenvalue among the plurality of eigenvalues, and the noise eigenvalue is less than a preset threshold.

[0027] Among them, based on the characteristics of each eigenvalue and the eigenvector corresponding to the eigenvalue, the eigenvector corresponding to the largest eigenvalue among the eigenvalues and the noise eigenvector corresponding to the noise eigenvalue are selected to form the target space matrix, so that the target space matrix can fully present the characteristic information of the signal data of the signal to be estimated, thereby more accurately estimating the angle of arrival of the signal.

[0028] Step 108: Based on the target space matrix, obtain the target correspondence between the target space spectrum and the incident angle of the signal to be estimated;

[0029] Among them, there is a target correspondence between the target spatial spectrum and the incident angle of the signal to be estimated, so that the angle of arrival of the signal can be estimated according to the characteristics of the target spatial spectrum and the target correspondence.

[0030] Step 110: Estimate the angle of arrival of the signal to be estimated based on the target correspondence.

[0031] In the embodiment of the present application, by receiving the signal data of the signal to be estimated, and constructing a data covariance matrix corresponding to the signal to be estimated according to the signal data, such as the incident signal vector, the direction vector of the incident angle, and the noise vector, the correlation relationship between the signal characteristics and the noise characteristics in the acquired signal data is characterized by this data covariance matrix. Then, eigenvalue decomposition is performed on the data covariance matrix to obtain multiple eigenvalues of the data covariance matrix and the eigenvectors corresponding to each eigenvalue. Based on the fact that the signal data includes signal characteristics and noise characteristics, and the mutual independence of the signal characteristics and the noise characteristics, the multiple eigenvalues obtained by the above decomposition may include signal eigenvalues and noise eigenvalues. Correspondingly, the eigenvectors may include signal eigenvectors corresponding to the signal eigenvalues and noise eigenvectors corresponding to the noise eigenvalues. Therefore, the eigenvector corresponding to the largest eigenvalue and the noise eigenvector are selected from the multiple eigenvalues and the eigenvectors corresponding to each eigenvalue to form a target space matrix. Adding the eigenvector corresponding to the largest eigenvalue, that is, the largest signal eigenvector, strengthens the original space matrix composed of noise eigenvectors, increases the effective information of the target space matrix, and thus can more accurately estimate the angle of arrival of the signal corresponding to the signal to be estimated according to the target correspondence between the target spatial spectrum obtained from this target space matrix and the incident angle of the signal to be estimated, realizing the solution of the estimation bias existing in non-ideal situations and improving the resolution ability of the angle of arrival of the signal.

[0032] In one implementation, step 106 of obtaining the target space matrix based on the multiple eigenvalues and the eigenvectors corresponding to each of the eigenvalues may include:

[0033] Step 1061: Select the eigenvector corresponding to the largest eigenvalue from the multiple eigenvalues as the largest signal eigenvector.

[0034] Among them, for the multiple eigenvalues obtained from the data covariance matrix and the eigenvectors corresponding to each eigenvalue, the signal eigenvalue is greater than the noise eigenvalue. Select the largest eigenvalue from the multiple signal eigenvalues, and use the eigenvector corresponding to the largest eigenvalue as the above-mentioned largest signal eigenvector to strengthen the effective information of the target space matrix through this largest signal eigenvector.

[0035] Step 1062: Sort the multiple noise feature vectors in descending order according to the noise eigenvalues to obtain an ordered noise subspace matrix.

[0036] Step 1063: Construct the target space matrix according to the maximum signal feature vector and the ordered noise subspace matrix.

[0037] In the embodiment of the present application, the target space matrix includes a maximum signal feature vector and noise feature vectors. The maximum signal feature vector is the feature vector corresponding to the largest eigenvalue selected from multiple eigenvalues. The noise feature vectors in the target space matrix are structured in descending order according to the noise eigenvalues. Thus, the target space matrix is composed of the maximum signal feature vector and the noise feature vectors sorted in descending order according to the noise eigenvalues, so that the constructed target space matrix has better performance, thereby improving the resolution ability of the signal arrival angle.

[0038] In one implementation, step 108 above, based on the target space matrix, obtaining the target correspondence between the target space spectrum and the incident angle of the signal to be estimated may include:

[0039] Step 1081: Obtain a first correspondence, where the first correspondence is the correspondence between the first space spectrum of the multiple signal classification algorithm corresponding to the noise subspace matrix and the incident angle of the signal to be estimated, and the noise subspace matrix is composed of multiple noise feature vectors.

