Two-dimensional direction-of-arrival estimation method based on L-array prior waveform information source

Abstract

The invention discloses a two-dimensional direction-of-arrival estimation method based on an L-array prior waveform information source, and the method comprises the steps: solving a two-dimensional direction-of-arrival angle of a prior waveform based on a first convex optimization function, so as to solve a problem that an underdetermined equation cannot be solved when the two-dimensional direction-of-arrival angle is solved according to an estimation angle vector model, obtaining an initial estimation value of the two-dimensional direction-of-arrival angle; performing optimization solution on the two-dimensional direction-of-arrival angle of the prior waveform based on a second convex optimization function so as to solve an off-lattice phenomenon generated when the steps are executed, and obtaining an optimal estimation value of the two-dimensional direction-of-arrival angle; determining a complex amplitude estimation value of each group of two-dimensional direction-of-arrival angles according to the optimal estimation value; and matching prior waveforms corresponding to the two-dimensional direction-of-arrival angles according to the complex amplitude estimation values to obtain the optimal estimation value of the two-dimensional direction-of-arrival angle of each prior waveform. The method is relatively low in calculation complexity and can be suitable for scenes with relatively high requirements on real-time performance.

Description

Technical Field

[0001] The invention belongs to the technical field of radar signal processing, and in particular relates to a two-dimensional direction of arrival estimation method based on an L-array priori waveform signal source. Background Art

[0002] Source direction of arrival estimation is an important part of radar signal processing, and its application scenarios are mainly divided into two categories. One is the application on fixed-position reconnaissance equipment. In order to achieve high-precision direction finding in a multi-signal electromagnetic environment, the passive direction finding subsystem of this reconnaissance equipment must use a large number of antennas to form a large array for direction finding. If the traditional array direction finding algorithm is applied to a passive direction finding subsystem with too many antennas, the computational complexity is too high, which is not conducive to real-time signal processing. The other is the application on fast-moving reconnaissance equipment. In order to improve its anti-interference ability, this reconnaissance equipment must have the ability to find the direction in a multi-signal electromagnetic environment. Due to the limited size of fast-moving reconnaissance equipment, the number of array elements on it is small, and the moving speed is fast, there are high requirements for direction finding time and direction finding accuracy; the current source direction of arrival estimation also cannot meet its demand for direction finding time.

[0003] Therefore, the current source direction of arrival estimation method cannot be applied to these two devices due to its high computational complexity and limited application scenarios. Summary of the invention

[0004] The embodiment of the present invention provides a method for estimating a two-dimensional direction of arrival of a source based on an L-array priori waveform, which can solve the problem that the current method for estimating the direction of arrival of a source is limited in application scenarios due to high computational complexity.

[0005] In a first aspect, an embodiment of the present invention provides a two-dimensional wave direction of arrival estimation method based on an L-array prior waveform source, the method comprising: S1, solving the two-dimensional wave direction of arrival angle of the prior waveform based on a first convex optimization function to solve the unsolvable problem of the underdetermined equation generated when solving the two-dimensional wave direction of arrival angle according to an estimated angle vector model, and obtaining an initial estimated value of the two-dimensional wave direction of arrival angle, wherein the estimated angle vector model is used to solve the estimated angle vector determined according to the two-dimensional wave direction of arrival angle, and the two-dimensional wave direction of arrival angle includes an azimuth angle and a pitch angle;

[0006] S2, optimizing and solving the two-dimensional direction of arrival angle of the priori waveform based on the second convex optimization function to solve the grid deviation phenomenon generated when executing step S1, and obtaining the best estimated value of the two-dimensional direction of arrival angle;

[0007] S3, determining a complex amplitude estimate of each set of two-dimensional direction of arrival angles according to the best estimate;

[0008] S4. Match the priori waveform corresponding to the two-dimensional direction of arrival angle according to the complex amplitude estimation value to obtain the best estimation value of the two-dimensional direction of arrival angle of each priori waveform.

