A method, system, terminal device and storage medium for estimating the direction of underwater acoustic targets

Abstract

The present invention relates to a method, system, terminal device and storage medium for estimating the direction of underwater acoustic targets. The method comprises: calculating the fourth-order cumulant matrix of the sound source signal; removing redundancy from the fourth-order cumulant matrix; performing vectorization operation on the transformed fourth-order cumulant after removing redundancy to obtain a single observation vector model; removing redundancy from the single observation vector model to obtain a final vector observation model; establishing a sparse representation model; converting the direction of arrival (DOA) estimation problem into an l1 norm sparse constrained optimization problem to solve the DOA estimation value. The scheme provided by the present invention combines the fourth-order cumulant and the sparse representation method, and is applied to the direction of shallow water underwater acoustic target estimation for Gaussian color noise, effectively expanding the array aperture, and reducing the computational complexity by removing redundant data twice. The method achieves a stronger ability to suppress color noise, a higher resolution, and a lower root mean square error under the conditions of a small number of snapshots and a low signal-to-noise ratio.

Description

Technical Field

[0001] The present invention relates to the field of array signal processing, and in particular to an underwater acoustic target azimuth estimation method, system, terminal equipment and storage medium. Background Art

[0002] In view of the fact that Gaussian white noise is mostly used in shallow sea research, in the existing technology, most of the traditional Direction of Arrival (DOA) estimation techniques are suitable for white noise background. Domestic and foreign scholars have mainly proposed three methods for DOA estimation under colored noise background, namely parameterization method, covariance matrix difference method and fourth-order cumulant method. However, the actual underwater environmental noise is often spatial colored noise. Under the colored noise background, the existing underwater acoustic target DOA estimation methods have serious performance degradation. Among them, the parameterization method will be seriously affected when the noise covariance matrix is ​​unknown. The covariance matrix difference method does not require noise parameters, but this type of method will have mirror pseudo peaks. The traditional fourth-order cumulant method has limited estimation accuracy and large amount of calculation. Summary of the invention

[0003] In order to overcome the problems existing in the related art, the present invention provides a method, system, terminal device and storage medium for estimating the direction of an underwater acoustic target, which can effectively expand the array aperture and reduce the computational complexity by removing redundant data twice.

[0004] According to a first aspect of an embodiment of the present invention, a method for estimating an underwater acoustic target direction is provided, comprising:

[0005] Step 1, calculating the fourth-order cumulant matrix of the sound source signal;

[0006] Step 2: construct a transformation matrix to remove redundancy from the fourth-order cumulant matrix, and obtain a transformed fourth-order cumulant with redundancy removed;

[0007] Step 3, performing vectorization operation on the de-redundant transformed fourth-order cumulants to obtain a single observation vector model;

[0008] Step 4: construct a selection matrix to remove redundancy of the single observation vector model to obtain a final vector observation model;

[0009] Step 5: Establish a sparse representation model based on the final vector observation model;

[0010] Step 6: Convert the DOA estimation problem into an l1-norm sparse constrained optimization problem and solve the DOA estimation value.

[0011] According to a second aspect of an embodiment of the present invention, there is provided a system for estimating an underwater acoustic target direction, comprising:

[0012] A calculation module, used for calculating a fourth-order cumulant matrix of a sound source signal;

[0013] A first redundancy removal module is used to construct a transformation matrix to remove redundancy from the fourth-order cumulant matrix, so as to obtain a redundancy-removed transformed fourth-order cumulant;

[0014] A vectorization module, used for performing vectorization operation on the de-redundant transformed fourth-order cumulants to obtain a single observation vector model;

[0015] A second redundancy removal module is used to construct a selection matrix to remove redundancy from the single observation vector model to obtain a final vector observation model;

[0016] An establishing module, used for establishing a sparse representation model according to the final vector observation model;

[0017] The solution module is used to convert the DOA estimation problem into an l1-norm sparse constrained optimization problem and solve the DOA estimation value.

