Signal direction finding method, device, system and storage medium

Abstract

The present disclosure relates to a signal direction-finding method, device, system, and storage medium, which are applied to an antenna array comprising multiple antenna elements. This solution constructs a covariance matrix from the data received by the antenna elements, converting the data into a matrix consisting of vectors. The covariance matrix is ​​subjected to singular value decomposition to obtain a singular space consisting of singular vectors corresponding to the signal and singular vectors corresponding to the noise, as well as the singular values ​​corresponding to each singular vector. A noise subspace consisting of singular vectors corresponding to the noise is then extracted. The noise subspace is processed based on spatial spectrum estimation, and the direction of arrival of all data received by the antenna array is determined based on the obtained spectrum values, thereby determining the direction of the signal source.

Description

Technical Field

[0001] The present disclosure relates to the technical field of wireless point detection, and in particular to a signal direction finding method, device, system and storage medium. Background Art

[0002] Radio direction finding (RDF) uses a radio direction finder to measure the direction of radio waves emitted by a radio station to determine the transmitter's location. It is widely used in military, public security, aviation, navigation, land and water transportation, disaster relief, and scientific research. Traditional DF methods include amplitude comparison DF and interferometer DF.

[0003] Amplitude direction finding requires at least two antenna beams to determine the direction of the signal. Common amplitude direction finding methods include: maximum signal method, equal signal method and comparative signal method. Figure 1 High consistency is required, and there is a conflict between beamwidth and direction-finding sensitivity. Because the antenna's pattern can be distorted by factors such as the surrounding environment, frequency, layout, and multipath, frequency measurement and auxiliary correction in the actual installation environment are usually required, resulting in low actual direction-finding accuracy. Furthermore, the consistency requirements between antennas are very high.

[0004] Phase interferometer direction finding uses an antenna array to obtain the phase difference of the incident signal to determine the direction of the signal. The distribution of the antenna elements determines the size of the phase difference, but the mutual coupling between the antenna elements and various reasons of the antenna device cause the incident signal to be distorted on the wavefront, resulting in a deviation between the measurement result and the actual result. The correlation interferometer algorithm can avoid the ambiguity problem caused by the phase interferometer algorithm. However, when using the correlation interferometer for direction finding, it is necessary to traverse the phase differences in the sample library and calculate the correlation between the various phase differences, which greatly increases the amount of calculation. The higher the required direction finding accuracy, the greater the corresponding amount of calculation. Summary of the Invention

[0005] In order to solve the problems in the prior art of high computational complexity in detecting the direction of a signal and being easily affected by the environment, at least one embodiment of the present disclosure provides a signal direction finding method, device, system, and storage medium.

[0006] In a first aspect, embodiments of the present disclosure provide a signal direction finding method, which is applied to an antenna array comprising multiple antenna elements. The direction finding method includes:

[0007] Constructing a covariance matrix by cross-correlating the signal received by each antenna array element with each data received by other antenna array elements;

[0008] Performing singular value decomposition on the covariance matrix to obtain a singular space composed of multiple groups of singular vectors and a singular value corresponding to each group of the singular vectors;

[0009] Obtaining a noise subspace composed of singular vectors corresponding to noise according to the singular space and the singular values;

[0010] A two-dimensional spatial spectrum matrix composed of spectral values ​​is obtained based on the noise subspace and the preset steering vectors, and the arrival direction of all the data is determined based on the size of the elements in the two-dimensional spatial spectrum matrix. The preset steering vector set is composed of multiple preset steering vectors representing different directions, and the spectral values ​​are obtained from the noise subspace and the preset steering vectors in the preset steering vector set.

[0011] Based on the above technical solution, the embodiment of the present disclosure may also be improved as follows.

[0012] In combination with the first aspect, in a first embodiment of the first aspect, obtaining, according to the singular space and the singular values, a noise subspace composed of singular vectors corresponding to the noise includes:

[0013] Obtaining an accumulated value of the absolute values ​​of all the singular values ​​as a first reference value;

[0014] Accumulating the singular values ​​in descending order of absolute value of the singular values ​​to obtain a second reference value; when the proportion of the second reference value to the first reference value reaches a preset proportion, calculating the singular value of the second reference value as the signal singular value corresponding to the signal; and calculating the number of the singular values ​​of the second reference value as the number of information sources of the signal; wherein the number of information sources is less than the number of antenna array elements;

[0015] The singular vectors corresponding to the signal singular values ​​in the singular space are eliminated to obtain the noise subspace.

[0016] In combination with the first embodiment of the first aspect, in the second embodiment of the first aspect, obtaining a two-dimensional spatial spectrum matrix composed of spectral values ​​according to the noise subspace and the preset steering vector set includes:

[0017] Calculating corresponding spectral values ​​according to the noise subspace and the preset steering vectors in the preset steering vector set;

[0018] The reciprocals of the spectrum values ​​correspond to the positions of the preset steering vectors in the preset steering vector set to form the two-dimensional spatial spectrum matrix.

[0019] In combination with the second embodiment of the first aspect, in a third embodiment of the first aspect, determining the arrival direction of all the data according to the size of the elements in the two-dimensional spatial spectrum matrix includes:

[0020] Determining a reference spectrum value in the two-dimensional spatial spectrum matrix; wherein the reference spectrum value is a maximum value among the spectrum values ​​in the row and column of the two-dimensional spatial spectrum matrix;

[0021] Obtaining a first number of reference spectrum values ​​from the two-dimensional spatial spectrum matrix in descending order as incoming wave spectrum values; wherein the first number is the number of the information sources;

[0022] The preset steering vector corresponding to each incoming wave spectrum value is used as the incoming wave direction of the data.

[0023] In combination with the third embodiment of the first aspect, in a fourth embodiment of the first aspect, before obtaining a first number of reference spectrum values ​​from the two-dimensional spatial spectrum matrix in descending order as the incoming wave spectrum value, the direction finding method further includes:

[0024] Determining whether the reference spectrum value is greater than or equal to a first preset threshold value;

[0025] When the reference spectrum value is less than the first preset threshold value, the reference spectrum value is deleted.

[0026] In combination with the first aspect, in a fifth embodiment of the first aspect, obtaining, according to the singular space and the singular values, a noise subspace composed of singular vectors corresponding to the noise includes:

[0027] Adjusting the singular values ​​corresponding to each group of the singular vectors respectively so that the singular values ​​corresponding to the signal are amplified and the singular values ​​corresponding to the noise are reduced; obtaining adjusted values ​​respectively;

[0028] For each group of singular vectors in the singular space, the elements in the singular vectors are divided by the adjustment values ​​corresponding to the singular vectors to obtain a singular space in which the singular vectors corresponding to the signal are suppressed and the singular vectors corresponding to the noise are amplified, as a noise subspace composed of the singular vectors corresponding to the noise.

[0029] In combination with the fifth embodiment of the first aspect, in a sixth embodiment of the first aspect, adjusting the singular values ​​corresponding to each group of the singular vectors to obtain the adjusted values ​​includes:

[0030] The singular values ​​corresponding to each group of the singular vectors are respectively raised to N powers to obtain the adjustment value; wherein N is greater than or equal to 1.

