Array shape calibration method, device and equipment of long linear array and storage medium

Abstract

The invention relates to an array shape calibration method and device of a long linear array, equipment and a storage medium. The method comprises the following steps: calculating a covariance matrix based on an obtained array receiving signal of a long linear array, performing eigenvalue decomposition on the covariance matrix, obtaining a signal eigenvector, calculating an ordinate initial phase of each array element based on the signal eigenvector, calculating a spatial spectrum based on the ordinate initial phase and according to an initial array guide vector, and obtaining a linear array; estimating a target orientation based on the maximum value of the spatial spectrum; calculating a vertical coordinate and a horizontal coordinate corresponding to each array element based on the target orientation, and updating an initial array guide vector according to the horizontal coordinate and the vertical coordinate of each array element; calculating the spatial spectrum again, judging whether the difference value between the maximum value of the current spatial spectrum and the maximum value of the previous spatial spectrum is within a preset difference value range, and if so, completing array calibration of the long linear array; compared with the prior art, the method can achieve the calibration of the position coordinates of each array element, and achieves the accurate estimation of the target orientation.

Description

Technical Field

[0001] The present application relates to the technical field of array sonar signal processing, and in particular to a formation calibration method, device, equipment and storage medium for a long linear array. Background Art

[0002] Due to its advantage of not being restricted by the physical space of the hull and the towed body, the towed array sonar system can achieve a longer array length and a larger array aperture. Compared with traditional hull sonar, the towed array sonar can operate at a lower operating frequency, thereby achieving a longer detection distance and better detection performance. However, in actual applications, the towed array sonar faces the problem of formation distortion, which is mainly due to the influence of factors such as the ship's maneuvering operation and ocean currents, especially under low-speed towing conditions, where formation distortion is more common.

[0003] Formation distortion has a significant impact on the direction-finding performance of the sonar system. Slight formation distortion may have little effect on the direction-finding accuracy, but severe formation distortion will cause some direction-of-arrival (DOA) estimation algorithms to fail, thereby affecting the performance of the entire system. In order to solve the DOA estimation problem under formation distortion, the usual method is to first estimate the formation and then use the estimated formation to perform DOA estimation. At present, the common method of formation estimation is to install multiple heading and depth sensors on the towed array and fit the formation through the data of these sensors; this method can realize real-time correction of the formation, but it also increases the complexity of the system and may reduce the reliability of the system. In addition, due to the limited number of sensors, when the formation distortion is large, it is difficult to accurately reconstruct the formation relying solely on the data of these sensors.

[0004] Moreover, most of the existing long-line array calibration methods are based on plane wave and spherical wave models, without fully considering the sound propagation characteristics of low-frequency shallow sea channels. This results in the formation calibration accuracy and target azimuth estimation accuracy being difficult to meet the requirements in practical applications. Summary of the invention

[0005] The present application provides a formation calibration method, device, equipment and storage medium for a long linear array, which can calibrate the position coordinates of each array element and simultaneously achieve accurate estimation of the target orientation.

[0006] In a first aspect, the present application provides a formation calibration method for a long linear array, comprising: obtaining an array receiving signal of the long linear array, calculating a covariance matrix corresponding to the array receiving signal, performing eigenvalue decomposition on the covariance matrix to obtain a signal eigenvector; calculating an initial phase of a ordinate corresponding to each array element in the long linear array based on the signal eigenvector, and calculating an initial array steering vector based on the initial phase of the ordinate; calculating a spatial spectrum according to the initial array steering vector, and estimating a target azimuth based on a maximum value of the spatial spectrum; calculating a ordinate corresponding to each array element in the long linear array based on the target azimuth, and estimating a horizontal coordinate corresponding to each array element in the long linear array based on the ordinate, and updating the initial array steering vector according to the horizontal coordinate and the ordinate corresponding to each array element; recalculating the spatial spectrum according to the updated initial array steering vector, judging whether a difference between a current maximum value of the spatial spectrum and a previous maximum value of the spatial spectrum is within a preset difference range, and if so, completing the formation calibration of the long linear array.

[0007] In one possible implementation, the array receives a signal as follows:

[0008] X = BΓS + N;

[0009] in,

[0010] X=[x1(t),...,x M (t)] T ,N=[n1(t),...,n M (t)] T ;

[0011] S=[η1(t),...,η J (t)] T ,B=[A(θ1),...,A(θ J )];

[0012] A(θ j )=[a(k1,θ j ),...,a(k L ,θ j )];

[0013]

[0014] Γ=diag[P1,...,P J ],P j =[b 1j ,...,b Lj ];

[0015] Where X is the array received signal, B is the array steering matrix, Γ is the source amplitude diagonal matrix, S is the source signal vector, and N is the noise vector; x1(t) is the signal received by the first array element, x M (t) is the signal received by the Mth array element, n1(t) is the zero-mean Gaussian white noise received by the 1st array element, and n M (t) is the zero-mean Gaussian white noise received by the Mth array element, η1(t) represents the source signal of the first signal source, η J (t) represents the source signal of the Jth signal source, A(θ1) is the steering vector matrix of the first signal source, A(θ j ) is the steering vector matrix of the jth signal source, A(θ J ) is the steering vector matrix of the Jth signal source, a(k l ,θ j ) is the steering vector, k l is the horizontal wave number of the lth-order simple normal wave, θ j is the direction of the jth signal source, L is the highest order normal wave, k L is the horizontal wave number of the L-th order simple normal wave, P1 is the simple normal wave amplitude vector of the first signal source, P J The amplitude vector of the simple normal wave of the Jth signal source, b 1j is the amplitude of the lth order simple normal wave generated by the jth signal source, b Lj is the amplitude of the Lth order simple normal wave generated by the jth signal source, M is the number of array elements of the long linear array, J is the number of signal sources, diag() represents the diagonal matrix, e -i() represents the complex exponential function, and d is the array element spacing.

