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

Abstract

The application discloses a two-dimensional direction of arrival estimation method based on L-array prior waveform signal source, comprising the following steps: solving the two-dimensional direction of arrival angle of the prior waveform based on a first convex optimization function to solve the underdetermined equation insoluble problem generated when solving the two-dimensional direction of arrival angle according to an estimated angle vector model, and obtaining an initial estimated value of the two-dimensional direction of arrival angle; solving the two-dimensional direction of arrival angle of the prior waveform based on a second convex optimization function to solve the off-lattice phenomenon generated when executing the above step, and obtaining an optimal estimated value of the two-dimensional direction of arrival angle; determining the complex amplitude estimated value of each group of two-dimensional direction of arrival angle according to the optimal estimated value; and matching the prior waveform corresponding to the two-dimensional direction of arrival angle according to the complex amplitude estimated value, and obtaining the optimal estimated value of the two-dimensional direction of arrival angle of each prior waveform. The application has a smaller calculation complexity and can be applied to a scene with a higher real-time requirement.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of radar signal processing, and particularly relates to a two-dimensional wave direction estimation method based on an L-array prior waveform signal source. BACKGROUND

[0002] The source wave direction estimation is an important part of radar signal processing, and its application scenarios mainly include two kinds. One is the application on a fixed-position reconnaissance device. In order to realize high-precision direction finding in a simultaneous multi-signal electromagnetic environment, the passive direction finding subsystem of the reconnaissance device must select a large number of antennas to form a large array for direction finding. If the traditional array direction finding algorithm is applied to the passive direction finding subsystem with too many antennas, the calculation complexity is too high, which is not conducive to real-time signal processing. The other is the application on a fast-moving reconnaissance device. In order to improve the anti-interference ability of the reconnaissance device, the reconnaissance device must have the ability of direction finding in a simultaneous multi-signal electromagnetic environment. Due to the limited size of the fast-moving reconnaissance device, the number of array elements on the device is small, and the moving speed is fast, so the direction finding time and the direction finding accuracy are required to be high. The current source wave direction estimation cannot meet the requirement of the direction finding time.

[0003] Therefore, the current source wave direction estimation method has a high calculation complexity, so its application scenarios are limited and cannot be applied to the two kinds of devices. SUMMARY

[0004] The embodiment of the application provides a two-dimensional wave direction estimation method based on an L-array prior waveform signal source, which can solve the problem that the current source wave direction estimation method has a high calculation complexity and its application scenarios are limited.

[0005] In a first aspect, the embodiment of the application provides a two-dimensional wave direction estimation method based on an L-array prior waveform signal source, which comprises the following steps: S1, solving the two-dimensional wave direction angle of a prior waveform based on a first convex optimization function, so as to solve the underdetermined equation insolvable problem caused by solving the two-dimensional wave direction angle according to an estimated angle vector model, and obtaining an initial estimated value of the two-dimensional wave direction angle, wherein the estimated angle vector model is used to solve an estimated angle vector determined according to the two-dimensional wave direction angle, and the two-dimensional wave direction angle comprises an azimuth angle and an elevation angle;

[0006] S2, optimizing and solving the two-dimensional wave direction angle of the prior waveform based on a second convex optimization function, so as to solve the off-lattice phenomenon caused by step S1, and obtaining an optimal estimated value of the two-dimensional wave direction angle;

[0007] S3, determining a complex amplitude estimated value of each group of two-dimensional wave direction angles according to the optimal estimated value;

[0008] S4, matching the prior 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 prior waveform.

[0009] In a second aspect, an embodiment of the present application provides a two-dimensional direction of arrival estimation method and device based on an L-array prior waveform signal source, comprising:

[0010] A first optimization solving module is configured to solve the two-dimensional direction of arrival angle of a prior waveform based on a first convex optimization function, to solve an underdetermined equation insolvable problem caused by solving the two-dimensional direction of arrival angle according to an estimated angle vector model, to obtain an initial estimation value of the two-dimensional direction of arrival angle, wherein the estimated angle vector model is configured to solve an estimated angle vector determined according to the two-dimensional direction of arrival angle, and the two-dimensional direction of arrival angle comprises an azimuth angle and an elevation angle.

[0011] A second optimization solving module is configured to optimize the two-dimensional direction of arrival angle of the prior waveform based on a second convex optimization function, to solve a gridding-out phenomenon caused by the execution of the first optimization solving module, to obtain a best estimation value of the two-dimensional direction of arrival angle.

