Sparse direction finding method capable of resisting far and near field interference and suitable for underwater unmanned platform sonar array

Abstract

The invention discloses a far and near field interference resistant sparse direction finding method suitable for an underwater unmanned platform sonar array, belongs to the field of underwater acoustic signal processing, and particularly relates to a far and near field interference resistant sparse direction finding method. The objective of the invention is to solve the problems of low resolution probability and low direction finding precision of far-field weak target signals under the condition of strong interference of far and near fields in the prior art. The far and near field interference resisting sparse direction finding method suitable for the underwater unmanned platform sonar array comprises the specific steps that 1, a near field region set is determined according to the platform size and far and near field steering vector correlation; 2, performing subspace projection processing on the data received by the receiving array to obtain projection of a covariance matrix in a signal interference subspace; and step 3, processing the projection of the covariance matrix in the signal interference subspace based on a sparse reconstruction method to obtain a direction finding result.

Description

Technical Field

[0001] The invention belongs to the field of underwater acoustic signal processing, and in particular relates to a sparse direction finding method resistant to far and near field interference. Background Art

[0002] Underwater Unmanned Vehicle (UUV) array direction finding is an important research topic in current passive sonar signal processing. Unlike arrays equipped on ships, the small size of UUV makes it difficult to build a vibration-isolating and noise-reducing working environment for the array, which makes the array signal processing seriously affected by the strong near-field interference caused by the UUV maneuvers. Under the influence of strong near-field interference, the direction finding algorithm for far-field target signals has serious performance degradation at this time, which is specifically manifested in the decrease of angular resolution and direction finding accuracy, and multiple groups of pseudo peaks will appear in the far-field spatial spectrum. At the same time, when one of the far-field weak targets is the target of interest, the remaining far-field strong targets will appear as far-field strong interference and have an adverse effect on the detection of the weak target.

[0003] The current direction-finding methods can be mainly divided into maximum likelihood (ML), beamforming, subspace and sparse reconstruction. Under the condition of model adaptation, ML algorithms often have the results closest to the Cramér–Rao Bound (CRB), but their huge computational complexity will not meet the real-time processing requirements of UUV platforms. Conventional beamforming (CBF) is the most common beamforming method in the beamforming method. It is robust to various mismatch conditions and is widely used in sonar signal processing. Its defects are that it cannot break through the Rayleigh limit and cannot distinguish adjacent targets in the same beam. At the same time, strong interference components leaked by side lobes will also cover weak targets. The minimum variance distortionless response (MVDR) beamforming algorithm has a good suppression effect on both near and far field strong interference, and has higher resolution than CBF. However, since MVDR is more sensitive to model mismatch, when there is a model mismatch, the performance of MVDR is seriously degraded. Subspace algorithms use the orthogonality of signal subspace and noise subspace to achieve high-resolution direction finding of target signals. However, in the UUV working environment, the received data contains far-field weak target signals, far-field and near-field interference signals, and ocean environmental noise. At this time, the basis of the signal subspace and the interference subspace will cross and overlap, and the signal subspace cannot be accurately estimated by eigenvalue decomposition. Compared with subspace algorithms, sparse reconstruction methods, beamforming algorithms and ML algorithms have higher angular resolution, but their sparse priors often need to be based on an over-complete dictionary set under ideal conditions. The strong near-field interference caused by the maneuvering of the UUV platform will lead to a dictionary set mismatch problem, that is, the basis in the far-field dictionary set matrix cannot accurately represent the near-field strong interference signal in the array received signal, resulting in an increase in fitting errors and a degradation of the far-field signal direction finding capability. In view of the above problems, the present invention proposes a sparse direction finding method that is resistant to far-field and near-field interference and is suitable for small platform sonar arrays. Summary of the invention

[0004] The purpose of the present invention is to solve the problems of low resolution probability and direction finding accuracy of far-field weak target signals under the existing far-field and near-field strong interference conditions, and to propose a sparse direction finding method suitable for underwater unmanned platform sonar arrays that is resistant to far-field and near-field interference.