[0009] In a second aspect, an embodiment of the present invention provides a method and device for estimating a two-dimensional direction of arrival of a source based on an L-array priori waveform, comprising:

[0010] A first optimization solution module, the first optimization solution module is used to solve the two-dimensional wave direction of arrival angle of the prior waveform based on a first convex optimization function to solve the unsolvable problem of the underdetermined equation generated when the two-dimensional wave direction of arrival angle is solved according to the estimated angle vector model, and obtain an initial estimated value of the two-dimensional wave direction of arrival angle, wherein the estimated angle vector model is used to solve the estimated angle vector determined according to the two-dimensional wave direction of arrival angle, and the two-dimensional wave direction of arrival angle includes an azimuth angle and a pitch angle;

[0011] A second optimization solution module, the second optimization solution module is used to optimize and solve the two-dimensional wave direction of arrival angle of the priori waveform based on a second convex optimization function to solve the grid deviation phenomenon generated when the first optimization solution module is executed, and obtain the best estimated value of the two-dimensional wave direction of arrival angle;

[0012] A complex amplitude solving module, the complex amplitude solving module is used to determine the complex amplitude estimation value of each group of two-dimensional wave arrival direction angles according to the best estimation value;

[0013] A matching module is used to match the priori waveform corresponding to the two-dimensional wave direction of arrival angle according to the complex amplitude estimation value to obtain the best estimation value of the two-dimensional wave direction of arrival angle of each priori waveform.

[0014] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory is used to store computer programs; the processor can be used to execute the computer program (instructions) stored in the memory to implement the method of the first aspect above.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed, the method of the first aspect described above can be implemented.

[0016] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0017] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1A schematic diagram of the structure of an L-array receiver provided by an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of the implementation flow of a method for estimating a two-dimensional direction of arrival based on an L-array priori waveform signal source provided by an embodiment of the present invention;

[0020] Figure 3 A schematic diagram of the structure of a two-dimensional direction of arrival estimation device based on an L-array priori waveform signal source provided by an embodiment of the present invention;

[0021] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0023] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0024] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0025] As used in the present 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 phrase "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.

[0026] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0027] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0028] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0029] Figure 1 Shown is a schematic structural diagram of an L-array receiver provided by an embodiment of the present invention.

[0030] In one possible implementation, see Figure 1 , the receiver may include M a array elements, of which M x The array elements are arranged along the x-axis, M z The array elements are arranged along the z-axis, and the reference array element is set at the origin. The adjacent spacing between coaxial array elements is d.

[0031] For example, the receiver may receive P narrowband far-field a priori waveform source signals with a carrier wavelength of λ, and the a priori waveform signal s p The set of (n) is Each prior waveform can include K p The direction is (θ pk ,φ pk ) of the multipath signal, where θ pk is the azimuth of the kth source corresponding to the pth prior waveform, φ pk is the elevation angle of the kth source corresponding to the pth prior waveform.

[0032] In one example, the method for estimating the two-dimensional direction of arrival of an L-array priori waveform signal source provided by the present invention can estimate the two-dimensional direction of arrival angle of each priori waveform based on the received data of the receiver.

[0033] Illustratively, the two-dimensional direction of arrival angle may include an azimuth angle and an elevation angle.

[0034] The two-dimensional direction of arrival estimation method based on L-array prior waveform signal source provided by the embodiment of the present invention can be applied to electronic devices such as mobile terminals, personal notebook computers, supercomputers, etc. The embodiment of the present invention does not impose any restrictions on the specific type of electronic devices.

[0035] Figure 2 The figure shows a schematic diagram of the implementation process of a method for estimating a two-dimensional direction of arrival based on an L-array priori waveform signal source provided by an embodiment of the present invention. As an example but not a limitation, the method can be applied to the above electronic device, and the method can include S1-S4, and each step is described below.