[0012] A complex amplitude solving module is configured to determine a complex amplitude estimation value of each group of two-dimensional direction of arrival angles according to the best estimation value.

[0013] A matching module is configured to match the prior 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 prior waveform.

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

[0015] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and when the computer program is executed, the method of the first aspect can be implemented.

[0016] It can be understood that the beneficial effects of the second aspect to the fourth aspect can be referred to the related description of the first aspect, and will not be repeated here.

[0017] Compared with the prior art, the embodiment of the present application has the following beneficial effects. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1A structural schematic diagram of an L-array receiver provided for an embodiment of the present application is shown in FIG. 1.

[0019] Figure 2 An implementation flowchart of a two-dimensional direction of arrival estimation method based on an L-array prior waveform source provided for an embodiment of the present application is shown in FIG. 2.

[0020] Figure 3 A structural schematic diagram of a two-dimensional direction of arrival estimation device based on an L-array prior waveform source provided for an embodiment of the present application is shown in FIG. 3.

[0021] Figure 4 A structural schematic diagram of an electronic device provided for an embodiment of the present application is shown in FIG. 4. DETAILED DESCRIPTION

[0022] In the following description, for the purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc., in order to provide a thorough understanding of the embodiments of the application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

[0023] It is to be understood that the terminology "includes", "has", "holds", "contains" and / or "comprising", when used in the present specification and in the accompanying claims, means the inclusion of but not limited to, such that there might be additional items which are not included.

[0024] It is also to be understood that the terminology "and / or" when used in the present specification and in the accompanying claims, means that there is at least one of the items, or any combination of the items, which are recited.

[0025] As used in the present specification and in the accompanying claims, the term "if" can be interpreted as meaning "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 [a described condition or event] is detected" can be interpreted to mean "upon determining" or "in response to determining" or "upon detecting [the described condition or event]" or "in response to detecting [the described condition or event]", depending on the context.

[0026] In addition, the terms "first", "second", "third", etc. are used herein only to describe different instances of the same item, and are not to be construed as indicating or implying relative importance of the items.

[0027] References to "one embodiment" or "some embodiments" in the present specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present invention. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

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

[0029] Figure 1 FIG2 shows 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 can 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 pitch angle of the kth signal 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 received data of a 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 laptops, supercomputers, etc. The embodiment of the present invention does not impose any restrictions on the specific type of electronic devices.

[0035] Figure 2 Fig. 1 shows a flowchart of a method for estimating a two-dimensional direction of arrival of a signal source based on a L-matrix prior waveform according to an embodiment of the present application. By way of example and without limitation, the method can be implemented in an electronic device as described above. The method can include steps S1-S4, which are described below.

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

[0037] In some embodiments, the estimated angle vector model can be used to solve an estimated angle vector determined according to a two-dimensional direction of arrival angle of a prior waveform.

[0038] For example, the estimated angle vector model can satisfy the following formula:

[0039]

[0040] wherein, are an x-matrix estimated angle vector and a z-matrix estimated angle vector of the pth prior waveform, θ p is an azimuth angle vector of the pth prior waveform, φ p is an elevation angle vector of the pth prior waveform, γ p is a complex amplitude vector of the pth prior waveform, is an identity matrix, K p is a number of signal directions included in the pth prior waveform, A x (θ p ), A z (φ p ) are an x-matrix submanifold matrix and a z-matrix submanifold matrix of the two-dimensional direction of arrival angle of the pth prior waveform, respectively, Δb xp (θ p , γ p ), Δb zp (φ p , γ p ) are errors of the real values corresponding thereto, respectively.

[0041] In one possible implementation, the estimated angle vector model can be obtained by solving a reduced dimension original spatial feature matrix based on a maximum likelihood algorithm.

[0042] For example, the reduced dimension original spatial feature matrix can satisfy the following formula:

[0043] ​

[0044] wherein:

[0045]

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

[0047] In one example, the original spatial feature matrix can be constructed according to an L-array received data model considering the difference between actual received data and the prior waveform.

[0048] The prior two-dimensional direction of arrival angle estimation method often directly calculates based on the prior waveform, ignores the error between the actual received data and the prior waveform, and results in low estimation accuracy of the two-dimensional direction of arrival angle. The estimation angle vector model constructed by the above L-array received data model can overcome this problem and improve the estimation accuracy.

[0049] For example, the L-array received data model can satisfy the following formula

[0050]

[0051] wherein, y(n) is L-array received data of the receiver, A x (θ), A z (φ) are x-array submanifold matrix and z-array submanifold matrix of the two-dimensional direction of arrival angle of the prior waveform respectively, 1 1 × K is a full 1 vector, K is the total number of signal directions included by all prior waveforms, Γ(γ) represents a block diagonal matrix constructed by 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) respectively represent the noise of the x-array, the noise of the z-array, and the noise at the reference array element.