[0005] A sparse direction finding method suitable for underwater unmanned platform sonar array with resistance to near and far field interference is described as follows:

[0006] Step 1: Determine the near-field region set based on the platform size and the correlation between the near-field and far-field steering vectors;

[0007] Step 2: Perform subspace projection processing on the data received by the receiving array to obtain the projection R of the covariance matrix in the signal interference subspace s ;

[0008] Step 3: Process the projection R of the covariance matrix in the signal interference subspace based on the sparse reconstruction method s , and get the direction finding result.

[0009] The beneficial effects of the present invention are:

[0010] The purpose of the present invention is to construct a sparse representation model of the far-field and near-field joint, and reconstruct the covariance matrix of the far-field part and the near-field part of the received signal respectively, so as to achieve high-precision and high-resolution direction finding of far-field weak target signals under the conditions of strong interference between the far-field and near-field.

[0011] The present invention determines the range of the near-field dictionary set according to the platform size and the correlation between the far-field and near-field steering vectors; performs subspace projection processing on the received data to obtain the projection of the covariance matrix in the signal interference subspace; processes the projection of the covariance matrix in the signal interference subspace based on the sparse reconstruction method, and outputs the direction finding result;

[0012] The core technical content of the present invention is to use the sparse priors of far-field and near-field sound sources in the spatial domain to achieve the separation of far-field and near-field covariance matrices. On this basis, in order to solve the problem of large difference in power between interference and target signals, the subspace projection method is used to normalize each signal source.

[0013] The present invention is based on sparse reconstruction technology and utilizes the sparse priors of far-field and near-field sound sources in the spatial domain to separate far-field target signals from near-field interference signals, thereby reducing the impact of near-field interference caused by platform self-noise on far-field direction finding.

[0014] The present invention pre-processes the input covariance matrix based on the subspace projection technology and normalizes the power of each information source, thereby suppressing the interference of far and near field strengths at the same time.

[0015] The present invention constructs a direction finding method suitable for underwater small platform arrays, and realizes high-precision and high-resolution direction finding of far-field weak targets under finite array element conditions;

[0016] The present invention takes into account the influence of far-field interference and near-field interference caused by the self-noise of underwater unmanned platform maneuvers on the direction finding of far-field weak targets, combines sparse reconstruction and subspace method to suppress far-field and near-field interference, and improves the direction finding capability of far-field weak target signals in scenarios with strong far-field and near-field interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the present invention;

[0018] Figure 2Schematic diagram of the division of the far and near fields applicable to the UUV platform, (a) the relationship diagram of the far and near field guidance vectors, (b) the schematic diagram of the array coordinate system;

[0019] Figure 3 A map of China's South China Sea test situation;

[0020] Figure 4 are the true value of the target ship's position and heading angle, (a) the true value of the target ship's position, (b) the target ship's heading angle;

[0021] Figure 5 The time-direction history diagram of far-field and near-field interference suppression before and after, (a) before far-field and near-field interference suppression, (b) after far-field and near-field interference suppression;

[0022] Figure 6 This is a comparison chart of the direction finding results between the improved method and the original method. DETAILED DESCRIPTION

[0023] Specific implementation method 1: Combination Figure 1 This embodiment is described. The specific process of this embodiment is a sparse direction finding method suitable for underwater unmanned platform sonar array to resist far and near field interference:

[0024] Step 1: Determine the near-field region set based on the platform size and the correlation between the near-field and far-field steering vectors;

[0025] Step 2: Perform subspace projection processing on the data received by the receiving array to obtain the projection R of the covariance matrix in the signal interference subspace s ;

[0026] Step 3: Process the projection R of the covariance matrix in the signal interference subspace based on the sparse reconstruction method s , and get the direction finding result.