[0031] In combination with the fifth embodiment of the first aspect, in a seventh embodiment of the first aspect, a two-dimensional spatial spectrum matrix consisting of spectral values ​​is obtained according to the noise subspace and the preset steering vector set, including:

[0032] Calculating corresponding spectral values ​​according to the noise subspace and the preset steering vectors in the preset steering vector set;

[0033] The reciprocals of the spectrum values ​​correspond to the positions of the preset steering vectors in the preset steering vector set to form the two-dimensional spatial spectrum matrix.

[0034] In conjunction with the seventh embodiment of the first aspect, in an eighth embodiment of the first aspect, determining the arrival direction of all the data according to the size of the elements in the two-dimensional spatial spectrum matrix includes:

[0035] Determining a reference spectrum value in the two-dimensional spatial spectrum matrix; wherein the reference spectrum value is a maximum value among the spectrum values ​​in the row and column of the two-dimensional spatial spectrum matrix;

[0036] When the number of the reference spectrum values ​​is greater than the number of antenna array elements of the antenna array, obtaining a second number of reference spectrum values ​​in descending order as the incoming wave spectrum value; wherein the second number is the number of the antenna array elements;

[0037] When the number of the reference spectrum values ​​is less than the number of antenna elements of the antenna array, all the reference spectrum values ​​are used as the incoming wave spectrum values.

[0038] The preset steering vector corresponding to each incoming wave spectrum value is used as the incoming wave direction of the data.

[0039] In conjunction with the eighth embodiment of the first aspect, in a ninth embodiment of the first aspect, the direction finding method further includes:

[0040] Determining whether the reference spectrum value is greater than or equal to a second preset threshold value;

[0041] When the reference spectrum value is less than the second preset threshold value, the reference spectrum value is deleted.

[0042] In combination with the first aspect or the first, second, third, fourth, fifth, sixth, seventh, eighth or ninth embodiment of the first aspect, in a tenth embodiment of the first aspect, the direction finding method further includes:

[0043] When an antenna array element is connected to each other through dual channels and data is received respectively, the received data is compensated according to the phase difference of the dual channels.

[0044] In conjunction with the tenth embodiment of the first aspect, in an eleventh embodiment of the first aspect, the direction finding method further includes:

[0045] connecting a first antenna array element through a first channel of the dual channel, connecting a second antenna array element through a second channel of the dual channel, and measuring a phase difference between the first antenna array element and the second antenna array element as a first phase difference;

[0046] connecting a first antenna array element through the first channel of the dual channel, connecting a third antenna array element through the second channel of the dual channel, and measuring a phase difference between the first antenna array element and the third antenna array element as a second phase difference;

[0047] The first channel of the dual channel is connected to the second antenna array element, and the second channel of the dual channel is connected to the third antenna array element, and a phase difference between the second antenna array element and the third antenna array element is measured as a third phase difference;

[0048] The phase difference of the dual channels is obtained by subtracting the difference between the first phase difference and the second phase difference from the third phase difference.

[0049] In a second aspect, an embodiment of the present disclosure provides a dual-channel signal direction finding device, which is applied to an antenna array including multiple antenna elements; the direction finding device includes:

[0050] A first processing unit, configured to construct a covariance matrix by cross-correlating the signal received by each antenna array element with each data received by other antenna array elements;

[0051] A second processing unit is configured to perform singular value decomposition on the covariance matrix to obtain a singular space composed of multiple groups of singular vectors and a singular value corresponding to each group of the singular vectors;

[0052] a third processing unit, configured to obtain a noise subspace composed of singular vectors corresponding to the noise according to the singular space and the singular values;

[0053] A fourth processing unit is configured to obtain a two-dimensional spatial spectrum matrix composed of spectral values ​​based on the noise subspace and a preset steering vector set, and determine the arrival direction of all the data based on the magnitude of the elements in the two-dimensional spatial spectrum matrix; wherein the preset steering vector set is composed of a plurality of preset steering vectors representing different directions; and the spectral values ​​are obtained from the noise subspace and the preset steering vectors.

[0054] In a third aspect, an embodiment of the present disclosure provides a signal direction finding system, the direction finding system comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0055] Memory for storing computer programs;

[0056] The processor is configured to implement the signal direction finding method described in any one of the embodiments of the first aspect when executing the program stored in the memory.

[0057] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the signal direction finding method described in any embodiment of the first aspect.

[0058] The above-mentioned technical solution disclosed in the present invention has the following advantages over the existing technology: This solution constructs a covariance matrix using data received by antenna array elements, converting the data into a matrix composed of vectors. By performing singular value decomposition on the covariance matrix, a singular space consisting of singular vectors corresponding to the signal and singular vectors corresponding to the noise, and the singular values ​​corresponding to each singular vector, is obtained. Then, a noise subspace consisting of singular vectors corresponding to the noise is extracted. Based on spatial spectrum estimation, the noise subspace is processed, and the arrival direction of all data received by the antenna array is determined based on the obtained spectral values, completing the determination of the direction of the signal source. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flow chart of a signal direction finding method provided by an embodiment of the present disclosure;

[0060] Figure 2 This is a flow chart of a signal direction finding method provided by another embodiment of the present disclosure;

[0061] Figure 3 This is a flow chart of a signal direction finding method provided by another embodiment of the present disclosure;

[0062] Figure 4 This is a second flow chart of a signal direction finding method provided by another embodiment of the present disclosure;

[0063] Figure 5 This is a third flow chart of a signal direction finding method provided by another embodiment of the present disclosure;

[0064] Figure 6 This is a fourth flow chart of a signal direction finding method provided by another embodiment of the present disclosure;

[0065] Figure 7 This is a fifth flow chart of a signal direction finding method provided by another embodiment of the present disclosure;

[0066] Figure 8 This is a sixth flow chart of a signal direction finding method provided by another embodiment of the present disclosure;

[0067] Figure 9 1 is a structural diagram of a signal direction finding device provided by another embodiment of the present disclosure;

[0068] Figure 10This is a structural diagram of a signal direction finding system provided by another embodiment of the present disclosure. DETAILED DESCRIPTION

[0069] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0070] like Figure 1 As shown, the embodiment of the present disclosure provides a signal direction finding method, which is applied to an antenna array including multiple antenna elements. Figure 1 , the direction finding method comprises the following steps:

[0071] S11. Construct a covariance matrix through cross-correlation between the signal received by each antenna array element and each data received by other antenna array elements.

[0072] In this embodiment, cross-correlation is the infinite integral of two functions, complex conjugated and inversely translated, and then multiplied together. Alternatively, it can be expressed as the infinite integral of the first function, complex conjugated and translated, and then multiplied by the second function. It can be shown that the two definitions are completely equivalent (and can be derived from each other). Physically, the result of the cross-correlation operation reflects a measure of the similarity between the two signals. Specifically for the real functions f(x) and h(x), the correlation operation is equivalent to finding the area enclosed by the horizontal axis and the overlapping portion of the curves of the two functions, which are shifted by the parameter x.

[0073] In this embodiment, in statistics and probability theory, each element of the covariance matrix is ​​the covariance between each vector element, which is a natural extension from scalar random variables to high-dimensional random vectors.