[0036] S1, solving the two-dimensional wave direction of arrival angle of the prior waveform based on the first convex optimization function to solve the unsolvable problem of the underdetermined equation generated when solving the two-dimensional wave direction of arrival angle according to the estimated angle vector model, and obtaining an initial estimated value of the two-dimensional wave direction of arrival angle.

[0037] In some embodiments, the estimated angle vector model may be used to solve for an estimated angle vector determined from a two-dimensional direction-of-arrival angle of a priori waveform.

[0038] Exemplarily, the estimated angle vector model may satisfy the following formula:

[0039]

[0040] in, are the estimated angle vectors of the x-matrix and z-matrix of the p-th prior waveform, respectively, p is the azimuth vector of the pth prior waveform, φ p The pitch angle vector of the pth prior waveform, γ p is the complex amplitude vector of the pth prior waveform, is the identity matrix, K p is the number of signal directions included in the pth prior waveform, A x (θ p ), A z (φ p ) are the x-array manifold matrix and the z-array manifold matrix of the two-dimensional wave direction of arrival angle of the p-th prior waveform, Δb xp (θ p ,γ p ), Δb zp (φ p ,γ p ) are respectively The error from its true value.

[0041] In a possible implementation, the estimated angle vector model may be obtained by solving the original spatial feature matrix after dimensionality reduction based on a maximum likelihood algorithm.

[0042] For example, the original spatial feature matrix after dimensionality reduction can satisfy the following formula:

[0043]

[0044] in:

[0045]

[0046] in, are the original x-array spatial feature matrix after dimensionality reduction and the original z-array residual feature matrix after dimensionality reduction, s(n) represents the prior waveform, n=1,2,…,N, N is the number of snapshots, x(n) and z(n) are the x-array received data and the z-array received data, θ is the azimuth matrix of the prior waveform, φ is the pitch angle matrix of the prior waveform, and γ is the complex amplitude matrix of the prior waveform.

[0047] In one example, the original spatial feature matrix may be constructed based on an L-matrix received data model that takes into account the difference between actual received data and a priori waveforms.

[0048] Previous two-dimensional wave direction of arrival angle estimation methods often directly calculate based on the prior waveform, ignoring the error between the actual received data and the prior waveform, resulting in low estimation accuracy of the two-dimensional wave direction of arrival angle. Constructing an estimated angle vector model through the above-mentioned L-array receiving data model can overcome this problem and improve the estimation accuracy.

[0049] Exemplarily, the L array receiving data model can satisfy the following formula

[0050]

[0051] Among them, y(n) is the L array received data of the receiver, A x (θ), A z (φ) are the x-array manifold matrix and z-array manifold matrix of the two-dimensional wave direction of arrival angle of the prior waveform, 11×K is an all-one vector, K is the total number of signal directions included in all prior waveforms, Γ(γ) represents a block diagonal matrix constructed from the complex amplitude of the prior waveform, s(n) represents the prior waveform, n=1,2,…,N, N is the number of snapshots, w x (n), w z (n), w0(n) represent the noise of x array, the noise of z array and the noise at the reference array element respectively.

[0052] in:

[0053] A x (θ)=[a x (θ1),a x (θ2),…,a x (θ P )]

[0054] A z (φ)=[a z (φ1),a z(φ2),…,a z (φ P )]

[0055] Γ(γ)=blkdiag{γ (1) ,γ (2) ,…,γ (P)}

[0056] s(n)=[s1(n),s2(t),…,s P (n)] T

[0057] in, a x (θ pk ) is the x-array steering vector of the k-th source corresponding to the p-th prior waveform, a z (θ pk ) is the z-matrix steering vector of the k-th source corresponding to the p-th prior waveform, blkdiag represents the building block diagonal matrix, γ pk is the complex amplitude of the kth source corresponding to the pth prior waveform, s p (n) is the pth prior waveform.