[0018] According to a third aspect of an embodiment of the present invention, a terminal device is provided, including:

[0019] Processor; and

[0020] The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is caused to execute the method as described above.

[0021] According to a fourth aspect of an embodiment of the present invention, there is provided a non-transitory machine-readable storage medium having executable code stored thereon, which, when executed by a processor of an electronic device, causes the processor to execute the method as described above.

[0022] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0023] Combining the fourth-order cumulant and sparse representation method, the method is applied to the azimuth estimation of shallow sea acoustic targets for Gaussian colored noise, effectively expanding the array aperture and reducing the computational complexity by removing redundant data twice. This method has a stronger ability to suppress colored noise, higher resolution, and lower root mean square error under the conditions of a small number of snapshots and low signal-to-noise ratio.

[0024] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0026] Figure 1 is a flow chart of a method for estimating the direction of an underwater acoustic target according to an exemplary embodiment of the present invention;

[0027] Figure 2 is a schematic diagram of a hydrophone array model;

[0028] Figure 3 is a schematic diagram of the expanded array element;

[0029] Figure 4 is a structural block diagram of a system for estimating the direction of an underwater acoustic target according to an exemplary embodiment of the present invention;

[0030] Figure 5 It is a schematic diagram showing the structure of a computing device according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0031] The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0032] The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0033] It should be understood that although the terms "first", "second", "third", etc. may be used to describe various information in the present invention, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0034] The invention provides a method for obtaining a DOA estimation value of an underwater acoustic target by solving an l1 norm sparse constraint optimization problem by using a sparse representation model established by a fourth-order cumulant.

[0035] The technical solutions of the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0036] Figure 1 The figure is a flow chart of a method for estimating the direction of an underwater acoustic target according to an exemplary embodiment of the present invention.

[0037] See also Figure 1 , the method comprising:

[0038] 110. Calculate the fourth-order cumulant matrix of the sound source signal;

[0039] Specifically, Figure 2 As shown in the figure, it is assumed that an array of M evenly placed hydrophones with an array element spacing of d is used for underwater acoustic signal positioning. Under the plane wave assumption, K far-field sound source signals are incident on the hydrophone array with the incoming wave direction vector θ, which is expressed as θ = [θ1, θ2, …, θ K ] T , T represents transposition, and the fourth-order cumulant matrix of the sound source signal is:

[0040]

[0041] in,(·) * represents conjugate, H represents conjugate transpose, represents the Kronecker product, E represents the expectation, and the fourth-order cumulant C of the signal x K 2 ×K 2 dimensional matrix, X is the sound source signal amplitude matrix.

[0042] According to the fourth-order cumulant property, C x There are only K non-zero elements in C. x On the diagonal, that is, C x The (i-1)K+i(i=1,…,K)th element of the diagonal of is non-zero. x All the zero-valued elements in the matrix C are removed. x By reducing the order, the fourth-order cumulant matrix of the signal can be simplified to a diagonal matrix, and its order is reduced from K 2 ×K 2 Reduced to K×K, that is, the fourth-order cumulant matrix after reduction is where diag(·) represents a diagonal matrix, represents the non-zero elements on the diagonal of the matrix;

[0043] The fourth-order cumulant of the new array received data obtained after dimensionality reduction is:

[0044]

[0045] Among them, ⊙ represents the Khatri-Rao product, A(θ)=[a(θ1),a(θ2)…,a(θ K )] is the array manifold matrix, and the definition in is the steering vector after the array is expanded. λ is the wavelength of the signal source, a m (θ k ) contains the delay information of the kth signal received by the mth array sensor. The process of constructing the fourth-order cumulant will produce many overlapping virtual array elements, which are symmetrically distributed with the real array elements. The schematic diagram of the expanded array elements is shown in Figure 3 shown.