[0016] In a possible implementation, the covariance matrix corresponding to the array received signal is calculated, and the eigenvalue decomposition of the covariance matrix is ​​performed to obtain the signal eigenvector, which specifically includes: obtaining the conjugate transpose of the array received signal, and calculating the covariance matrix corresponding to the array received signal based on the array received signal and the conjugate transpose of the array received signal; based on the eigenvalue decomposition formula, the eigenvalue decomposition of the covariance matrix is ​​performed to obtain the signal eigenvector; wherein the eigenvalue decomposition formula is as follows:

[0017]

[0018] Where R is the covariance matrix, U s It is the subspace spanned by the eigenvectors (i.e., signal eigenvectors) corresponding to the J largest eigenvalues ​​of the covariance matrix, that is, the signal subspace; U J It is the subspace spanned by the eigenvectors corresponding to the MJ small eigenvalues ​​of the covariance matrix, that is, the noise subspace; Σ sis the diagonal matrix consisting of the J largest eigenvalues ​​of the covariance matrix, Σ J is a diagonal matrix composed of MJ small eigenvalues ​​of the covariance matrix, J is the number of information sources, M is the number of elements of the long-line array, is the conjugate transpose of the signal subspace, is the conjugate transpose of the noise subspace.

[0019] In a possible implementation, based on the signal characteristic vector, the initial phase of the ordinate corresponding to each array element in the long linear array is calculated, and based on the initial phase of the ordinate, the initial array steering vector is calculated, specifically including: obtaining the signal characteristic vector phase corresponding to the signal characteristic vector, and based on the number of array elements in the long linear array, obtaining the target array element phase corresponding to the target array element in the signal characteristic vector; inputting the signal characteristic vector phase, the target array element phase and the number of array elements into a preset initial phase calculation formula of the ordinate to calculate the initial phase of the ordinate corresponding to each array element in the long linear array; inputting the initial phase of the ordinate corresponding to each array element in the long linear array into a preset array steering vector calculation formula to obtain the initial array steering vector; wherein the array steering vector calculation formula is as follows:

[0020]

[0021] Where a(θ) is the initial array steering vector, θ is the scanning angle, and k l is the horizontal wave number, d is the array element spacing, and are the initial phases corresponding to the y coordinates of the 2nd and Mth array elements in the signal eigenvector, respectively. T represents transpose, e -i() represents the complex exponential function.

[0022] In a possible implementation, calculating the spatial spectrum according to the initial array steering vector specifically includes: obtaining a signal subspace formed based on the signal feature vector and a conjugate transpose of the signal subspace; obtaining the initial array steering vector and the conjugate transpose of the initial array steering vector; inputting the signal subspace, the conjugate transpose of the signal subspace, the initial array steering vector, and the conjugate transpose of the initial array steering vector into a preset spatial spectrum calculation formula to obtain a spatial spectrum; wherein the preset spatial spectrum calculation formula is as follows:

[0023]

[0024] Where P(θ) is the spatial spectrum, U S is the signal subspace, () H represents the conjugate transpose, ||·|| Fis the Frobenius norm, is the conjugate transpose of the signal subspace, a(θ) is the initial array steering vector, and a H (θ) is the conjugate transpose of the initial array steering vector.

[0025] In a possible implementation, based on the target azimuth, calculating the ordinate corresponding to each array element in the long linear array specifically includes: obtaining a signal characteristic vector phase corresponding to the signal characteristic vector, and obtaining a horizontal coordinate phase of each array element in the signal characteristic vector at the target azimuth; respectively substituting the signal characteristic vector phase, the horizontal coordinate phase corresponding to each array element, and the target azimuth into a preset ordinate calculation formula to calculate the ordinate corresponding to each array element in the long linear array, wherein the ordinate calculation formula is as follows:

[0026]

[0027] Where θ0 is the target orientation, y m is the y coordinate of the mth array element in the long linear array, Φ(u) is the phase of the signal eigenvector, Φ(x m ,θ0) is the horizontal coordinate phase of the mth array element in the signal characteristic vector at the target azimuth, and λ is the signal wavelength.

[0028] In a possible implementation manner, estimating the abscissa corresponding to each array element in the long line array based on the target orientation and the ordinate specifically includes: acquiring an array element spacing, substituting the array element spacing and the ordinate into a preset abscissa calculation formula to obtain the abscissa corresponding to each array element in the long line array; wherein the abscissa calculation formula is as follows:

[0029] (x m -x m-1 ) 2 +(y m -y m-1 ) 2 =d 2 , m=1,2,…,M;

[0030] In the formula, x m is the horizontal coordinate of the mth array element in the long line array, x m-1 is the horizontal coordinate of the m-1th array element in the long line array, y m is the ordinate corresponding to the mth array element in the long line array, y m-1 is the ordinate corresponding to the m-1th array element in the long linear array, and d is the array element spacing.

[0031] In a possible implementation, estimating a second array steering vector according to the abscissa and the ordinate corresponding to each array element specifically includes: inputting the abscissa and the ordinate corresponding to each array element into a preset array steering vector estimation formula to obtain a second array steering vector, wherein the array steering vector estimation formula is as follows:

[0032]

[0033] Where a1(θ) is the second array steering vector, θ is the scanning angle, and k l is the horizontal wave number, (x2,y2) and (x M ,y M ) are the horizontal coordinate and the vertical coordinate of the 2nd array element and the Mth array element respectively, () T represents transpose, e -i() represents the complex exponential function.

[0034] In a second aspect, the present application provides a formation calibration device for a long linear array, comprising: an array reception signal processing module, an initial array steering vector calculation module, a spatial spectrum calculation module, an initial array steering vector update module and a spatial spectrum data comparison module; wherein the array reception signal processing module is used to obtain the array reception signal of the long linear array, and calculate the covariance matrix corresponding to the array reception signal, perform eigenvalue decomposition on the covariance matrix to obtain a signal eigenvector; the initial array steering vector calculation module is used to calculate the initial phase of the ordinate corresponding to each array element in the long linear array based on the signal eigenvector, and calculate the initial array steering vector based on the initial phase of the ordinate; the spatial a spectrum calculation module, configured to calculate a spatial spectrum according to the initial array steering vector, and estimate a target position based on a maximum value of the spatial spectrum; a first array steering vector updating module, configured to calculate a ordinate corresponding to each array element in the long linear array based on the target position, and estimate a horizontal coordinate corresponding to each array element in the long linear array based on the ordinate, and update the initial array steering vector according to the horizontal coordinate and the ordinate corresponding to each array element; and a spatial spectrum data comparison module, configured to recalculate a spatial spectrum according to the updated initial array steering vector, and determine whether a difference between a current maximum value of the spatial spectrum and a previous maximum value of the spatial spectrum is within a preset difference range, and if so, complete formation calibration of the long linear array.