[0040] Among them, based on the noise subspace matrix and the incident angle of the signal to be estimated, the first correspondence between the first space spectrum and the incident angle of the signal to be estimated can be obtained. According to this first correspondence, the signal arrival angle can be estimated, but due to the influence of non-ideal situations, the estimation accuracy is not high. Therefore, the second correspondence is added as follows to improve the estimation accuracy of the signal arrival angle.

[0041] Step 1082: Obtain a second correspondence, where the second correspondence is the correspondence between the second space spectrum of the multiple signal classification algorithm corresponding to the target space matrix and the incident angle of the signal to be estimated.

[0042] Among them, the second correspondence is the correspondence between the target space matrix with the maximum signal feature vector and the incident angle of the signal to be estimated. Based on the better performance of the target space matrix, the second correspondence can better reflect the correspondence between the second space spectrum and the incident angle of the signal to be estimated.

[0043] Step 1083: Combine the first correspondence and the second correspondence to obtain the target correspondence.

[0044] In the embodiments of the present application, through the combination of the second correspondence relationship between the second spatial spectrum corresponding to the target spatial matrix and the incident angle of the signal to be estimated and the basic first correspondence relationship, the combined target correspondence relationship can fully represent the correspondence relationship between the target spatial spectrum and the incident angle of the signal to be estimated. Therefore, according to this target correspondence relationship, the angle of arrival of the signal corresponding to the signal to be estimated can be estimated more accurately, improving the resolution ability of the angle of arrival of the signal.

[0045] In one implementation, the above-mentioned obtaining of the first correspondence relationship may include: according to the multiple signal classification algorithm, obtaining the following correspondence relationship between the first spatial spectrum and the incident angle of the signal to be estimated:

[0046]

[0047] where P1 represents the first spatial spectrum, θ represents the incident angle, a(θ) represents the direction vector formed by the incident angle, a H (θ) represents the direction vector after conjugate transpose, and U N represents the noise subspace matrix; represents the noise subspace matrix after conjugate transpose.

[0048] In the embodiments of the present application, based on the orthogonality between the direction vector formed by the incident angle and the noise subspace matrix, the correspondence relationship between the first spatial spectrum and the incident angle of the signal to be estimated can be constructed.

[0049] In one implementation, the above-mentioned obtaining of the second correspondence relationship may include: according to the multiple signal classification algorithm, obtaining the following correspondence relationship between the second spatial spectrum and the incident angle of the signal to be estimated:

[0050]

[0051] where P2 represents the second spatial spectrum, θ represents the incident angle, a(θ) represents the direction vector formed by the incident angle, a H (θ) represents the direction vector after conjugate transpose, and U 目标 represents the target spatial matrix; represents the target spatial matrix after conjugate transpose.

[0052] In the embodiments of the present application, based on the orthogonality between the direction vector formed by the incident angle and the target spatial matrix, the correspondence relationship between the second spatial spectrum and the incident angle of the signal to be estimated can be constructed.

[0053] In one implementation, the combination of the first correspondence and the second correspondence to obtain the target correspondence may include: obtaining the target correspondence in the following manner:

[0054] P 目标 = P1 * P2;

[0055] where P 目标 represents the target spatial spectrum, P1 represents the first spatial spectrum, and P2 represents the second spatial spectrum.

[0056] In the embodiments of the present application, by combining the first correspondence and the second correspondence to obtain the target correspondence, that is, obtaining the target correspondence between the target spatial spectrum and the incident angle of the signal to be estimated, the angle of arrival of the signal can be estimated according to the characteristics of the target spatial spectrum and the target correspondence.

[0057] In one implementation, the above signal data includes a direction vector composed of the incident angles, an incident signal vector, and a noise vector. The above step 102 constructs a data covariance matrix corresponding to the signal to be estimated according to the received signal data of the signal to be estimated, and may include:

[0058] Step 1021, constructing an array manifold matrix according to the direction vector.

[0059] Among them, signal data is received through antennas in multiple directions of the base station. Antennas in different directions receive different signal data. An array manifold matrix is constructed from the direction vectors formed by the incident angles in each signal data, and this array manifold matrix is used as a part of the received signal matrix.

