Direction of Arrival Reliability Estimation Method Based on Multiple Signal Classification Algorithm

Abstract

The present invention provides a method for estimating the reliability of a direction of arrival based on a multiple signal classification algorithm, which is applied to the technical field of direction of arrival estimation. The method comprises: determining a correlation matrix corresponding to received data; performing eigenvalue decomposition on the correlation matrix to obtain eigenvectors and eigenvalues; determining a noise vector matrix based on the eigenvectors and eigenvalues; determining a multiple signal classification correlation value based on an array manifold and a correlation matrix corresponding to the noise vector matrix; performing a peak search based on the multiple signal classification correlation value to determine a corresponding position of a maximum peak; determining an azimuth and an elevation angle of the direction of arrival of received data based on the corresponding position of the maximum peak; determining an array manifold vector and a signal vector corresponding to the azimuth and the elevation angle; and determining an angular reliability value of the direction of arrival of received data based on the array manifold vector and the signal vector. The present invention can improve the reliability of the estimation of the direction of arrival of the multiple signal classification algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of direction of arrival estimation, and in particular to a method for estimating the reliability of direction of arrival based on a multiple signal classification algorithm. Background Art

[0002] In the multiple signal classification algorithm, the array antenna receives signal data to generate a covariance matrix, which is decomposed to obtain the signal subspace and noise subspace. The angle information of the incoming signal is obtained through the orthogonal relationship between the signal direction vector and the noise subspace.

[0003] The multiple signal classification algorithm is used to calculate the direction of arrival of the signal, mainly by calculating the correlation matrix of the data and then performing matrix decomposition to obtain the signal subspace and noise subspace. According to the orthogonality of the array flow pattern and the noise subspace, the angle of the incoming wave is estimated. However, in actual engineering applications, the performance of the direction of arrival estimation will be reduced due to various errors in the array.

[0004] It can be seen that the direction of arrival estimation method in the related art cannot determine the reliability of the arrival angle. Summary of the invention

[0005] The present invention provides a method for estimating the reliability of a direction of arrival based on a multiple signal classification algorithm, which is used to solve the problem that the direction of arrival estimation method in the prior art cannot determine the reliability of the arrival angle, thereby improving the reliability of the direction of arrival estimation of the multiple signal classification algorithm.

[0006] The present invention provides a method for estimating the reliability of a direction of arrival based on a multiple signal classification algorithm, comprising the following steps: determining the received data of a preset uniform circular array array and the array manifold of the uniform circular array; determining a correlation matrix corresponding to the received data; performing eigenvalue decomposition on the correlation matrix to obtain eigenvectors and eigenvalues; determining a noise vector matrix based on the eigenvectors and the eigenvalues; determining multiple signal classification correlation values ​​based on the array manifold and the correlation matrix corresponding to the noise vector matrix; performing peak search based on the multiple signal classification correlation values ​​to determine the corresponding position of the maximum peak; determining the azimuth and elevation of the direction of arrival of the received data based on the corresponding position of the maximum peak; determining the array manifold vector and signal vector corresponding to the azimuth and elevation based on the corresponding position of the maximum peak; determining the angular reliability value of the direction of arrival of the received data based on the array manifold vector and the signal vector.

[0007] According to a wave direction reliability estimation method based on a multiple signal classification algorithm provided by the present invention, the noise vector matrix is ​​determined based on the eigenvector and the eigenvalue, including: sorting the eigenvalues ​​in order of numerical value to obtain a first eigenvalue; adjusting the eigenvector based on the first eigenvalue to obtain a first eigenvector; determining the number of signal sources based on the first eigenvalue; and determining the noise vector matrix based on the number of signal sources and the first eigenvector.

[0008] According to a method for estimating the direction of arrival reliability based on a multiple signal classification algorithm provided by the present invention, the method for determining the angular reliability value of the direction of arrival of the received data based on the array manifold vector and the signal vector comprises:

[0009] The angle reliability value is calculated according to the following formula:

[0010] ;

[0011] Among them, angleArrayFold represents the array manifold vector, allArrayMainFold represents the array manifold, pos represents the corresponding position of the maximum peak, signalEV represents the signal vector, EV1 represents the first eigenvector, angleReability represents the angle reliability value, || represents the modulus value; H represents the conjugate transpose.

[0012] According to a method for estimating the reliability of the direction of arrival of the received data based on a multiple signal classification algorithm provided by the present invention, after determining the angular reliability value of the direction of arrival of the received data based on the array manifold vector and the signal vector, the method further includes: when the angular reliability value of the direction of arrival of the received data is greater than a first reliability threshold, outputting the angular reliability value and the azimuth angle and the elevation angle.

[0013] According to a method for estimating the reliability of a direction of arrival based on a multiple signal classification algorithm provided by the present invention, after determining the angular reliability value of the direction of arrival of the received data based on the array manifold vector and the signal vector, the method further includes: when the angular reliability value of the direction of arrival of the received data is less than a first reliability threshold, determining multiple target peaks greater than a second reliability threshold and a position index corresponding to each of the multiple target peaks from the multiple signal classification correlation values; determining, according to the array manifold and based on the position index corresponding to each target peak, the array manifold vector corresponding to each target peak and the signal vector corresponding to each target peak; determining an array of angular reliability values ​​corresponding to the multiple target peaks based on the array manifold vector corresponding to each target peak and the signal vector corresponding to each target peak; determining a target position corresponding to the maximum angular reliability value in the angular reliability value array; and determining the azimuth and elevation angles corresponding to the maximum angular reliability value based on the target position.