[0027] Specific implementation method 2: This implementation method is different from specific implementation method 1 in that: in step 1, the near field area set is determined according to the platform size and the correlation between the far and near field steering vectors;

[0028] The specific process is:

[0029] Step 11: Determine the correlation between the far and near field steering vectors;

[0030] Step 12: Define the set Ω based on the correlation of the near and far field steering vectors 1 ;

[0031] Step 13: Define the set Ω based on the platform size 2 ;

[0032] Step 14: Based on the set Ω 1 and the set Ω 2 Determine the near field region set.

[0033] The other steps and parameters are the same as those in the first embodiment.

[0034] Specific implementation method three: This implementation method is different from specific implementation methods one or two in that: in step one, the correlation between the far-field and near-field steering vectors is determined; the specific process is:

[0035] The receiving array is a uniform linear array (ULA), in which the uniform linear array is uniformly arranged by M array elements, the array element spacing is d = λ / 2, λ is the wavelength corresponding to the center frequency of the narrowband signal; M is 4≤M≤50; the center of the array is taken as the origin of the coordinate system, the bow direction of the platform is the positive direction of the horizontal axis, and the normal direction of the center of the array is taken as the positive direction of the vertical axis. A Cartesian coordinate system is established, and the position coordinates of each array element are (d m ,0), where d m is the coordinate position of each array element on the horizontal axis in the Cartesian coordinate system, d m =[m-1-(M-1) / 2]d,m=1...M;

[0036] The distribution of near-field interference sources studied in the present invention is related to the size of the UUV platform, and the interference sources are often not distributed in the Fresnel zone, so the approximate conclusion of the Fresnel zone and the near-field division criteria are not applicable.

[0037] The correlation expressions for the near and far field steering vectors are as follows:

[0038]

[0039] Among them, κ(α, β) is the correlation between the far-field and near-field steering vectors. The larger the value of κ(α, β), the stronger the correlation between the near-field steering vector and the far-field steering vector when the near-field source coordinates are (α, β). means taking θ as the variable and taking the maximum value among all the values; |·| means modulo operation; a(θ) is the far-field steering vector, θ is the far-field signal arrival direction, and the value range of θ is any angle in the far-field space; a(α,β) is the near-field steering vector, (α,β) is the near-field source coordinate (in the above Cartesian coordinate system), and the value range of (α,β) is: {(α,β)|α 2 +β 2 ≤4D 4 / λ 2}, where D is the array aperture, λ is the wavelength corresponding to the center frequency of the narrowband signal; || || 2 is the two-norm; the superscript H indicates conjugation;

[0040] The mathematical expressions of a(θ) and a(α,β) are:

[0041]

[0042] Among them, d 1 is the horizontal coordinate of the first array element in the Cartesian coordinate system; d M is the horizontal coordinate of the Mth array element in the Cartesian coordinate system; the polar coordinate position corresponding to (α, β) is (r, δ), where r is the polar diameter and δ is the polar angle, that is, sin -1 (·) represents the arc sine operation; r 1 is the distance from the coordinate (α, β) to the first array element; r M is the distance from the coordinate (α, β) to the Mth array element; j is an imaginary unit, j 2 = -1; (·) T Represents a transpose operation.

[0043] When κ(α, β)→1 in equation (1), the sound source is considered to be closer to a far-field sound source. Consider the following scenario: a 10-element ULA with an array element spacing of 0.5 m. The variation trend of κ(α, β) is as follows: Figure 2 As shown in (a), contour lines are drawn for the positions where the κ values ​​are 0.6, 0.8, and 0.9. The sound source in the area where κ(α, β) < 0.9 is regarded as a near-field sound source, and the sound source in the area where κ(α, β) > 0.9 is considered to have characteristics closer to the far-field sound source and is not considered.

[0044] The other steps and parameters are the same as those in the first or second embodiment.

[0045] Specific implementation method 4: This implementation method is different from one of the specific implementation methods 1 to 3 in that: in the steps 1 and 2, the set Ω is defined based on the correlation of the far-field and near-field steering vectors. 1 ;

[0046] The expression is as follows:

[0047]

[0048] Among them, @ is a definition symbol.