[0060] Step 1022, constructing a received signal matrix according to the array manifold matrix, the incident signal vector, and the noise vector.

[0061] Step 1023, constructing the data covariance matrix according to the received signal matrix and the received signal matrix after conjugate transpose.

[0062] In the embodiments of the present application, a received signal matrix is first constructed according to the received signal data of the signal to be estimated, and then a conjugate transpose operation is performed on the received signal matrix. Thus, the data covariance matrix corresponding to the signal to be estimated is obtained by processing the received signal matrix and the received signal matrix after conjugate transpose.

[0063] Exemplarily, the received signal matrix X(t) = A(θ)S(t) + N(t), where t is time, X(t) represents the received signal matrix, S(t) represents the incident signal vector, A represents the array manifold matrix, a(θ) is the direction vector, and N(t) is the noise vector. Based on the fact that the received additive noise N(t) is stationary, zero-mean Gaussian white noise and is uncorrelated with the signal, the autocorrelation of the received signal matrix X(t) is performed to obtain the data covariance matrix R as follows:

[0064] R = E[XX H ; where H represents the conjugate transpose operation.

[0065] It should be noted that the actually received signal data is obtained by sampling through a digital receiver, and the data sampling covariance matrix of the signal data is used to approximate the data covariance matrix. The data sampling covariance matrix is:

[0066] where L represents the number of data sampling snapshots of the signal data.

[0067] In one implementation, the signal data of the signal to be estimated above is received by a plurality of array elements arranged in a uniform linear array; where the interval between two adjacent array elements is not greater than half the wavelength of the signal to be estimated.

[0068] In an exemplary embodiment of the present application, the application environment is configured to establish a base station equipped with a plurality of array elements arranged in a uniform linear array in a free and open environment, and the signal to be estimated sent by a plurality of terminal devices in this scenario is received by the plurality of array elements, where the interval between two adjacent array elements is not greater than half the wavelength of the signal to be estimated.

[0069] In one implementation, step 110 above for estimating the angle of arrival of the signal corresponding to the signal to be estimated based on the target correspondence relationship may include:

[0070] Step 1101, obtaining the peak value of the target spatial spectrum based on the target correspondence relationship.

[0071] Step 1102, determining the angle value of the incident angle corresponding to the peak value as the angle of arrival of the signal.

[0072] In the embodiment of the present application, the angle value of the incident angle corresponding to the peak value of the target spatial spectrum can be obtained by searching for the peak value of the target spatial spectrum, and this angle value is the angle of arrival of the signal.

[0073] Figure 2 Shows a schematic flowchart of another method for estimating the angle of arrival of a signal provided by an exemplary embodiment of the present application in one implementation. For the application scenario of this method, see Figure 3, including: taking the element at the coordinate origin as the reference element, configuring a total of M elements arranged in a uniform linear array, where the interval between two adjacent elements is d, and d ≤ λ / 2, and λ is the wavelength of the signal to be estimated. There are K terminal devices in this application scenario, and the terminal devices send a total of K narrowband signals with a wavelength of λ. See Figure 2 , the method includes the following steps:

[0074] Step 201, construct the received signal matrix X(t) according to the received signal data.

[0075] Among them, the received signal matrix X(t) = A(θ)S(t) + N(t), X(t) represents the received signal matrix, S(t) represents the incident signal vector, A represents the array manifold matrix, a(θ) represents the direction vector, and θ k respectively represent the incident angle corresponding to the k-th signal, N(t) represents the noise vector, k = 1, 2, 3,..., K, t represents time, and H represents the conjugate transpose operation.

[0076] X(t) = [x1(t) x2(t)... x M (n)] T ,

[0077] S(t) = [S1(t) S2(t)... S K (t)] T ,

[0078] N(t) = [n1(t) n2(t)... n M (t)] T ,

[0079]

[0080]

[0081] Based on the fact that the received additive noise N(t) is stationary, zero-mean Gaussian white noise and is uncorrelated with the signal, the autocorrelation of the received signal matrix X(t) is performed to obtain the data covariance matrix R as:

[0082] R = E[XX H ; where H represents the conjugate transpose operation.