[0014] According to a method for estimating the reliability of a direction of arrival based on a multiple signal classification algorithm provided by the present invention, the array manifold of the uniform circular array is allArrayMainFold, and the dimension of the array manifold is K N, where K=360 numPha, where 360 ​​represents the number of azimuth angles, numPha represents the number of elevation angles, and N represents the number of array elements.

[0015] The present invention also provides a wave direction reliability estimation device based on a multiple signal classification algorithm, comprising the following modules: a receiving module, used to determine the received data of a preset uniform circular array array, and the array manifold of the uniform circular array array; a determining module, used to determine the correlation matrix corresponding to the received data; a decomposition module, used to perform eigenvalue decomposition on the correlation matrix to obtain eigenvectors and eigenvalues; a noise module, used to determine the noise vector matrix based on the eigenvectors and the eigenvalues; a correlation value module, used to determine the multiple signal classification correlation value based on the array manifold and the correlation matrix corresponding to the noise vector matrix; a peak module, used to perform peak search based on the multiple signal classification correlation value to determine the corresponding position of the maximum peak; a direction module, used to determine the azimuth and elevation of the wave direction of the received data based on the corresponding position of the maximum peak; a vector module, used to determine the array manifold vector and the signal vector corresponding to the azimuth and the elevation based on the corresponding position of the maximum peak; an angle reliability module, used to determine the angle reliability value of the wave direction of the received data based on the array manifold vector and the signal vector.

[0016] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for estimating the reliability of the direction of arrival based on the multiple signal classification algorithm as described above is implemented.

[0017] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for estimating the reliability of the direction of arrival based on any of the multiple signal classification algorithms described above is implemented.

[0018] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any of the above-mentioned methods for estimating the reliability of a direction of arrival based on a multiple signal classification algorithm.

[0019] The method for estimating the reliability of the direction of arrival based on the multiple signal classification algorithm provided by the present invention determines a correlation matrix corresponding to the received data, the correlation matrix contains the statistical relationship between the received data and is the basis for subsequent eigenvalue decomposition; decomposes the correlation matrix to obtain eigenvectors and eigenvalues, and determines a noise vector matrix based on the eigenvectors and eigenvalues, the noise vector matrix is ​​the key to constructing a pseudo-spectral function in the multiple signal classification algorithm; determines a multiple signal classification correlation value based on an array manifold and a correlation matrix corresponding to the noise vector matrix, the multiple signal classification correlation value is a measure for evaluating the possibility of signals in different directions; searches for multiple signal classification related information, and obtains the eigenvalues ​​of the eigenvalues. The peak value of the relevant value is obtained, and the position of the maximum peak value is found, which corresponds to the most likely signal source direction; based on the corresponding position of the maximum peak value, the array manifold vector and the signal vector corresponding to the azimuth angle and the elevation angle are determined; based on the array manifold vector and the signal vector, the angular reliability value of the arrival direction of the received data is determined; thereby, not only the estimated value of the arrival direction is provided, but also the angular reliability value of the arrival direction of the received data is determined based on the array manifold vector and the signal vector, which is helpful to improve the stability and reliability of the arrival direction estimation in practical applications, and further solves the technical problem that the arrival direction estimation method in the related art cannot determine the reliability of the arrival angle. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced one by one below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 It is a flow chart of a method for estimating direction of arrival reliability based on a multiple signal classification algorithm provided by the present invention.

[0022] Figure 2 It is a flow chart of eigenvalue decomposition and music peak search provided by the present invention.

[0023] Figure 3 It is a schematic diagram of the process of calculating the angle reliability value provided by the present invention.

[0024] Figure 4 It is a flow chart of reliability value judgment and post-processing provided by the present invention.

[0025] Figure 5 It is a structural schematic diagram of a direction of arrival reliability estimation device based on a multiple signal classification algorithm provided by the present invention.

[0026] Figure 6 It is a schematic diagram of the physical structure of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The multiple signal classification (MUSIC) algorithm and the Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm are iconic algorithms for spatial spectrum estimation, and their emergence has achieved a huge leap in spatial spectrum estimation technology. In the MUSIC algorithm, the array antenna receives signal data to generate a covariance matrix, which is decomposed to obtain the signal subspace and noise subspace. The angle information of the incoming signal is obtained through the orthogonal relationship between the signal direction vector and the noise subspace.

[0029] The super-resolution direction finding performance of the above-mentioned super-resolution algorithms is obtained under the premise that the array flow pattern is accurately known. However, in actual engineering applications, there will be various errors, such as amplitude and phase inconsistency between array elements, coupling between array elements, array element position deviation, channel amplitude and phase inconsistency, etc. These errors will cause a large deviation between the array flow pattern and the ideal array flow pattern. At this time, the performance of these super-resolution direction finding algorithms will be seriously deteriorated or even fail.