[0049] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0050] Specific implementation mode 5: This implementation mode is different from the specific implementation modes 1 to 4 in that: in step 13, the set Ω is defined based on the platform size. 2 ; The specific process is:

[0051] At the same time, the near-field interference signal in the UUV array receiving signal has a distribution range related to the platform size. Figure 2 As shown in (b), in the established Cartesian coordinate system, the length of the UUV is Xu , width is Y u , assuming that the horizontal axis range of the platform in the Cartesian coordinate system is The vertical axis range is

[0052] in is the left boundary of UUV in Cartesian coordinate system, is the right boundary of UUV in the Cartesian coordinate system, is the upper boundary of the UUV in the Cartesian coordinate system, Y u - is the lower boundary of the UUV in the Cartesian coordinate system;

[0053] Define the set Ω 2 :

[0054]

[0055] The other steps and parameters are the same as those in Specific Implementation 1 to 4-1.

[0056] Specific implementation method 6: This implementation method is different from the specific implementation methods 1 to 5 in that: in step 14, based on the set Ω 1 and the set Ω 2 Determine the set of near-field regions; expressed as:

[0057] Ω=Ω 1 IΩ 2 (5)

[0058] Among them, I is the intersection.

[0059] The other steps and parameters are the same as those in Specific Implementation Methods 1 to 5-1.

[0060] Specific implementation method 7: The difference between this implementation method and any one of the specific implementation methods 1 to 6 is that in step 2, subspace projection processing is performed on the data received by the receiving array to obtain the projection R of the covariance matrix in the signal interference subspace s ; The specific process is:

[0061] Step 21: Estimate the covariance matrix of the received data:

[0062]

[0063] in, represents the sample covariance matrix estimated by the received snapshots, Y represents the original data received by the receiving array, and T is the number of snapshots;

[0064] Step 22: Estimate the sample covariance matrix Perform eigendecomposition; expressed as:

[0065]

[0066] Among them, U s Represents the subspace matrix of the far-field target signal plus the far-field and near-field interference, referred to as the signal-interference subspace matrix; Σ s Denotes the matrix U s The eigenvalue matrix of e represents the noise subspace matrix, Σ e Denotes the noise subspace matrix U e The eigenvalue matrix of

[0067] Step 23: Based on the signal interference subspace matrix U s , define the projection R of the covariance matrix in the signal interference subspace s .

[0068] The other steps and parameters are the same as those in Specific Embodiments 1 to 6.

[0069] Specific implementation eight: This implementation differs from one of the specific implementations one to seven in that: the signal interference subspace matrix U s , define the projection R of the covariance matrix in the signal interference subspace s ;

[0070] The expression is:

[0071]

[0072] Among them, R s represents the projection of the covariance matrix in the signal interference subspace, U s represents the signal-interference subspace matrix.

[0073] The other steps and parameters are the same as those in Specific Embodiments 1 to 7.

[0074] Specific implementation method 9: This implementation method is different from any one of specific implementation methods 1 to 8 in that: in step 3, the projection R of the covariance matrix in the signal interference subspace is processed based on the sparse reconstruction method. s , get the direction finding result;

[0075] The specific process is:

[0076] Step 31: Divide the far and near field grids to generate a mixed overcomplete dictionary set Ψ;

[0077] The specific process is:

[0078] For the far field, the full spatial angle of the far field from 0° to 180° is divided into L 1 grid points, represented as a set

[0079] θ 1 Indicates the angle of the first grid point in the far field, θ 2 represents the angle of the second grid point in the far field, Indicates the far field L 1 grid point angles;

[0080] For the near field, the near field area set Ω is evenly divided into L 2 grid points, represented as a set

[0081] (α 1 ,β 1 ) represents the position of the first grid point in the near field, (α 2 ,β 2 ) represents the position of the second grid point in the near field, Indicates the near field L 2 grid point locations;

[0082] Defining the Grid Hereinafter, the near-field grid and the far-field grid are collectively referred to as grids 1 to L;