[0058] in:

[0059]

[0060] ξ pk =exp(j2πdcosθ pk / λ)

[0061]

[0062] η pk =exp(j2πdcosφ pk / λ)

[0063] Among them, θ pk ,φ pk are respectively the azimuth and elevation angles of the kth source corresponding to the pth prior waveform.

[0064] Exemplarily, the original spatial feature matrix can satisfy the following formula:

[0065]

[0066] Among them, B x (θ,γ),B z (φ,γ) are the original x-matrix spatial feature matrix and the original z-matrix spatial feature matrix respectively.

[0067] Similarly, the x-array received data and the z-array received data can also be obtained according to the L-array received data model, which can satisfy the following formula:

[0068]

[0069] In some embodiments, for the unsolvable problem of underdetermined equations generated when solving the two-dimensional direction of arrival angle according to the estimated angle vector model, the current two-dimensional direction of arrival angle estimation method usually uses a two-dimensional multi-step iterative search method to solve it. The computational complexity of this method is too high and the calculation process is cumbersome. Solving the unsolvable problem of underdetermined equations based on convex optimization functions can reduce the number of iterations and reduce the computational complexity.

[0070] Exemplarily, the first convex optimization function may satisfy the following formula:

[0071]

[0072] Among them, h xp 、h zp are the x-matrix coefficient vector and z-matrix coefficient vector of the p-th prior waveform, respectively. are the estimated angle vectors of the x-matrix and z-matrix of the p-th prior waveform, respectively, p ,φ p are the azimuth vector and pitch angle vector of the pth prior waveform, γ p is the complex amplitude of the pth prior waveform connected to the azimuth measurement matrix of the two-dimensional wave arrival direction angle of interest Pitch measurement matrix of the 2D direction of arrival angle of interest are the x-array manifold matrix and the z-array manifold matrix of the two-dimensional wave arrival direction angle of interest, are the azimuth vector and elevation vector of interest in the two-dimensional direction of arrival, ε xp , ε zp are the x-array error threshold and z-array error threshold of the p-th prior waveform respectively.

[0073] Exemplarily, the two-dimensional direction of arrival angle of interest may include an elevation angle of interest and an azimuth angle of interest, which are respectively possible values ​​of the elevation angle and the azimuth angle of the prior waveform.

[0074] Specifically:

[0075]

[0076] in, is the azimuth measurement angle, i is less than or equal to G x A positive integer, G x is the number of azimuths of interest, is the pitch measurement angle, i′ is less than or equal to G z A positive integer, G z is the number of pitch angles of interest, h xp 、h zp The row number where the non-zero value of and The column numbers are equal

[0077] S2, optimizing and solving the two-dimensional direction of arrival angle of the prior waveform based on the second convex optimization function to solve the grid deviation phenomenon generated when executing step S1, and obtaining the best estimated value of the two-dimensional direction of arrival angle.

[0078] Exemplarily, the optimal two-dimensional direction of arrival angle is a value determined according to the initial estimation value that is closest to the two-dimensional direction of arrival angle of the priori waveform.

[0079] Exemplarily, after performing P iterations on the second convex optimization function, the best estimate of the azimuth angle of the kth prior waveform can be obtained: Its set can be expressed as Get the best estimate of the pitch angle of the kth prior waveform Its set can be expressed as

[0080] In one example, the real manifold matrix can be calculated at the grid point Ne where the azimuth measurement matrix and the elevation measurement matrix are closest to the real angle (i.e., the best two-dimensional wave arrival direction angle) Perform a first-order Taylor expansion, and then construct a second convex optimization function based on the expansion result.

[0081] Exemplarily, the result of the first-order Taylor expansion may satisfy the following formula:

[0082]

[0083] Among them, diag means constructing a diagonal matrix, are the azimuth measurement matrix and elevation measurement matrix of the best two-dimensional direction of arrival angle, for The derivative of for The derivative of .