[0030] When using the above super-resolution algorithm to calculate the direction of arrival (DOA), the reliability of the output angle should be evaluated in order to improve the accuracy and efficiency of direction finding.

[0031] In the existing scheme, the MUSIC algorithm is used to calculate the DOA of the signal, mainly by calculating the correlation matrix of the data and then performing matrix decomposition to obtain the signal subspace and noise subspace. According to the orthogonality of the array flow pattern and the noise subspace, the incoming wave angle is estimated.

[0032] However, in actual engineering applications, the DOA estimation performance will be degraded due to various errors in the array. That is, when the MUSIC algorithm outputs the incoming wave angle, no reliability information corresponding to the angle is given; when the measured angle is inaccurate, no corresponding solution is given.

[0033] According to the method for estimating the reliability of the direction of arrival based on the multiple signal classification algorithm provided by the present invention, on the one hand, a reliability calculation method corresponding to the direction of arrival can be provided, and on the other hand, when it is determined that the measured angle is inaccurate based on the angle reliability information, a corresponding processing solution can be provided.

[0034] Optionally, the method for estimating the direction of arrival reliability based on a multiple signal classification algorithm in an embodiment of the present application can be executed by a server, or by a terminal device, or jointly by a server and a terminal device, taking the example of the method for estimating the direction of arrival reliability based on a multiple signal classification algorithm in this embodiment being executed by a server.

[0035] Figure 1 FIG. 1 is a flow chart of a method for estimating the reliability of a direction of arrival based on a multiple signal classification algorithm provided by the present invention. Figure 1 As shown, the method comprises the following steps:

[0036] Step 101: determine the received data of a preset uniform circular array and the array manifold of the uniform circular array.

[0037] Uniform Circular Array (UCA) is a special type of antenna array. A uniform circular array refers to an antenna array in which all array elements are evenly distributed on a circle with a radius of R and all array elements are coplanar. This array structure enables the signal to be beamformed and the direction of arrival estimated in three-dimensional space.

[0038] According to a DOA reliability estimation method based on a multiple signal classification algorithm provided by the present invention, the array manifold of the uniform circular array is allArrayMainFold, and the dimension of the array manifold is K N, where K=360 numPha, where 360 ​​represents the number of azimuth angles, numPha represents the number of elevation angles, and N represents the number of array elements.

[0039] In the embodiment of the present invention, it is assumed that the array is a uniform circular array, the number of array elements is N, the number of signal sampling points is M, and the radius of the array element is r;

[0040] Assume the array manifold is allArrayMainFold and its dimension is K N,K=360 numPha, where 360 ​​represents the number of azimuth angles and numPha represents the number of elevation angles. The array manifold can be obtained by measurement.

[0041] Among them, the number of array elements (N) is used to indicate the number of sensors evenly distributed on the circular array; the array element radius (r) is the radius of the circular array; the number of rows of the array manifold (K) is determined by the combination of azimuth and elevation, specifically K = 360 × numPha, where 360 ​​represents the number of azimuth angles (usually divided into degrees, with one sample per degree), and numPha represents the number of elevation angles.

[0042] The received data is the signal data received from each array element of the Uniform Circular Array (UCA).

[0043] An array manifold is a pre-computed or measured matrix that describes the phase differences produced by signals in different directions on the array.

[0044] Through the embodiments of the present invention, an accurate array manifold and the dimension of the array manifold can be provided, and the propagation process of the signal in space can be accurately simulated, including the phase and amplitude changes of the signal at different azimuth and elevation angles.

[0045] Step 102: determine a correlation matrix corresponding to the received data.

[0046] The correlation matrix (also called the correlation coefficient matrix) is composed of the correlation coefficients between the columns of the matrix. That is, the element in the i-th row and j-th column of the correlation matrix is ​​the correlation coefficient between the i-th column and the j-th column of the original matrix.

[0047] In the embodiment of the present invention, the correlation matrix RR is calculated based on the user received data data, and the dimension of data is N M, RR dimension is N N can be specifically expressed by the following formula:

[0048]

[0049] in, represents the correlation matrix of the received data, Indicates receiving data. Here, N represents the number of sensors (or array elements); M represents the number of signal sampling points received by each sensor.

[0050] For each sensor, the mean of the received signal is calculated, and the mean is subtracted from the signal of each sensor to obtain the de-meaned signal.

[0051] Each element R of the correlation matrix RR ij represents the correlation between sensor i and sensor j, for example, R ij It is to calculate the dot product between the signal vectors after removing the mean and divide it by the number of sampling points M for normalization.

[0052] The de-averaged signal can be represented as an N×M matrix D′, where each row corresponds to the de-averaged signal of a sensor. The correlation matrix RR can be obtained by calculating D′TD′ and dividing it by M, where D′T is the transposed matrix of D′.

[0053] Step 103, perform eigenvalue decomposition on the correlation matrix to obtain eigenvectors and eigenvalues.