[0035] In a third aspect, an embodiment of the present application further provides a computer device, which includes a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0036] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program can implement the above method when executed by a processor.

[0037] The embodiments of the present application provide a method, device, equipment and storage medium for calibrating a long linear array, which have the following advantages over the prior art:

[0038] By acquiring the array receiving signal of the long linear array and calculating the covariance matrix corresponding to the array receiving signal, the covariance matrix is ​​subjected to eigenvalue decomposition to obtain the signal eigenvectors; these signal eigenvectors contain the position information of each array element in the array and the azimuth of the target, which is conducive to improving the accuracy of the array calibration in the future; the signal eigenvectors are used to calculate the initial phase of the ordinate of each array element, and the initial array steering vector is calculated based on the initial phase of the ordinate, and then the spatial spectrum is calculated, and the target azimuth is estimated based on the maximum value of the spatial spectrum. This method can improve the accuracy of the target azimuth estimation; subsequently, based on the target azimuth, the ordinate corresponding to each array element in the long linear array is calculated, and based on the ordinate, the abscissa corresponding to each array element in the long linear array is estimated, according to The abscissa and the ordinate corresponding to each array element are used to update the initial array steering vector; the spatial spectrum is recalculated according to the updated initial array steering vector to determine whether the difference between the current maximum value of the spatial spectrum and the previous maximum value of the spatial spectrum is within a preset difference range; if so, the formation calibration of the long linear array is completed; by comparing the maximum values ​​of the spatial spectrum in an iterative process, the method can adaptively calibrate the formation until the preset accuracy requirement is met; this adaptive calibration mechanism ensures the efficiency and accuracy of the calibration process and can adapt to formation distortion; even when the formation is severely distorted, the array steering vector can be iteratively updated to effectively perform DOA estimation, thereby solving the problem that the traditional method is difficult to reconstruct the correct formation when the formation distortion is large. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0042] Figure 1 It is a flow chart of an embodiment of a formation calibration method of a long linear array provided by the present application;

[0043] Figure 2 It is a structural schematic diagram of an embodiment of a formation calibration device for a long linear array provided by the present application;

[0044] Figure 3 This is a schematic diagram of the spatial spectrum of a long linear array before and after calibration according to an embodiment of the present application;

[0045] Figure 4 It is a structural schematic diagram of an electronic device provided by this application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0047] The disclosure below provides many different embodiments or examples to realize the different structures of the present application. In order to simplify the disclosure of the present application, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the present application. In addition, the present application can repeat reference numbers and / or letters in different examples. This repetition is for the purpose of simplification and clarity, and does not itself indicate the relationship between the various embodiments and / or settings discussed.

[0048] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0049] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0050] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0051] As used in this specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0052] Example 1, see Figure 1 , Figure 1 FIG. 1 is a flow chart of an embodiment of a method for calibrating a long linear array provided by the present application. Figure 1 As shown, the method includes steps 101 to 105, which are specifically as follows:

[0053] Step 101: Acquire an array receiving signal of a long linear array, calculate a covariance matrix corresponding to the array receiving signal, perform eigenvalue decomposition on the covariance matrix, and obtain a signal eigenvector.

[0054] In one embodiment, based on the KRAKEN sound field model, the horizontal wave number and eigenfunction of the simple normal wave are calculated to generate a shallow sea long linear array receiving signal.

[0055] Specifically, KRAKEN is an ocean acoustic model that can be used to conduct numerical experiments on sound propagation and attenuation. Therefore, the KRAKEN acoustic field model is used to simulate sound wave propagation in shallow sea environments.

[0056] Specifically, the horizontal wave number and eigenfunction of the simple normal wave are key parameters in the sound field simulation, and the sound field can be calculated through the horizontal wave number and the eigenfunction.

[0057] Specifically, the KRAKEN model is used to simulate the sound wave propagation of the sound source at a specific frequency and direction. According to the given simulation parameters, the receiving signal of the simple normal wave on the shallow sea long linear array is simulated to obtain the array receiving signal of the long linear array.

[0058] Preferably, the simulation parameters include but are not limited to a signal frequency of 400 Hz, a target azimuth of 40 degrees, a target and receiving array depth of 30 m, a horizontal distance of 10 km between the sound source and the first array element of the long linear array, a sea depth of 100 m, a seabed sound speed of 1650 m / s, a density of 1.8 g / cm3, array element spacing, an array element number of 256, a snapshot number of 1024, a distorted array shape of a sinusoidal array, and a disturbance amplitude of 2 times the signal wavelength.

[0059] In this embodiment, based on the KRAKEN sound field model, the sound propagation characteristics of the low-frequency shallow sea channel are fully considered to generate the array receiving signal of the long linear array, which can improve the subsequent formation calibration accuracy and target azimuth estimation accuracy.

[0060] In one embodiment, the array receives a signal as follows:

[0061] X = BΓS + N;

[0062] in,

[0063] X=[x1(t),...,x M (t)] T ,N=[n1(t),...,n M (t)] T ;

[0064] S=[η1(t),.,η J (t)] T ,B=[A(θ1),...,A(θ J )];

[0065] A(θ j )=[a(k1,θ j ),..,a(k L ,θ j )];

[0066]

[0067] Γ=diag[P1,...,P J ],P j =[b 1j ,...,b Lj ];

[0068] Where X is the array received signal, B is the array steering matrix, Γ is the source amplitude diagonal matrix, S is the source signal vector, and N is the noise vector; x1(t) is the signal received by the first array element, x M (t) is the signal received by the Mth array element, n1(t) is the zero-mean Gaussian white noise received by the 1st array element, and n M(t) is the zero-mean Gaussian white noise received by the Mth array element, η1(t) represents the source signal of the first signal source, η J (t) represents the source signal of the Jth signal source, A(θ1) is the steering vector matrix of the first signal source, A(θ j ) is the steering vector matrix of the jth signal source, A(θ J ) is the steering vector matrix of the Jth signal source, a(k l ,θ j ) is the steering vector, k l is the horizontal wave number of the lth-order simple normal wave, θ j is the direction of the jth signal source, L is the highest order normal wave, k L is the horizontal wave number of the L-th order simple normal wave, P1 is the simple normal wave amplitude vector of the first signal source, P J The amplitude vector of the simple normal wave of the Jth signal source, b 1j is the amplitude of the lth order simple normal wave generated by the jth signal source, b Lj is the amplitude of the Lth order simple normal wave generated by the jth signal source, M is the number of array elements of the long linear array, J is the number of signal sources, diag() represents the diagonal matrix, e -i() represents the complex exponential function, and d is the array element spacing.