[0074] In this embodiment, conventional antenna arrays mainly include uniform linear arrays and uniform circular arrays. The direction-finding capability of linear arrays is limited, so circular arrays are usually used, including five-antenna and nine-antenna uniform circular arrays. The more array elements there are, the more targets can be identified, but the corresponding equipment will also be larger, which is not in line with engineering practice. Therefore, a 9-element uniform circular array is usually used.

[0075] like Figure 2 As shown, in this embodiment, data reception can be completed in sequence through a dual-channel nine-element device, and the dual channels can receive data from two elements each time.

[0076] In this embodiment, for a dual-channel receiver, only two array elements are connected at a time, so only two sets of data can be received at a time. These two sets of data are synchronized in time, with the only difference being the arrival delay caused by the direction of the signal. The received signals are usually discrete sequences, so in the discrete domain, the cross-correlation can be expressed as:

[0077]

[0078] Among them, R 12 (τ) is the cross-correlation of the data received by the first array element and the second array element, R 21 (τ), N is the number of sampling time points for receiving data, x1(n) is the data obtained by the first array element at the nth sampling time point, x2(n+τ) is the data obtained by the second array element at the nth sampling time point, τ is the arrival delay of the second array element compared to the first array element, x1(n+τ) is the data obtained by the first array element at the nth sampling time point, τ is the arrival delay of the first array element compared to the second array element, and x2(n) is the data obtained by the second array element at the nth sampling time point.

[0079] In this embodiment, if there are nine array elements in total, the covariance matrix is ​​constructed by receiving data through dual channels. By connecting two array elements through dual channels, only two sets of data can be received. For example, the data x1 and x2 of the first array element and the second array element are received. Only R in the covariance matrix can be calculated through the two data. 11 、R 12 、R 21 and R 22 In order to calculate the 81 values ​​in the covariance matrix, two-by-two switching is required, so a total of times The combined calculation can complete the covariance matrix, and the R on the diagonal ii and R jj There will be redundant data in each calculation. Since the results of each calculation are basically the same, only the value calculated for the first time is selected. There is no need to update the corresponding value on the covariance diagonal in subsequent calculations. Of course, you can also choose to calculate the cross-correlations on all diagonals and then use the cumulative averaging method to obtain the average value.

[0080] In this embodiment, the same logic can be applied to cases with more channels and different numbers of antenna elements, which will not be described in detail in this solution.

[0081] S12. Perform singular value decomposition on the covariance matrix to obtain a singular space composed of multiple groups of singular vectors and a singular value corresponding to each group of singular vectors.

[0082] In this embodiment, singular value decomposition (SVD) is an important matrix decomposition in linear algebra, and SVD is a generalization of eigendecomposition on arbitrary matrices. It has important applications in signal processing, statistics and other fields.

[0083] For example, let the complex symmetric matrix be C=A+iB, where C=C H A=A T B=-B T , where H is the conjugate transpose in linear algebra, T is the transpose in linear algebra, A and B are both real matrices, the singular vectors corresponding to the singular values ​​s of C are set to u+iv, and u and v are both real numbers. Since the hardware implementation of complex numbers is performed separately, we can get:

[0084]

[0085] So the corresponding ones are:

[0086] In this embodiment, based on the principle of singular value decomposition, a 9×9 complex symmetric matrix can be converted into an 18×18 real matrix singular value decomposition. After decomposition, the corresponding singular values ​​and singular vectors can be obtained and restored to complex form.

[0087] In this embodiment, the unitary matrices U, V and the singular value matrix S are obtained by singular value decomposition, where U is a singular space composed of eigenvectors corresponding to signal singular values ​​and eigenvectors corresponding to noise singular values, and S is a matrix composed of singular values, with singular values ​​on the diagonal and elements in other positions are all 0.

[0088] S13. According to the singular space and the singular values, a noise subspace composed of singular vectors corresponding to the noise is obtained.

[0089] In this embodiment, the singular vectors corresponding to the noise are extracted from the singular space according to the singular values. Since the signal-guided vector and the noise subspace are orthogonal, and since the magnitudes of the data corresponding to the signal and the noise are quite different, the singular values ​​of the eigenvectors corresponding to different noises obtained in the above steps will be very small, while the singular values ​​of the eigenvectors corresponding to the signal will be relatively large. Therefore, the singular vectors in the singular space can be divided according to the numerical values ​​of the singular values.

[0090] S14. A two-dimensional spatial spectrum matrix consisting of spectral values ​​is obtained based on the noise subspace and the preset steering vector set, and the arrival direction of all data is determined based on the size of the elements in the two-dimensional spatial spectrum matrix; wherein the preset steering vector set is composed of multiple preset steering vectors representing different directions; and the spectral values ​​are obtained from the noise subspace and the preset steering vectors.

[0091] In this embodiment, spatial spectrum estimation is an important research direction in array signal processing, with broad application prospects in numerous fields, including radar, communications, and sonar. The two main research directions in array signal processing are adaptive spatial filtering (adaptive array processing) and spatial spectrum estimation. Unlike adaptive array technology, spatial spectrum estimation focuses on the ability of a processing system composed of a spatial multi-sensor array to accurately estimate multiple parameters of a spatial signal of interest. Its primary goal is to estimate the spatial parameters of the signal or the location of the signal source.

[0092] In this embodiment, calculations are performed based on the preset steering vectors in the preset steering vector set and the noise subspace to obtain corresponding spectral values. Since the eigenvector corresponding to the noise in the noise subspace is orthogonal to the signal steering vector, the value obtained by multiplying the eigenvector corresponding to the noise by the preset steering vector corresponding to the true incoming wave direction in the preset steering vector set is theoretically 0. However, it is impossible to put all the preset steering vectors in the preset steering vector set. Therefore, the incoming wave directions of all signal sources in the data can be determined based on the spectral values.

[0093] In this embodiment, for example, for the obtained noise subspace, the spatial spectrum is solved as follows:

[0094]

[0095] P MUSIC is the spectrum value, α(φ) is the steering vector, is the noise subspace, H is the conjugate transpose in linear algebra, and each preset steering vector in the preset steering vector set is composed of a pitch angle and an azimuth angle. Through the above calculation formula, the noise subspace and each preset steering vector will obtain a specific spectral value. Since the above spectral value is the inverse, the value obtained after multiplying the eigenvector corresponding to the final noise with the preset steering vector corresponding to the true incoming wave direction in the preset steering vector set is theoretically infinite, that is, the corresponding spectral value will be very large. By finding the maximum value in the final spectral value, the incoming wave direction can be determined.

[0096] In this embodiment, this solution constructs a covariance matrix using data received by antenna array elements to convert the data into a matrix consisting of vectors. Singular value decomposition is performed on the covariance matrix to obtain a singular space consisting of singular vectors corresponding to the signal and singular vectors corresponding to the noise, as well as singular values ​​corresponding to each singular vector. A noise subspace consisting of singular vectors corresponding to the noise is then extracted. Based on spatial spectrum estimation, the noise subspace is processed, and the arrival direction of all data received by the antenna array is determined based on the obtained spectrum values, thereby determining the direction of the signal source.

[0097] In a specific embodiment, in this embodiment, the direction finding method further includes the following steps: when connecting to an antenna array element through two channels respectively and receiving data respectively, compensating the received data according to the phase difference of the two channels.