[0054] In the embodiment of the present invention, the matrix RR is subjected to eigenvalue decomposition to obtain eigenvectors and eigenvalues, which are EV and D respectively, where the dimension of EV is N. N, the dimension of D is N 1.

[0055] The dimension of EV is N×N, where N is the dimension of matrix RR. Each column of EV is an eigenvector. The dimension of D is N×1, containing all the eigenvalues ​​of matrix RR.

[0056] Through eigenvalue decomposition, the eigenvectors associated with the noise can be identified and subsequently used to construct the noise vector matrix.

[0057] Step 104: Determine a noise vector matrix based on the eigenvectors and eigenvalues.

[0058] According to the DOA reliability estimation method based on the multiple signal classification algorithm provided by the present invention, a noise vector matrix is ​​determined based on eigenvectors and eigenvalues, including:

[0059] Sort the eigenvalues ​​in order of numerical value to obtain the first eigenvalue;

[0060] Adjusting the eigenvector based on the first eigenvalue to obtain a first eigenvector;

[0061] Determining the number of signal sources based on the first eigenvalue;

[0062] Based on the number of signal sources and the first eigenvector, a noise vector matrix is ​​determined.

[0063] In the embodiment of the present invention, the eigenvalues ​​D are sorted in descending order to obtain a first eigenvalue D1, and the eigenvector EV is readjusted according to the sorting of the first eigenvalues ​​to obtain a new first eigenvector EV1.

[0064] According to the first eigenvalue D1, the number of signal sources doaNum is obtained by calculation according to existing rules, that is, the number of incident angles.

[0065] According to the number of signal sources doaNum and EV1, the noise vector matrix G is determined, where the noise vector matrix G can be expressed by the following formula:

[0066] G=EV1(:,doaNum+1:N);

[0067] Wherein, G represents the noise vector matrix, and EV1(:,doaNum+1:N) represents taking the doaNum+1 to N columns of the first eigenvector EV1.

[0068] Here, the eigenvector matrix EV is adjusted accordingly according to the order of the eigenvalues ​​D to obtain the first eigenvector EV1. This means that the columns of EV1 correspond one-to-one to the elements of D1.

[0069] According to the sorted first eigenvalue D1, a certain rule (such as information theory method, AIC criterion, MDL criterion, eigenvalue drop method, etc.) is used to calculate the number of signal sources doaNum, where doaNum represents the number of independent signal sources (or incident angles) received.

[0070] Once the number of signal sources doaNum is determined, the first eigenvector EV1 can be divided into two parts: one part belongs to the signal subspace (composed of the first doaNum eigenvectors), and the other part belongs to the noise subspace (composed of the remaining feature vectors).

[0071] The noise vector matrix G is composed of these eigenvectors belonging to the noise subspace. In the MUSIC algorithm, the noise vector matrix G is used to calculate the noise correlation matrix, which is then used to estimate the arrival direction of the signal.

[0072] Through the embodiments of the present invention, the eigenvalues ​​are sorted according to their numerical values, and the most important eigenvalue, i.e., the first eigenvalue, can be identified. Based on the first eigenvalue, the corresponding eigenvector can be adjusted or optimized to better reflect the most important information or structure in the data set; the number of signal sources is usually related to the distribution or order of magnitude of the eigenvalues; based on the known number of signal sources and the first eigenvector, the noise vector matrix can be further determined, and the noise vector matrix includes the signal-independent or noise-related parts of the data set.

[0073] Step 105 : determining a multiple signal classification correlation value based on the array manifold and a correlation matrix corresponding to the noise vector matrix.

[0074] The Multiple Signal Classification (MUSIC) algorithm is a high-resolution direction estimation method that uses the orthogonality of the signal and noise subspaces to estimate the direction of arrival (DOA) of the signal. In the MUSIC algorithm, based on the array manifold and the noise vector matrix, the MUSIC spectrum (or MUSIC correlation value) can be calculated, and the peak position of the spectrum corresponds to the direction of arrival of the signal.

[0075] The array manifold describes how the array response vector varies with the signal arrival direction. For a uniform linear array (ULA), the array manifold is usually a matrix containing the array response vector, where each column corresponds to a specific arrival direction.

[0076] The noise vector matrix is ​​obtained from the eigenvalue decomposition and is a matrix composed of eigenvectors belonging to the noise subspace. After the eigenvalue decomposition, the eigenvectors corresponding to the smaller eigenvalues ​​are considered to be components of the noise subspace.

[0077] The correlation matrix corresponding to the noise vector matrix (equivalent to the noise correlation matrix) is usually obtained by multiplying the conjugate transpose of the noise vector matrix with itself, and is used to describe the characteristics of the noise subspace in the MUSIC algorithm.

[0078] In the embodiment of the present invention, according to the noise vector matrix G, the correlation matrix corresponding to the noise vector matrix is ​​calculated: , The dimension is N N,

[0079] .

[0080] in, represents the correlation matrix corresponding to the noise vector matrix, represents the noise vector matrix, represents the conjugate transpose.

