WO2020143606A1 - Method for estimating angle of arrival of signal, and base station - Google Patents

Abstract

A method for estimating an angle of arrival of a signal, and a base station. The method comprises: obtaining an approximate autocorrelation matrix of signals received by a surface antenna array (11); adjusting the approximate autocorrelation matrix according to a noise transmission characteristic so as to obtain an adjusted autocorrelation matrix (12); and determining, according to the adjusted autocorrelation matrix, an estimated value of an angle of arrival of a signal (13).

Description

Signal arrival angle estimation method and base station

Cross-reference of related applications

This application claims the priority of Chinese Patent Application No. 201910028442.X filed in China on January 11, 2019, the entire contents of which are hereby incorporated by reference.

Technical field

The present disclosure relates to the field of communication technology, and in particular, to a method for estimating a signal angle of arrival and a base station.

Background technique

Based on the multiple signal classification (Multiple Signal Classification, MUSIC) method based on matrix feature space decomposition, the observation space of signal processing in geometry can be decomposed into orthogonal signal subspace and noise subspace. The signal subspace consists of the feature vectors corresponding to the signals in the data covariance matrix received by the array, and the noise subspace consists of the feature vectors corresponding to all the smallest eigenvalues (noise variance) in the covariance matrix. The spatial spectral function is constructed according to its orthogonality, and the angle corresponding to its spectral peak is the signal arrival angle. Under the low signal-to-noise ratio environment, this algorithm has a significant difference between the signal and noise or the noise is stronger than the signal, which leads to a serious degradation in the estimation performance of the signal arrival angle; when the signal arrival angles of multiple coherent sources are similar, three Possible situations: (1) The strong signal in the spectrum function covers the peak of the weak signal, resulting in missed detection; (2) When the signal strength is equal, only one peak appears when the peaks of different signals are superimposed on each other, resulting in missed detection and Misdetection; (3) When the signal intensities are equal, the spectral peaks of different signals are superimposed on each other, and the spectral peaks move in the middle, and two spectral peaks still appear, leading to misdetection. In addition, the traditional signal parameter estimation based on rotation invariant technology (estimating signal parameter via variation in techniques (referred to as ESPRIT) method) is limited by the physical structure of the antenna array, and is also not conducive to the estimation performance of the signal angle of arrival.

Summary of the invention

The present disclosure provides a signal arrival angle estimation method and a base station to solve the problem of poor signal arrival angle estimation performance in a scenario where a low signal-to-noise ratio and multi-coherent source signals are close to each other.

An embodiment of the present disclosure provides a method for estimating the angle of arrival of a signal, including:

Obtain the approximate autocorrelation matrix of the received signal of the surface antenna array;

Adjust the approximate auto-correlation matrix according to the noise transmission characteristics to obtain the adjusted auto-correlation matrix;

According to the adjusted auto-correlation matrix, the estimated value of the signal arrival angle is determined.

Wherein, the adjusting the approximate auto-correlation matrix according to the noise transmission characteristics to obtain the adjusted auto-correlation matrix includes:

Adjust the diagonal elements in the approximate auto-correlation matrix according to the preset adjustment mode corresponding to the noise transmission characteristics to obtain the adjusted auto-correlation matrix.

Wherein, adjusting the diagonal elements in the approximate autocorrelation matrix to obtain the adjusted autocorrelation matrix includes:

Determine the adjustment target value of the diagonal elements in the approximate autocorrelation matrix according to the element values in the approximate autocorrelation matrix;

According to the adjustment target value, the approximate auto-correlation matrix is adjusted to a Toeplitz matrix to obtain an adjusted auto-correlation matrix.

Wherein, determining the adjustment target value of the diagonal elements in the approximate autocorrelation matrix according to the element values in the approximate autocorrelation matrix includes:

Calculate the average value of the diagonal elements on each diagonal in the approximate autocorrelation matrix separately;

Determining the average value as the adjustment target value of the diagonal elements on the respective diagonal lines;

Wherein, the diagonal line is a main diagonal line or a sub-diagonal line parallel to the main diagonal line.

Wherein, adjusting the diagonal elements in the approximate autocorrelation matrix to obtain the adjusted autocorrelation matrix includes:

Construct a diagonal diagonal noise matrix according to the approximate autocorrelation matrix; wherein the number of rows and columns of the diagonal diagonal matrix is equal to the number of rows and columns of the approximate autocorrelation matrix;

According to the noise diagonal matrix, the diagonal elements in the approximate auto-correlation matrix are adjusted to obtain an adjusted auto-correlation matrix.

Wherein, constructing the diagonal noise matrix according to the approximate autocorrelation matrix includes:

Eigenvalue decomposition of the approximate autocorrelation matrix to obtain P eigenvalues;

According to the P eigenvalues, construct the noise diagonal matrix; where P is a positive integer.

Wherein, the step of constructing the noise diagonal matrix according to the P eigenvalues includes:

Calculating the average value of the smallest P-D feature values among the P feature values;

The average value is determined as the value of the diagonal elements in the diagonal matrix to construct the noise diagonal matrix; where D is a positive integer and D is less than K.

Wherein, adjusting the diagonal elements in the approximate autocorrelation matrix according to the noise diagonal matrix to obtain the adjusted autocorrelation matrix includes:

The difference between the approximate auto-correlation matrix and the noise diagonal matrix is calculated to obtain an adjusted auto-correlation matrix.

Wherein, the determining the estimated value of the signal arrival angle according to the adjusted autocorrelation matrix includes:

Construct the signal subspace and noise subspace of the D signals according to the autocorrelation matrix; where D is a positive integer;

Constructing D spectral functions according to the signal subspace and the noise subspace of the D signal;

According to the D spectral functions, the estimated value of the angle of arrival of the D channel signal is determined.

Wherein, constructing the signal subspace and noise subspace of the D signal according to the autocorrelation matrix includes:

Perform eigenvalue decomposition on the adjusted autocorrelation matrix to obtain P eigenvalues and eigenvectors corresponding to the P eigenvalues respectively; where P is a positive integer;

Construct the signal subspace of the D-channel signal according to the top D eigenvalues among the P eigenvalues and the D eigenvectors corresponding to the D eigenvalues;

A noise subspace is constructed according to P-D feature vectors corresponding to P-D eigenvalues other than the D eigenvalues among the P eigenvalues.

Wherein, constructing the signal subspace of the D-channel signal according to the top D eigenvalues among the P eigenvalues and the D eigenvectors corresponding to the D eigenvalues includes:

Obtain signal conditioning factors;

Adjust the i-th feature vector according to the signal adjustment factor to obtain the adjusted i-th feature vector;

Construct a signal subspace of the i-th signal according to the adjusted i-th eigenvector and the eigenvectors of the D eigenvectors other than the i-th eigenvector; where i is a positive integer, And i is less than or equal to D.

Wherein, the acquisition signal adjustment factor includes:

According to the D characteristic values, a signal conditioning factor is determined.

Wherein, determining the signal adjustment factor according to the D characteristic values includes:

Calculating the average value of the D characteristic values;

The average value is determined as the signal adjustment factor.

Wherein, the adjusting the i-th feature vector according to the signal adjustment factor to obtain the adjusted i-th feature vector includes:

The product of the adjustment factor and the i-th feature vector is determined as the adjusted i-th feature vector.

Wherein, the i-th spectral function among the D spectral functions is:

Where P _i is the spectral function corresponding to the i-th signal, a(θ _i ,

) Is the steering vector of the i-th signal, θ _i is the azimuth of the i-th signal,

Elevation angle of the i-th channel signal, E _s ⁱ is the i-th path signal subspace signal, E _n is the noise subspace, H denotes the conjugate transpose of a matrix; i is a positive integer, and i is less than or equal to D.

Wherein, the determining the estimated value of the angle of arrival of the D signal according to the D spectral functions includes:

According to the order of the D characteristic values from large to small, the estimated value of the angle of arrival of the D signal is calculated according to the D spectral functions in sequence.

Wherein, calculating the estimated value of the angle of arrival of the D signal according to the D spectral functions in order from the largest to the smallest of the D characteristic values includes:

Calculate the D-pair angle value corresponding to the first D spectral peaks in the i-th spectrum function, where the angle value includes an azimuth angle value and an elevation angle value;

According to the i-1 pair of angle values corresponding to the previous i-1 signal, determine the pair of angle values corresponding to the maximum spectral peak value of the D pair of angle values as the estimated value of the angle of arrival of the i-th signal; i It is a positive integer, and i is less than or equal to D.

Wherein, according to the i-1 pair of angle values corresponding to the previous i-1 signal, the pair of angle values corresponding to the maximum spectral peak of the D pair of angle values is determined as the angle of arrival of the i-th signal Estimates, including:

Determining k pairs of angle values other than the i-1 pairs of angle values among the D pairs of angle values;

The pair of angle values corresponding to the maximum spectral peak among the k pairs of angle values is determined as the estimated value of the angle of arrival of the i-th signal; where k is a positive integer and k is less than or equal to D.

Among them, D is preset by the base station side, or determined from signaling, or set by the terminal side and fed back to the base station side.

Some embodiments of the present disclosure also provide a base station, including: a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the following steps are implemented :

Obtain the approximate autocorrelation matrix of the received signal of the surface antenna array;

Adjust the approximate auto-correlation matrix according to the noise transmission characteristics to obtain the adjusted auto-correlation matrix;

According to the adjusted auto-correlation matrix, the estimated value of the signal arrival angle is determined.