[0083] Substitute the sets Θ and χ into equation (2) to form a mixed overcomplete dictionary set Ψ, expressed as

[0084]

[0085] Where a(θ 1 ) is the steering vector corresponding to the first far-field grid angle, where For L 1 The steering vector corresponding to the far-field grid angle;

[0086] a(α 1 ,β 1 ) is the steering vector corresponding to the first near-field grid position, For L 2 The steering vector corresponding to the near-field grid position;

[0087] I M represents a unit matrix of dimension M×M, where M represents the total number of array elements;

[0088] represents the first column in the Ψ matrix, represents the second column in the Ψ matrix, represents the L+Mth column in the Ψ matrix;

[0089] Step 32: Projection R based on the covariance matrix in the signal interference subspace s And the mixed overcomplete dictionary set Ψ obtains the far-field spatial spectrum estimate.

[0090] The present invention needs to implement DOA estimation in an iterative manner. During the iterative process, the present invention uses the superscript (·) (i) Indicates the number of iterations.

[0091] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0092] Specific implementation method 10: This implementation method is different from the specific implementation methods 1 to 9 in that: Step 32: Based on the projection R of the covariance matrix in the signal interference subspace s and mixed with the overcomplete dictionary set Ψ to obtain the far-field spatial spectrum estimate;

[0093] The present invention needs to implement DOA estimation in an iterative manner. During the iterative process, the present invention uses the superscript (·) (i) Indicates the number of iterations.

[0094] The specific process is:

[0095] Input data:

[0096] The input data for this step are as follows: R obtained in formula (8) s , the mixed overcomplete dictionary set Ψ obtained by formula (9);

[0097] Step 321: Set the maximum number of iterations to iter max , the tolerance threshold is th;

[0098] Step 322: Initialize the number of iterations i = 0, and initialize the initial tolerance value ε = 1;

[0099] initialization and σ (0) :

[0100]

[0101] in, Indicates the initial value of the spatial spectrum when the grid number is l, the subscript value range is l=1,2LL, σ (0) represents the initial value of noise power; represents the lth column in the Ψ matrix;

[0102] definition for The smallest M values ​​in Represents a set, and the subscript value range is from 1 to M. Represents a set, and the subscript value range is from 1 to L;

[0103] Represents the set of columns in the mixed overcomplete dictionary set;

[0104] Step 323: Determine whether i < iter max or ε≤th;

[0105] If not, the far-field spatial spectrum estimate is obtained

[0106] If so, update the fitted covariance matrix R by the following formula (i) :

[0107] R (i) =ΨP (i) Ψ H (11)

[0108] Among them, P (i) is a diagonal matrix, P (i) It is expressed as:

[0109]

[0110] in is the spatial spectrum value corresponding to the first grid in the i-th iteration (obtained from the previous iteration or initialization); is the spatial spectrum value corresponding to the second grid in the i-th iteration; is the spatial spectrum value corresponding to the Lth grid in the i-th iteration; σ (i) is the value of the noise power in the i-th iteration (obtained from the previous iteration or initialization);

[0111] Based on the fitted covariance matrix R (i) The projection of the covariance matrix in the signal interference subspace R s , update the corresponding value of the i+1th iteration and σ (i+1) :

[0112]

[0113] Among them, p l (i+1) Represents the spatial spectrum value corresponding to the lth grid in the i+1th iteration; Indicates the spatial spectrum value corresponding to the lth grid in the i-th iteration;

[0114] R (i) represents the covariance matrix reconstructed in the i-th iteration;

[0115] w represents the intermediate variable, w=[w 1 ,L,w l ,L,w L ], where w 1 w l The value of l = 1, wL w l Where l = the value of L, w l represents the intermediate variable,

[0116] ρ (i) represents the intermediate variable in the i-th iteration;

[0117] l represents the spatial spectrum number, l = 1, 2, L, L, L represents the total number of grid points in the near and far fields;