[0081] According to the array manifold allArrayMainFold and , calculate the multiple signal classification correlation value , the calculation formula is as follows:

[0082]

[0083] in, represents the array response vector, represents the array manifold, express The matrix OK; Indicates MUSIC spectrum value or MUSIC correlation value at each scanning angle, represents the correlation matrix corresponding to the noise vector matrix, express The conjugate transpose of .

[0084] Step 106: Perform peak search based on the multiple signal classification correlation value to determine the corresponding position of the maximum peak.

[0085] The MUSIC correlation value is usually expressed as a function of the possible arrival direction, with the peak corresponding to the true signal source direction.

[0086] All calculated MUSIC correlation values ​​are used as function values ​​and the corresponding arrival directions are used as independent variables to construct the MUSIC spectrum. The MUSIC spectrum is a function image about the arrival direction, where the peak represents the possible signal source direction.

[0087] Peak search on the MUSIC spectrum can be achieved by traversing all points of the MUSIC spectrum and finding the point with the largest value. Since the MUSIC spectrum is usually continuous (or dense enough after discretization), the peak can be found by comparing the values ​​of adjacent points.

[0088] Step 107: Determine the azimuth and elevation of the direction of arrival of the received data based on the corresponding position of the maximum peak value.

[0089] Among them, the corresponding position of the maximum peak is the position of the maximum peak in the MUSIC spectrum. This position can be expressed by angles (azimuth and elevation) to determine the arrival direction of the signal source.

[0090] In the embodiment of the present invention, according to Perform peak search, obtain the maximum peak value peakValue and the corresponding position pos, and calculate the corresponding azimuth angle thetaTest and elevation angle phaTest. The calculation formula is as follows:

[0091] ;

[0092] ;

[0093] ;

[0094]

[0095] in, represents the elevation index, represents the corresponding position of the maximum peak, Indicates rounding down. Indicates the direction angle index, % indicates the modulo operation, represents the azimuth, represents the elevation angle, Indicates the elevation resolution.

[0096] refer to Figure 2 , Figure 2 The present invention provides a schematic flow diagram of eigenvalue decomposition and music peak search, which specifically includes: calculating the correlation matrix RR; eigenvalue decomposition, obtaining eigenvectors and eigenvalues; determining the number of signal sources according to the eigenvalues; determining the noise subspace according to the number of signal sources; music correlation value calculation; peak search, determining the maximum peak position;

[0097] The above specific process can be referred to the above embodiments, and the present invention will not be described in detail here.

[0098] Step 108, determining the array manifold vector and the signal vector corresponding to the azimuth angle and the elevation angle based on the corresponding position of the maximum peak.

[0099] In the embodiment of the present invention, the corresponding position of the maximum peak corresponds to the arrival direction of the signal, and the azimuth and elevation can be extracted from the position of the maximum peak.

[0100] The array manifold vector describes the relative phase difference on each array element when the signal reaches the array from a specific direction. The array manifold vector can be calculated based on the azimuth and elevation angles. The signal vector usually represents the complex amplitude of the signal on each element of the array and can be obtained through actual measurement.

[0101] Step 109: Determine the angular reliability value of the direction of arrival of the received data based on the array manifold vector and the signal vector.

[0102] A method for estimating the reliability of a direction of arrival based on a multiple signal classification algorithm provided by the present invention is characterized in that, based on an array manifold vector and a signal vector, an angular reliability value of the direction of arrival of received data is determined, including:

[0103] The angle reliability value is calculated according to the following formula:

[0104]

[0105] Among them, angleArrayFold represents the array manifold vector, allArrayMainFold represents the array manifold, pos represents the corresponding position of the maximum peak, signalEV represents the signal vector, EV1 represents the first eigenvector, angleReability represents the angle reliability value, || represents the modulus value; H represents the conjugate transpose.

[0106] In an embodiment of the present invention, the array manifold vector angleArrayFold corresponding to the angle (i.e., azimuth and elevation) and the signal vector signalEV are taken out according to the corresponding position pos of the maximum peak, and the angle reliability value angleReability is calculated according to the above formula. The angle reliability value is a value between 0 and 1.

[0107] Through the embodiments of the present invention, the angle reliability value corresponding to the arrival angle can be accurately calculated, so as to determine the reliability of determining the arrival direction according to the angle reliability value.

[0108] The wave direction reliability estimation method based on the multiple signal classification algorithm provided by the present invention not only provides an estimated value of the wave direction, but also determines the angular reliability value of the wave direction of the received data based on the array manifold vector and the signal vector, which helps to improve the stability and reliability of the system in practical applications, and further solves the technical problem that the wave direction estimation method in the related art cannot determine the reliability of the wave arrival angle.

[0109] According to a method for estimating the reliability of a direction of arrival based on a multiple signal classification algorithm provided by the present invention, after determining the angular reliability value of the direction of arrival of received data based on the array manifold vector and the signal vector, the method further includes:

[0110] When the angle reliability value of the arrival direction of the received data is greater than the first reliability threshold, the angle reliability value and the azimuth and elevation angles are output.