[0085] in:

[0086]

[0087] in, Represents the estimated azimuth angle of the kth source corresponding to the pth prior waveform at point Ne, Represents the estimated value of the pitch angle of the kth source corresponding to the pth prior waveform at point Ne.

[0088] Exemplarily, the second convex optimization function may satisfy the following formula:

[0089]

[0090] Where p = 1, 2, ..., P, || f xp || 2,1 is the weighted norm of the x submatrix vector, ||f zp || 2,1 is the weighted norm of the z sub-matrix vector, h xp is the x-submatrix angle grid coefficient vector, g xp is the x-submatrix angle grid coefficient vector, h zp is the grid coefficient vector of the z sub-matrix angle, g zp is the z-submatrix angle grid coefficient vector, They are The derivative of g xp , g zp are the coefficient vectors in the x and z directions respectively.

[0091] S3, determining a complex amplitude estimation value of each set of two-dimensional direction of arrival angles according to the best estimation value.

[0092] In one example, the complex amplitude estimate value of each set of two-dimensional direction-of-arrival angles may be determined based on a complex amplitude matching model.

[0093] Exemplarily, the complex amplitude matching model may satisfy the following formula:

[0094]

[0095] in, is the estimated value of the complex amplitude vector of the pth prior waveform, for The x-array manifold matrix, for The submanifold matrix of .

[0096] S4, matching the priori waveform corresponding to the two-dimensional direction of arrival angle according to the complex amplitude estimation value to obtain the best estimation value of the two-dimensional direction of arrival angle of each priori waveform.

[0097] According to the method provided by the present invention, the unsolvable problem of underdetermined equations generated when solving the estimated angle vector model is solved based on a convex optimization function, which can reduce the number of iterations and reduce the computational complexity, so that the method provided by the present invention can be applied to scenarios with high real-time requirements.

[0098] Figure 3 The structure diagram of a two-dimensional direction of arrival estimation device based on an L-array priori waveform source provided by an embodiment of the present invention is shown. As an example but not limitation, the device 300 may include a first optimization solution module 310, a second optimization solution module 320, a complex amplitude solution module 330, and a matching module 340.

[0099] Exemplarily, the first optimization solution module 310 is used to solve the two-dimensional wave direction of arrival angle of the prior waveform based on the first convex optimization function to solve the unsolvable problem of the underdetermined equation generated when solving the two-dimensional wave direction of arrival angle according to the estimated angle vector model, and obtain the initial estimated value of the two-dimensional wave direction of arrival angle, wherein the estimated angle vector model is used to solve the estimated angle vector determined according to the two-dimensional wave direction of arrival angle, and the two-dimensional wave direction of arrival angle includes the azimuth angle and the pitch angle; the second optimization solution module 320 is used to optimize and solve the two-dimensional wave direction of arrival angle of the prior waveform based on the second convex optimization function to solve the grid deviation phenomenon generated when the first optimization solution module 310 is executed, and obtain the best estimated value of the two-dimensional wave direction of arrival angle; the complex amplitude solution module 330 is used to determine the complex amplitude estimated value of each group of two-dimensional wave direction of arrival angle according to the best estimated value; the matching module 340 is used to match the prior waveform corresponding to the two-dimensional wave direction of arrival angle according to the complex amplitude estimated value, and obtain the best estimated value of the two-dimensional wave direction of arrival angle of each prior waveform.

[0100] According to the device provided by the present invention, the unsolvable problem of underdetermined equations generated when solving the estimated angle vector model is solved based on a convex optimization function, which can reduce the number of iterations and reduce the computational complexity, so that the method provided by the present invention can be applied to scenarios with high real-time requirements.

[0101] Figure 4 FIG. 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 4 The electronic device 400 shown may include: at least one processor 410 ( Figure 4 Only one processor is shown in the figure), a memory 420, and a computer program 430 stored in the memory 420 and executable on the at least one processor 410, wherein the processor 410 implements the steps of any of the above-mentioned method embodiments when executing the computer program 430.