[0052] wherein:

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

[0054] A z (φ) = [a z (φ1), a za 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] wherein, a x (θ pk ) is the x-array steering vector of the kth source corresponding to the pth prior waveform, a z (θ pk ) is the z-array steering vector of the kth source corresponding to the pth prior waveform, blkdiag represents constructing a 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] wherein:

[0059]

[0060] ξ pk =exp(j2πdcosθ pk / λ)

[0061]

[0062] η pk =exp(j2πdcosφ pk / λ)

[0063] wherein, θ pk , φ pk are the azimuth angle and the elevation angle of the kth source corresponding to the pth prior waveform.

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

[0065]

[0066] wherein, B x (θ, γ), B z (φ, γ) are the original x-array spatial feature matrix and the original z-array 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 underdetermined equation unsolvable problem 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 solves it by using a two-dimensional multi-step iterative search method, and the calculation complexity of this method is too high, and the calculation process is complicated. Based on the convex optimization function to solve the underdetermined equation unsolvable problem, the number of iterations can be reduced, and the calculation complexity can be reduced.

[0070] For example, the first convex optimization function can satisfy the following formula:

[0071]

[0072] Wherein, h xp , h zp are the x-array coefficient vector and the z-array coefficient vector of the pth prior waveform, are the x-array estimated angle vector and the z-array estimated angle vector of the pth prior waveform, θ p , φ p are the azimuth angle vector and the elevation angle vector of the pth prior waveform, γ p is the complex amplitude of the pth prior waveform, and the azimuth measurement matrix of the two-dimensional direction of arrival angle of interest the elevation measurement matrix of the two-dimensional direction of arrival angle of interest are the x-array submanifold matrix and the z-array submanifold matrix of the two-dimensional direction of arrival angle of interest, are the interested azimuth angle vector and the interested elevation angle vector in the two-dimensional direction of arrival angle of interest, ε xp , ε zp are the x-array error threshold and the z-array error threshold of the pth prior waveform.

[0073] For example, the two-dimensional direction of arrival angle of interest can include the interested elevation angle and the interested azimuth angle, which are the possible values of the elevation angle and the azimuth angle of the prior waveform.

[0074] Specifically:

[0075]

[0076] Wherein, is the azimuth measurement angle, i is a positive integer less than or equal to G x , and G x is the number of interested azimuth angles, 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 resolve 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 based on the initial estimation value that is closest to the two-dimensional direction of arrival angle of the priori waveform.

[0079] For example, 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 direction of arrival angle) Perform a first-order Taylor expansion, and then construct a second convex optimization function based on the expansion result.

[0081] For example, the result of the first-order Taylor expansion can satisfy the following formula:

[0082]

[0083] Among them, diag means constructing a diagonal matrix,

[0084] are the azimuth measurement matrix and elevation measurement matrix of the optimal two-dimensional direction of arrival angle, for The derivative of for The derivative of .

[0085] in:

[0086]

[0087] in, Indicates the estimated azimuth angle of the kth source corresponding to the pth prior waveform at point Ne, The p-th prior waveform corresponding to the k-th source of the Ne points.

[0088] The second convex optimization function satisfies the following formula:

[0089]

[0090] Wherein, p = 1, 2, …, P, ||f xp || 2,1 The x-subarray vector weighted norm is ||f zp || 2,1 The z-subarray vector weighted norm is h xp The x-subarray angle grid point coefficient vector is g xp The x-subarray angle off-grid coefficient vector is h zp The z-subarray angle grid point coefficient vector is g zp The z-subarray angle off-grid coefficient vector is h The derivative of g is g xp , g zp The coefficient vectors in the x and z directions are respectively.

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

[0092] In an example, the complex amplitude estimation value of each group of two-dimensional direction of arrival angles can be determined based on a complex amplitude matching model.

[0093] The complex amplitude matching model satisfies the following formula:

[0094]

[0095] Wherein, The estimation value of the complex amplitude vector of the p-th prior waveform is g The x-subarray manifold matrix of g The submanifold matrix of g The submanifold matrix of g

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

[0097] According to the method provided by the application, the underdetermined equation unsolvable problem caused by solving the estimated angle vector model is solved based on the convex optimization function, the number of iterations is reduced, the calculation complexity is reduced, and thus the method provided by the application can be applied to scenes with higher real-time requirements.