[0111] refer to Figure 3 , Figure 3It is a flow chart of calculating the angle reliability value provided by the present invention, which specifically includes: determining the signal vector; selecting the array manifold vector according to the maximum peak position; calculating the angle reliability value; judging whether the reliability value is greater than a preset threshold; when it is greater than the preset threshold, outputting the corresponding azimuth and pitch angles; when it is less than the preset threshold, performing post-processing.

[0112] In the embodiment of the present invention, an angle reliability threshold (ie, a first reliability threshold) thresold is set, and if the angle reliability value angleReability is greater than thresold, angleReability and the corresponding pitch angle are output.

[0113] It should be noted that the angle reliability threshold is preset according to actual application conditions, and the present invention does not limit it here.

[0114] Through the embodiment of the present invention, the first reliability threshold is set to ensure that the angle reliability value and the azimuth and elevation angles are output only when the angle reliability value of the arrival direction of the received data is higher than this threshold; this helps to avoid erroneous outputs caused by factors such as noise, interference or algorithm errors, thereby improving the accuracy and reliability of the data.

[0115] According to a method for estimating the reliability of a direction of arrival based on a multiple signal classification algorithm provided by the present invention, after determining the angular reliability value of the direction of arrival of received data based on the array manifold vector and the signal vector, the method further includes:

[0116] When the angle reliability value of the wave arrival direction of the received data is less than the first reliability threshold, determining a plurality of target peaks greater than the second reliability threshold from the multiple signal classification correlation values, and a position index corresponding to each of the plurality of target peaks;

[0117] According to the array manifold, based on the position index corresponding to each target peak, determine the array manifold vector corresponding to each target peak and the signal vector corresponding to each target peak;

[0118] Determine an array of angle reliability values ​​corresponding to the plurality of target peaks based on the array manifold vectors corresponding to each target peak and the signal vectors corresponding to each target peak;

[0119] Determine the target position corresponding to the maximum angle reliability value in the angle reliability value array;

[0120] The azimuth and elevation angles corresponding to the maximum angle reliability value are determined based on the target position.

[0121] refer to Figure 4 , Figure 4It is a flow chart of reliability value judgment and post-processing provided by the present invention, which specifically includes: finding all positions that meet the requirements based on the maximum peak value and threshold value; selecting array manifold vectors based on the position; calculating all angular reliability values; finding the maximum value and the corresponding position from the reliability values; and calculating the azimuth and elevation angles based on the maximum value position.

[0122] The direction of arrival (DOA) estimation of the received data generates an angle reliability value. If the angle reliability value is less than a preset first reliability threshold (for example, indicating that the estimation result is not reliable enough or has high uncertainty), the next step is entered.

[0123] Calculate the correlation value (usually the spatial spectrum) of the MUSIC algorithm, which represents the signal strength or possibility at different angles, and find multiple target peaks greater than the second reliability threshold in the correlation value. The multiple target peaks correspond to the actual signal sources.

[0124] For each target peak found, determine its position index in the correlation value array, which corresponds to the estimated angle of the signal source.

[0125] Using the geometry of the array and the position index of each target peak, the corresponding array manifold vector is calculated, and at the same time, the signal vector corresponding to each target peak is extracted from the received data.

[0126] For each target peak, an angle reliability value is determined using its corresponding array manifold vector and signal vector, the maximum value is found in the angle reliability value array, and its corresponding target position (i.e., angle index) is determined.

[0127] Using the array geometry and the target position index corresponding to the maximum angle reliability value, the azimuth and elevation angles are calculated as the final DOA estimation results.

[0128] In the embodiment of the present invention, a threshold value (i.e., the second reliability threshold value) thresold2 is set. Select all values ​​greater than peakValue The value of thresold2 and the corresponding position index positionInd.

[0129] According to the position index positionInd, select the corresponding array manifold vector from allArrayMainFold in turn Calculate the angle reliability value according to the above method and obtain the angle reliability value array ,from Find the position pos1 corresponding to the maximum value and calculate the pitch angle according to the following formula:

[0130] pos2=positionInd(pos1);

[0131] ;

[0132] ;

[0133] thetaTest = thetaInd;

[0134] phaTest=phaInd phaResolution.

[0135] Among them, pos1 represents the input position, pos2 represents the obtained position index, and positionInd represents the position index function. represents the elevation index, Indicates the direction angle index, % indicates the modulo operation, thetaTest indicates the azimuth, phaTest indicates the elevation, Indicates the elevation resolution.

[0136] Through the embodiment of the present invention, when the reliability value of the measurement angle is not high, a corresponding processing scheme is given, that is, by introducing a multiple signal classification method and a second reliability threshold, multiple target peaks greater than the second reliability threshold are screened out from the multiple signal classification related values, so that all possible peaks can be avoided from being processed indiscriminately, thereby saving computing resources and time; reducing misjudgments caused by unreliable single measurement values, and improving the stability and robustness of the system. The following describes the wave direction reliability estimation device based on the multiple signal classification algorithm provided by the present invention, and the wave direction reliability estimation device based on the multiple signal classification algorithm described below and the wave direction reliability estimation method based on the multiple signal classification algorithm described above can be referenced to each other.

[0137] refer to Figure 5 , Figure 5 It is a structural schematic diagram of a direction of arrival reliability estimation device based on a multiple signal classification algorithm provided by the present invention.