Wherein, the processor implements the following steps when executing the computer program:

Adjust the diagonal elements in the approximate auto-correlation matrix according to the preset adjustment mode corresponding to the noise transmission characteristics to obtain the adjusted auto-correlation matrix.

Wherein, the processor implements the following steps when executing the computer program:

Determine the adjustment target value of the diagonal elements in the approximate autocorrelation matrix according to the element values in the approximate autocorrelation matrix;

According to the adjustment target value, the approximate auto-correlation matrix is adjusted to a Toeplitz matrix to obtain an adjusted auto-correlation matrix.

Wherein, the processor implements the following steps when executing the computer program:

Calculate the average value of the diagonal elements on each diagonal in the approximate autocorrelation matrix separately;

Determining the average value as the adjustment target value of the diagonal elements on the respective diagonal lines;

Wherein, the diagonal line is a main diagonal line or a sub-diagonal line parallel to the main diagonal line.

Wherein, the processor implements the following steps when executing the computer program:

Construct a diagonal diagonal noise matrix according to the approximate autocorrelation matrix; wherein the number of rows and columns of the diagonal diagonal matrix is equal to the number of rows and columns of the approximate autocorrelation matrix;

According to the noise diagonal matrix, the diagonal elements in the approximate auto-correlation matrix are adjusted to obtain an adjusted auto-correlation matrix.

Wherein, the processor implements the following steps when executing the computer program:

Eigenvalue decomposition of the approximate autocorrelation matrix to obtain P eigenvalues;

According to the P eigenvalues, construct the noise diagonal matrix; where P is a positive integer.

Wherein, the processor implements the following steps when executing the computer program:

Calculating the average value of the smallest P-D feature values among the P feature values;

The average value is determined as the value of the diagonal elements in the diagonal matrix to construct the noise diagonal matrix; where D is a positive integer and D is less than K.

Wherein, the processor implements the following steps when executing the computer program:

The difference between the approximate auto-correlation matrix and the noise diagonal matrix is calculated to obtain an adjusted auto-correlation matrix.

Wherein, the processor implements the following steps when executing the computer program:

Construct the signal subspace and noise subspace of the D signals according to the autocorrelation matrix; where D is a positive integer;

Constructing D spectral functions according to the signal subspace and the noise subspace of the D signal;

According to the D spectral functions, the estimated value of the angle of arrival of the D channel signal is determined.

Wherein, the processor implements the following steps when executing the computer program:

Perform eigenvalue decomposition on the adjusted autocorrelation matrix to obtain P eigenvalues and eigenvectors corresponding to the P eigenvalues respectively; where P is a positive integer;

Construct the signal subspace of the D-channel signal according to the top D eigenvalues among the P eigenvalues and the D eigenvectors corresponding to the D eigenvalues;

According to the P-D feature vectors corresponding to the P-D feature values of the P feature values except the D feature values, a noise subspace is constructed.

Wherein, the processor implements the following steps when executing the computer program:

Obtain signal conditioning factors;

Adjust the i-th feature vector according to the signal adjustment factor to obtain the adjusted i-th feature vector;

Construct a signal subspace of the i-th signal according to the adjusted i-th eigenvector and the eigenvectors of the D eigenvectors other than the i-th eigenvector; where i is a positive integer, And i is less than or equal to D.

Wherein, the processor implements the following steps when executing the computer program:

According to the D characteristic values, a signal conditioning factor is determined.

Wherein, the processor implements the following steps when executing the computer program:

Calculating the average value of the D characteristic values;

The average value is determined as the signal adjustment factor.

Wherein, the processor implements the following steps when executing the computer program:

The product of the adjustment factor and the i-th feature vector is determined as the adjusted i-th feature vector.

Wherein, the i-th spectral function among the D spectral functions is:

Where P _i is the spectral function corresponding to the i-th signal, a(θ _i ,

) Is the steering vector of the i-th signal, θ _i is the azimuth of the i-th signal,

Elevation angle of the i-th channel signal, E _s ⁱ is the i-th path signal subspace signal, E _n is the noise subspace, H denotes the conjugate transpose of a matrix; i is a positive integer, and i is less than or equal to D.

Wherein, the processor implements the following steps when executing the computer program:

According to the order of the D characteristic values from large to small, the estimated value of the angle of arrival of the D signal is calculated according to the D spectral functions in sequence.

Wherein, the processor implements the following steps when executing the computer program:

Calculate the D-pair angle value corresponding to the first D spectral peaks in the i-th spectrum function, where the angle value includes an azimuth angle value and an elevation angle value;

According to the i-1 pair of angle values corresponding to the previous i-1 signal, determine the pair of angle values corresponding to the maximum spectral peak value of the D pair of angle values as the estimated value of the angle of arrival of the i-th signal; i It is a positive integer, and i is less than or equal to D.

Wherein, the processor implements the following steps when executing the computer program:

Determining k pairs of angle values other than the i-1 pairs of angle values among the D pairs of angle values;

The pair of angle values corresponding to the maximum spectral peak among the k pairs of angle values is determined as the estimated value of the angle of arrival of the i-th signal; where k is a positive integer and k is less than or equal to D.

Among them, D is preset by the base station side, or determined from signaling, or set by the terminal side and fed back to the base station side.

Some embodiments of the present disclosure also provide a base station, including:

An acquisition module for acquiring an approximate auto-correlation matrix of signals received by the surface antenna array;

An adjustment module, configured to adjust the approximate auto-correlation matrix according to noise transmission characteristics to obtain an adjusted auto-correlation matrix;

The determining module is used to determine the estimated value of the angle of arrival of the signal according to the adjusted autocorrelation matrix.

Some embodiments of the present disclosure also provide a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the steps of the signal arrival angle estimation method described above.

The beneficial effects of the above technical solutions of the present disclosure are:

In some embodiments of the present disclosure, by adjusting the noise transmission characteristics, the approximate auto-correlation matrix is adjusted, and then the estimated value of the signal angle of arrival is determined according to the adjusted auto-correlation matrix, so as to weaken the influence of noise on signal angle of arrival detection, Improve the accuracy of signal angle of arrival detection, and avoid missed detection and false detection. In addition, the scheme can also be applied to the estimation of signal angle of arrival of various types of planar antenna arrays, which is beneficial to improve the adaptability of signal angle of arrival estimation.

BRIEF DESCRIPTION

FIG. 1 shows a flowchart of a method for estimating the angle of arrival of signals according to some embodiments of the present disclosure;

2 shows a schematic diagram of a uniform surface antenna array according to some embodiments of the present disclosure;

3 shows a schematic diagram of a 5G indoor positioning scenario in some embodiments of the present disclosure;

FIG. 4 is a schematic diagram illustrating SRS reference signal SINR simulation results of some embodiments of the present disclosure;

FIG. 5a shows one of the spectral peak schematic diagrams using the traditional MUISC algorithm in some embodiments of the present disclosure;

FIG. 5b shows one of the spectrum peak schematic diagrams of the Topritz noise cancellation algorithm in some embodiments of the present disclosure;

FIG. 6a shows a second schematic diagram of the spectral peak using the traditional MUISC algorithm in some embodiments of the present disclosure;

FIG. 6b shows a second schematic diagram of the spectral peaks of the Topritz noise cancellation algorithm in some embodiments of the present disclosure;

7a shows one of the spectral peak schematic diagrams of the eigenvalue decomposition noise reduction algorithm in some embodiments of the present disclosure;

7b shows a second schematic diagram of the spectral peaks of the eigenvalue decomposition and noise reduction algorithm in some embodiments of the present disclosure;

7c shows a third schematic diagram of the spectral peaks of the eigenvalue decomposition noise reduction algorithm in some embodiments of the present disclosure;

FIG. 8a shows the curve of the RMSE of the elevation angle with the number of antenna elements in some embodiments of the present disclosure;

FIG. 8b shows a curve of the RMSE of the azimuth angle with the number of antenna elements in some embodiments of the present disclosure;

9 shows a block diagram of a base station according to some embodiments of the present disclosure;

FIG. 10 shows a structural block diagram of the base station of the present disclosure.

detailed description

In order to make the technical problems, technical solutions and advantages to be solved by the present disclosure more clear, the following will describe in detail with reference to the accompanying drawings and specific embodiments. In the following description, specific details such as specific configurations and components are provided only to assist in a comprehensive understanding of embodiments of the present disclosure. Therefore, it should be clear to those skilled in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. In addition, descriptions of known functions and constructions are omitted for clarity and conciseness.

It should be understood that “one embodiment” or “one embodiment” mentioned throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the present disclosure. Therefore, “in one embodiment” or “in one embodiment” appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics may be combined in one or more embodiments in any suitable manner.

In various embodiments of the present disclosure, it should be understood that the size of the sequence numbers of the following processes does not mean that the execution order is sequential, and the execution order of each process should be determined by its function and inherent logic, and should not deal with some of the disclosure. The implementation process of the embodiments constitutes no limitation.

In addition, the terms "system" and "network" are often used interchangeably in this document.

In the embodiments provided in this application, it should be understood that “B corresponding to A” means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B based on A does not mean determining B based on A alone, and B may also be determined based on A and/or other information.

In some embodiments of the present disclosure, the form of the access network is not limited, and may include a macro base station (Macro Base Station), a micro base station (Pico Base Station), a Node B (3G mobile base station), and an enhanced base station (eNB) , GNB (called 5G mobile base station), home enhanced base station (Femto eNB or Home eNode B or Home eNB or HeNB), relay station, access point, RRU (Remote Radio Unit), RRH (Remote Radio Unit) Head, RF remote head) and other access networks. The user terminal may be a mobile phone (or cell phone), or other devices capable of sending or receiving wireless signals, including user equipment, personal digital assistants (PDAs), wireless modems, wireless communication devices, handheld devices, laptop computers, cordless phones , Wireless Local Loop (WLL) station, CPE (Customer Equipment, Customer Terminal) capable of converting mobile signals into WiFi signals, or mobile smart hotspots, smart appliances, or other devices that can communicate with mobile communication networks spontaneously without human operation Equipment etc.