[0098] ​Figure 3 Fig. 1 shows a structural schematic diagram of a device for two-dimensional direction of arrival estimation based on L-matrix prior waveform source according to an embodiment of the present application. As an example but not limitation, the device 300 can include a first optimization solving module 310, a second optimization solving module 320, a complex amplitude solving module 330, and a matching module 340.

[0099] For example, the first optimization solving module 310 is configured to solve the two-dimensional direction of arrival angle of the prior waveform based on a first convex optimization function, to solve the underdetermined equation insolvable problem when solving the two-dimensional direction of arrival angle according to an estimated angle vector model, to obtain an initial estimated value of the two-dimensional direction of arrival angle, wherein the estimated angle vector model is configured to solve an estimated angle vector determined according to the two-dimensional direction of arrival angle, and the two-dimensional direction of arrival angle includes an azimuth angle and an elevation angle; the second optimization solving module 320 is configured to solve the two-dimensional direction of arrival angle of the prior waveform based on a second convex optimization function, to solve the off-lattice phenomenon generated when the first optimization solving module 310 is executed, to obtain an optimal estimated value of the two-dimensional direction of arrival angle; the complex amplitude solving module 330 is configured to determine a complex amplitude estimated value of each group of two-dimensional direction of arrival angle according to the optimal estimated value; and the matching module 340 is configured to match the prior waveform corresponding to the two-dimensional direction of arrival angle according to the complex amplitude estimated value, to obtain the optimal estimated value of the two-dimensional direction of arrival angle of each prior waveform.

[0100] According to the device provided by the present application, the underdetermined equation insolvable problem when solving the estimated angle vector model is solved based on the convex optimization function, the number of iterations can be reduced, the calculation complexity can be reduced, and thus the method provided by the present application can be applied to a scene with higher real-time requirement.

[0101] Figure 4 Fig. 4 shows a structural schematic diagram of an electronic device according to an embodiment of the present application. As an example but not limitation, the electronic device 400 can include at least one processor 410 (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 in any of the method embodiments described above when executing the computer program 430. Figure 4 Figure 4 The electronic device 400 can be a processing device such as a robot, which can implement the above method, and the embodiments of the present application do not make any limitation on the specific type of the electronic device.

[0102] The electronic device 400 can be a processing device such as a robot, which can implement the above method, and the embodiments of the present application do not make any limitation on the specific type of the electronic device.

[0103] Those skilled in the art can understand that Figure 4 ​The electronic device 400 is merely an example and does not limit the electronic device, which can include more or less components than shown, or combine some components, or have different components. For example, the electronic device 400 can also include an input / output interface.

[0104] The processor 410 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

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

[0106] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0107] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is exemplified, and in actual application, the above-mentioned functions can be completed by different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or software functional unit. In addition, the specific names of each functional unit and module are only for easy distinction, and do not limit the protection scope of the present application. The specific working process of the unit and module in the above system can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0108] The embodiment of the present application further provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps in each method embodiment.

[0109] The embodiment of the present application provides a computer program product, when the computer program product runs on an electronic device, so that the electronic device executes to realize the steps in each method embodiment.

[0110] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the present application realizes all or part of the processes in the above-mentioned embodiment methods, which can be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can realize the steps in each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable file or some intermediate form. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / terminal equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc. In some jurisdictions, according to legislation and patent practice, the computer readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0111] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0112] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

Claims

1. A two-dimensional direction of arrival estimation method based on L-matrix prior waveform source, characterized in that, The method comprises: S1. Solving the two-dimensional direction of arrival angle of the prior waveform based on a first convex optimization function to solve the underdetermined equation unsolvable problem generated when solving the two-dimensional direction of arrival angle according to an estimated angle vector model, to obtain an initial estimated value of the two-dimensional direction of arrival angle, wherein the estimated angle vector model is used to solve an estimated angle vector determined according to the two-dimensional direction of arrival angle, and the two-dimensional direction of arrival angle comprises an azimuth angle and an elevation angle; S2. Optimally solving the two-dimensional direction of arrival angle of the prior waveform based on a second convex optimization function to solve the off-grid phenomenon generated when performing step S1, to obtain an optimal estimated value of the two-dimensional direction of arrival angle; S3. Determining a complex amplitude estimated value of each group of two-dimensional direction of arrival angles according to the optimal estimated value; S4. Matching the prior waveform corresponding to the two-dimensional direction of arrival angle according to the complex amplitude estimated value, to obtain an optimal estimated value of the two-dimensional direction of arrival angle of each prior waveform.