[0083] The actually received signal data is obtained by sampling with a digital receiver, and the data sampling covariance matrix of the signal data is used to approximate the data covariance matrix. The data sampling covariance matrix is:

[0084] Among them, L represents the number of data sampling snapshots of the signal data.

[0085] Step 202: Perform eigenvalue decomposition on the data covariance matrix to obtain the signal subspace matrix and the noise subspace matrix.

[0086] Among them, due to the mutual independence of the signal and the noise, decomposing the data covariance matrix can obtain the signal subspace matrix and the noise subspace matrix as U S and U N , and U S is the signal subspace composed of the signal eigenvectors corresponding to the larger K signal eigenvalues, and U N is the noise subspace composed of the noise eigenvectors corresponding to the remaining (M - K) smaller noise eigenvalues.

[0087] Step 203: Construct the first spatial spectrum based on the direction vector and the noise subspace matrix.

[0088] Among them, based on the orthogonality between the direction vector a(θ) and the noise subspace matrix U N , construct the first spatial spectrum:

[0089] Step 204: Sort the M eigenvectors in descending order of eigenvalues and form an ordered eigenvector space matrix.

[0090] Among them, the ordered eigenvector space matrix U = [e1 e2 … e M , where the first K eigenvectors e1, e2, …, e K are signal eigenvectors, and the last (M - K) eigenvectors e K+1 , e K+2 , …, e M are noise eigenvectors.

[0091] Step 205: Select the largest signal eigenvector corresponding to the largest signal eigenvalue and the remaining (M - K) noise eigenvectors from the ordered eigenvector space matrix to form the maximum eigenvector subspace matrix.

[0092] Among them, the maximum eigenvector subspace matrix Umax = [e1 e K+1 … e M .

[0093] Step 206: Construct the second spatial spectrum based on the maximum eigenvector subspace matrix.

[0094] Among them, the second spatial spectrum:

[0095] Step 207: Combine the first spatial spectrum and the second spatial spectrum to obtain the superimposed spectrum.

[0096] Among them, the superimposed spectrum:

[0097] Step 208: Obtain the angular value of the incident angle corresponding to the peak value of the superimposed spectrum by searching for the peak value of the superimposed spectrum, and this angular value is the angle of arrival of the signal.

[0098] Through the embodiments of the present application, the maximum signal eigenvector is added to strengthen the maximum eigenvector subspace matrix to obtain the second spatial spectrum, and then the superimposed spectrum is obtained according to the combination of the first spatial spectrum and the second spatial spectrum to estimate the angle of arrival of the signal, solving the estimation deviation existing in non-ideal situations, and improving the resolution ability of the angle of arrival of the signal by using more stable signal characteristics.

[0099] Figures 4a - 4c The evaluation line chart showing the estimation performance of the embodiments of the present application in different application scenarios is shown. Among them, the root mean square error is used to measure the estimation error of the angle of arrival of two uncorrelated signals with similar incident angles estimated by the method embodiments of the present application. The root mean square error of the D angle of arrival estimates based on the Monte Carlo experiment:

[0100] where ρ represents the number of Monte Carlo experiments, represents the estimated value of the D-th angle of arrival in the ρ-th experiment, and θ D is the actual angle of the angle of arrival of the signal.

[0101] Application scenario 1: Set the antenna array to be a uniform array signal base station composed of 10 array elements. Two far-field mobile devices 1 and 2 respectively transmit narrowband signals and arrive at the array from two directions of 5° and 10°. The signals have the same center frequency of 3.5 GHz and the number of sampling beats is 50. Set the signal-to-noise ratio to increase from -5 dB in steps of 1 dB to 15 dB. Conduct 300 experiments on both the method of the present application and the MUSIC method and compare the root mean square error. As Figure 4a shown, as the signal-to-noise ratio increases, the estimated value of the angle of arrival of the signal gets closer and closer to the actual angle, and the resolution of the method for estimating the angle of arrival of the signal provided by the present application is also getting higher and higher.

[0102] Application scenario 2: Set two far-field mobile devices 1 and 2 to respectively transmit narrowband signals and arrive at the array from two directions of 5° and 10°. The signals have the same center frequency of 3.5 GHz and the number of sampling beats is 40. At a signal-to-noise ratio of 5 dB and under the condition of 40 beats, when the number of array elements is set to increase from 6 to 20, conduct 300 experiments on both the method of the present application and the MUSIC method and compare the root mean square error. As Figure 4b shown, as the number of array elements increases, the resolution of the method for estimating the angle of arrival of the signal provided by the present application is also getting higher and higher.