Specifically, the embodiments of the present disclosure provide a signal arrival angle estimation method, which solves the problem of poor signal arrival angle estimation performance in a scenario where a low signal-to-noise ratio and multi-coherent source signals are close to each other.

The method for estimating the angle of arrival of the signal in some embodiments of the present disclosure is executed by the base station side, and the terminal side reports the angle of arrival estimation request message.

As shown in FIG. 1, an embodiment of the present disclosure provides a method for estimating the angle of arrival of a signal, which specifically includes the following steps:

Step 11: Obtain the approximate auto-correlation matrix of the signal received by the surface antenna array.

Wherein, the planar antenna array includes but is not limited to: uniform array, non-uniform array, rectangular array, circular array, etc.

Among them, the approximate auto-correlation matrix is the mean value of the auto-correlation matrix of K received signals at different times; K is a positive integer.

Specifically, the autocorrelation matrix of the received signal at time t may be determined in the following manner: the received signal of the antenna array at time t is determined based on the flow matrix of the antenna array; the antenna array is determined according to the received signal of the antenna array at time t The autocorrelation matrix of the received signal.

Furthermore, based on the big data theorem, the mean value of the auto-correlation matrix of K received signals at different times is solved, and the approximate auto-correlation matrix of the received signal of the antenna array is determined. It should be noted that the larger the value of K, the better the approximation effect. The value of K can be determined according to actual needs, and is not specifically limited here.

For the convenience of explanation, the following uses a uniform surface array as an example:

As shown in FIG. 2, an example of a uniform planar antenna array is given. The uniform planar antenna array 20 has N rows and M columns of uniformly isotropic antenna elements 201 (M and N are positive integers), and D channels When the signal reaches the antenna array (D is a positive integer, and D is less than or equal to P, P is associated with M and N, and P is the product of M and N), the flow matrix of the antenna array is composed as follows:

Where A represents the flow matrix of the antenna array, a(θ _i ,

) Is the steering vector of the i-th signal;

Where a ^k (θ _i ,

) Is the steering vector of the i-th signal in the k-th row, T represents the transpose of the matrix; the steering vector of the i-th signal is the organic splicing of the steering vector of the i-th signal in the N-row of the antenna array.

Where θ _i is the azimuth of the i-th signal,

Elevation angle of the i-th signal path; d _r is the row pitch of the antenna array elements, d _c is the column of the antenna array element spacing; i is a positive integer less than or equal to D i; k and k is a positive integer less than or equal to N.

The received signal at the antenna array end at time t is:

x(t)=A×s(t)+n(t)

Where x(t) is the received signal at the antenna array at time t, s(t) is the complex amplitude vector of the D signal at time t, n(t) is the noise signal from the antenna array, and A is the flow pattern of the antenna array matrix.

According to the formula

Solve the autocorrelation of the received signal x(t) at the antenna array at time t. among them,

For the autocorrelation of the received signal x(t) at the antenna array at time t, E represents the expectation and H represents the conjugate transpose.

Further, by solving the mean value of the autocorrelation matrix of K received signals at different times, the approximate autocorrelation matrix of the received signal at the antenna array end is approximately solved. The approximate autocorrelation matrix is:

Where R _x is the approximate auto-correlation matrix of the received signal at the antenna array end, x _i (t) is the received signal at the i-th t time, and K is the number of snapshots.

Step 12: Adjust the approximate auto-correlation matrix according to the noise transmission characteristics to obtain the adjusted auto-correlation matrix.

Among them, the noise transmission characteristics include noise transmission parameters, such as: signal-to-noise ratio.

For example: when the difference in the direction of incoming waves is large, the approximate autocorrelation matrix is adjusted to the Toplitz matrix; when the difference in the direction of incoming waves is small, the approximate autocorrelation matrix is adjusted by the approximate variance of noise to weaken The influence of noise on the signal angle of arrival detection improves the signal angle of arrival detection accuracy.

Step 13: Determine the estimated value of the angle of arrival of the signal according to the adjusted autocorrelation matrix.

In the above solution, the approximate autocorrelation matrix is adjusted by adjusting the noise transmission characteristics, and then the estimated value of the signal angle of arrival is determined according to the adjusted autocorrelation matrix, so as to weaken the influence of noise on signal angle of arrival detection and improve the signal Arrival angle detection accuracy, and avoid missing detection and wrong detection; in addition, the scheme can also be applied to the estimation of signal arrival angles of various types of area antenna arrays, which is beneficial to improve the adaptability of signal arrival angle estimation.

Wherein, the above step 12 specifically includes: adjusting the diagonal elements in the approximate auto-correlation matrix according to the preset adjustment mode corresponding to the noise transmission characteristics to obtain the adjusted auto-correlation matrix.

Adjusting the diagonal elements in the approximate autocorrelation matrix includes but is not limited to the following ways:

Method 1: Determine the adjustment target value of the diagonal elements in the approximate autocorrelation matrix according to the element values in the approximate autocorrelation matrix;

According to the adjustment target value, the approximate auto-correlation matrix is adjusted to a Toeplitz matrix to obtain an adjusted auto-correlation matrix.

As a specific implementation manner, determining the adjustment target value of the diagonal elements in the approximate autocorrelation matrix according to the element values in the approximate autocorrelation matrix may include:

Calculate the average value of the diagonal elements on each diagonal in the approximate autocorrelation matrix separately;

Among them, the average value of the diagonal elements on each diagonal is:

among them,

Is the average value of the diagonal elements on the lth diagonal, i is the row index of the elements in the approximate autocorrelation matrix, j is the column index of the elements in the approximate autocorrelation matrix, l=- (MN-1),-(MN-2),...,-1,0,1,...,(MN-2),(MN-1).

The average value is determined as the adjustment target value of the diagonal elements on the respective diagonal lines; wherein the diagonal line is the main diagonal line or the sub-diagonal line parallel to the main diagonal line.

Further, according to the adjustment target value (the average value of the diagonal elements on each diagonal), the approximate autocorrelation matrix is adjusted to the Toplitz matrix, and the adjusted autocorrelation matrix is obtained as:

In this embodiment, when the statistical characteristics of noise are not ideal, or when the noise is large (such as when the direction of incoming waves is far away), the approximate autocorrelation matrix of the received and received signals is adjusted to the Toplitz matrix to weaken the noise on the signal The influence of angle of arrival detection is beneficial to improve the accuracy of signal angle of arrival detection.

Method 2: Construct a diagonal noise matrix according to the approximate autocorrelation matrix; wherein the number of rows and columns of the diagonal noise matrix is equal to the number of rows and columns of the approximate autocorrelation matrix;

According to the noise diagonal matrix, the diagonal elements in the approximate auto-correlation matrix are adjusted to obtain an adjusted auto-correlation matrix.

For example, the approximate variance of noise can be determined according to the approximate autocorrelation matrix; the diagonal diagonal matrix of noise can be constructed according to the approximate variance of the noise.

As a specific implementation manner, according to the approximate autocorrelation matrix, constructing the diagonal noise matrix may include:

Eigenvalue decomposition of the approximate autocorrelation matrix to obtain P eigenvalues;

According to the P eigenvalues, construct the diagonal diagonal matrix of the noise; where P is a positive integer and P is M*N.

Further, according to the P eigenvalues, constructing the noise diagonal matrix may include:

Calculate the average of the smallest P-D eigenvalues of the P eigenvalues (that is, the approximate variance of noise);

Specifically, P eigenvalues obtained by performing eigenvalue decomposition on the approximate autocorrelation matrix, and arranging the P eigenvalues in descending order as: λ ₁ , λ ₂ , ... λ _P ; The first D first eigenvalues that are largest among the P eigenvalues in a large to small order, and the second eigenvalues other than the D first eigenvalues in the P eigenvalues are the smallest of the P eigenvalues. PD characteristic values.

For example: the P feature values are: 15,13,10,10,7,6,5,5,5,4,2,1; the value of D is 5, then the largest top D feature values are: 15,13,10,10,7; the smallest PD feature value is: 6,5,5,5,4,2,1.

It should be noted that, some embodiments of the present disclosure only need to be able to distinguish the largest D feature values from the P feature values and the smallest P-D feature values, and the sorting step is not necessarily required. For example: in the case that the order of the P feature values is random (may not be in the order from large to small or from small to large), the P feature values may not be arranged in order, but the P The smallest PD feature value among the feature values.

The average value of the smallest P-D feature values among the P feature values is:

among them,

Is the average value of the smallest PD feature values among the P feature values; λ _D+1 , λ _D+2 ,..., Λ _P are the smallest PD feature values among the P feature values.

The average value is determined as the value of the diagonal elements in the diagonal matrix to construct the noise diagonal matrix; where D is a positive integer and D is less than K.

Among them, D may be preset by the base station side, or determined from signaling, or set by the terminal side and fed back to the base station side.

As a specific implementation manner, adjusting the diagonal elements in the approximate autocorrelation matrix according to the noise diagonal matrix, and the adjusted autocorrelation matrix may include:

The difference between the approximate auto-correlation matrix and the noise diagonal matrix is calculated to obtain an adjusted auto-correlation matrix.