[0098] In this embodiment, since data received through the communication channel may have a phase deviation, and the phase deviations between data received through the dual channels may also have different deviations, in this step, the received data is compensated according to the phase difference between the dual channels.

[0099] like Figure 2 As shown, in this embodiment, the direction finding method further includes the following steps:

[0100] S21. Connect a first antenna array element through a first channel of the dual channel, connect a second antenna array element through a second channel of the dual channel, and measure a phase difference between the first antenna array element and the second antenna array element as a first phase difference.

[0101] In this embodiment, it is assumed that the first channel A has a fixed phase offset phiA and the second channel B has a fixed phase offset phiB. When measuring the phase difference, the phase difference between the first channel A and the second channel B is measured in sequence by switching the matrix switch. Therefore,

[0102] P p,mn =(P m +phiA)-(P n +phiB);

[0103] Among them, P p,mn is the phase difference between the data received by the first antenna array element and the data received by the second antenna array element; P m is the phase of the data received by the first antenna array element, P n is the phase of the data received by the second antenna element.

[0104] S22: Connect the first antenna array element through the first channel of the dual channel, connect the third antenna array element through the second channel of the dual channel, and measure the phase difference between the first antenna array element and the third antenna array element as the second phase difference.

[0105] In this embodiment, as above,

[0106] P p,mb =(P m +phiA)-(P b +phiB);

[0107] Among them, P p,mb is the phase difference between the data received by the first antenna array element and the data received by the third antenna array element; P m is the phase of the data received by the first antenna array element, Pb is the phase of the data received by the third antenna array element.

[0108] S23. Connect the first channel of the dual channel to the second antenna array element, connect the second channel of the dual channel to the third antenna array element, and measure the phase difference between the second antenna array element and the third antenna array element as the third phase difference.

[0109] In this embodiment, as above,

[0110] P p,nb =(P n +phiA)-(P b +phiB);

[0111] Among them, P p,nb is the phase difference between the data received by the second antenna element and the data received by the third antenna element; P n is the phase of the data received by the second antenna element, P b is the phase of the data received by the third antenna array element.

[0112] S24. Subtract the difference between the first phase difference and the second phase difference from the third phase difference to obtain a dual-channel phase difference.

[0113] According to the above embodiment, the first phase difference, the second phase difference, and the third phase difference can be obtained:

[0114] P p,mn -P p,mb =(P m +phiA)-(P n +phiB)-[(P m +phiA)-(P b +phiB)];

[0115] Further, we can get:

[0116] P p,mn -P p,mb =(P n +phiA)-(P b +phiB)-(phiA-phiB);

[0117] Further, we can get:

[0118] (phiA-phiB)=P p,nb -(P p,mn -P p,mb );

[0119] Therefore, the phase difference of the two channels is obtained by the first phase difference, the second phase difference and the third phase difference.

[0120] like Figure 3 As shown, the embodiment of the present disclosure provides a signal direction finding method. Figure 2 , the direction finding method comprises the following steps:

[0121] S31 , constructing a covariance matrix through cross-correlation between the signal received by each antenna array element and each data received by other antenna array elements.

[0122] For details about step S31 , please refer to the description of step S11 , which will not be repeated here in this embodiment.

[0123] S32. Perform singular value decomposition on the covariance matrix to obtain a singular space composed of multiple groups of singular vectors and a singular value corresponding to each group of singular vectors.

[0124] For details about step S32, please refer to the description of step S12, which will not be repeated here in this embodiment.

[0125] S33. Obtain the accumulated value of the absolute values ​​of all singular values ​​as a first reference value.

[0126] S34. Accumulate the singular values ​​in descending order of their absolute values ​​to obtain a second reference value. When the proportion of the second reference value in the first reference value reaches a preset proportion, calculate the singular value of the second reference value as the signal singular value corresponding to the signal, and calculate the number of singular values ​​of the second reference value as the number of signal sources of the signal; wherein the number of signal sources is less than or equal to the number of antenna array elements.

[0127] In this embodiment, the obtained covariance matrix R is subjected to singular value decomposition to obtain a series of singular values ​​arranged from large to small. The part of the singular values ​​that contributes the most is selected, that is, after the singular value decomposition, the absolute values ​​are taken and summed. Then, the absolute values ​​of the singular values ​​are taken from large to small and added together until the accumulated value reaches a preset proportion of the total singular values. It is then considered that the remaining singular values ​​are generated by noise, and the number of signal sources is obtained from this, which is convenient for the subsequent determination of the number of incoming wave directions. The preset proportion can be seventy-five percent or greater, and this scheme does not impose any special limitation on this.

[0128] S35. Remove the singular vectors corresponding to the signal singular values ​​in the singular space to obtain a noise subspace.

[0129] In this embodiment, after the signal singular values ​​are determined, the singular vectors corresponding to the signal singular values ​​are eliminated in the singular space. Alternatively, the singular vectors corresponding to the singular values ​​other than the signal singular values ​​are obtained from the singular space to form a noise subspace.

[0130] S36. A two-dimensional spatial spectrum matrix composed of spectral values ​​is obtained based on the noise subspace and the preset steering vector set, and the arrival direction of all data is determined based on the size of the elements in the two-dimensional spatial spectrum matrix; wherein the preset steering vector set is composed of multiple preset steering vectors representing different directions; and the spectral values ​​are obtained from the noise subspace and the preset steering vectors in the preset steering vector set.

[0131] For details of step S36 , please refer to the description of step S14 , which will not be repeated here in this embodiment.

[0132] In this embodiment, the singular values ​​are divided according to the numerical difference between the singular values ​​corresponding to the signal and the singular values ​​corresponding to the noise, and the singular values ​​corresponding to the signal are determined. Thus, a noise subspace composed of singular vectors corresponding to the noise in the singular space is obtained, and the various incoming wave directions of the signal are determined through the noise subspace.

[0133] like Figure 4 As shown, in this embodiment, S36 obtains a two-dimensional spatial spectrum matrix composed of spectrum values ​​according to the noise subspace and the preset steering vector set, including the following steps:

[0134] S41. Calculate and obtain corresponding spectrum values ​​according to the noise subspace and the preset steering vector in the preset steering vector set.

[0135] S42. Corresponding the reciprocals of the spectrum values ​​to the positions of the preset steering vectors in the preset steering vector set constitutes a two-dimensional spatial spectrum matrix.

[0136] In this embodiment, for the obtained noise subspace, the spatial spectrum is solved as follows:

[0137]

[0138] P MUSIC is the spectrum value, α(φ) is the steering vector, is the noise subspace, H is the conjugate transpose in linear algebra, and each preset steering vector in the preset steering vector set is composed of a pitch angle and an azimuth angle. Through the above calculation formula, the noise subspace and each preset steering vector will obtain a specific spectral value. Since the above spectral value is the inverse, the value obtained by multiplying the singular vector corresponding to the final noise with the preset steering vector corresponding to the true incoming wave direction in the preset steering vector set is theoretically infinite, that is, the corresponding spectral value will be very large. By finding the maximum value in the final spectral value, the incoming wave direction can be determined.