[0046] 120. Construct a transformation matrix to remove redundancy from the fourth-order cumulant matrix, and obtain a transformed fourth-order cumulant with redundancy removed;

[0047] Specifically, construct an M 2 ×(2M-1)-dimensional transformation matrix Q removes redundancy, and the expression of Q is as follows:

[0048]

[0049] Q m =[0 M×(M-m) ,I M ,0 M×(m-1) ],1≤m≤M

[0050] Among them I M is the unit matrix of dimension M, then there is a corresponding relationship Make the new array manifold steering vector satisfy the conjugate symmetric structure, that is:

[0051]

[0052] Define a (2M-1)×(2M-1)-dimensional matrix G=Q T Q = diag (1, 2, ..., M-1, M, M-1, ..., 1), using the matrix G, Q for linear transformation, we get the fourth-order cumulant matrix of redundancy removal The new array manifold matrix is ​​B(θ)=[b(θ1),b(θ2),…,b(θ K )], the fourth-order cumulant matrix C is derived from M 2 ×M 2 The dimension is reduced to a (2M-1)×(2M-1) dimensional matrix R4.

[0053] Therefore, the matrix R4 not only utilizes all the information but also eliminates the redundant data, and the advantage of expanding the array aperture is maintained.

[0054] 130. Performing vectorization operation on the de-redundant transformed fourth-order cumulants to obtain a single observation vector model;

[0055] Specifically, in order to obtain an observation model with better performance, the redundant fourth-order cumulant R4 is vectorized to obtain a single observation vector model:

[0056]

[0057] where vec(·) represents the vectorization operator, B * ()⊙B(θ) serves as the new array manifold matrix.

[0058] 140. Construct a selection matrix to remove redundancy of the single observation vector model to obtain a final vector observation model;

[0059] Specifically, in the same way, construct another (2M-1) 2 ×(4M-3)-dimensional selection matrix Perform secondary dimensionality reduction on Γ, and the construction method is similar to Q. The selection matrix can be expressed as definition Similarly where D(θ)=[d(θ1),d(θ2),…,d(θ K )] is the new array manifold matrix after secondary dimensionality reduction, and the corresponding steering vector is The single vector observation model is obtained as Left multiplication Remove redundancy to obtain the final vector observation model D(θ) is the new array manifold matrix.

[0060] 150. Establish a sparse representation model according to the final vector observation model;

[0061] Specifically, assuming To fix the sampling grid points, the spatial domain can be divided. N is the number of grids. Under general conditions, N>>M is satisfied. The method of constructing D(θ) is used to construct an overcomplete dictionary. Then the array manifold matrix of the entire spatial domain can be expressed as It contains all possible orientation information in space, so there is a sparse observation vector where Γ N is a K sparse vector if and only if When Γ N The corresponding element is non-zero.

[0062] 160. The DOA estimation problem is converted into an l1-norm sparse constrained optimization problem to solve the DOA estimation value.

[0063] Specifically, the DOA estimation problem is transformed into an l1-norm sparse constrained optimization problem, namely

[0064] where ||·||1 and ||·||2 represent the l1 norm and l2 norm respectively, λ represents the regularization factor, whose value is related to the noise energy, Z is the single observation vector of the receiving array, is the array manifold matrix in the spatial domain. The CVX convex optimization toolbox is used to solve the above l1-norm sparse constrained optimization problem and obtain K sparse vectors Γ N , thus obtaining the estimated azimuth values ​​of K signal sources from the grid positions of non-zero elements.

[0065] An embodiment of the present invention provides a method for estimating the direction of an underwater acoustic target. In view of the fact that the environmental noise in shallow water underwater is often spatial colored noise and the traditional fourth-order cumulant algorithm has high computational complexity, a quadratic fourth-order cumulant dimensionality reduction is used to eliminate redundant data in the fourth-order cumulant, and a sparse representation method is used for DOA estimation. The present invention is applicable to Gaussian colored noise background and expands the array aperture. Simulation and sea trial data tests show that the technology is applicable to shallow water environments. Compared with the traditional fourth-order cumulant method, this method has a stronger ability to suppress colored noise, a higher resolution, and a lower root mean square error under the conditions of a small number of snapshots and a low signal-to-noise ratio.