[0138] The receiving module 501 is used to determine the reception data of the preset uniform circular array and the array manifold of the uniform circular array;

[0139] A determination module 502, configured to determine a correlation matrix corresponding to the received data;

[0140] A decomposition module 503 is used to perform eigenvalue decomposition on the correlation matrix to obtain eigenvectors and eigenvalues;

[0141] A noise module 504, configured to determine a noise vector matrix based on the eigenvectors and the eigenvalues;

[0142] A correlation value module 505 for determining a multiple signal classification correlation value based on the array manifold and a correlation matrix corresponding to the noise vector matrix;

[0143] A peak module 506, configured to perform peak search based on the multiple signal classification correlation value to determine the corresponding position of the maximum peak;

[0144] A direction module 507, used to determine the azimuth and elevation of the direction of arrival of the received data based on the corresponding position of the maximum peak value;

[0145] A vector module 508, for determining an array manifold vector and a signal vector corresponding to an azimuth angle and an elevation angle based on a corresponding position of a maximum peak;

[0146] The angle reliability module 509 is used to determine the angle reliability value of the direction of arrival of the received data based on the array manifold vector and the signal vector.

[0147] Specifically, the above-mentioned wave direction reliability estimation device based on multiple signal classification algorithm provided by the present invention can implement all the method steps implemented by the above-mentioned wave direction reliability estimation method embodiment based on multiple signal classification algorithm, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0148] Figure 6 is a schematic diagram of the physical structure of the electronic device provided by the present invention, such as Figure 6As shown, the electronic device may include: a processor (processor) 610 , a communication interface (Communications Interface) 620 , a memory (memory) 630 and a communication bus 640 , wherein the processor 610 , the communication interface 620 , and the memory 630 communicate with each other through the communication bus 640 . The processor 610 can call the logic instructions in the memory 630 to execute the wave direction reliability estimation method based on the multiple signal classification algorithm, which includes: determining the received data of the preset uniform circular array array and the array manifold of the uniform circular array; determining the correlation matrix corresponding to the received data; performing eigenvalue decomposition on the correlation matrix to obtain eigenvectors and eigenvalues; determining the noise vector matrix based on the eigenvectors and eigenvalues; determining the multiple signal classification correlation values ​​based on the array manifold and the correlation matrix corresponding to the noise vector matrix; performing peak search based on the multiple signal classification correlation values ​​to determine the corresponding position of the maximum peak; determining the azimuth and elevation of the wave direction of the received data based on the corresponding position of the maximum peak; determining the array manifold vector and signal vector corresponding to the azimuth and elevation based on the corresponding position of the maximum peak; determining the angular reliability value of the wave direction of the received data based on the array manifold vector and the signal vector.

[0149] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0150] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the wave direction reliability estimation method based on the multiple signal classification algorithm provided by the above methods, and the method includes: determining the received data of a preset uniform circular array array and the array manifold of the uniform circular array; determining the correlation matrix corresponding to the received data; performing eigenvalue decomposition on the correlation matrix to obtain eigenvectors and eigenvalues; determining a noise vector matrix based on the eigenvectors and eigenvalues; determining multiple signal classification correlation values ​​based on the array manifold and the correlation matrix corresponding to the noise vector matrix; performing peak search based on the multiple signal classification correlation values ​​to determine the corresponding position of the maximum peak; determining the azimuth and elevation of the wave direction of the received data based on the corresponding position of the maximum peak; determining the array manifold vector and signal vector corresponding to the azimuth and elevation based on the corresponding position of the maximum peak; determining the angular reliability value of the wave direction of the received data based on the array manifold vector and the signal vector.

[0151] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the wave direction reliability estimation method based on the multiple signal classification algorithm provided by the above-mentioned methods, the method comprising: determining the received data of a preset uniform circular array array and the array manifold of the uniform circular array; determining a correlation matrix corresponding to the received data; performing eigenvalue decomposition on the correlation matrix to obtain eigenvectors and eigenvalues; determining a noise vector matrix based on the eigenvectors and eigenvalues; determining multiple signal classification correlation values ​​based on the array manifold and the correlation matrix corresponding to the noise vector matrix; performing peak search based on the multiple signal classification correlation values ​​to determine the corresponding position of the maximum peak; determining the azimuth and elevation of the wave direction of the received data based on the corresponding position of the maximum peak; determining the array manifold vector and signal vector corresponding to the azimuth and elevation based on the corresponding position of the maximum peak; determining the angular reliability value of the wave direction of the received data based on the array manifold vector and the signal vector.