[0102] The electronic device 400 may be a processing device such as a robot that can implement the above method. The embodiment of the present invention does not impose any limitation on the specific type of the electronic device.

[0103] Those skilled in the art will understand that Figure 4The electronic device 400 is merely an example and does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 400 may also include an input and output interface.

[0104] The processor 410 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASTC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0105] The memory 420 may be an internal storage unit in some embodiments, such as a hard disk or a memory. The memory 420 may also be an external storage device in other embodiments, such as a plug-in hard disk, a smart memory card (SmartMemory Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Further, the memory 420 may also include both an internal storage unit and an external storage device. The memory 420 is used to store an operating system, an application program, a boot loader (Boot Loader), data, and other programs, such as the program code of the computer program, etc. The memory 420 may also be used to temporarily store data that has been output or is to be output.

[0106] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0107] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0108] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0109] An embodiment of the present invention provides a computer program product. When the computer program product runs on an electronic device, the electronic device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0110] 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 computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include at least: any entity or device that can carry the computer program code to the camera / terminal device, a recording medium, a computer memory, a read-only memory (ROM, Memory), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. For example, USB flash drives, mobile hard disks, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0111] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0112] Those skilled 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, or a combination of computer software and electronic hardware. 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 the present invention.

Claims

1. A two-dimensional direction of arrival estimation method based on L-array prior waveform source, characterized in that: include: S1. Solving the two-dimensional direction of arrival of the prior waveform based on a first convex optimization function to solve the unsolvable problem of the underdetermined equation generated when solving the two-dimensional direction of arrival according to an estimated angle vector model, and obtaining an initial estimate of the two-dimensional direction of arrival, wherein the estimated angle vector model is used to solve the estimated angle vector determined according to the two-dimensional direction of arrival, and the two-dimensional direction of arrival includes an azimuth angle and a pitch angle; S2, optimizing and solving the two-dimensional direction of arrival angle of the priori waveform based on the second convex optimization function to solve the grid deviation phenomenon generated when executing step S1, and obtaining the best estimated value of the two-dimensional direction of arrival angle; S3, determining a complex amplitude estimate of each set of two-dimensional direction of arrival angles according to the best estimate; S4. Match the priori waveform corresponding to the two-dimensional direction of arrival angle according to the complex amplitude estimation value to obtain the best estimation value of the two-dimensional direction of arrival angle of each priori waveform.

2. The method according to claim 1, characterized in that The first convex optimization function satisfies the following formula: Among them, h xp 、h zp are the x-matrix coefficient vector and z-matrix coefficient vector of the p-th prior waveform, respectively. are the x-matrix estimated angle vector and the z-matrix estimated angle vector of the p-th priori waveform, θ p ,φ p are the azimuth vector and pitch angle vector of the pth prior waveform, γ p is the complex amplitude of the pth prior waveform, the azimuth measurement matrix of the two-dimensional wave arrival direction angle of interest The elevation measurement matrix of the two-dimensional direction of arrival angle of interest Wherein, the two-dimensional direction of arrival angle of interest is a possible value of the two-dimensional direction of arrival angle; are the x-array manifold matrix and the z-array manifold matrix of the two-dimensional wave arrival direction angle of interest, respectively, are respectively the azimuth vector and elevation vector of interest in the two-dimensional direction of arrival angle of interest, ε xp , ε zp are respectively the x-array error threshold and the z-array error threshold of the p-th priori waveform.

3. The method according to claim 2, characterized in that The second convex optimization function satisfies the following formula: Where p = 1, 2, ..., P, || f xp || 2,1 is the weighted norm of the x submatrix vector, ||f zp || 2,1 is the weighted norm of the z sub-matrix vector, h xp is the x-submatrix angle grid coefficient vector, g xp is the x-submatrix angle grid coefficient vector, h zp is the grid coefficient vector of the z sub-matrix angle, g zp is the z-submatrix angle grid coefficient vector, They are The derivative of g xp , g zp are the coefficient vectors in the x and z directions respectively.