Specifically, you can use the formula:

The adjusted autocorrelation matrix is obtained. among them,

For the adjusted auto-correlation matrix, R _x is the approximate auto-correlation matrix, and R _n is the diagonal noise matrix.

In this embodiment, by calculating the average of the PD minimum eigenvalues of the P eigenvalues, that is, the average value of the noise eigenvalues, to characterize the noise level, that is, the variance of the noise, and through this The noise diagonal matrix constructed by the average value adjusts the approximate auto-correlation matrix of the received signal to eliminate the influence of part of the noise on the eigenvalue decomposition (such as the effect of noise on the eigenvalue decomposition when the incoming direction is closer), Furthermore, the influence of noise on signal angle of arrival detection is weakened, which is beneficial to improve the accuracy of signal angle of arrival detection.

Wherein, the above step 13 specifically includes: constructing the signal subspace and noise subspace of the D-channel signal according to the autocorrelation matrix; where D is a positive integer, D can be preset by the base station side, or determined from signaling , Or set by the terminal side and fed back to the base station side;

Constructing D spectral functions according to the signal subspace and the noise subspace of the D signal;

According to the D spectral functions, the estimated value of the angle of arrival of the D channel signal is determined.

Further, according to the autocorrelation matrix, constructing the signal subspace and noise subspace of the D-channel signal may include:

Perform eigenvalue decomposition on the adjusted autocorrelation matrix to obtain P eigenvalues and eigenvectors corresponding to the P eigenvalues respectively; where P is a positive integer;

Construct the signal subspace of the D-channel signal according to the top D eigenvalues among the P eigenvalues and the D eigenvectors corresponding to the D eigenvalues;

A noise subspace is constructed according to P-D feature vectors corresponding to P-D eigenvalues other than the D eigenvalues among the P eigenvalues.

Specifically, eigenvalue decomposition is performed on the adjusted autocorrelation matrix to obtain P eigenvalues, feature vectors corresponding to the P eigenvalues, respectively, and a feature matrix composed of the feature vectors; the P eigenvalues Sorting according to the order from large to small, the feature matrix is adjusted according to the correspondence between the feature vector and the feature value, and finally the P feature values after sorting, the feature vectors corresponding to the P feature values, and the adjusted Feature matrix. Among them, the largest first D eigenvalues of the P eigenvalues correspond to the signal, and the smallest last P-D feature values correspond to noise.

Further, according to the largest D feature values among the P feature values and the D feature vectors corresponding to the D feature values, constructing the signal subspace of the D channel signal may include:

Obtain signal conditioning factors;

Specifically, a signal adjustment factor may be determined according to the D characteristic values to characterize the energy sum of the signal. As a specific implementation manner, the average value of the D characteristic values may be calculated; the average value is determined as the signal adjustment factor.

Among them, the signal adjustment factor is: α=λ ₁ ² +λ ₂ ² +...+λ _D ² , where α is the signal adjustment factor; λ ₁ , λ ₂ ,..., λ _D is the largest _{D among} the P eigenvalues Characteristic values.

Adjust the i-th feature vector according to the signal adjustment factor to obtain the adjusted i-th feature vector;

As a specific implementation manner, the product of the adjustment factor and the i-th feature vector may be determined as the adjusted i-th feature vector, that is, by increasing the amplitude of the i-th feature vector, Adjustment of the i-th feature vector.

Construct a signal subspace of the i-th signal according to the adjusted i-th eigenvector and the eigenvectors of the D eigenvectors other than the i-th eigenvector; where i is a positive integer, And i is less than or equal to D.

The signal subspace of the i-th signal is:

E _s ⁱ =[ν ₁ ν ₂ ν ₃ … α*ν _i … ν _D ]

Where ν ₁ , ν ₂ , ν ₃ …, ν _D are the eigenvectors corresponding to the D eigenvalues respectively, ν _i is the eigenvector corresponding to the eigenvalue λ _i , which is also the i-th column in the feature matrix .

The noise subspace is:

E _n =[ν _D+1 ν _D+2 ν ₃ … ν _P ]

Where ν _D+1 , ν _D+2 , ..., ν _P are eigenvectors corresponding to the PD eigenvalues, respectively.

The i-th spectral function among the D spectral functions is:

Where P _i is the spectral function corresponding to the i-th signal, a(θ _i ,

) Is the steering vector of the i-th signal, θ _i is the azimuth of the i-th signal,

Elevation angle of the i-th channel signal, E _s ⁱ is the i-th path signal subspace signal, E _n is the noise subspace, H denotes the conjugate transpose of a matrix; i is a positive integer, and i is less than or equal to D.

In this embodiment, adjusting the i-th feature vector by an adjustment factor is equivalent to using the energy of all signals and adjusting the i-th signal to make it a strong signal, avoiding that the spectral peak of the i-th signal may be affected by a strong signal The problem of spectral peak coverage can also avoid the leakage and false detection problems caused by the signal strength of the i-th signal and other signals may be equivalent, so as to solve the problem of spectral peak interference of neighbor signals, which is beneficial to increase the angle of arrival of the signal The estimation accuracy of the angle of arrival of the signal when the difference is small, and has better stability, effectively reducing the missed detection and false detection rate.

Further, determining the estimated value of the angle of arrival of the D channel signal according to the D spectral functions may include: sequentially calculating the D channels according to the D spectral functions according to the order of the D characteristic values from large to small Estimated angle of arrival of the signal.

Among them, the eigenvalue corresponds to the eigenvector, the signal subspace is associated with the eigenvector, and the D spectral functions correspond one-to-one to the signal subspace of the D signal, then the calculation order of the D spectral functions is that the D eigenvalues are To small order.

Furthermore, the step of sequentially calculating the estimated value of the angle of arrival of the D signal according to the D spectral functions in descending order of the D feature values includes:

Calculate the D-pair angle value corresponding to the first D spectral peaks in the i-th spectrum function, where the angle value includes an azimuth angle value and an elevation angle value;

According to the i-1 pair of angle values corresponding to the previous i-1 signal, determine the pair of angle values corresponding to the maximum spectral peak value of the D pair of angle values as the estimated value of the angle of arrival of the i-th signal; i It is a positive integer, and i is less than or equal to D.

As a specific implementation manner, it may be to determine k pairs of angle values other than the i-1 pairs of angle values among the D pairs of angle values; and map a pair corresponding to the largest spectral peak value among the k pairs of angle values The angle value is determined to be the estimated value of the angle of arrival of the i-th signal; where k is a positive integer, and k is less than or equal to D.

For example: the verification process of the i-th pair of angle values (estimated value of the i-th signal arrival angle) is: store the detected first i-1 pair of angle values in the array Angle, and perform spectral peak on the i-th spectral function search for. Take out the D pairs of angle values corresponding to the first D spectral peaks, and remove the detected i-1 pairs of angles in the array Angle from the D pairs of angle values, and the remaining D-i+1 (that is, k) pairs of angles Among the values, the pair of angle values corresponding to the maximum spectral peak value is the i-th pair of angle values (estimated value of the i-th signal arrival angle). In this way, after D times of searching for the same spectral peak, the azimuth and elevation angles of the D pairs corresponding to the D signals can be successfully detected, and have good accuracy.

The method for estimating the angle of arrival of signals in some embodiments of the present disclosure will be described below in conjunction with specific application scenarios:

Figure 3 shows a 5G indoor positioning scenario. In the indoor space of 120×50m, the distance between base stations is 20m, and the height is 3m, which is consistent with the indoor environment. In this scenario, due to the dense deployment of base stations, the user terminal may have a direct path with multiple base stations at the same time. In FIG. 3, BS represents a base station (Base Station).

The uplink channel sounding signal (SRS) is reported by the user terminal to the base station periodically and is independent of the data sent. It occupies independent frequency domain resources. It was originally used to estimate the frequency domain information of the uplink channel. Do frequency selective scheduling; used to estimate uplink channels, do downlink beamforming, etc. Due to the periodicity, configurability and independent existence of the SRS signal, it does not need to send data, and it can also exist independently, which is beneficial as a real-time positioning reference signal.

The process of SRS signal sequence generation and physical resource mapping in 5G is as follows:

The SRS resource is configured by the SRS resource information part (SRS-Resource) IE, which mainly includes:

Refers to the number of antenna ports

p _i ∈{1000,1001,...}. SRS antenna ports are between 1000 and 2000, and the number of ports can be selected from 1, 2, and 4.

Refers to the number of consecutive OFDM symbols.

l ₀ , refers to the start position of the time domain, by

It is concluded that l _offset ∈{0, 1, ..., 5},

Represents the number of symbols in a single time slot,

Indicates that a single time slot is guaranteed to be completed

Symbol transmission.

k ₀ refers to the beginning of the frequency domain.

SRS sequence generation:

Is the length of the SRS sequence,

It is a random sequence generator, δ=log ₂ (K _TC ), K _TC is the transmission comb number (transmission comb number), the value is 2 or 4, and α _i is the cyclic shift of the antenna port pi.

The SRS sequence is mapped to the physical layer:

That is, the (k′, l′) symbol in the SRS sequence is mapped to the resource block’s

For transmission.

m _{SRS, b} can be obtained by looking at Table 1 for different cell reference signal (CRS) configuration modes,

Represents the number of subcarriers in a Radio Bearer (Radio Bearer, RB for short), otherwise represents other.

It is the transmission comb offse configured by the upper layer. n _shift is the amount of frequency shift configured by the upper layer. The SRS allocation is adjusted to the common resource block grid by a multiple of four.

n _RRC is the quantity of the upper layer configuration, and N _b can be obtained by referring to Table 1.