[0152] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0153] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating direction of arrival reliability based on a multiple signal classification algorithm, characterized in that: include: Determining received data of a preset uniform circular array and an array manifold of the uniform circular array; determining a correlation matrix corresponding to the received data; Performing eigenvalue decomposition on the correlation matrix to obtain eigenvectors and eigenvalues; Determining a noise vector matrix based on the eigenvectors and the eigenvalues; determining a multiple signal classification correlation value based on the array manifold and a correlation matrix corresponding to the noise vector matrix; Performing a peak search based on the multiple signal classification correlation value to determine a corresponding position of a maximum peak; Determine the azimuth and elevation of the direction of arrival of the received data based on the corresponding position of the maximum peak; Determining an array manifold vector and a signal vector corresponding to the azimuth angle and the elevation angle based on a corresponding position of the maximum peak; Determining an angular reliability value of a direction of arrival of the received data based on the array manifold vector and the signal vector; The determining of the noise vector matrix based on the eigenvector and the eigenvalue comprises: sorting the eigenvalues ​​in order of numerical values ​​to obtain a first eigenvalue; adjusting the eigenvector based on the first eigenvalue to obtain a first eigenvector; determining the number of signal sources based on the first eigenvalue; and determining the noise vector matrix based on the number of signal sources and the first eigenvector. The determining, based on the array manifold vector and the signal vector, an angular reliability value of the direction of arrival of the received data comprises: The angle reliability value is calculated according to the following formula: ； Among them, angleArrayFold represents the array manifold vector, allArrayMainFold represents the array manifold, pos represents the corresponding position of the maximum peak, signalEV represents the signal vector, EV1 represents the first eigenvector, angleReability represents the angle reliability value, || represents the modulus value; H represents the conjugate transpose.

2. The method for estimating direction of arrival reliability based on multiple signal classification algorithm according to claim 1, characterized in that: After determining the angular reliability value of the direction of arrival of the received data based on the array manifold vector and the signal vector, the method further includes: When the angle reliability value of the arrival direction of the received data is greater than a first reliability threshold, the angle reliability value and the azimuth angle and the elevation angle are output.

3. The method for estimating direction of arrival reliability based on multiple signal classification algorithm according to claim 1, characterized in that: After determining the angular reliability value of the direction of arrival of the received data based on the array manifold vector and the signal vector, the method further includes: When the angle reliability value of the direction of arrival of the received data is less than a first reliability threshold, determining a plurality of target peaks greater than a second reliability threshold from the multiple signal classification correlation values, and a position index corresponding to each of the plurality of target peaks; According to the array manifold, based on the position index corresponding to each target peak, determine the array manifold vector corresponding to each target peak and the signal vector corresponding to each target peak; Determine an array of angle reliability values ​​corresponding to the multiple target peaks based on the array manifold vector corresponding to each target peak and the signal vector corresponding to each target peak; Determine a target position corresponding to a maximum angle reliability value in the angle reliability value array; The azimuth and elevation angles corresponding to the maximum angle reliability value are determined based on the target position.

4. The method for estimating direction of arrival reliability based on multiple signal classification algorithm according to claim 1, characterized in that: The array manifold of the uniform circular array is allArrayMainFold, and the dimension of the array manifold is K N, where K=360 numPha, where 360 ​​represents the number of azimuth angles, numPha represents the number of elevation angles, and N represents the number of array elements.

5. A device for estimating direction of arrival reliability based on a multiple signal classification algorithm, characterized in that: include: A receiving module, used to determine the reception data of a preset uniform circular array and the array manifold of the uniform circular array; A determination module, used to determine a correlation matrix corresponding to the received data; A decomposition module, used for performing eigenvalue decomposition on the correlation matrix to obtain eigenvectors and eigenvalues; A noise module, used for determining a noise vector matrix based on the eigenvector and the eigenvalue; A correlation value module, configured to determine a multiple signal classification correlation value based on the array manifold and a correlation matrix corresponding to the noise vector matrix; A peak module, used to perform peak search based on the multiple signal classification correlation value to determine the corresponding position of the maximum peak; A direction module, used to determine the azimuth and elevation of the direction of arrival of the received data based on the corresponding position of the maximum peak; A vector module, configured to determine an array manifold vector and a signal vector corresponding to the azimuth angle and the elevation angle based on a corresponding position of the maximum peak; An angle reliability module, used to determine an angle reliability value of the direction of arrival of the received data based on the array manifold vector and the signal vector; The determining of the noise vector matrix based on the eigenvector and the eigenvalue comprises: sorting the eigenvalues ​​in order of numerical values ​​to obtain a first eigenvalue; adjusting the eigenvector based on the first eigenvalue to obtain a first eigenvector; determining the number of signal sources based on the first eigenvalue; and determining the noise vector matrix based on the number of signal sources and the first eigenvector. The determining, based on the array manifold vector and the signal vector, an angular reliability value of the direction of arrival of the received data comprises: The angle reliability value is calculated according to the following formula: ； Among them, angleArrayFold represents the array manifold vector, allArrayMainFold represents the array manifold, pos represents the corresponding position of the maximum peak, signalEV represents the signal vector, EV1 represents the first eigenvector, angleReability represents the angle reliability value, || represents the modulus value; H represents the conjugate transpose.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for estimating the reliability of the direction of arrival based on the multiple signal classification algorithm as claimed in any one of claims 1 to 4 is implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for estimating the reliability of the direction of arrival based on the multiple signal classification algorithm as claimed in any one of claims 1 to 4 is implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for estimating the reliability of the direction of arrival based on the multiple signal classification algorithm as claimed in any one of claims 1 to 4 is implemented.

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