[0069] In one embodiment, the conjugate transpose of the array received signal is obtained, and based on the array received signal and the conjugate transpose of the array received signal, a covariance matrix corresponding to the array received signal is calculated.

[0070] Specifically, the array received signal and the conjugate transpose of the array received signal are input into a preset covariance matrix calculation formula to obtain a covariance matrix corresponding to the array received signal, wherein the covariance matrix calculation formula is as follows:

[0071] R=XX H ;

[0072] Where R is the covariance moment, X H is the conjugate transpose of the array receiving signal, and X is the array receiving signal.

[0073] In one embodiment, based on the eigenvalue decomposition formula, the covariance matrix is ​​subjected to eigenvalue decomposition to obtain a signal eigenvector; wherein the eigenvalue decomposition formula is as follows:

[0074]

[0075] Where R is the covariance matrix, U s It is the subspace spanned by the eigenvectors (i.e., signal eigenvectors) corresponding to the J largest eigenvalues ​​of the covariance matrix, that is, the signal subspace; UJ It is the subspace spanned by the eigenvectors corresponding to the MJ small eigenvalues ​​of the covariance matrix, that is, the noise subspace; Σ s is the diagonal matrix consisting of the J largest eigenvalues ​​of the covariance matrix, Σ J is a diagonal matrix composed of MJ small eigenvalues ​​of the covariance matrix, J is the number of information sources, M is the number of elements of the long-line array, is the conjugate transpose of the signal subspace, is the conjugate transpose of the noise subspace.

[0076] Specifically, the covariance matrix is ​​subjected to eigenvalue decomposition, and the result of eigenvalue decomposition is: R = U∑U H Among them, U is a matrix containing all eigenvectors of the covariance matrix, Σ is a diagonal matrix whose diagonal elements are the corresponding eigenvalues; since signal eigenvectors are usually associated with larger eigenvalues ​​and noise eigenvectors are associated with smaller eigenvalues, the eigenvalues ​​are arranged in descending order, and the corresponding eigenvectors are also arranged in this order.

[0077] Specifically, according to the number of signal sources J, the eigenvectors corresponding to the first J largest eigenvalues ​​are selected. These eigenvectors constitute the signal subspace U s , the remaining MJ eigenvectors constitute the noise subspace U J .

[0078] Step 102: Based on the signal eigenvector, the initial phase of the ordinate corresponding to each array element in the long linear array is calculated, and based on the initial phase of the ordinate, an initial array steering vector is calculated.

[0079] In one embodiment, a signal characteristic vector phase corresponding to the signal characteristic vector is obtained, and based on the number of array elements of the long linear array, a target array element phase corresponding to the target array element in the signal characteristic vector is obtained.

[0080] Specifically, based on the number of array elements in the long linear array, the Mth array element in the long linear array is used as the target array element.

[0081] In one embodiment, the signal characteristic vector phase, the target array element phase and the number of array elements are input into a preset ordinate initial phase calculation formula to calculate the ordinate initial phase corresponding to each array element in the long linear array.

[0082] Specifically, the calculation formula of the initial phase of the ordinate is as follows:

[0083]

[0084] In the formula, is the initial phase of the ordinate corresponding to the ordinate of the mth array element in the signal eigenvector, Φ(u) is the phase of the signal eigenvector, Φ[u(M)] is the phase of the Mth array element in the signal eigenvector, and M is the number of array elements.

[0085] In one embodiment, the initial phase of the ordinate corresponding to each array element in the long linear array is respectively input into a preset array steering vector calculation formula to obtain an initial array steering vector; wherein the array steering vector calculation formula is as follows:

[0086]

[0087] Where a(θ) is the initial array steering vector, θ is the scanning angle, and k l is the horizontal wave number, d is the array element spacing, and are the initial phases corresponding to the y coordinates of the 2nd and Mth array elements in the signal eigenvector, respectively. T represents transpose, e -i () represents a complex exponential function.

[0088] Step 103: Calculate the spatial spectrum according to the initial array steering vector, and estimate the target orientation based on the maximum value of the spatial spectrum.

[0089] In one embodiment, a signal subspace formed based on the signal feature vector and a conjugate transpose of the signal subspace are obtained, and the initial array steering vector and a conjugate transpose of the initial array steering vector are obtained.

[0090] In one embodiment, the signal subspace, the conjugate transpose of the signal subspace, the initial array steering vector, and the conjugate transpose of the initial array steering vector are input into a preset spatial spectrum calculation formula to obtain a spatial spectrum; wherein the preset spatial spectrum calculation formula is as follows:

[0091]

[0092] Where P(θ) is the spatial spectrum, U S is the signal subspace, () H represents the conjugate transpose, ||·|| F is the Frobenius norm, is the conjugate transpose of the signal subspace, a(θ) is the initial array steering vector, and a H (θ) is the conjugate transpose of the initial array steering vector.

[0093] In one embodiment, when estimating the target orientation based on the maximum value of the spatial spectrum, the spatial spectrum is input into a preset target orientation estimation formula to obtain the target orientation, wherein the target orientation estimation formula is as follows:

[0094] θ0=arg max θ P(θ);

[0095] Where θ0 is the target orientation.

[0096] Step 104: Based on the target orientation, the ordinate corresponding to each array element in the long linear array is calculated, and based on the ordinate, the abscissa corresponding to each array element in the long linear array is estimated, and according to the abscissa and the ordinate corresponding to each array element, the initial array steering vector is updated.

[0097] In one embodiment, when calculating the ordinate corresponding to each array element in the long linear array based on the target azimuth, the signal characteristic vector phase corresponding to the signal characteristic vector is obtained, and the abscissa phase of each array element in the signal characteristic vector at the target azimuth is obtained; the signal characteristic vector phase, the abscissa phase corresponding to each array element, and the target azimuth are respectively substituted into a preset ordinate calculation formula to calculate the ordinate corresponding to each array element in the long linear array, wherein the ordinate calculation formula is as follows:

[0098]

[0099] Where θ0 is the target orientation, y m is the y coordinate of the mth array element in the long linear array, Φ(u) is the phase of the signal eigenvector, Φ(x m ,θ0) is the horizontal coordinate phase of the mth array element in the signal characteristic vector at the target azimuth, and λ is the signal wavelength.