4. The method according to claim 1, characterized in that The estimated angle vector model is obtained by solving the original spatial feature matrix after dimensionality reduction based on the maximum likelihood algorithm. The original spatial feature matrix after dimensionality reduction satisfies the following formula: in: in, They are respectively the original x-array spatial domain feature matrix after dimensionality reduction and the original z-array spatial domain feature matrix after dimensionality reduction, s(n) represents the prior waveform, n=1,2,…,N, N is the number of snapshots, x(n) and z(n) are the x-array received data and the z-array received data, respectively.

5. The method according to claim 4, characterized in that The original spatial feature matrix is ​​constructed based on the L-array receiving data model, which satisfies the following formula: Among them, y(n) is the L array received data of the receiver, A x (θ), A z (φ) represents the x-array manifold matrix and the z-array manifold matrix of the two-dimensional wave arrival direction angle, θ and φ represent the azimuth matrix and the elevation matrix of the prior waveform, respectively. 1×K is the unit matrix, K is the total number of signal directions included in all prior waveforms, Γ(γ) represents a block diagonal matrix constructed from the complex amplitude of the prior waveform, s(n) represents the prior waveform, n = 1, 2, ..., N, N is the number of snapshots, w x (n), w z (n), w0(n) represent the noise of x array, the noise of z array and the noise at the reference array element respectively.

6. The method according to claim 1, characterized in that The step of determining the complex amplitude estimation value of each group of two-dimensional direction of arrival angles according to the best estimation value comprises: Based on the complex amplitude matching model, determining the complex amplitude estimation value of each group of two-dimensional direction of arrival angles according to the best estimation value; The complex amplitude matching model satisfies the following formula: in, is the estimated value of the complex amplitude vector of the pth prior waveform, 1 1×Kp is the identity matrix, K p is the number of signal directions included in the pth prior waveform, for The x-array manifold matrix, is composed of the best estimate of the azimuth angle of the pth prior waveform, Based on Determine the estimated angle vector of the x matrix of the pth prior waveform, θ p represents the azimuth vector of the Pth prior waveform, γ p represents the complex amplitude vector of the pth prior waveform, for The z-matrix matrix, φ p represents the pitch angle vector of the pth prior waveform, is φ p The best estimate of Based on The z-matrix estimated angle vector of the p-th priori waveform is determined.

7. A two-dimensional direction of arrival estimation device based on L-array priori waveform signal source, characterized in that: include: A first optimization solution module, the first optimization solution module is used to solve the two-dimensional wave direction of arrival angle of the prior waveform based on a first convex optimization function to solve the unsolvable problem of the underdetermined equation generated when the two-dimensional wave direction of arrival angle is solved according to the estimated angle vector model, and obtain an initial estimated value of the two-dimensional wave direction of arrival angle, wherein the estimated angle vector model is used to solve the estimated angle vector determined according to the two-dimensional wave direction of arrival angle, and the two-dimensional wave direction of arrival angle includes an azimuth angle and a pitch angle; A second optimization solution module, the second optimization solution module is used to optimize and solve the two-dimensional wave direction of arrival angle of the priori waveform based on a second convex optimization function to solve the grid deviation phenomenon generated when executing the first optimization solution module, and obtain the best estimated value of the two-dimensional wave direction of arrival angle; A complex amplitude solving module, the complex amplitude solving module is used to determine the complex amplitude estimation value of each group of two-dimensional wave arrival direction angles according to the best estimation value; A matching module is used to match the priori waveform corresponding to the two-dimensional wave direction of arrival angle according to the complex amplitude estimation value to obtain the best estimation value of the two-dimensional wave direction of arrival angle of each priori waveform.

8. An electronic device comprising a memory, a processor and a computer program stored in the memory, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the electronic device, the method according to any one of claims 1 to 6 is implemented.

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