[0103] Application Scenario 3: Set the antenna array as a uniform array signal base station composed of 10 array elements. Two far-field mobile devices 1 and 2 respectively transmit narrowband signals and arrive at the array from two directions of 5° and 10°. The signals have the same center frequency of 3.5 GHz. Under the conditions of a signal-to-noise ratio of 5 dB and 10 array elements, 300 experiments are carried out on the method of this application and the MUSIC method with the number of beats ranging from 10 to 100, and the root mean square error is compared. As Figure 4c shown, as the number of sampling beats increases, the root mean square error of both methods decreases. Obviously, the method of this application can greatly reduce the number of samplings required for the signal base station to estimate the direction of the mobile device, greatly saving the overhead of the signal base station, and as the number of sampling beats increases, the estimation error of this application becomes smaller and smaller.

[0104] The embodiment of this application also provides an electronic device. Figure 5 The following shows a schematic structural diagram of an electronic device provided by an exemplary embodiment of this application. Refer to Figure 5 This electronic device is used to execute the above method for estimating the angle of arrival of the signal. Figure 5 The following is a schematic structural diagram of an electronic device for implementing various embodiments of this application. The electronic device may vary greatly due to different configurations or performances. It may include a processor 501, a communication interface 502, a memory 503, and a communication bus 504. Among them, the processor 501, the communication interface 502, and the memory 503 communicate with each other through the communication bus 504. The processor 501 can call a computer program stored in the memory 503 and running on the processor 501 to execute the various steps of the above embodiment of the method for estimating the angle of arrival of the signal, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0105] The above structure of the electronic device does not limit the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, the input unit may include a graphics processing unit (GPU) and a microphone, and the display unit may be configured with a display panel in the form of a liquid crystal display, an organic light-emitting diode, etc. The user input unit includes at least one of a touch panel and other input devices. The touch panel is also called a touch screen. Other input devices may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be elaborated here.

[0106] The memory can be used to store software programs and various data. The memory may mainly include a first storage area for storing programs or instructions and a second storage area for storing data. Among them, the first storage area can store an operating system, application programs or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory can include volatile memory or non-volatile memory, or the memory can include both volatile and non-volatile memory. Among them, the non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. The volatile memory can be a Random Access Memory (RAM), a Static RAM (SRAM), a Dynamic RAM (DRAM), a Synchronous DRAM (SDRAM), a Double Data Rate SDRAM (DDR SDRAM), an Enhanced SDRAM (ESDRAM), a Synchlink DRAM (SLDRAM), and a Direct Rambus RAM (DRRAM).

[0107] The processor may include one or more processing units; optionally, the processor integrates an application processor and a modem processor. Among them, the application processor mainly processes operations related to the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication signals, such as a baseband processor. It can be understood that the above-mentioned modem processor may not be integrated into the processor either.

[0108] The embodiment of the present application also provides a readable storage medium, in which at least one computer program is stored, and the computer program is loaded and executed by the processor to implement all or part of the steps in the above method for estimating the angle of arrival of a signal. For example, the readable storage medium can be a Read-Only Memory (ROM), a Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0109] It should be noted that in this document, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the statement "including one..." does not exclude the presence of additional identical elements in the process, method, article or apparatus including such element. In addition, it should be pointed out that the scope of the methods and apparatuses in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0110] From the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0111] After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily conceive of other embodiments of the present application. The present application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include known common knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and examples are only to be considered as exemplary, and the true scope and spirit of the present application are pointed out by the claims.

[0112] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present application is only limited by the appended claims.

Claims

1. A method for estimating the angle of arrival of a signal, characterized in that, Including: Construct a data covariance matrix corresponding to the signal to be estimated according to the signal data of the received signal to be estimated; Obtain multiple eigenvalues of the data covariance matrix and the eigenvectors corresponding to each of the eigenvalues; Based on the multiple eigenvalues and the eigenvectors corresponding to each of the eigenvalues, obtain a target space matrix, where the target space matrix is composed of a maximum signal eigenvector and a noise eigenvector, the maximum signal eigenvector is the eigenvector corresponding to the largest eigenvalue among the multiple eigenvalues, the noise eigenvector is the eigenvector corresponding to the noise eigenvalue among the multiple eigenvalues, and the noise eigenvalue is less than a preset threshold; Based on the target space matrix, obtain a target correspondence between the target space spectrum and the incident angle of the signal to be estimated; Based on the target correspondence, estimate the signal arrival angle corresponding to the signal to be estimated.