Based on the above formula, the multiplexing factor of the SRS signal can be roughly expressed as K _TC *N _b , and K _TC takes a value of 2 or 4. Check the value of N _b (N1, N2, N3) from Table 1 below. The value ranges from 1 to 17 depending on the CRS configuration method.

Table 1: SRS CRS configuration method.

In some embodiments of the present disclosure, user density, SRS multiplexing factor, interference model combined with Point-to-Point Protocol (PPP) are used to derive the typical signal and interference of SRS as the uplink positioning reference signal in the 5G indoor (Indoor) scenario Add noise ratio (Signal to Interference plus Noise Ratio, referred to as SINR) for the next simulation, the specific process is as follows:

The user's position can be modeled by the homogeneous Poisson point process Φ _u with density λ _u , and the location of the base station in the network is also modeled by the homogeneous Poisson point process Φ with density λ independent of Φ _u .

The SINR of the user i reference signal received by the base station may be defined as:

Among them, h is the small-scale fading between the user and the base station, h~exp(1).

For a certain SINR threshold τ, the typical user's probability of successful reception can be expressed as:

The probability that there is one less user in the radius R area is

The probability density function of the distance from the user to the base station can be expressed as:

Rewrite the reference signal SINR as

Where Q=I+σ ² . therefore,

Among them, the Laplace transform of interference term I is expressed as:

among them,

In summary, we can get:

and so,

Table 2: SRS reference signal SINR simulation parameter table.

Simulation parameters	Value
λ u	1/16
P	23dBm
α	4
Minimum distance from base station	5m
noise	-174dBm/Hz
σ 2	10^-13.4

As shown in Figure 4, an example of SRS reference signal SINR simulation results is given, where the y-axis represents the probability of successful reception, and the x-axis represents the correct reception threshold setting value τ (in dB). As can be seen from Figure 4, when the When the receiving threshold is set to -10dB, nearly 90% of the transmission can be correctly received, which means that in this scenario, 90% of the user SRS signal SINR is -10dB and above.

The following is a simulation analysis of the signal angle of arrival estimation method of some embodiments of the present disclosure:

Toplitz attenuates noise:

In the simulation, three signals reach the antenna array at 30°, 34°, and 38° respectively. The signal-to-noise ratio is -10dB, the number of snapshots is 1024, the spectral peak scanning step is 0.05°, and the number of antenna elements in the antenna array is 20. Fig. 5a is a schematic diagram of the spectral peak of the traditional MUISC algorithm using the above parameters, and there is only a sharp peak point in Fig. 5a. FIG. 5b is a schematic diagram of the spectral peaks of the Topritz noise reduction algorithm in some embodiments of the present disclosure using the above parameters. It can be seen that some embodiments of the present disclosure can clearly distinguish three peak points. In Figs. 5a and 5b, the horizontal axis represents the signal arrival angle (unit: °), and the vertical axis represents the signal-to-noise ratio (unit: dB).

In the simulation, three signals reach the antenna array at 30°, 33°, and 36° respectively. The signal-to-noise ratio is -10dB, the number of snapshots is 1024, the spectral peak scanning step is 0.05°, and the number of antenna elements in the antenna array is 20. As shown in FIG. 6a, it is a schematic diagram of a modified spectral peak based on FIG. 5a. FIG. 6a is a schematic diagram of a spectral peak using the above parameters for a traditional MUISC algorithm. Schematic diagram of the spectral peaks of the algorithm. In Figs. 6a and 6b, the horizontal axis represents the signal arrival angle (unit: °), and the vertical axis represents the signal-to-noise ratio (unit: dB). By comparing the simulation results before and after the modification, it is found that this operation only increases the sharpness of the spectral peak and does not improve the accuracy and resolution of the angle estimation. The Topritz noise cancellation algorithm has limitations when the signal arrival angles are small, and when the signal arrives When the angle difference is large, it has a significant effect of improving the accuracy and resolution of the angle estimation.

Eigenvalue decomposition attenuates noise:

In the same simulation environment (that is, the three signals reach the antenna array at 30°, 33°, and 36°, respectively, the signal-to-noise ratio is -10dB, the number of snapshots is 1024, the peak scan step is 0.05°, and the antenna elements of the antenna array The number is 20). Using the eigenvalue decomposition noise reduction method and the arrival angle spectrum peak scanning strategy, three signal arrival angles can be obtained, respectively 30.95°, 35.65°, and 33.25°. The corresponding peaks are shown in Figures 7a and 7b. As shown in Fig. 7c, the horizontal axis represents the signal arrival angle (unit: °), and the vertical axis represents the signal-to-noise ratio (unit: dB). The difference from the true value is 0.95°, 0.25°, 0.35°. It can be seen that the eigenvalue decomposition reduces the influence of noise, and adopts the spectral function construction method and the spectral peak scanning strategy in some embodiments of the present disclosure to provide a more accurate angle estimation result when the signal arrival angle is closer.

The root-mean-square error (RMSE) simulation of each scheme under different signal arrival scenarios:

Simulation environment:

Elevation angle θ = [51 55 59 63 67; 23 35 47 59 71], azimuth

Set two sets of three-dimensional angle values, which are the scenarios where the incoming angles differ by about 10° and 4°. The RMSE results are shown in Table 3.

Table 3:

Simulation environment: The signal-to-noise ratio is fixed at -5dB, 200 drops, and the number of snapshots is 1024.

As shown in Figure 8a, it is a curve of the RMSE of elevation angle with the number of antenna elements, and as shown in Figure 8b, it is a curve of the RMSE of azimuth angle with the number of antenna elements.

When the signal arrival angle is far away, and the signal-to-noise ratio of each channel is randomly generated from -10 to 0dB, the eigenvalue decomposition weakens the noise. The MSEIC algorithm's RMSE is about 0.004° for the elevation angle, and the RMSE for the azimuth is about 7e-4°. The TOPLZ noise reduction MUSIC algorithm has an RMSE of elevation angle of about 0.001°, and an RMSE of azimuth angle of about 4e-4°, which can provide more accurate accuracy.

When the signal arrival angle is relatively close and the signal-to-noise ratio of each signal is randomly generated from -10 to 0dB, the Topsight's noise reduction MUSIC algorithm has an elevation RMSE of about 20° and an azimuth RMSE of about 15°. The eigenvalue decomposition weakens the noise. The RMSE of the elevation angle of the MUSIC algorithm is around 0.9°, and the RMSE of the azimuth angle is around 0.6°.

It can be known from the above simulation results that the signal arrival angle estimation methods in some embodiments of the present disclosure can better cope with the environment with a low signal-to-noise ratio and the multi-channel signal arrival angles are similar. The accuracy of the azimuth angle is generally higher than the estimation accuracy of the elevation angle. A stereo antenna array can be used to improve the estimation accuracy of the elevation angle.

By estimating the RMSE from the angle of different antenna array elements, it can be seen that this method has a lower limit on the number of antenna array elements. That is, in order to ensure the accuracy of the signal angle of arrival estimation, an antenna array of a certain size needs to be provided. It can be known from the simulation results that when the number of antenna elements reaches the threshold, the angle estimation accuracy is no longer sensitive to the antenna size. Therefore, as long as the antenna size reaches the threshold value in production, the angle estimation can obtain a better profit.

The above embodiment introduces the method for estimating the signal arrival angle of the present disclosure. The following embodiment will further describe its corresponding base station with reference to the drawings.

Specifically, as shown in FIG. 9, the base station 900 of some embodiments of the present disclosure includes:

The obtaining module 910 is used to obtain an approximate auto-correlation matrix of signals received by the surface antenna array;

The adjustment module 920 is configured to adjust the approximate auto-correlation matrix according to the noise transmission characteristics to obtain an adjusted auto-correlation matrix;

The determining module 930 is configured to determine the estimated value of the angle of arrival of the signal according to the adjusted auto-correlation matrix.

Wherein, the adjustment module 920 includes:

The adjustment sub-module is used to adjust the diagonal elements in the approximate auto-correlation matrix according to the preset adjustment mode corresponding to the noise transmission characteristics to obtain the adjusted auto-correlation matrix.

Wherein, the adjustment sub-module includes:

A determining unit, configured to determine the adjustment target value of diagonal elements in the approximate autocorrelation matrix according to the element values in the approximate autocorrelation matrix;

The first adjustment unit is configured to adjust the approximate auto-correlation matrix to a Toeplitz matrix according to the adjustment target value to obtain an adjusted auto-correlation matrix.

Wherein, the determining unit includes:

The first calculation subunit is used to calculate the average value of the diagonal elements on each diagonal line in the approximate autocorrelation matrix respectively;

A first determining subunit, configured to determine the average value as the adjustment target value of the diagonal elements on the respective diagonal lines;

Wherein, the diagonal line is a main diagonal line or a sub-diagonal line parallel to the main diagonal line.

Wherein, the adjustment sub-module includes:

A construction unit, configured to construct a diagonal diagonal noise matrix based on the approximate autocorrelation matrix; wherein the number of rows and columns of the diagonal diagonal matrix is equal to the number of rows and columns of the approximate autocorrelation matrix;

The second adjusting unit is configured to adjust the diagonal elements in the approximate auto-correlation matrix according to the noise diagonal matrix to obtain an adjusted auto-correlation matrix.

Wherein, the building unit includes:

A decomposition subunit, configured to perform eigenvalue decomposition on the approximate autocorrelation matrix to obtain P eigenvalues;

The first construction subunit is configured to construct the diagonal diagonal matrix of noise according to the P eigenvalues; where P is a positive integer.