[0139] like Figure 5 As shown, in this embodiment, determining the arrival direction of all data according to the size of the elements in the two-dimensional spatial spectrum matrix in S36 includes the following steps:

[0140] S51. Determine a reference spectrum value in the two-dimensional spatial spectrum matrix; wherein the reference spectrum value is a maximum value among the spectrum values ​​in the row and column of the two-dimensional spatial spectrum matrix.

[0141] In this embodiment, the maximum value search of the two-dimensional spatial spectrum matrix cannot be achieved through difference analysis like that of a one-dimensional matrix. Two-dimensional difference analysis is more complicated. Therefore, in order to achieve the maximum value search, the two-dimensional problem is converted into a one-dimensional problem. That is, if it is a maximum value, it should be a maximum value in the row and the column. If all the above conditions are met, it is the maximum value of the two-dimensional spatial spectrum matrix.

[0142] In this embodiment, since the reference spectrum value is the maximum value in the row and column of the matrix, there will be at least one reference spectrum value that meets the conditions in a matrix.

[0143] S52. Obtain a first number of reference spectrum values ​​from the two-dimensional spatial spectrum matrix in descending order as incoming wave spectrum values; wherein the first number is the number of information sources.

[0144] S53: Use the preset steering vector corresponding to each incoming wave spectrum value as the incoming wave direction of the data.

[0145] In this embodiment, since the spectrum value is calculated through the noise subspace and the preset steering vector, and the preset steering vector is composed of the preset steering vector obtained by the step angle combination, there will be a large number of reference spectrum values ​​with similar values. In this scheme, the incoming wave spectrum value is determined according to the number of signal sources obtained in the above embodiment, and the corresponding preset steering vector in the preset steering vector set is used as the incoming wave direction according to the position of the incoming wave spectrum value in the two-dimensional spatial spectrum matrix.

[0146] In this embodiment, S52 obtains a first number of reference spectrum values ​​from the two-dimensional spatial spectrum matrix in order from large to small as the incoming wave spectrum value. The direction finding method also includes: determining whether the reference spectrum value is greater than or equal to a first preset threshold value; when the reference spectrum value is less than the first preset threshold value, deleting the reference spectrum value.

[0147] In this embodiment, after determining the reference spectrum value, the reference spectrum value that is less than the threshold value is deleted by setting the threshold value. It can be seen from the above steps that the value of the element in the two-dimensional spatial spectrum matrix finally calculated will be very large. Therefore, the threshold value can be set according to the actual operation. The value less than a certain threshold value can be directly identified as an interference item, where the first preset threshold value can be 10dB.

[0148] like Figure 6 As shown, the embodiment of the present disclosure provides a signal direction finding method. Figure 5 , the direction finding method comprises the following steps:

[0149] S61 : Construct a covariance matrix through cross-correlation between the signal received by each antenna array element and each data received by other antenna array elements.

[0150] For details of step S61 , please refer to the description of step S11 , which will not be repeated here in this embodiment.

[0151] S62. Perform singular value decomposition on the covariance matrix to obtain a singular space composed of multiple groups of singular vectors and a singular value corresponding to each group of singular vectors.

[0152] For details of step S62 , please refer to the description of step S12 , which will not be repeated here in this embodiment.

[0153] S63. Adjust the singular values ​​corresponding to each group of singular vectors respectively, so that the singular values ​​corresponding to the signal are amplified and the singular values ​​corresponding to the noise are reduced; and obtain adjusted values ​​respectively.

[0154] S64. For each group of singular vectors in the singular space, divide the elements in the singular vectors by the adjustment values ​​corresponding to the singular vectors, to obtain a singular space in which the singular vectors corresponding to the signal are suppressed and the singular vectors corresponding to the noise are amplified, as a noise subspace composed of the singular vectors corresponding to the noise.

[0155] In this embodiment, since the singular values ​​corresponding to the singular vectors corresponding to the signal are larger, while the singular values ​​corresponding to the singular vectors corresponding to the noise are smaller, and the singular values ​​corresponding to the signal are further amplified and the singular values ​​corresponding to the noise are further reduced through the above steps, in this step, the elements in the singular vectors are scaled separately by adjusting the values, so that the singular vectors corresponding to the signal are suppressed and the singular vectors corresponding to the noise are amplified. Under ideal conditions, the singular vectors corresponding to the signal can be suppressed to approach zero. In actual situations, the singular vectors corresponding to the signal will still have a certain value, but this value will no longer affect the subsequent judgment of the incoming wave direction. Therefore, in this step, the processed singular space is used as the noise subspace composed of the singular vectors corresponding to the noise. This can reduce the problem of judging the identity of the singular vectors in the singular space in the above embodiment and improve data processing efficiency.

[0156] In this embodiment, according to the spectrum value expression of the MUSIC algorithm, take:

[0157]

[0158] in, refers to the noise subspace, K refers to the number of sources, M refers to the number of singular vectors in the singular space, u iIt refers to the i-th singular vector in the singular space, and H is the conjugate transpose in linear algebra. From the above, we can find that the MUSIC algorithm only uses the singular vectors corresponding to the noise to calculate the noise subspace. This method has high requirements for the accuracy of the number of source estimation, and in actual situations, the process of estimating the number of source will also increase the amount of calculation.

[0159] For the received signal covariance matrix, we currently only use the noise subspace vectors, and have not fully utilized the signal subspace vectors and singular values. Therefore, we hope to use the singular values ​​as weights to adjust the proportion of each eigenvector in the spatial spectrum estimation.

[0160] In this embodiment, the singular values ​​of the receive covariance matrix are formed as follows:

[0161]

[0162] where λ i is the singular value corresponding to the i-th singular vector, K refers to the number of information sources, M refers to the number of singular vectors in the singular space, μ i is the signal characteristic value, is the variance of the noise, theoretically From the above formula, we can see that when the signal exists, the singular value corresponding to the noise subspace is λ i (i=K+1,K+2,...,M), these singular values ​​are small and basically equal, so these singular values ​​can be used to consistently retain or amplify the noise eigenvector. The singular value λ corresponding to the signal subspace i (i = 1, 2, ..., K) are large, and can be used to shrink the signal eigenvectors and reduce their proportion, thereby suppressing the signal subspace components. Through the above processing, the sorting of eigenvalues ​​and the partitioning of the noise subspace are avoided. The singular vectors can be directly weighted by the singular values, which is not affected by the fuzzy eigenvalue boundary problem.

[0163] The new eigenvector space can approximate the noise subspace and can preserve the orthogonality of the subspace to a large extent. Therefore, the received signal eigenvector space is redefined as:

[0164]

[0165] Among them, U refers to the singular space, β is the weighting coefficient, and β ≥ 1, so we can get:

[0166]

[0167] The parameters in the formula can refer to the description in the above embodiment, and this solution will not go into details.

[0168] In this embodiment, the singular values ​​corresponding to each group of singular vectors are adjusted separately in S63. This can be done based on the difference in size between the singular values ​​corresponding to the signal and the singular values ​​corresponding to the noise. For example, the singular values ​​corresponding to the signal are larger, while those corresponding to the noise are smaller. Therefore, by raising the singular values ​​to the power of β, the singular values ​​corresponding to the signal can be amplified, while those corresponding to the noise can be reduced. We want a smaller value for sharper spectral peaks, but we also want a larger value to suppress signal subspace components. This creates a conflicting trend in the value of , so we need to select a compromise value that achieves the desired performance. Typically, β = 1 is used to reduce computational complexity while ensuring direction finding performance.