[0100] Specifically, the abscissa phase of each array element in the signal characteristic vector at the target azimuth is calculated based on the abscissa phase calculation formula of the signal characteristic vector, wherein the abscissa phase calculation formula of the signal characteristic vector is as follows:

[0101]

[0102] Where d is the array element spacing, λ is the signal wavelength, and m is the array element index.

[0103] In one embodiment, when estimating the abscissa corresponding to each array element in the long line array based on the target orientation and the ordinate, the array element spacing is obtained, and the array element spacing and the ordinate are substituted into a preset abscissa calculation formula to obtain the abscissa corresponding to each array element in the long line array; wherein the abscissa calculation formula is as follows:

[0104] (x m -x m-1 ) 2 +(y m -y m-1 ) 2 =d 2 , m=1,2,…,M;

[0105] In the formula, x m is the horizontal coordinate of the mth array element in the long line array, x m-1 is the horizontal coordinate of the m-1th array element in the long line array, y m is the ordinate corresponding to the mth array element in the long line array, y m-1 is the ordinate corresponding to the m-1th array element in the long linear array, and d is the array element spacing.

[0106] In one embodiment, updating the initial array steering vector according to the abscissa and the ordinate corresponding to each array element specifically includes:

[0107] The horizontal coordinate and the vertical coordinate corresponding to each array element are input into a preset array steering vector estimation formula to obtain an updated initial array steering vector, wherein the array steering vector estimation formula is as follows:

[0108]

[0109] Where a(θ) is the updated initial array steering vector, θ is the scanning angle, and k l is the horizontal wave number, (x2,y2) and (x M ,y M ) are the horizontal coordinate and the vertical coordinate of the 2nd array element and the Mth array element respectively, () T represents transpose, e -i() represents the complex exponential function.

[0110] Step 105: recalculate the spatial spectrum according to the updated initial array steering vector, and determine whether the difference between the current maximum value of the spatial spectrum and the previous maximum value of the spatial spectrum is within a preset difference range. If so, the formation calibration of the long linear array is completed.

[0111] In one embodiment, after recalculating the spatial spectrum according to the updated initial array steering vector, the current spatial spectrum maximum value is determined based on the spatial spectrum. The process of calculating the spatial spectrum is as shown in step 103 above, which will not be repeated here.

[0112] Specifically, a previous spatial spectrum maximum value corresponding to the current spatial spectrum maximum value is obtained, wherein the previous spatial spectrum maximum value is calculated based on the initial array steering vector before updating.

[0113] In one embodiment, the preset difference range may be 0, or may be a numerical interval set based on user needs.

[0114] Specifically, when it is determined that the difference between the current maximum spatial spectrum value and the previous maximum spatial spectrum value is within a preset difference range, it is considered that the maximum spatial spectrum values ​​obtained by the two previous and subsequent calculations are substantially unchanged.

[0115] In one embodiment, when it is determined that the difference between the current maximum value of the spatial spectrum and the previous maximum value of the spatial spectrum is not within a preset difference range, that is, the spatial spectrum maximum values ​​obtained by two previous and subsequent calculations are greatly different, the current maximum value of the spatial spectrum is updated to the maximum value of the spatial spectrum, and the target orientation is estimated based on the updated maximum value of the spatial spectrum, and the ordinate corresponding to each array element in the long line array is calculated based on the target orientation, and the abscissa corresponding to each array element in the long line array is estimated based on the ordinate, and the initial array steering vector is updated according to the abscissa and the ordinate corresponding to each array element, so that the spatial spectrum is recalculated according to the updated initial array steering vector, and it is determined whether the difference between the current maximum value of the spatial spectrum and the previous maximum value of the spatial spectrum is within the preset difference range, until it is determined that the difference between the current maximum value of the spatial spectrum and the previous maximum value of the spatial spectrum is within the preset difference range, and the iterative calculation of the current maximum value of the spatial spectrum is stopped; that is, the above steps 103 and 104 are repeated until it is determined that the difference between the current maximum value of the spatial spectrum and the previous maximum value of the spatial spectrum is within the preset difference range.

[0116] like Figure 3 As shown, Figure 3 : is a schematic diagram of the spatial spectrum before and after the long-line array formation calibration of an embodiment provided by the present application; the dotted line in the figure is the spatial spectrum before calibration. Due to the formation distortion and the unknown position of the array elements, the spatial spectrum calculated according to the uniform long-line array formation has no obvious spectrum peak at the target azimuth, and the spatial spectrum side lobe is relatively high, and the target azimuth cannot be accurately estimated; the solid line is the spatial spectrum after formation calibration calculated by the method provided in this embodiment, which has an obvious spectrum peak at the target azimuth, and the spatial spectrum side lobe is relatively low, less than -10dB, and the target azimuth can be accurately estimated.

[0117] In summary, the method provided in the embodiment of the present application utilizes the signal eigenvector of the array receiving signal of the long linear array, that is, the signal subspace formed by the eigenvectors with large eigenvalues ​​and the subspace formed by the sound pressure vector of the simple normal wave sound field are the same subspace, and subsequently based on the signal eigenvector, the position coordinates of each array element and the target azimuth are estimated to solve the problem that the existing long linear array formation calibration method based on plane waves or spherical waves does not take into account the sound propagation characteristics of the low-frequency shallow sea channel, resulting in the formation calibration accuracy and the shallow sea azimuth estimation accuracy being difficult to meet application requirements. Compared with the prior art, there is no need to deploy a calibration sound source, nor is there any information from auxiliary sensors such as heading and depth. The target azimuth can be estimated while calibrating the formation, and the implementation is simple and the calculation amount is small.

[0118] Example 2, see Figure 2 , Figure 2 : is a structural schematic diagram of a formation calibration device for a long linear array provided by the present application. Corresponding to the formation calibration method for a long linear array described above, the present application also provides a formation calibration device for a long linear array. The formation calibration device for a long linear array includes a module for executing the formation calibration method for a long linear array described above, and the formation calibration device for a long linear array can be configured in a desktop computer, a tablet computer, a laptop computer, and other terminals. Specifically, the formation calibration device for a long linear array includes an array receiving signal processing module 201, an initial array steering vector calculation module 202, a spatial spectrum calculation module 203, an initial array steering vector update module 204, and a spatial spectrum data comparison module 205.