Wherein, the first constructing subunit is specifically used to: calculate the average value of the smallest PD characteristic values among the P characteristic values; determine the average value as the value of the diagonal element in the diagonal matrix, to Construct the noise diagonal matrix; where D is a positive integer and D is less than K.

Wherein, the second adjustment unit includes:

The second calculation subunit is used to calculate the difference between the approximate auto-correlation matrix and the noise diagonal matrix to obtain an adjusted auto-correlation matrix.

Wherein, the determining module 930 includes:

A first construction submodule, configured to construct a signal subspace and a noise subspace of the D signals according to the autocorrelation matrix; where D is a positive integer;

A second construction submodule, configured to construct D spectral functions according to the signal subspace and the noise subspace of the D channel signals;

The determination submodule is used for determining the estimated value of the angle of arrival of the D channel signal according to the D spectral functions.

Wherein, the first building sub-module includes:

A first decomposition unit, configured to perform eigenvalue decomposition on the adjusted autocorrelation matrix to obtain P eigenvalues and eigenvectors corresponding to the P eigenvalues respectively; where P is a positive integer;

A first construction unit, configured to construct a signal subspace of the D-channel signal according to the largest D of the P feature values and the D feature vectors corresponding to the D feature values;

The second construction unit is configured to construct a noise subspace according to P-D feature vectors corresponding to P-D feature values of the P feature values except the D feature values.

Wherein, the first building unit includes:

Acquisition subunit for acquiring signal conditioning factors;

An adjustment subunit, configured to adjust the i-th feature vector according to the signal adjustment factor to obtain the adjusted i-th feature vector;

A second constructing subunit, configured to construct a signal subspace of the i-th signal according to the adjusted i-th feature vector and the feature vectors other than the i-th feature vector among the D feature vectors ; Where i is a positive integer and i is less than or equal to D.

Wherein, the obtaining subunit is specifically used to determine the signal adjustment factor according to the D characteristic values.

Wherein, the obtaining subunit is further specifically used for: calculating an average value of the D characteristic values; and determining the average value as the signal adjustment factor.

Wherein, the adjustment subunit is specifically used to determine the product of the adjustment factor and the i-th feature vector as the adjusted i-th feature vector.

Wherein, the i-th spectral function among the D spectral functions is:

Where P _i is the spectral function corresponding to the i-th signal, α(θ _i ,

) Is the steering vector of the i-th signal, θ _i is the azimuth of the i-th signal,

Elevation angle of the i-th channel signal, E _s ⁱ is the i-th path signal subspace signal, E _n is the noise subspace, H denotes the conjugate transpose of a matrix; i is a positive integer, and i is less than or equal to D.

Wherein, the determining sub-module includes:

The calculation unit is configured to sequentially calculate the estimated value of the angle of arrival of the D-channel signal according to the D spectral functions in order from the largest to the smallest.

Wherein, the calculation unit includes:

The third calculation subunit is used to calculate the D-pair angle value corresponding to the first D spectral peaks in the i-th spectrum function, where the angle value includes an azimuth angle value and an elevation angle value;

A second determining subunit, configured to determine a pair of angle values corresponding to the maximum spectral peak value of the D pair angle values as the i-th signal according to the i-1 pair angle values corresponding to the previous i-1 way signals The estimated value of the angle of arrival of; i is a positive integer, and i is less than or equal to D.

Wherein, the second determining subunit is specifically configured to: determine k pairs of angle values other than the i-1 pairs of angle values among the D pairs of angle values; correspond to the maximum spectral peak value of the k pairs of angle values The pair of angle values of is determined as the estimated value of the angle of arrival of the i-th signal; where k is a positive integer and k is less than or equal to D.

Where D is preset in the base station side or determined through signaling or set in the terminal side and fed back to the base station side.

The embodiment of the base station of the present disclosure corresponds to the embodiment of the method for estimating the angle of arrival of the signal described above. All the implementation means in the embodiment of the method described above are applicable to the embodiment of the base station, and the same technical effect can also be achieved.

In some embodiments of the present disclosure, the base station 900 adjusts the approximate auto-correlation matrix by adjusting the noise transmission characteristics, and then determines the estimated value of the signal arrival angle according to the adjusted auto-correlation matrix, so as to weaken the noise The impact of detection improves the accuracy of signal arrival angle detection and avoids missed detection and false detection; in addition, the scheme can also be applied to the estimation of signal arrival angles of various types of area antenna arrays, which is beneficial to improve the appropriateness of signal arrival angle estimation Matching.

In order to better achieve the above object, as shown in FIG. 10, some embodiments of the present disclosure further provide a base station, which includes: a processor 1000; a memory 1020 connected to the processor 1000 through a bus interface 1030, And a transceiver 1010 connected to the processor 1000 through a bus interface 1030; the memory 1020 is used to store programs and data used by the processor 1000 when performing operations; and send data information or guides through the transceiver 1010 Frequency, it also receives the uplink control channel through the transceiver 1010; when the processor 1000 calls and executes the programs and data stored in the memory 1020, the following functions are realized.

The processor 1000 is used to read the program in the memory 1020. When the processor 1000 executes the computer program, the following steps are realized: obtaining an approximate autocorrelation matrix of the received signal of the surface antenna array; according to the noise transmission characteristics, the approximate self The correlation matrix is adjusted to obtain an adjusted auto-correlation matrix; according to the adjusted auto-correlation matrix, the estimated value of the signal arrival angle is determined.

When the processor 1000 executes the computer program, the following steps are implemented: adjusting the diagonal elements in the approximate auto-correlation matrix according to the preset adjustment mode corresponding to the noise transmission characteristics to obtain the adjusted auto-correlation matrix.

When the processor 1000 executes the computer program, the following steps are implemented: according to the element values in the approximate autocorrelation matrix, determine the adjustment target value of the diagonal elements in the approximate autocorrelation matrix; according to the adjustment target value , Adjust the approximate auto-correlation matrix to a Toplitz matrix to obtain an adjusted auto-correlation matrix.

When the processor 1000 executes the computer program, the following steps are implemented: separately calculating the average value of the diagonal elements on each diagonal line in the approximate autocorrelation matrix; determining the average value as the respective diagonal line The adjustment target value of the diagonal elements on the above; wherein the diagonal line is the main diagonal line or the sub-diagonal line parallel to the main diagonal line.

When the processor 1000 executes the computer program, the following steps are implemented: constructing a diagonal diagonal noise matrix according to the approximate autocorrelation matrix; wherein the number of rows and columns of the diagonal diagonal matrix and the number of rows and columns of the approximate autocorrelation matrix Equality; adjust the diagonal elements in the approximate auto-correlation matrix according to the noise diagonal matrix to obtain the adjusted auto-correlation matrix.

When the processor 1000 executes the computer program, the following steps are implemented: performing eigenvalue decomposition on the approximate autocorrelation matrix to obtain P eigenvalues; constructing the noise diagonal matrix according to the P eigenvalues; wherein , P is a positive integer.

When the processor 1000 executes the computer program, the following steps are realized: calculating the average value of the smallest PD characteristic values among the P characteristic values; determining the average value as the value of diagonal elements in the diagonal matrix To construct the diagonal matrix of noise; where D is a positive integer and D is less than K.

When the processor 1000 executes the computer program, the following steps are implemented: calculating the difference between the approximate auto-correlation matrix and the diagonal noise matrix to obtain an adjusted auto-correlation matrix.

When the processor 1000 executes the computer program, the following steps are implemented: constructing the signal subspace and noise subspace of the D-channel signal according to the autocorrelation matrix; where D is a positive integer; and the signal of the D-channel signal Constructing D spectral functions in the subspace and the noise subspace; according to the D spectral functions, the estimated value of the angle of arrival of the D channel signal is determined.

When the processor 1000 executes the computer program, the following steps are implemented: performing eigenvalue decomposition on the adjusted autocorrelation matrix to obtain P eigenvalues and eigenvectors corresponding to the P eigenvalues respectively; wherein, P It is a positive integer; according to the largest D first eigenvalues of the P eigenvalues, and the D eigenvectors corresponding to the D eigenvalues, construct a signal subspace of the D channel signal; according to the P eigenvalues In the PD feature vectors corresponding to the PD feature values except the D feature values, a noise subspace is constructed.

When the processor 1000 executes the computer program, the following steps are realized: acquiring a signal adjustment factor; adjusting the i-th feature vector according to the signal adjustment factor to obtain the adjusted i-th feature vector; according to the adjustment The i-th eigenvector and the eigenvectors of the D eigenvectors other than the i-th eigenvector to construct the signal subspace of the i-th signal; where i is a positive integer and i is less than or equal to D.

When the processor 1000 executes the computer program, the following steps are implemented: according to the D feature values, a signal adjustment factor is determined.

When the processor 1000 executes the computer program, the following steps are implemented: calculating an average value of the D feature values; and determining the average value as the signal adjustment factor.

When the processor 1000 executes the computer program, the following steps are implemented: the product of the adjustment factor and the i-th feature vector is determined as the adjusted i-th feature vector.

Wherein, the i-th spectral function among the D spectral functions is:

Where P _i is the spectral function corresponding to the i-th signal, α(θ _i ,

) Is the steering vector of the i-th signal, θ _i is the azimuth of the i-th signal,

Elevation angle of the i-th channel signal, E _s ⁱ is the i-th path signal subspace signal, E _n is the noise subspace, H denotes the conjugate transpose of a matrix; i is a positive integer, and i is less than or equal to D.