[0169] S65. A two-dimensional spatial spectrum matrix composed of spectral values ​​is obtained based on the noise subspace and the preset steering vector set, and the arrival direction of all data is determined based on the size of the elements in the two-dimensional spatial spectrum matrix; wherein the preset steering vector set is composed of multiple preset steering vectors representing different directions; and the spectral values ​​are obtained from the noise subspace and the preset steering vectors.

[0170] For details of step S65 , please refer to the description of step S14 , which will not be repeated here in this embodiment.

[0171] Specifically, adjusting the singular values ​​corresponding to each group of singular vectors to obtain adjusted values ​​includes: raising the singular values ​​corresponding to each group of singular vectors to the power of N to obtain adjusted values; where N is greater than or equal to 1. In this embodiment, N is β in the above embodiment, and this step is not further described.

[0172] like Figure 7 As shown, in this embodiment, according to the noise subspace and the preset steering vector, a two-dimensional spatial spectrum matrix composed of spectrum values ​​is obtained, which includes the following steps:

[0173] S71. Calculate corresponding spectrum values ​​according to the noise subspace and the preset steering vector in the preset steering vector set.

[0174] S72. Corresponding the reciprocals of the spectrum values ​​to the positions of the preset steering vectors in the preset steering vector set constitutes a two-dimensional spatial spectrum matrix.

[0175] In this embodiment, for the obtained noise subspace, the spatial spectrum is solved as follows:

[0176]

[0177] P MUSIC is the spectrum value, α(φ) is the steering vector, is the noise subspace, H is the conjugate transpose in linear algebra, and each preset steering vector in the preset steering vector set is composed of a pitch angle and an azimuth angle. Through the above calculation formula, the noise subspace and each preset steering vector will obtain a specific spectral value. Since the above spectral value is the inverse, the value obtained after multiplying the eigenvector corresponding to the final noise with the preset steering vector corresponding to the true incoming wave direction in the preset steering vector set is theoretically infinite, that is, the corresponding spectral value will be very large. By finding the maximum value in the final spectral value, the incoming wave direction can be determined.

[0178] like Figure 8 As shown, in this embodiment, determining the arrival direction of all data according to the size of the elements in the two-dimensional spatial spectrum matrix in S65 includes the following steps:

[0179] S81. Determine a reference spectrum value in the two-dimensional spatial spectrum matrix; wherein the reference spectrum value is the maximum value among the spectrum values ​​in the row and column of the two-dimensional spatial spectrum matrix.

[0180] In this embodiment, the maximum value search of the two-dimensional spatial spectrum matrix cannot be achieved through difference analysis like that of a one-dimensional matrix. Two-dimensional difference analysis is more complicated. Therefore, in order to achieve the maximum value search, the two-dimensional problem is converted into a one-dimensional problem. That is, if it is a maximum value, it should be a maximum value in the row and the column. If all the above conditions are met, it is the maximum value of the two-dimensional spatial spectrum matrix.

[0181] In this embodiment, since the reference spectrum value is the maximum value in the row and column of the matrix, there will be at least one reference spectrum value that meets the conditions in a matrix.

[0182] S82. When the number of reference spectrum values ​​is greater than the number of antenna elements in the antenna array, obtain a second number of reference spectrum values ​​in descending order as the incoming wave spectrum values; wherein the second number is the number of antenna elements.

[0183] S83. When the number of reference spectrum values ​​is less than the number of antenna elements in the antenna array, all the reference spectrum values ​​are used as incoming wave spectrum values.

[0184] S84. Use the preset steering vector corresponding to each incoming wave spectrum value as the incoming wave direction of the data.

[0185] In this embodiment, since the spectrum value is calculated through the noise subspace and the steering vector, and the steering vector set is composed of preset steering vectors obtained by stepping the angle combination, there will be a large number of reference spectrum values ​​with similar values. Therefore, in this step, the number of reference spectrum values ​​and the number of antenna elements are determined. Since the number of antenna elements will affect the number of signal sources that can be detected, in this step, the number of incoming spectrum values ​​is limited based on the antenna elements.

[0186] In this embodiment, the direction finding method further includes: determining whether the reference spectrum value is greater than or equal to a second preset threshold value; when the reference spectrum value is less than the second preset threshold value, deleting the reference spectrum value.

[0187] In this embodiment, after the reference spectrum value is determined, the reference spectrum value that is less than the threshold value is deleted by setting a threshold value. It can be seen from the above steps that the value of the element in the two-dimensional spatial spectrum matrix finally calculated will be very large, so the threshold value can be set according to the actual operation, and the value less than a certain threshold value can be directly identified as an interference item, where the first preset threshold value can be 10dB or 20dB. Since the number of signal sources is not confirmed in this scheme, but the singular vector is limited by the singular value, a higher threshold value can be set to avoid misjudgment.

[0188] like Figure 9 As shown, an embodiment of the present disclosure provides a dual-channel signal direction finding device, which is applied to an antenna array including multiple antenna elements; the direction finding device includes: a first processing unit 11, a second processing unit 12, a third processing unit 13 and a fourth processing unit 14.

[0189] In this embodiment, the first processing unit 11 is configured to construct a covariance matrix by cross-correlating the signal received by each antenna array element with each data received by other antenna array elements;

[0190] In this embodiment, the second processing unit 12 is used to perform singular value decomposition on the covariance matrix to obtain a singular space composed of multiple groups of singular vectors and a singular value corresponding to each group of singular vectors;

[0191] In this embodiment, the third processing unit 13 is configured to obtain a noise subspace composed of singular vectors corresponding to the noise according to the singular space and the singular values;

[0192] In this embodiment, the fourth processing unit 14 obtains a two-dimensional spatial spectrum matrix composed of spectral values ​​based on the noise subspace and a preset steering vector set, and determines the arrival direction of all data based on the size of the elements in the two-dimensional spatial spectrum matrix. The preset steering vector set is composed of multiple preset steering vectors representing different directions, and the spectral values ​​are obtained from the noise subspace and the preset steering vectors.

[0193] In this embodiment, the third processing unit 13 is specifically used to obtain the cumulative value of the absolute values ​​of all singular values ​​as a first reference value; accumulate the singular values ​​in order of the absolute values ​​of the singular values ​​from large to small to obtain a second reference value; when the proportion of the second reference value in the first reference value reaches a preset proportion, the singular value of the second reference value is calculated as the signal singular value corresponding to the signal, and the number of singular values ​​calculated for the second reference value is used as the number of signal sources of the signal; wherein the number of signal sources is less than or equal to the number of antenna array elements; and the singular vectors corresponding to the signal singular values ​​in the singular space are eliminated to obtain a noise subspace.

[0194] In this embodiment, the fourth processing unit 14 is specifically configured to calculate corresponding spectrum values ​​according to the noise subspace and the preset steering vector set; and construct a two-dimensional spatial spectrum matrix by corresponding the reciprocal of the spectrum value to the position of the preset steering vector in the preset steering vector set.