[0119] The array received signal processing module 201 is used to obtain the array received signal of the long linear array, calculate the covariance matrix corresponding to the array received signal, perform eigenvalue decomposition on the covariance matrix, and obtain a signal eigenvector.

[0120] The initial array steering vector calculation module 202 is used to calculate the initial phase of the ordinate corresponding to each array element in the long linear array based on the signal characteristic vector, and calculate the initial array steering vector based on the initial phase of the ordinate.

[0121] The spatial spectrum calculation module 203 is used to calculate the spatial spectrum according to the initial array steering vector, and estimate the target direction based on the maximum value of the spatial spectrum.

[0122] The second array steering vector calculation module 204 is used to calculate the longitudinal coordinate corresponding to each array element in the long line array based on the target orientation, and estimate the transverse coordinate corresponding to each array element in the long line array based on the longitudinal coordinate, and update the initial array steering vector according to the transverse coordinate and the longitudinal coordinate corresponding to each array element.

[0123] The spatial spectrum data comparison module 205 is used to recalculate the spatial spectrum according to the updated initial array steering vector, and determine whether the difference between the current spatial spectrum maximum value and the previous spatial spectrum maximum value is within a preset difference range. If so, the formation calibration of the long linear array is completed.

[0124] In one embodiment, the array receives a signal as follows:

[0125] X = BΓS + N;

[0126] in,

[0127] X=[x1(t),...,x M (t)] T ,N=[n1(t),...,n M (t)] T ;

[0128] S=[η1(t),...,η J (t)] T ,B=[A(θ1),...,A(θ J )];

[0129] A(θ j )=[a(k1,θ j ),...,a(k L ,θ j )];

[0130]

[0131] Γ=diag[P1,...,P J ],P j =[b 1j ,...,b Lj ];

[0132] Where X is the array received signal, B is the array steering matrix, Γ is the source amplitude diagonal matrix, S is the source signal vector, and N is the noise vector; x1(t) is the signal received by the first array element, x M (t) is the signal received by the Mth array element, n1(t) is the zero-mean Gaussian white noise received by the 1st array element, and n M (t) is the zero-mean Gaussian white noise received by the Mth array element, η1(t) represents the source signal of the first signal source, η J (t) represents the source signal of the Jth signal source, A(θ1) is the steering vector matrix of the first signal source, A(θ j ) is the steering vector matrix of the jth signal source, A(θ J ) is the steering vector matrix of the Jth signal source, a(k l ,θj ) is the steering vector, k l is the horizontal wave number of the lth-order simple normal wave, θ j is the direction of the jth signal source, L is the highest order normal wave, k L is the horizontal wave number of the L-th order simple normal wave, P1 is the simple normal wave amplitude vector of the first signal source, P J The amplitude vector of the simple normal wave of the Jth signal source, b 1j is the amplitude of the lth order simple normal wave generated by the jth signal source, b Lj is the amplitude of the Lth order simple normal wave generated by the jth signal source, M is the number of array elements of the long linear array, J is the number of signal sources, diag() represents the diagonal matrix, e -i() represents the complex exponential function, and d is the array element spacing.

[0133] In one embodiment, the array received signal processing module 201 is used to calculate the covariance matrix corresponding to the array received signal, perform eigenvalue decomposition on the covariance matrix, and obtain a signal eigenvector, specifically including: obtaining the conjugate transpose of the array received signal, and calculating the covariance matrix corresponding to the array received signal based on the array received signal and the conjugate transpose of the array received signal; performing eigenvalue decomposition on the covariance matrix based on the eigenvalue decomposition formula to obtain a signal eigenvector; wherein the eigenvalue decomposition formula is as follows:

[0134]

[0135] Where R is the covariance matrix, U s It is the subspace spanned by the eigenvectors (i.e., signal eigenvectors) corresponding to the J largest eigenvalues ​​of the covariance matrix, that is, the signal subspace; U J It is the subspace spanned by the eigenvectors corresponding to the MJ small eigenvalues ​​of the covariance matrix, that is, the noise subspace; Σ s is the diagonal matrix consisting of the J largest eigenvalues ​​of the covariance matrix, Σ J is a diagonal matrix composed of MJ small eigenvalues ​​of the covariance matrix, J is the number of information sources, M is the number of elements of the long-line array, is the conjugate transpose of the signal subspace, is the conjugate transpose of the noise subspace.

[0136] In one embodiment, the initial array steering vector calculation module 202 is used to calculate the initial phase of the ordinate corresponding to each array element in the long linear array based on the signal characteristic vector, and calculate the initial array steering vector based on the initial phase of the ordinate, specifically including: obtaining the signal characteristic vector phase corresponding to the signal characteristic vector, and obtaining the target array element phase corresponding to the target array element in the signal characteristic vector based on the number of array elements in the long linear array; inputting the signal characteristic vector phase, the target array element phase and the number of array elements into a preset initial phase calculation formula of the ordinate to calculate the initial phase of the ordinate corresponding to each array element in the long linear array; inputting the initial phase of the ordinate corresponding to each array element in the long linear array into a preset array steering vector calculation formula to obtain the initial array steering vector; wherein the array steering vector calculation formula is as follows:

[0137]

[0138] Where a(θ) is the initial array steering vector, θ is the scanning angle, and k l is the horizontal wave number, d is the array element spacing, and are the initial phases corresponding to the y coordinates of the 2nd and Mth array elements in the signal eigenvector, respectively. T represents transpose, e -i() represents the complex exponential function.

[0139] In one embodiment, the spatial spectrum calculation module 203 is used to calculate the spatial spectrum according to the initial array steering vector, specifically including: obtaining a signal subspace formed based on a signal feature vector and a conjugate transpose of the signal subspace; obtaining the initial array steering vector and the conjugate transpose of the initial array steering vector; inputting the signal subspace, the conjugate transpose of the signal subspace, the initial array steering vector and the conjugate transpose of the initial array steering vector into a preset spatial spectrum calculation formula to obtain a spatial spectrum; wherein the preset spatial spectrum calculation formula is as follows:

[0140]

[0141] Where P(θ) is the spatial spectrum, U S is the signal subspace, () H represents the conjugate transpose, ||·|| F is the Frobenius norm, is the conjugate transpose of the signal subspace, a(θ) is the initial array steering vector, and a H (θ) is the conjugate transpose of the initial array steering vector.