2. The method according to claim 1, characterized in that, The obtaining the target space matrix based on the multiple eigenvalues and the eigenvectors corresponding to each of the eigenvalues includes: Select the eigenvector corresponding to the largest eigenvalue from the multiple eigenvalues as the maximum signal eigenvector; Sort the multiple noise eigenvectors in descending order of the noise eigenvalue to obtain an ordered noise subspace matrix; According to the maximum signal eigenvector and the ordered noise subspace matrix, form the target space matrix.

3. The method according to claim 1 or 2, characterized in that, The obtaining the target correspondence between the target space spectrum and the incident angle of the signal to be estimated based on the target space matrix includes: Obtain a first correspondence, where the first correspondence is the correspondence between the first space spectrum of the multiple signal classification algorithm corresponding to the noise subspace matrix and the incident angle of the signal to be estimated, and the noise subspace matrix is composed of multiple noise eigenvectors; Obtain a second correspondence, where the second correspondence is the correspondence between the second space spectrum of the multiple signal classification algorithm corresponding to the target space matrix and the incident angle of the signal to be estimated; Combine the first correspondence and the second correspondence to obtain the target correspondence.

4. The method according to claim 3, characterized in that, The obtaining the first correspondence includes: According to the multiple signal classification algorithm, obtain the following correspondence between the first space spectrum and the incident angle of the signal to be estimated: Among them, P1 represents the first spatial spectrum, θ represents the incident angle, a(θ) represents the direction vector formed by the incident angle, and a H (θ) represents the direction vector after conjugate transpose, and U N represents the noise subspace matrix; represents the noise subspace matrix after conjugate transpose.

5. The method according to claim 3, characterized in that, The obtaining the second correspondence includes: According to the multiple signal classification algorithm, obtain the following correspondence between the second space spectrum and the incident angle of the signal to be estimated: Among them, P2 represents the second spatial spectrum, θ represents the incident angle, a(θ) represents the direction vector composed of the incident angle, and a H (θ) represents the direction vector after conjugate transpose, and U 目标 represents the target space matrix; represents the target space matrix after conjugate transpose.

6. The method according to claim 3, characterized in that, The combining the first correspondence and the second correspondence to obtain the target correspondence includes: Obtain the target correspondence in the following manner: P 目标 = P1 * P2; Among them, P 目标 represents the target spatial spectrum, P1 represents the first spatial spectrum, and P2 represents the second spatial spectrum.

7. The method according to claim 1, characterized in that, The signal data includes a direction vector, an incident signal vector, and a noise vector composed of the incident angles; the constructing a data covariance matrix corresponding to the signal to be estimated according to the signal data of the received signal to be estimated includes: Construct an array manifold matrix according to the direction vector; Construct a received signal matrix according to the array manifold matrix, the incident signal vector, and the noise vector; Construct the data covariance matrix according to the received signal matrix and the received signal matrix after conjugate transposition.

8. The method according to claim 1, characterized in that, The signal data of the signal to be estimated is received by a plurality of array elements arranged in a uniform linear array; wherein, the interval between two adjacent array elements is not greater than the half wavelength of the signal to be estimated.

9. The method according to claim 1, characterized in that, Estimating the angle of arrival of the signal corresponding to the signal to be estimated based on the target correspondence includes: Obtain the peak value of the target spatial spectrum based on the target correspondence; Determine the angle value of the incident angle corresponding to the peak value as the angle of arrival of the signal.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory, and the memory stores a program or instruction that can run on the processor. When the program or instruction is executed by the processor, the steps of the method for estimating the angle of arrival of the signal according to any one of claims 1 to 9 are implemented.

11. A readable storage medium, characterized in that, A program or instruction is stored on the readable storage medium. When the program or instruction is executed by a processor, the steps of the method for estimating the angle of arrival of the signal according to any one of claims 1 to 9 are implemented.

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