[0195] In this embodiment, the fourth processing unit 14 is specifically configured to determine reference spectrum values ​​in a two-dimensional spatial spectrum matrix; obtain a first number of reference spectrum values ​​from the two-dimensional spatial spectrum matrix in descending order as incoming spectrum values, wherein the first number is the number of signal sources; and use a preset steering vector corresponding to each incoming spectrum value as the incoming direction of the data; wherein the reference spectrum value is the maximum value among the spectrum values ​​in the corresponding row and column of the two-dimensional spatial spectrum matrix.

[0196] In this embodiment, the direction finding device further includes: a fifth processing unit, configured to determine whether the reference spectrum value is greater than or equal to a first preset threshold value; and when the reference spectrum value is less than the first preset threshold value, deleting the reference spectrum value.

[0197] In this embodiment, the third processing unit 13 is specifically used to adjust the singular values ​​corresponding to each group of singular vectors, so that the singular values ​​corresponding to the signal are amplified and the singular values ​​corresponding to the noise are reduced; and obtain adjustment values ​​respectively; for each group of singular vectors in the singular space, divide the elements in the singular vectors by the adjustment values ​​corresponding to the singular vectors, and obtain a singular space in which the singular vectors corresponding to the signal are suppressed and the singular vectors corresponding to the noise are amplified, as a noise subspace composed of the singular vectors corresponding to the noise.

[0198] In this embodiment, the third processing unit 13 is specifically configured to raise the singular values ​​corresponding to each group of singular vectors to N powers to obtain adjustment values; wherein N is greater than or equal to 1.

[0199] In this embodiment, the fourth processing unit 14 is specifically configured to calculate corresponding spectrum values ​​according to the noise subspace and the preset steering vector set; and construct a two-dimensional spatial spectrum matrix by corresponding the reciprocal of the spectrum value to the position of the preset steering vector in the preset steering vector set.

[0200] In this embodiment, the fourth processing unit 14 is specifically configured to determine reference spectrum values ​​in a two-dimensional spatial spectrum matrix. When the number of reference spectrum values ​​is greater than the number of antenna elements in the antenna array, a second number of reference spectrum values ​​are obtained in descending order as incoming spectrum values, where the second number is the number of antenna elements. When the number of reference spectrum values ​​is less than or equal to the number of antenna elements in the antenna array, all reference spectrum values ​​are used as incoming spectrum values. The preset steering vector corresponding to each incoming spectrum value is used as the incoming direction of the data. The reference spectrum value is the maximum value among the spectrum values ​​in the corresponding row and column of the two-dimensional spatial spectrum matrix.

[0201] In this embodiment, the direction finding device further includes: a sixth processing unit, configured to determine whether the reference spectrum value is greater than or equal to a second preset threshold value; and when the reference spectrum value is less than the second preset threshold value, deleting the reference spectrum value.

[0202] In this embodiment, the direction finding device further includes: a seventh processing unit, configured to compensate the received data according to a phase difference between the two channels when the two channels are connected to an antenna array element respectively and receive data respectively.

[0203] In this embodiment, the direction-finding device further includes: connecting the first antenna array element through the first channel of the dual channel, connecting the second antenna array element through the second channel of the dual channel, and measuring the phase difference between the first antenna array element and the second antenna array element as the first phase difference; connecting the first antenna array element through the first channel of the dual channel, connecting the third antenna array element through the second channel of the dual channel, and measuring the phase difference between the first antenna array element and the third antenna array element as the second phase difference; connecting the second antenna array element through the first channel of the dual channel, connecting the third antenna array element through the second channel of the dual channel, and measuring the phase difference between the second antenna array element and the third antenna array element as the third phase difference; and subtracting the difference between the first phase difference and the second phase difference from the third phase difference to obtain the phase difference of the dual channel.

[0204] like Figure 10 As shown, an embodiment of the present disclosure provides a signal direction finding system, which includes: a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140;

[0205] Memory 1130, for storing computer programs;

[0206] The processor 1110 is configured to implement the following method when executing the program stored in the memory 1130:

[0207] A covariance matrix is ​​constructed by cross-correlating the signal received by each antenna array element with each data received by other antenna array elements;

[0208] Perform singular value decomposition on the covariance matrix to obtain a singular space composed of multiple groups of singular vectors and the singular values ​​corresponding to each group of singular vectors;

[0209] According to the singular space and singular values, a noise subspace consisting of the singular vectors corresponding to the noise is obtained;

[0210] A two-dimensional spatial spectrum matrix consisting of spectral values ​​is obtained based on the noise subspace and a preset steering vector set. The arrival direction of all data is determined based on the size of the elements in the two-dimensional spatial spectrum matrix. The preset steering vector set is composed of multiple preset steering vectors representing different directions. The spectral values ​​are obtained from the noise subspace and the preset steering vectors in the preset steering vector set.

[0211] In the electronic device provided by the embodiments of the present disclosure, the processor 1110 constructs a covariance matrix using data received by antenna array elements by executing a program stored in the memory 1130, thereby converting the data into a matrix composed of vectors. The processor 1110 then performs singular value decomposition on the covariance matrix to obtain a singular space composed of singular vectors corresponding to the signal and singular vectors corresponding to the noise, and the singular values ​​corresponding to each singular vector. The processor then extracts a noise subspace composed of singular vectors corresponding to the noise. The processor processes the noise subspace based on spatial spectrum estimation, and determines the direction of arrival of all data received by the antenna array based on the obtained spectrum values, thereby determining the direction of the signal source.

[0212] The communication bus 1140 mentioned in the electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the figure shows only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0213] The communication interface 1120 is used for communication between the electronic device and other devices.

[0214] The memory 1130 may include a random access memory (RAM) or a non-volatile memory (non-volatile memory), such as at least one disk storage. Alternatively, the memory 1130 may be at least one storage device located away from the processor 1110.

[0215] The above-mentioned processor 1110 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0216] The embodiments of the present disclosure provide a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the direction finding method of any of the above embodiments.

[0217] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present disclosure is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)).

[0218] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure 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 various embodiments of the present disclosure.

Claims

1. A signal direction finding method, characterized in that: Applicable to antenna arrays containing multiple antenna elements; The direction finding method comprises: Constructing a covariance matrix by cross-correlating the signal received by each antenna array element with each data received by other antenna array elements; Performing singular value decomposition on the covariance matrix to obtain a singular space composed of multiple groups of singular vectors and a singular value corresponding to each group of the singular vectors; Obtaining a noise subspace composed of singular vectors corresponding to noise according to the singular space and the singular values; The step of obtaining a noise subspace composed of singular vectors corresponding to noise according to the singular space and the singular values ​​includes: Obtaining an accumulated value of the absolute values ​​of all the singular values ​​as a first reference value; Accumulating the singular values ​​in descending order of absolute value of the singular values ​​to obtain a second reference value; when the proportion of the second reference value to the first reference value reaches a preset proportion, calculating the singular value of the second reference value as the signal singular value corresponding to the signal; and calculating the number of the singular values ​​of the second reference value as the number of information sources of the signal; wherein the number of information sources is less than the number of antenna array elements; Eliminating the singular vectors corresponding to the signal singular values ​​in the singular space to obtain the noise subspace; A two-dimensional spatial spectrum matrix composed of spectral values ​​is obtained based on the noise subspace and the preset steering vector set, and the arrival direction of all the data is determined based on the size of the elements in the two-dimensional spatial spectrum matrix; wherein the preset steering vector set is composed of multiple preset steering vectors representing different directions; and the spectral values ​​are obtained from the noise subspace and the preset steering vectors.