[0142] In one embodiment, the second array steering vector calculation module 204 is used to calculate the ordinate corresponding to each array element in the long linear array based on the target azimuth, specifically including: obtaining the signal characteristic vector phase corresponding to the signal characteristic vector, and obtaining the abscissa phase of each array element in the signal characteristic vector at the target azimuth; respectively substituting the signal characteristic vector phase, the abscissa phase corresponding to each array element and the target azimuth into a preset ordinate calculation formula to calculate the ordinate corresponding to each array element in the long linear array, wherein the ordinate calculation formula is as follows:

[0143]

[0144] Where θ0 is the target orientation, y m is the y coordinate of the mth array element in the long linear array, Φ(u) is the phase of the signal eigenvector, Φ(x m ,θ0) is the horizontal coordinate phase of the mth array element in the signal characteristic vector at the target azimuth, and λ is the signal wavelength.

[0145] In one embodiment, the second array steering vector calculation module 204 is used to estimate the horizontal coordinate corresponding to each array element in the long line array based on the target orientation and the vertical coordinate, specifically including: obtaining the array element spacing, substituting the array element spacing and the vertical coordinate into a preset horizontal coordinate calculation formula to obtain the horizontal coordinate corresponding to each array element in the long line array; wherein the horizontal coordinate calculation formula is as follows:

[0146] (x m -x m-1 ) 2 +(y m -y m-1 ) 2 =d 2 , m=1,2,…,M;

[0147] In the formula, x m is the horizontal coordinate of the mth array element in the long line array, x m-1 is the horizontal coordinate corresponding to the m-1th array element in the long line array, y m is the ordinate corresponding to the mth array element in the long line array, y m-1 is the ordinate corresponding to the m-1th array element in the long linear array, and d is the array element spacing.

[0148] In one embodiment, the second array steering vector calculation module 204 is used to estimate the second array steering vector according to the horizontal coordinate and the vertical coordinate corresponding to each array element, specifically comprising: inputting the horizontal coordinate and the vertical coordinate corresponding to each array element into a preset array steering vector estimation formula to obtain the second array steering vector, wherein the array steering vector estimation formula is as follows:

[0149]

[0150] Where a1(θ) is the second array steering vector, θ is the scanning angle, and k l is the horizontal wave number, (x2,y2) and (x M ,y M ) are the horizontal coordinate and the vertical coordinate of the 2nd array element and the Mth array element respectively, () T represents transpose, e -i() represents the complex exponential function.

[0151] The above-mentioned long linear array formation calibration device can implement the long linear array formation calibration method of the above-mentioned method embodiment. The options in the above-mentioned method embodiment are also applicable to this embodiment and will not be described in detail here.

[0152] like Figure 4 As shown, Figure 4 It is a structural diagram of an electronic device provided by the present application; it includes a processor 111, a communication interface 112, a memory 113 and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114, and the memory 113 is used to store computer programs.

[0153] In one embodiment of the present application, the processor 111 is used to implement the long linear array formation calibration method provided by any one of the aforementioned method embodiments when executing the program stored in the memory 113.

[0154] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0155] Therefore, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the formation calibration method for a long linear array provided in any of the aforementioned method embodiments are implemented.

[0156] The storage medium is a physical, non-transient storage medium, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which can store program codes. The computer-readable storage medium can be non-volatile or volatile.

[0157] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0158] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0159] The steps in the method of the embodiment of the present application can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present application can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0160] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art, or all or 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, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0161] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0162] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0163] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for calibrating a long linear array, characterized in that: include: Acquire an array receiving signal of a long linear array, calculate a covariance matrix corresponding to the array receiving signal, perform eigenvalue decomposition on the covariance matrix, and obtain a signal eigenvector; Based on the signal characteristic vector, calculating the initial phase of the ordinate corresponding to each array element in the long linear array, and based on the initial phase of the ordinate, calculating the initial array steering vector; Calculating a spatial spectrum according to the initial array steering vector, and estimating a target orientation based on a maximum value of the spatial spectrum; Based on the target orientation, calculate the ordinate corresponding to each array element in the long linear array, and based on the ordinate, estimate the abscissa corresponding to each array element in the long linear array, and update the initial array steering vector according to the abscissa and the ordinate corresponding to each array element; The spatial spectrum is recalculated according to the updated initial array steering vector to determine whether the difference between the current maximum value of the spatial spectrum and the previous maximum value of the spatial spectrum is within a preset difference range. If so, the formation calibration of the long linear array is completed.

2. The method according to claim 1, characterized in that The array receives signals as follows: X = BΓS + N; in, X=[x1(t),...,x M (t)] T ,N=[n1(t),...,n M (t)] T ; S=[η1(t),..,η J (t)] T ,B=[A(θ1),...,A(θ J )]; A(θ j )=[a(k1,θ j ),...,a(k L ,i j )]; Γ0diag[P1,...,P J ],P j J[b 1j ,..,b Lj ]4 Where X is the array received signal, B is the array steering matrix, Γ is the source amplitude diagonal matrix, S is the source signal vector, and N is the noise vector; x1(t) is the signal received by the first array element, x M (t) is the signal received by the Mth array element, n1(t) is the zero-mean Gaussian white noise received by the 1st array element, and n M (t) is the zero-mean Gaussian white noise received by the Mth array element, η1(t) represents the source signal of the first signal source, η J (t) represents the source signal of the Jth signal source, A(θ1) is the steering vector matrix of the first signal source, A(θ j ) is the steering vector matrix of the jth signal source, A(θ J ) is the steering vector matrix of the Jth signal source, a(k l ,θ j ) is the steering vector, k l is the horizontal wave number of the lth-order simple normal wave, θ j is the direction of the jth signal source, L is the highest order normal wave, k L is the horizontal wave number of the L-th order simple normal wave, P1 is the simple normal wave amplitude vector of the first signal source, P J The amplitude vector of the simple normal wave of the Jth signal source, b 1j is the amplitude of the lth order simple normal wave generated by the jth signal source, b Lj is the amplitude of the Lth order simple normal wave generated by the jth signal source, M is the number of array elements of the long linear array, J is the number of signal sources, diag() represents the diagonal matrix, e -i() represents the complex exponential function, and d is the array element spacing.