[0066] Figure 4 It is a structural block diagram of a system for estimating the direction of an underwater acoustic target according to an exemplary embodiment of the present invention.

[0067] See also Figure 4 The system comprises:

[0068] A calculation module, used for calculating a fourth-order cumulant matrix of a sound source signal;

[0069] A first redundancy removal module is used to construct a transformation matrix to remove redundancy from the fourth-order cumulant matrix, so as to obtain a redundancy-removed transformed fourth-order cumulant;

[0070] A vectorization module, used for performing vectorization operation on the de-redundant transformed fourth-order cumulants to obtain a single observation vector model;

[0071] A second redundancy removal module is used to construct a selection matrix to remove redundancy from the single observation vector model to obtain a final vector observation model;

[0072] An establishing module, used for establishing a sparse representation model according to the final vector observation model;

[0073] The solution module is used to convert the DOA estimation problem into an l1-norm sparse constrained optimization problem and solve the DOA estimation value.

[0074] Regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0075] Figure 5 It is a schematic diagram showing the structure of a computing device according to an exemplary embodiment of the present invention.

[0076] See also Figure 5 , the computing device 500 includes a memory 510 and a processor 520 .

[0077] The processor 520 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0078] The memory 510 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage. Among them, ROM can store static data or instructions required by the processor 520 or other modules of the computer. The permanent storage device may be a readable and writable storage device. The permanent storage device may be a non-volatile storage device that does not lose the stored instructions and data even after the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (such as a magnetic or optical disk, flash memory) as a permanent storage device. In some other embodiments, the permanent storage device may be a removable storage device (such as a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as a dynamic random access memory. The system memory may store some or all instructions and data required by the processor at run time. In addition, the memory 510 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks may also be used. In some embodiments, the memory 510 may include a readable and / or writable removable storage device, such as a laser disc (CD), a read-only digital versatile disc (e.g., DVD-ROM, double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. The computer-readable storage medium does not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0079] The memory 510 stores executable codes, and when the executable codes are processed by the processor 520 , the processor 520 can execute part or all of the methods described above.

[0080] In addition, the method according to the present invention may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing part or all of the steps in the above method of the present invention.

[0081] Alternatively, the present invention may also be implemented as a non-temporary machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) on which executable code (or computer program, or computer instruction code) is stored. When the executable code (or computer program, or computer instruction code) is executed by a processor of an electronic device (or computing device, server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present invention.

[0082] The scheme of the present invention has been described in detail above with reference to the accompanying drawings. 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. Those skilled in the art should also be aware that the actions and modules involved in the specification are not necessarily required by the present invention. In addition, it can be understood that the steps in the method of the embodiment of the present invention can be adjusted in order, merged and deleted according to actual needs, and the modules in the device of the embodiment of the present invention can be merged, divided and deleted according to actual needs.

[0083] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or a combination of both.