2. The direction finding method according to claim 1, wherein: The method of obtaining a two-dimensional spatial spectrum matrix composed of spectrum values ​​according to the noise subspace and the preset steering vector set includes: Calculating corresponding spectral values ​​according to the noise subspace and the preset steering vectors in the preset steering vector set; The reciprocals of the spectrum values ​​correspond to the positions of the preset steering vectors in the preset steering vector set to form the two-dimensional spatial spectrum matrix.

3. The direction finding method according to claim 2, wherein: The determining the arrival direction of all the data according to the size of the elements in the two-dimensional spatial spectrum matrix includes: Determining a reference spectrum value in the two-dimensional spatial spectrum matrix; wherein the reference spectrum value is a maximum value among the spectrum values ​​in the row and column of the two-dimensional spatial spectrum matrix; Obtaining a first number of reference spectrum values ​​from the two-dimensional spatial spectrum matrix in descending order as incoming wave spectrum values; wherein the first number is the number of the information sources; The preset steering vector corresponding to each incoming wave spectrum value is used as the incoming wave direction of the data.

4. The direction finding method according to claim 3, wherein: Before obtaining a first number of reference spectrum values ​​from the two-dimensional spatial spectrum matrix in descending order as the incoming wave spectrum values, the direction finding method further includes: Determining whether the reference spectrum value is greater than or equal to a first preset threshold value; When the reference spectrum value is less than the first preset threshold value, the reference spectrum value is deleted.

5. The direction finding method according to claim 1, wherein: The step of obtaining a noise subspace composed of singular vectors corresponding to noise according to the singular space and the singular values ​​includes: Adjusting the singular values ​​corresponding to each group of the singular vectors respectively so that the singular values ​​corresponding to the signal are amplified and the singular values ​​corresponding to the noise are reduced; obtaining adjusted values ​​respectively; For each group of singular vectors in the singular space, the elements in the singular vectors are divided by the adjustment values ​​corresponding to the singular vectors to obtain a singular space in which the singular vectors corresponding to the signal are suppressed and the singular vectors corresponding to the noise are amplified, as a noise subspace composed of the singular vectors corresponding to the noise.

6. The direction finding method according to claim 5, characterized in that: The step of adjusting the singular values ​​corresponding to each group of the singular vectors to obtain adjusted values ​​includes: The singular values ​​corresponding to each group of the singular vectors are respectively raised to N powers to obtain the adjustment value; wherein N is greater than or equal to 1.

7. The direction finding method according to claim 5, characterized in that: According to the noise subspace and the preset steering vector set, a two-dimensional spatial spectrum matrix consisting of spectrum values ​​is obtained, including: Calculating corresponding spectral values ​​according to the noise subspace and the preset steering vectors in the preset steering vector set; The reciprocals of the spectrum values ​​correspond to the positions of the preset steering vectors in the preset steering vector set to form the two-dimensional spatial spectrum matrix.

8. The direction finding method according to claim 7, characterized in that: The determining the arrival direction of all the data according to the size of the elements in the two-dimensional spatial spectrum matrix includes: Determining a reference spectrum value in the two-dimensional spatial spectrum matrix; wherein the reference spectrum value is a maximum value among the spectrum values ​​in the row and column of the two-dimensional spatial spectrum matrix; When the number of the reference spectrum values ​​is greater than the number of antenna array elements of the antenna array, obtaining a second number of reference spectrum values ​​in descending order as the incoming wave spectrum value; wherein the second number is the number of the antenna array elements; When the number of the reference spectrum values ​​is less than or equal to the number of antenna elements of the antenna array, all the reference spectrum values ​​are used as the incoming wave spectrum values; The preset steering vector corresponding to each incoming wave spectrum value is used as the incoming wave direction of the data.

9. The direction finding method according to claim 8, characterized in that: The direction finding method further comprises: Determining whether the reference spectrum value is greater than or equal to a second preset threshold value; When the reference spectrum value is less than the second preset threshold value, the reference spectrum value is deleted.

10. The direction finding method according to any one of claims 1 to 9, characterized in that: The direction finding method further comprises: When an antenna array element is connected to each other through dual channels and data is received respectively, the received data is compensated according to the phase difference of the dual channels.

11. The direction finding method according to claim 10, characterized in that: Direction finding methods also include: connecting a first antenna array element through a first channel of the dual channel, connecting a second antenna array element through a second channel of the dual channel, and measuring a phase difference between the first antenna array element and the second antenna array element as a first phase difference; connecting a first antenna array element through the first channel of the dual channel, connecting a third antenna array element through the second channel of the dual channel, and measuring a phase difference between the first antenna array element and the third antenna array element as a second phase difference; The first channel of the dual channel is connected to the second antenna array element, and the second channel of the dual channel is connected to the third antenna array element, and a phase difference between the second antenna array element and the third antenna array element is measured as a third phase difference; The phase difference of the dual channels is obtained by subtracting the difference between the first phase difference and the second phase difference from the third phase difference.

12. A dual-channel signal direction finding device, characterized in that: Applicable to antenna arrays containing multiple antenna elements; The direction finding device comprises: A first processing unit, configured to construct a covariance matrix by cross-correlating the signal received by each antenna array element with each data received by other antenna array elements; A second processing unit is configured to perform singular value decomposition on the covariance matrix to obtain a singular space composed of multiple groups of singular vectors and a singular value corresponding to each group of the singular vectors; a third processing unit, configured to obtain a noise subspace composed of singular vectors corresponding to the noise according to the singular space and the singular values; The step of obtaining a noise subspace composed of singular vectors corresponding to noise according to the singular space and the singular values ​​includes: Obtaining an accumulated value of the absolute values ​​of all the singular values ​​as a first reference value; Accumulating the singular values ​​in descending order of absolute value of the singular values ​​to obtain a second reference value; when the proportion of the second reference value to the first reference value reaches a preset proportion, calculating the singular value of the second reference value as the signal singular value corresponding to the signal; and calculating the number of the singular values ​​of the second reference value as the number of information sources of the signal; wherein the number of information sources is less than the number of antenna array elements; Eliminating the singular vectors corresponding to the signal singular values ​​in the singular space to obtain the noise subspace; a fourth processing unit configured to obtain a two-dimensional spatial spectrum matrix composed of spectral values ​​based on the noise subspace and a preset steering vector set, and determine the arrival direction of all the data based on the magnitude of the elements in the two-dimensional spatial spectrum matrix; wherein the preset steering vector set is composed of a plurality of preset steering vectors representing different directions; and the spectral values ​​are obtained from the noise subspace and the preset steering vectors.

13. A signal direction finding system, characterized in that: The direction finding system includes: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory for storing computer programs; A processor is configured to implement the signal direction finding method according to any one of claims 1 to 11 when executing a program stored in a memory.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the signal direction finding method according to any one of claims 1 to 11.

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