2. The method of claim 1, wherein, The first convex optimization function satisfies the following formula: in, 、 Respectively p A priori waveform x Matrix coefficient vector, z Matrix coefficient vector, 、 They are respectively p A priori waveform x Matrix estimated angle vector, z Matrix estimated angle vector, 、 Respectively p The azimuth and elevation vectors of the prior waveforms, For the p The complex amplitude of the prior waveform, the azimuth measurement matrix of the two-dimensional direction of arrival 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 two-dimensional direction of arrival angles of interest, x array manifold matrix and z The array manifold matrix, 、 are respectively the azimuth angle vector of interest and the elevation angle vector of interest in the two-dimensional direction of arrival angle of interest, 、 They are respectively p A priori waveform x Array error threshold, z Array error threshold.

3. The method of claim 2, wherein, The second convex optimization function satisfies the following formula: wherein , is x subarray vector weighted norm, is the z subarray vector weighted norm, is x subarray angle grid point coefficient vector, is x subarray angle off-grid coefficient vector, is z subarray angle grid point coefficient vector, is z subarray angle off-grid coefficient vector, , are , derivatives of , are x , z coefficient vectors in the directions 4. The method of claim 1, wherein, The estimated angle vector model is obtained by solving a reduced original spatial domain feature matrix based on a maximum likelihood algorithm, and the reduced original spatial domain feature matrix satisfies the following formula: Wherein: wherein, , are the original x matrix spatial feature matrix and the original z matrix spatial feature matrix after dimension reduction, respectively, denotes the prior waveform, N is the number of snapshots, , are the x matrix received data and the z matrix received data, respectively.

5. The method of claim 4, wherein, The original spatial domain feature matrix is constructed according to an L matrix reception data model, and the L matrix reception data model satisfies the following formula: in, The L array of the receiver receives data, 、 Respectively represent the two-dimensional direction of arrival angle x array manifold matrix and z The array manifold matrix, 、 represent the azimuth matrix and elevation matrix of the prior waveform respectively, is the unit matrix, is the total number of signal directions included in all prior waveforms, represents the block diagonal matrix constructed from the complex amplitudes of the prior waveform, represents the prior waveform, , N is the number of snapshots, 、 , Respectively x Array noise, z The noise of the array and the noise at the reference element.

6. The method of claim 1, wherein, The method further comprises: Determining a complex amplitude estimated value of each group of two-dimensional direction of arrival angles according to the optimal estimated value based on a complex amplitude matching model; Wherein, the complex amplitude matching model satisfies the following formula: wherein is an estimate of the complex amplitude vector of the p th prior waveform, is an identity matrix, is the number of signal directions included in the p th prior waveform, is is the x submanifold matrix of is composed of the best estimate of the azimuth angle of the p th prior waveform, is the submanifold estimate angle vector of the p th prior waveform determined according to x is the azimuth angle vector of the th prior waveform, P is the complex amplitude vector of the th prior waveform, p is the submanifold matrix of is the elevation angle vector of the z th prior waveform, is the best estimate of p is the submanifold estimate angle vector of the th prior waveform determined according to is the submanifold estimate angle vector of the p th prior waveform determined according to z is the 7. A two-dimensional direction of arrival estimation device based on L-matrix prior waveform source, characterized in that, The method comprises: A first optimization solving module, which is configured to solve the two-dimensional direction of arrival angle of the prior waveform based on a first convex optimization function to solve the underdetermined equation unsolvable problem generated when solving the two-dimensional direction of arrival angle according to an estimated angle vector model, to obtain an initial estimated value of the two-dimensional direction of arrival angle, wherein the estimated angle vector model is used to solve an estimated angle vector determined according to the two-dimensional direction of arrival angle, and the two-dimensional direction of arrival angle comprises an azimuth angle and an elevation angle; A second optimization solving module, which is configured to optimally solve the two-dimensional direction of arrival angle of the prior waveform based on a second convex optimization function to solve the off-grid phenomenon generated when performing the first optimization solving module, to obtain an optimal estimated value of the two-dimensional direction of arrival angle; A complex amplitude solving module, which is configured to determine a complex amplitude estimated value of each group of two-dimensional direction of arrival angles according to the optimal estimated value; A matching module, which is configured to match the prior waveform corresponding to the two-dimensional direction of arrival angle according to the complex amplitude estimated value, to obtain an optimal estimated value of the two-dimensional direction of arrival angle of each prior waveform.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method of any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the electronic device to implement the method of any one of claims 1-6.

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