[0084] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system and method according to multiple embodiments of the present invention. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or code, and the part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0085] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terms used herein are selected to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for estimating the direction of an underwater acoustic target, characterized in that: include: Step 1, calculating the fourth-order cumulant matrix of the sound source signal; Step 2: construct a transformation matrix to remove redundancy from the fourth-order cumulant matrix, and obtain a transformed fourth-order cumulant with redundancy removed; Step 3, performing vectorization operation on the de-redundant transformed fourth-order cumulants to obtain a single observation vector model; Step 4: construct a selection matrix to remove redundancy of the single observation vector model to obtain a final vector observation model; Step 5: Establish a sparse representation model based on the final vector observation model; Step 6: Convert the DOA estimation problem into an l1-norm sparse constrained optimization problem to solve the DOA estimation value; Step 1 specifically includes: Assume that an array of M evenly spaced hydrophones with an array element spacing of d is used for underwater acoustic signal positioning. Under the plane wave assumption, K far-field sound source signals are incident on the hydrophone array with the incoming wave direction vector θ, expressed as θ = [θ1, θ2, …, θ K ] T , T represents transposition, and the fourth-order cumulant matrix of the sound source signal is: in,(·) * represents conjugate, H represents conjugate transpose, represents the Kronecker product, E represents the expectation, and the fourth-order cumulant C of the signal x K 2 ×K 2 dimensional matrix, X is the sound source signal amplitude matrix; According to the fourth-order cumulant property, C x There are only K non-zero elements in C. x On the diagonal, that is, C x The (i-1)K+ith element of the diagonal of is non-zero, i=1,...,K; x All the zero-valued elements in the matrix C are removed. x By reducing the order, the fourth-order cumulant matrix of the signal can be simplified to a diagonal matrix, and its order is reduced from K 2 ×K 2 Reduced to K×K, that is, the fourth-order cumulant matrix after reduction is where diag(·) represents a diagonal matrix, γ xk =C x ((i-1)K+i,(i-1)K+i) represents the non-zero elements on the diagonal of the matrix; The fourth-order cumulant of the new array received data obtained after dimensionality reduction is: Among them, ⊙ represents the Khatri-Rao product, A(θ)=[a(θ1),a(θ2)…,a(θ K )] is the array manifold matrix, and the definition in is the steering vector after the array is expanded; λ is the wavelength of the signal source, a(θ k ) contains the delay information of the kth signal received by the mth array sensor; Step 2 specifically includes: Construct an M 2 ×(2M-1)-dimensional transformation matrix Q removes redundancy, and the expression of Q is as follows: Q m =[0 M×(M-m) ,I M ,0 M×(m-1) ],1≤m≤M Among them I M is the unit matrix of dimension M, then there is a corresponding relationship Make the new array manifold steering vector satisfy the conjugate symmetric structure, that is: Define a (2M-1)×(2M-1)-dimensional matrix G=Q T Q = diag (1, 2, ..., M-1, M, M-1, ..., 1), using the matrix G, Q for linear transformation, we get the fourth-order cumulant matrix of redundancy removal The new array manifold matrix is ​​B(θ)=[b(θ1),b(θ2),…,b(θ K )], the fourth-order cumulant matrix C is derived from M 2 ×M 2 The dimension is reduced to a (2M-1)×(2M-1) dimensional matrix R4.

2. The method according to claim 1, characterized in that Step 3 specifically includes: The vectorized operation of the fourth-order cumulant R4 after removing redundancy is performed, and the single observation vector model is obtained as follows: where vec(·) represents the vectorization operator, B * (θ)⊙B(θ) serves as the new array manifold matrix.

3. The method according to claim 2, characterized in that Step 4 specifically includes: Construct a (2M-1) 2 ×(4M-3)-dimensional selection matrix Perform secondary dimensionality reduction on Γ, and the construction method is similar to Q. The selection matrix can be expressed as definition Similarly where D(θ)=[d(θ1),d(θ2),…,d(θ K )] is the new array manifold matrix after secondary dimensionality reduction, and the corresponding steering vector is The single vector observation model is obtained as Left multiplication Remove redundancy to obtain the final vector observation model D(θ) is the new array manifold matrix.

4. The method according to claim 3, characterized in that Step 5 specifically includes: Assumptions To fix the sampling grid points, the spatial domain can be divided. N is the number of grids. The condition N>>M must be satisfied. The overcomplete dictionary is constructed by constructing D(θ). The array manifold matrix of the entire spatial domain can be expressed as It contains all possible orientation information in space, so there is a sparse observation vector where Γ N is a K sparse vector if and only if When θ k is the incident signal direction Γ N The corresponding element is non-zero.