[0118] σ (i+1) represents the noise power value in the i+1th iteration, σ (i) Represents the noise power value in the i-th iteration;

[0119] (·) -1 Indicates matrix inversion operation; || || 2 represents the two-norm;

[0120] Update the tolerance value ε by the following formula:

[0121]

[0122] Among them, p s (i+1) represents the mixed spatial spectrum value in the i+1th iteration, expressed as in represents the spatial spectrum value corresponding to the first grid in the i+1th iteration, represents the spatial spectrum value corresponding to the Lth grid in the i+1th iteration; p s (i) represents the mixed spatial spectrum value in the i-th iteration, in represents the spatial spectrum value corresponding to the first grid in the i-th iteration, represents the spatial spectrum value corresponding to the Lth grid in the i-th iteration;

[0123] Step 324: Set i=i+1, and repeat steps 323 until i<iter max Or ε≤th; get the far-field spatial spectrum estimate

[0124] The other steps and parameters are the same as those in Specific Embodiments 1 to 9.

[0125] Figure 3 A map of China's South China Sea test situation;

[0126] Figure 4 is the true value of the target ship's position and heading angle, Figure 4 (a) is the true value of the target ship's position. Figure 4(b) is the heading angle of the target ship;

[0127] Figure 5 This is the time-direction history diagram before and after the near-field and far-field interference suppression. Figure 5 (a) is before the near-field and far-field interference is suppressed. Figure 5 Middle (b) is after far-field and near-field interference suppression;

[0128] Figure 6 This is a comparison chart of the direction finding results between the improved method and the original method.

[0129] The pseudo code of the process of the present invention is shown in the following table:

[0130]

[0131]

[0132] The present invention may also have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A sparse direction finding method suitable for underwater unmanned platform sonar array with resistance to near and far field interference, characterized in that: The specific process of the method is: Step 1: Determine the near-field region set based on the platform size and the correlation between the near-field and far-field steering vectors; Step 2: Perform subspace projection processing on the data received by the receiving array to obtain the projection R of the covariance matrix in the signal interference subspace s ; Step 3: Process the projection R of the covariance matrix in the signal interference subspace based on the sparse reconstruction method s , and get the direction finding result.

2. The sparse direction finding method for underwater unmanned platform sonar array with resistance to far and near field interference according to claim 1 is characterized in that: In the step 1, a near field region set is determined according to the platform size and the correlation between the near and far field steering vectors; The specific process is: Step 11: Determine the correlation between the far and near field steering vectors; Step 1 and 2: Define the set Ω1 based on the correlation between the far and near field steering vectors; Step 13: Define the set Ω2 based on the platform size; Step 14: Determine the near field area set based on the set Ω1 and the set Ω2.

3. The sparse direction finding method for underwater unmanned platform sonar array with resistance to far and near field interference according to claim 2 is characterized in that: In the step 1, the correlation between the far and near field steering vectors is determined; the specific process is: The receiving array is a uniform linear array, in which the uniform linear array is uniformly arranged by M array elements, and the array element spacing is d = λ / 2, where λ is the wavelength corresponding to the center frequency of the narrowband signal; the center of the array is taken as the origin of the coordinate system, the bow direction of the platform is the positive direction of the horizontal axis, and the normal direction of the center of the array is taken as the positive direction of the vertical axis. A Cartesian coordinate system is established, and the position coordinates of each array element are (d m ,0), where d m is the coordinate position of each array element on the horizontal axis in the Cartesian coordinate system, d m =[m-1-(M-1) / 2]d,m=1...M; The correlation expressions for the near and far field steering vectors are as follows: Among them, κ(α,β) is the correlation between the far-field and near-field steering vectors; means taking θ as the variable and taking the maximum value among all the values; |g| means modulo operation; a(θ) is the far-field steering vector, θ is the far-field signal arrival direction; a(α,β) is the near-field steering vector, (α,β) are the near-field source coordinates, and the value range of (α,β) is: {(α,β)|α 2 +β 2 ≤4D 4 / λ 2 }, where D is the array aperture, λ is the wavelength corresponding to the center frequency of the narrowband signal; || ||2 is the two-norm; the superscript H indicates conjugation; The mathematical expressions of a(θ) and a(α,β) are: Where d1 is the horizontal coordinate of the first array element in the Cartesian coordinate system; d M is the horizontal coordinate of the Mth array element in the Cartesian coordinate system; the polar coordinate position corresponding to (α, β) is (r, δ), where r is the polar diameter and δ is the polar angle, that is, sin -1 (·) represents the arcsine operation; r1 is the distance from the coordinate (α, β) to the first array element; r M is the distance from the coordinate (α, β) to the Mth array element; j is an imaginary unit, j 2 = -1; (·) T Represents a transpose operation.