3. The method according to claim 1, characterized in that: Calculating the covariance matrix corresponding to the array received signal, performing eigenvalue decomposition on the covariance matrix, and obtaining a signal eigenvector, specifically including: Obtaining the conjugate transpose of the array received signal, and calculating a covariance matrix corresponding to the array received signal based on the array received signal and the conjugate transpose of the array received signal; Based on the eigenvalue decomposition formula, the covariance matrix is ​​subjected to eigenvalue decomposition to obtain a signal eigenvector; wherein the eigenvalue decomposition formula is as follows: Where R is the covariance matrix, U s It is the subspace spanned by the eigenvectors (i.e., signal eigenvectors) corresponding to the J largest eigenvalues ​​of the covariance matrix, that is, the signal subspace; U J It is the subspace spanned by the eigenvectors corresponding to the MJ small eigenvalues ​​of the covariance matrix, that is, the noise subspace; Σ s is the diagonal matrix consisting of the J largest eigenvalues ​​of the covariance matrix, Σ J is a diagonal matrix composed of MJ small eigenvalues ​​of the covariance matrix, J is the number of information sources, M is the number of elements of the long-line array, is the conjugate transpose of the signal subspace, is the conjugate transpose of the noise subspace.

4. The method according to claim 1, characterized in that: Calculating the initial ordinate phase corresponding to each array element in the long linear array based on the signal characteristic vector, and calculating the initial array steering vector based on the initial ordinate phase, specifically includes: Acquire a signal characteristic vector phase corresponding to the signal characteristic vector, and acquire a target array element phase corresponding to a target array element in the signal characteristic vector based on the number of array elements of the long linear array; Inputting the signal characteristic vector phase, the target array element phase and the number of array elements into a preset ordinate initial phase calculation formula to calculate the ordinate initial phase corresponding to each array element in the long linear array; The initial phase of the ordinate corresponding to each array element in the long linear array is input into a preset array steering vector calculation formula to obtain an initial array steering vector; wherein the array steering vector calculation formula is as follows: Where a(θ) is the initial array steering vector, θ is the scanning angle, and k l is the horizontal wave number, d is the array element spacing, and are the initial phases corresponding to the y coordinates of the 2nd and Mth array elements in the signal eigenvector, respectively. T represents transpose, e -i() represents the complex exponential function.

5. The method according to claim 1, characterized in that: Calculating a spatial spectrum according to the initial array steering vector specifically includes: Acquire a signal subspace formed based on the signal feature vector and a conjugate transpose of the signal subspace; Obtaining the initial array steering vector and a conjugate transpose of the initial array steering vector; The signal subspace, the conjugate transpose of the signal subspace, the initial array steering vector, and the conjugate transpose of the initial array steering vector are input into a preset spatial spectrum calculation formula to obtain a spatial spectrum; wherein the preset spatial spectrum calculation formula is as follows: Where P(θ) is the spatial spectrum, U S is the signal subspace, () H represents the conjugate transpose, ||·|| F is the Frobenius norm, is the conjugate transpose of the signal subspace, a(θ) is the initial array steering vector, and a H (θ) is the conjugate transpose of the initial array steering vector.

6. The method according to claim 1, characterized in that: Calculating the ordinate corresponding to each array element in the long linear array based on the target orientation specifically includes: Obtaining a signal characteristic vector phase corresponding to the signal characteristic vector, and obtaining a horizontal coordinate phase of each array element in the signal characteristic vector at the target azimuth; The signal characteristic vector phase, the horizontal coordinate phase corresponding to each array element, and the target azimuth are respectively substituted into a preset vertical coordinate calculation formula to calculate the vertical coordinate corresponding to each array element in the long linear array, wherein the vertical coordinate calculation formula is as follows: Where θ0 is the target orientation, y m is the y coordinate of the mth array element in the long linear array, Φ(u) is the phase of the signal eigenvector, Φ(x m ,θ0) is the horizontal coordinate phase of the mth array element in the signal characteristic vector at the target azimuth, and λ is the signal wavelength.

7. The method according to claim 1, characterized in that: Based on the target orientation and the ordinate, estimating the abscissa corresponding to each array element in the long linear array specifically includes: Obtain the array element spacing, substitute the array element spacing and the ordinate into a preset abscissa calculation formula, and obtain the abscissa corresponding to each array element in the long linear array; wherein the abscissa calculation formula is as follows: (x m -x m-1 ) 2 +(y m -y m-1 ) 2 =d 2 ,m=1,2,…,M; In the formula, x m is the horizontal coordinate of the mth array element in the long line array, x m-1 is the horizontal coordinate corresponding to the m-1th array element in the long line array, y m is the ordinate corresponding to the mth array element in the long line array, y m-1 is the ordinate corresponding to the m-1th array element in the long linear array, and d is the array element spacing.

8. A long-line array calibration device, characterized in that: include: An array receiving signal processing module, an initial array steering vector calculation module, a spatial spectrum calculation module, an initial array steering vector update module and a spatial spectrum data comparison module; The array receiving signal processing module is used to obtain the array receiving signal of the long linear array, calculate the covariance matrix corresponding to the array receiving signal, perform eigenvalue decomposition on the covariance matrix, and obtain the signal eigenvector; The initial array steering vector calculation module is used to calculate the initial phase of the ordinate corresponding to each array element in the long linear array based on the signal characteristic vector, and calculate the initial array steering vector based on the initial phase of the ordinate; The spatial spectrum calculation module is used to calculate the spatial spectrum according to the initial array steering vector, and estimate the target direction based on the maximum value of the spatial spectrum; The initial array steering vector updating module is used to calculate the ordinate corresponding to each array element in the long line array based on the target orientation, and estimate the abscissa corresponding to each array element in the long line array based on the ordinate, and update the initial array steering vector according to the abscissa and the ordinate corresponding to each array element; The spatial spectrum data comparison module is used to recalculate the spatial spectrum according to the updated initial array steering vector, and determine whether the difference between the current spatial spectrum maximum value and the previous spatial spectrum maximum value is within a preset difference range. If so, the formation calibration of the long linear array is completed.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 can be implemented.

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