When the processor 1000 executes the computer program, the following steps are implemented: in accordance with the order of the D feature values from largest to smallest, the estimated value of the angle of arrival of the D signal is calculated according to the D spectral functions in sequence.

When the processor 1000 executes the computer program, the following steps are realized: calculating the D-pair angle value corresponding to the first D spectral peaks in the i-th spectrum function, the angle value includes the azimuth angle value and the elevation angle value; according to the previous i- I-1 pair of angle values corresponding to one signal, the pair of angle values corresponding to the maximum spectral peak among the D pair of angle values is determined as the estimated value of the angle of arrival of the i-th signal; i is a positive integer, And i is less than or equal to D.

When the processor 1000 executes the computer program, the following steps are realized: determining k pairs of angle values other than the i-1 pairs of angle values among the D pairs of angle values; and determining the maximum spectrum among the k pairs of angle values The pair of angle values corresponding to the peak value is determined to be the estimated value of the angle of arrival of the i-th signal; where k is a positive integer, and k is less than or equal to D.

Where D is preset in the base station side or determined by signaling or set in the terminal side and fed back to the base station side.

The base station in some embodiments of the present disclosure adjusts the approximate auto-correlation matrix by adjusting the noise transmission characteristics, and then determines the estimated value of the signal angle of arrival according to the adjusted auto-correlation matrix, so as to weaken the noise for signal angle of arrival detection Impact, improve the accuracy of signal arrival angle detection, and avoid missed detection and false detection; in addition, the scheme can also be applied to the estimation of the signal arrival angle of many types of surface antenna arrays, which is beneficial to improve the adaptation of the signal arrival angle estimation Sex.

The transceiver 1010 is used to receive and transmit data under the control of the processor 1000.

Among them, in FIG. 10, the bus architecture may include any number of interconnected buses and bridges, specifically one or more processors represented by the processor 1000 and various circuits of the memory represented by the memory 1020 are linked together. The bus architecture can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, etc., which are well known in the art, and therefore, they will not be further described in this article. The bus interface provides an interface. The transceiver 1010 may be a plurality of elements, including a transmitter and a transceiver, and provides a unit for communicating with various other devices on a transmission medium. The processor 1000 is responsible for managing the bus architecture and general processing, and the memory 1020 may store data used by the processor 1000 when performing operations.

Some embodiments of the present disclosure also provide a computer-readable storage medium that stores a computer program on the computer-readable storage medium. When the computer program is executed by a processor, each process of the above-described signal angle of arrival estimation method embodiments is implemented, and can To achieve the same technical effect, in order to avoid repetition, it will not be repeated here. Wherein, the computer-readable storage medium, such as read-only memory (Read-Only Memory, ROM for short), random access memory (Random Access Memory, RAM for short), magnetic disk or optical disk, etc.

Those skilled in the art can understand that all or part of the steps of the above embodiments can be completed by hardware, or can be completed by instructing related hardware through a computer program, and the computer program includes instructions to perform part or all of the steps of the above method. ; And the computer program may be stored in a readable storage medium, the storage medium may be any form of storage medium.

In addition, it should be pointed out that, in the device and method of the present disclosure, obviously, each component or each step can be decomposed and/or recombined. These decompositions and/or recombinations should be regarded as equivalent solutions of the present disclosure. Moreover, the steps for performing the above-mentioned series of processing can naturally be executed in chronological order in the order described, but it does not necessarily need to be executed in chronological order, and some steps can be executed in parallel or independently of each other. For those of ordinary skill in the art, all or any steps or components of the methods and devices of the present disclosure can be understood, and can be implemented in hardware, firmware in any computing device (including a processor, a storage medium, etc.) or a network of computing devices , Software, or a combination thereof, which can be realized by those of ordinary skill in the art using their basic programming skills after reading the description of the present disclosure.

Therefore, the purpose of the present disclosure can also be achieved by running a program or a group of programs on any computing device. The computing device may be a well-known general-purpose device. Therefore, the object of the present disclosure can also be achieved only by providing a program product containing program code for implementing the method or device. That is, such a program product also constitutes the present disclosure, and a storage medium storing such a program product also constitutes the present disclosure. Obviously, the storage medium may be any known storage medium or any storage medium developed in the future. It should also be noted that, in the device and method of the present disclosure, obviously, each component or each step can be decomposed and/or recombined. These decompositions and/or recombinations should be regarded as equivalent solutions of the present disclosure. In addition, the steps for performing the above-mentioned series of processing may naturally be performed in chronological order in the order described, but it is not necessary to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

The above are some embodiments of the present disclosure. It should be noted that for those of ordinary skill in the art, without departing from the principles described in the present disclosure, several improvements and retouches can be made. These improvements and retouches also It should be regarded as the scope of protection of this disclosure.

Claims (40)

1. A method for estimating the angle of arrival of a signal, including:

   Obtain the approximate autocorrelation matrix of the received signal of the surface antenna array;

   Adjust the approximate auto-correlation matrix according to the noise transmission characteristics to obtain the adjusted auto-correlation matrix;

   According to the adjusted auto-correlation matrix, the estimated value of the signal arrival angle is determined.
2. The method for estimating the angle of arrival of a signal according to claim 1, wherein the adjusting the approximate auto-correlation matrix according to the noise transmission characteristics to obtain the adjusted auto-correlation matrix includes:

   Adjust the diagonal elements in the approximate auto-correlation matrix according to the preset adjustment mode corresponding to the noise transmission characteristics to obtain the adjusted auto-correlation matrix.
3. The method for estimating the angle of arrival of a signal according to claim 2, wherein the adjusting the diagonal elements in the approximate autocorrelation matrix to obtain the adjusted autocorrelation matrix includes:

   Determine the adjustment target value of the diagonal elements in the approximate autocorrelation matrix according to the element values in the approximate autocorrelation matrix;

   According to the adjustment target value, the approximate auto-correlation matrix is adjusted to a Toeplitz matrix to obtain an adjusted auto-correlation matrix.
4. The method for estimating the angle of arrival of a signal according to claim 3, wherein the determining the adjustment target value of the diagonal elements in the approximate autocorrelation matrix according to the element values in the approximate autocorrelation matrix includes:

   Calculate the average value of the diagonal elements on each diagonal in the approximate autocorrelation matrix separately;

   Determining the average value as the adjustment target value of the diagonal elements on the respective diagonal lines;

   Wherein, the diagonal line is a main diagonal line or a sub-diagonal line parallel to the main diagonal line.
5. The method for estimating the angle of arrival of a signal according to claim 2, wherein the adjusting the diagonal elements in the approximate autocorrelation matrix to obtain the adjusted autocorrelation matrix includes:

   Construct a diagonal diagonal noise matrix according to the approximate autocorrelation matrix; wherein the number of rows and columns of the diagonal diagonal matrix is equal to the number of rows and columns of the approximate autocorrelation matrix;

   According to the diagonal diagonal matrix, the diagonal elements in the approximate autocorrelation matrix are adjusted to obtain an adjusted autocorrelation matrix.
6. The method for estimating the angle of arrival of a signal according to claim 5, wherein the constructing a diagonal diagonal noise matrix according to the approximate autocorrelation matrix includes:

   Eigenvalue decomposition of the approximate autocorrelation matrix to obtain P eigenvalues;

   According to the P eigenvalues, construct the noise diagonal matrix; where P is a positive integer.
7. The method for estimating the angle of arrival of a signal according to claim 6, wherein the constructing the noise diagonal matrix according to the P eigenvalues includes:

   Calculating the average value of the smallest P-D feature values among the P feature values;

   The average value is determined as the value of the diagonal elements in the diagonal matrix to construct the noise diagonal matrix; where D is a positive integer and D is less than K.
8. The method for estimating the angle of arrival of a signal according to claim 5, wherein said adjusting the diagonal elements in said approximate autocorrelation matrix according to said noise diagonal matrix to obtain an adjusted autocorrelation matrix, include:

   The difference between the approximate auto-correlation matrix and the noise diagonal matrix is calculated to obtain an adjusted auto-correlation matrix.
9. The method for estimating the angle of arrival of a signal according to claim 1, wherein the determining the estimated value of the angle of arrival of the signal according to the adjusted autocorrelation matrix includes:

   Construct the signal subspace and noise subspace of the D signals according to the autocorrelation matrix; where D is a positive integer;

   Constructing D spectral functions according to the signal subspace and the noise subspace of the D signal;

   According to the D spectral functions, the estimated value of the angle of arrival of the D channel signal is determined.
10. The method for estimating the angle of arrival of the signal according to claim 9, wherein the constructing the signal subspace and the noise subspace of the D signal according to the autocorrelation matrix includes:

    Perform eigenvalue decomposition on the adjusted autocorrelation matrix to obtain P eigenvalues and eigenvectors corresponding to the P eigenvalues respectively; where P is a positive integer;

    Construct the signal subspace of the D-channel signal according to the top D eigenvalues among the P eigenvalues and the D eigenvectors corresponding to the D eigenvalues;

    A noise subspace is constructed according to P-D feature vectors corresponding to P-D eigenvalues other than the D eigenvalues among the P eigenvalues.
11. The method for estimating the angle of arrival of a signal according to claim 10, wherein the D is constructed according to the top D eigenvalues among the P eigenvalues and the D eigenvectors corresponding to the D eigenvalues The signal subspace of the road signal includes:

    Obtain signal conditioning factors;

    Adjust the i-th feature vector according to the signal adjustment factor to obtain the adjusted i-th feature vector;