4. The sparse direction finding method for underwater unmanned platform sonar array with resistance to far and near field interference according to claim 3 is characterized in that: In the steps 1 and 2, a set Ω1 is defined based on the correlation of the near and far field steering vectors; The expression is as follows: in, To define symbols.

5. The sparse direction finding method for underwater unmanned platform sonar array with resistance to far and near field interference according to claim 4 is characterized in that: In step 1-3, a set Ω2 is defined based on the platform size; the specific process is: In the established Cartesian coordinate system, the length of the UUV is X u , width is Y u , assuming that the horizontal axis range of the platform in the Cartesian coordinate system is The vertical axis range is in is the left boundary of UUV in Cartesian coordinate system, is the right boundary of the UUV in the Cartesian coordinate system, Y u + is the upper boundary of the UUV in the Cartesian coordinate system, is the lower boundary of the UUV in the Cartesian coordinate system; Define the set Ω2:

6. The sparse direction finding method for underwater unmanned platform sonar array with resistance to far and near field interference according to claim 5 is characterized in that: In the step 1-4, the near field area set is determined based on the set Ω1 and the set Ω2; it is expressed as: Ω=Ω1 IΩ2 (5) Among them, I is the intersection.

7. The sparse direction finding method for underwater unmanned platform sonar array with resistance to far and near field interference according to claim 6 is characterized in that: In step 2, subspace projection processing is performed on the data received by the receiving array to obtain the projection R of the covariance matrix in the signal interference subspace. s ; The specific process is: Step 21: Estimate the covariance matrix of the received data: in, represents the sample covariance matrix estimated by the received snapshots, Y represents the original data received by the receiving array, and T is the number of snapshots; Step 22: Estimate the sample covariance matrix Perform eigendecomposition; expressed as: Among them, U s represents the signal interference subspace matrix; Σ s Denotes the matrix U s The eigenvalue matrix of e represents the noise subspace matrix, Σ e Denotes the noise subspace matrix U e The eigenvalue matrix of Step 23: Based on the signal interference subspace matrix U s , define the projection R of the covariance matrix in the signal interference subspace s .

8. The sparse direction finding method for underwater unmanned platform sonar array with resistance to far and near field interference according to claim 7 is characterized in that: In the steps 2 and 3, based on the signal interference subspace matrix U s , define the projection R of the covariance matrix in the signal interference subspace s ; The expression is: Among them, R s represents the projection of the covariance matrix in the signal interference subspace, U s represents the signal-interference subspace matrix.