5. The method according to claim 4, characterized in that Step 6 specifically includes: The DOA estimation problem is transformed into an l1-norm sparse constrained optimization problem, namely where ||·||1 and ||·||2 represent the l1 norm and l2 norm respectively, β represents the regularization factor, whose value is related to the noise energy, and Z is the single observation vector of the receiving array. is the array manifold matrix in the spatial domain. The CVX convex optimization toolbox is used to solve the above l1-norm sparse constrained optimization problem and obtain K sparse vectors Γ N , thus obtaining the estimated azimuth values ​​of K signal sources from the grid positions of non-zero elements.

6. A system for estimating the direction of an underwater acoustic target, characterized in that: include: A calculation module, used for calculating a fourth-order cumulant matrix of a sound source signal; A first redundancy removal module is used to construct a transformation matrix to remove redundancy from the fourth-order cumulant matrix, so as to obtain a redundancy-removed transformed fourth-order cumulant; A vectorization module, used for performing vectorization operation on the de-redundant transformed fourth-order cumulants to obtain a single observation vector model; A second redundancy removal module is used to construct a selection matrix to remove redundancy from the single observation vector model to obtain a final vector observation model; An establishing module, used for establishing a sparse representation model according to the final vector observation model; A solution module is used to convert the DOA estimation problem into an l1-norm sparse constrained optimization problem and solve the DOA estimation value; The calculation module is specifically used for: Assume that an array of M evenly spaced hydrophones with an array element spacing of d is used for underwater acoustic signal positioning. Under the plane wave assumption, K far-field sound source signals are incident on the hydrophone array with the incoming wave direction vector θ, expressed as θ = [θ1, θ2, …, θ K ] T , T represents transposition, and the fourth-order cumulant matrix of the sound source signal is: in,(·) * represents conjugate, H represents conjugate transpose, represents the Kronecker product, E represents the expectation, and the fourth-order cumulant C of the signal x K 2 ×K 2 dimensional matrix, X is the sound source signal amplitude matrix; According to the fourth-order cumulant property, C x There are only K non-zero elements in C. x On the diagonal, that is, C x The (i-1)K+ith element of the diagonal of is non-zero, i=1,...,K; x All the zero-valued elements in the matrix C are removed. x By reducing the order, the fourth-order cumulant matrix of the signal can be simplified to a diagonal matrix, and its order is reduced from K 2 ×K 2 Reduced to K×K, that is, the fourth-order cumulant matrix after reduction is where diag(·) represents a diagonal matrix, γ xk =C x ((i-1)K+i,(i-1)K+i) represents the non-zero elements on the diagonal of the matrix; The fourth-order cumulant of the new array received data obtained after dimensionality reduction is: Among them, ⊙ represents the Khatri-Rao product, A(θ)=[a(θ1),a(θ2)…,a(θ K )] is the array manifold matrix, and the definition in is the steering vector after the array is expanded; λ is the wavelength of the signal source, a(θ k ) contains the delay information of the kth signal received by the mth array sensor; The first redundancy removal module is specifically used for: Construct an M 2 ×(2M-1)-dimensional transformation matrix Q removes redundancy, and the expression of Q is as follows: Q m =[0 M×(M-m) ,I M ,0 M×(m-1) ],1≤m≤M Among them I M is the unit matrix of dimension M, then there is a corresponding relationship Make the new array manifold steering vector satisfy the conjugate symmetric structure, that is: Define a (2M-1)×(2M-1)-dimensional matrix G=Q T Q = diag (1, 2, ..., M-1, M, M-1, ..., 1), using the matrix G, Q for linear transformation, we get the fourth-order cumulant matrix of redundancy removal The new array manifold matrix is ​​B(θ)=[b(θ1),b(θ2),…,b(θ K )], the fourth-order cumulant matrix C is derived from M 2 ×M 2 The dimension is reduced to a (2M-1)×(2M-1) dimensional matrix R4.

7. A terminal device, characterized in that: include: processor; as well as A memory having executable codes stored thereon, which, when executed by the processor, causes the processor to execute the method according to any one of claims 1 to 5.

8. A non-transitory machine-readable storage medium having executable code stored thereon, characterized in that: When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method as claimed in any one of claims 1 to 5.

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