    Construct a signal subspace of the i-th signal according to the adjusted i-th eigenvector and the eigenvectors of the D eigenvectors other than the i-th eigenvector; where i is a positive integer, And i is less than or equal to D.
12. The method for estimating the angle of arrival of a signal according to claim 11, wherein the acquisition signal adjustment factor comprises:

    According to the D characteristic values, a signal conditioning factor is determined.
13. The method for estimating the angle of arrival of the signal according to claim 12, wherein the determining the signal adjustment factor according to the D characteristic values includes:

    Calculating the average value of the D characteristic values;

    The average value is determined as the signal adjustment factor.
14. The method for estimating the angle of arrival of a signal according to claim 11, wherein the adjusting the i-th feature vector according to the signal adjustment factor to obtain the adjusted i-th feature vector includes:

    The product of the adjustment factor and the i-th feature vector is determined as the adjusted i-th feature vector.
15. The method for estimating the angle of arrival of a signal according to claim 9, wherein the i-th spectral function of the D spectral functions is:

    

    Where P _i is the spectral function corresponding to the i-th signal,

    

    Is the steering vector of the i-th signal, θ _i is the azimuth of the i-th signal,

    

    Elevation angle of the i-th channel signal, E _s ⁱ is the i-th path signal subspace signal, E _n is the noise subspace, H denotes the conjugate transpose of a matrix; i is a positive integer, and i is less than or equal to D.
16. The method for estimating the angle of arrival of the signal according to claim 9, wherein the determining the estimated value of the angle of arrival of the D channel signal according to the D spectral functions includes:

    According to the order of the D characteristic values from large to small, the estimated value of the angle of arrival of the D signal is calculated according to the D spectral functions in sequence.
17. The method for estimating the angle of arrival of a signal according to claim 16, wherein the estimation of the angle of arrival of the D channel signal is sequentially calculated according to the D spectral functions according to the order of the D characteristic values from large to small Values, including:

    Calculate the D-pair angle value corresponding to the first D spectral peaks in the i-th spectrum function, where the angle value includes an azimuth angle value and an elevation angle value;

    According to the i-1 pair of angle values corresponding to the previous i-1 signal, determine the pair of angle values corresponding to the maximum spectral peak value of the D pair of angle values as the estimated value of the angle of arrival of the i-th signal; i It is a positive integer, and i is less than or equal to D.
18. The method for estimating the angle of arrival of a signal according to claim 17, wherein the pair of angles corresponding to the maximum spectral peak of the D pair of angle values is determined according to the i-1 pair of angle values corresponding to the previous i-1 signal Value, determined as the estimated value of the angle of arrival of the i-th signal, including:

    Determining k pairs of angle values other than the i-1 pairs of angle values among the D pairs of angle values;

    The pair of angle values corresponding to the maximum spectral peak among the k pairs of angle values is determined as the estimated value of the angle of arrival of the i-th signal; where k is a positive integer and k is less than or equal to D.
19. The method for estimating the angle of arrival of a signal according to any one of claims 7, 9 to 18, wherein D is preset by the base station side, or determined from signaling, or set by the terminal side and fed back to the base station side .
20. A base station includes: a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the following steps when executing the computer program:

    Obtain the approximate autocorrelation matrix of the received signal of the surface antenna array;

    Adjust the approximate auto-correlation matrix according to the noise transmission characteristics to obtain the adjusted auto-correlation matrix;

    According to the adjusted auto-correlation matrix, the estimated value of the signal arrival angle is determined.
21. The base station according to claim 20, wherein the processor implements the following steps when executing the computer program:

    Adjust the diagonal elements in the approximate auto-correlation matrix according to the preset adjustment mode corresponding to the noise transmission characteristics to obtain the adjusted auto-correlation matrix.
22. The base station according to claim 21, wherein the processor implements the following steps when executing the computer program:

    Determine the adjustment target value of the diagonal elements in the approximate autocorrelation matrix according to the element values in the approximate autocorrelation matrix;

    According to the adjustment target value, the approximate auto-correlation matrix is adjusted to a Toeplitz matrix to obtain an adjusted auto-correlation matrix.
23. The base station according to claim 22, wherein the processor implements the following steps when executing the computer program:

    Calculate the average value of the diagonal elements on each diagonal in the approximate autocorrelation matrix separately;

    Determining the average value as the adjustment target value of the diagonal elements on the respective diagonal lines;

    Wherein, the diagonal line is a main diagonal line or a sub-diagonal line parallel to the main diagonal line.
24. The base station according to claim 21, wherein the processor implements the following steps when executing the computer program:

    Construct a diagonal diagonal noise matrix according to the approximate autocorrelation matrix; wherein the number of rows and columns of the diagonal diagonal matrix is equal to the number of rows and columns of the approximate autocorrelation matrix;

    According to the noise diagonal matrix, the diagonal elements in the approximate auto-correlation matrix are adjusted to obtain an adjusted auto-correlation matrix.
25. The base station according to claim 24, wherein the processor implements the following steps when executing the computer program:

    Eigenvalue decomposition of the approximate autocorrelation matrix to obtain P eigenvalues;

    According to the P eigenvalues, construct the noise diagonal matrix; where P is a positive integer.
26. The base station according to claim 25, wherein the processor implements the following steps when executing the computer program:

    Calculating the average value of the smallest P-D feature values among the P feature values;

    The average value is determined as the value of the diagonal elements in the diagonal matrix to construct the noise diagonal matrix; where D is a positive integer and D is less than K.
27. The base station according to claim 24, wherein the processor implements the following steps when executing the computer program:

    The difference between the approximate auto-correlation matrix and the noise diagonal matrix is calculated to obtain an adjusted auto-correlation matrix.
28. The base station according to claim 20, wherein the processor implements the following steps when executing the computer program:

    Construct the signal subspace and noise subspace of the D signals according to the autocorrelation matrix; where D is a positive integer;

    Constructing D spectral functions according to the signal subspace and the noise subspace of the D signal;

    According to the D spectral functions, the estimated value of the angle of arrival of the D channel signal is determined.
29. The base station according to claim 28, wherein the processor implements the following steps when executing the computer program:

    Perform eigenvalue decomposition on the adjusted autocorrelation matrix to obtain P eigenvalues and eigenvectors corresponding to the P eigenvalues respectively; where P is a positive integer;

    Construct the signal subspace of the D-channel signal according to the top D eigenvalues among the P eigenvalues and the D eigenvectors corresponding to the D eigenvalues;

    A noise subspace is constructed according to P-D feature vectors corresponding to P-D eigenvalues other than the D eigenvalues among the P eigenvalues.
30. The base station according to claim 29, wherein the processor implements the following steps when executing the computer program:

    Obtain signal conditioning factors;

    Adjust the i-th feature vector according to the signal adjustment factor to obtain the adjusted i-th feature vector;

    Construct a signal subspace of the i-th signal according to the adjusted i-th eigenvector and the eigenvectors of the D eigenvectors other than the i-th eigenvector; where i is a positive integer, And i is less than or equal to D.
31. The base station according to claim 30, wherein the processor implements the following steps when executing the computer program:

    According to the D characteristic values, a signal conditioning factor is determined.
32. The base station according to claim 31, wherein the processor implements the following steps when executing the computer program:

    Calculating the average value of the D characteristic values;

    The average value is determined as the signal adjustment factor.
33. The base station according to claim 30, wherein the processor implements the following steps when executing the computer program:

    The product of the adjustment factor and the i-th feature vector is determined as the adjusted i-th feature vector.
34. The base station according to claim 28, wherein the i-th spectral function of the D spectral functions is:

    

    Where P _i is the spectral function corresponding to the i-th signal,

    

    Is the steering vector of the i-th signal, θ _i is the azimuth of the i-th signal,

    

    Elevation angle of the i-th channel signal, E _s ⁱ is the i-th path signal subspace signal, E _n is the noise subspace, H denotes the conjugate transpose of a matrix; i is a positive integer, and i is less than or equal to D.
35. The base station according to claim 28, wherein the processor implements the following steps when executing the computer program:

    According to the order of the D characteristic values from large to small, the estimated value of the angle of arrival of the D signal is calculated according to the D spectral functions in sequence.
36. The base station according to claim 35, wherein the processor implements the following steps when executing the computer program:

    Calculate the D-pair angle value corresponding to the first D spectral peaks in the i-th spectrum function, where the angle value includes an azimuth angle value and an elevation angle value;

    According to the i-1 pair of angle values corresponding to the previous i-1 signal, determine the pair of angle values corresponding to the maximum spectral peak value of the D pair of angle values as the estimated value of the angle of arrival of the i-th signal; i It is a positive integer, and i is less than or equal to D.
37. The base station according to claim 36, wherein the processor implements the following steps when executing the computer program:

    Determining k pairs of angle values other than the i-1 pairs of angle values among the D pairs of angle values;

    The pair of angle values corresponding to the maximum spectral peak among the k pairs of angle values is determined as the estimated value of the angle of arrival of the i-th signal; where k is a positive integer and k is less than or equal to D.
38. The base station according to any one of claims 26, 28 to 37, wherein D is preset by the base station side, or determined from signaling, or set by the terminal side and fed back to the base station side.
39. A base station, including:

    An acquisition module for acquiring an approximate auto-correlation matrix of signals received by the surface antenna array;

    An adjustment module, configured to adjust the approximate auto-correlation matrix according to noise transmission characteristics to obtain an adjusted auto-correlation matrix;

    The determining module is used to determine the estimated value of the angle of arrival of the signal according to the adjusted autocorrelation matrix.
40. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method for estimating a signal arrival angle according to any one of claims 1 to 19 are realized.

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