9. The sparse direction finding method for underwater unmanned platform sonar array with resistance to far and near field interference according to claim 8, characterized in that: In the step 3, the projection R of the covariance matrix in the signal interference subspace is processed based on the sparse reconstruction method. s , get the direction finding result; the specific process is: Step 31: Generate a mixed overcomplete dictionary set Ψ; The specific process is: For the far field, the full spatial angle of the far field from 0° to 180° is divided into L1 grid points, represented as a set θ1 represents the angle of the first grid point in the far field, θ2 represents the angle of the second grid point in the far field, Represents the angle of the L1th grid point in the far field; For the near field, the near field area set Ω is evenly divided into L2 grid points, represented as a set (α1, β1) represents the position of the first grid point in the near field, (α2, β2) represents the position of the second grid point in the near field, Indicates the position of the L2th grid point in the near field; Defining the Grid Substitute the sets Θ and χ into equation (2) to form a mixed overcomplete dictionary set Ψ, expressed as Where a(θ1) is the steering vector corresponding to the first far-field grid angle, is the steering vector corresponding to the L1th far-field grid angle; a(α1,β1) is the steering vector corresponding to the first near-field grid position, is the steering vector corresponding to the L2th near-field grid position; I M represents a unit matrix of dimension M×M, where M represents the total number of array elements; represents the first column in the Ψ matrix, represents the second column in the Ψ matrix, represents the L+Mth column in the Ψ matrix; Step 32: Projection R based on the covariance matrix in the signal interference subspace s And the mixed overcomplete dictionary set Ψ obtains the far-field spatial spectrum estimate.

10. The sparse direction finding method for underwater unmanned platform sonar array with resistance to far and near field interference according to claim 9, characterized in that: The projection R of the covariance matrix in the signal interference subspace in step 32 is based on s and mixed with the overcomplete dictionary set Ψ to obtain the far-field spatial spectrum estimate; The specific process is: Step 321: Set the maximum number of iterations to iter max , the tolerance threshold is th; Step 322: Initialize the number of iterations i = 0, and initialize the initial tolerance value ε = 1; initialization and σ (0) : in, Indicates the initial value of the spatial spectrum when the grid number is l, the subscript value range is l=1,2LL, σ (0) represents the initial value of noise power; represents the lth column in the Ψ matrix; definition for The smallest M values ​​in Represents a set, and the subscript value range is from 1 to M. Represents a set, and the subscript value range is from 1 to L; Represents the set of columns in the mixed overcomplete dictionary set; Step 323: Determine whether i < iter max or ε≤th; If not, the far-field spatial spectrum estimate is obtained If so, update the fitted covariance matrix R by the following formula (i) : R (i) =ΨP (i) P H (11) Among them, P (i) is a diagonal matrix, P (i) It is expressed as: in is the spatial spectrum value corresponding to the first grid in the i-th iteration; is the spatial spectrum value corresponding to the second grid in the i-th iteration; is the spatial spectrum value corresponding to the Lth grid in the i-th iteration; σ (i) is the value of the noise power in the i-th iteration; Based on the fitted covariance matrix R (i) The projection of the covariance matrix in the signal interference subspace R s , update the p corresponding to the i+1th iteration l (i+1) and σ (i+1) : Among them, p l (i+1) Represents the spatial spectrum value corresponding to the lth grid in the i+1th iteration; Indicates the spatial spectrum value corresponding to the lth grid in the i-th iteration; R (i) represents the covariance matrix reconstructed in the i-th iteration; w represents the intermediate variable, w=[w1,L,w l ,L,w L ], where w1 is w l The value of l = 1, w L w l Where l = the value of L, w l represents the intermediate variable, ρ (i) represents the intermediate variable in the i-th iteration; l represents the spatial spectrum number, l = 1, 2, L, L, L represents the total number of grid points in the near and far fields; σ (i+1) represents the noise power value in the i+1th iteration, σ (i) Represents the noise power value in the i-th iteration; (·) -1 represents the matrix inversion operation; || ||2 represents the binary norm; Update the tolerance value ε by the following formula: Among them, p s (i+1) represents the mixed spatial spectrum value in the i+1th iteration, expressed as in represents the spatial spectrum value corresponding to the first grid in the i+1th iteration, represents the spatial spectrum value corresponding to the Lth grid in the i+1th iteration; p s (i) represents the mixed spatial spectrum value in the i-th iteration, in represents the spatial spectrum value corresponding to the first grid in the i-th iteration, represents the spatial spectrum value corresponding to the Lth grid in the i-th iteration; Step 324: Set i=i+1, and repeat steps 323 until i<iter max Or ε≤th; get the far-field spatial spectrum estimate

Images