L-shaped Array Signal Processing Method, System, Readable Storage Medium and Computer

Abstract

The present invention provides an L-shaped array signal processing method, system, readable storage medium and computer. The method includes: constructing an L-shaped array and dividing the L-shaped array into multiple overlapping sub-arrays in the X and Y directions; merging the data of each of the overlapping sub-arrays to form a sixth-order tensor, and performing tensor expansion on the sixth-order tensor to obtain a corresponding eighth-order tensor; recombining the eighth-order tensor to obtain a virtual array with a preset degree of freedom, and performing tensor decomposition on the virtual array to obtain a factor matrix of the virtual array; obtaining a plurality of signal direction parameters from the factor matrix of the virtual array, and performing signal processing on each of the signal direction parameters to obtain corresponding two-dimensional target azimuth angles of the signals. The present invention fully exploits the structural information contained in the array signals to improve the potential of the array degree of freedom and parameter estimation accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of array signal processing, and particularly relates to an L-shaped array signal processing method, system, readable storage medium and computer. Background Art

[0002] A segmented array of discrete electromagnetic vector sensors is composed of two uniform linear arrays of discrete electromagnetic vector sensors with a coprime relationship between the adjacent element spacings of two segments. First, the ESPRIT algorithm is used to obtain the array manifold of a single discrete electromagnetic vector sensor array and the signal DOA with angle ambiguity, and then the coprime relationship and the characteristics of obtaining the signal DOA by a single discrete electromagnetic vector sensor are used to eliminate the angle ambiguity, so as to obtain the accurate signal DOA.

[0003] The array structure of the prior art only expands the one-dimensional aperture, and the aperture in the other dimension is small, so it is difficult to obtain high-precision two-dimensional signal DOA. It uses the ESPRIT algorithm for some elements in the array respectively to implement signal parameter estimation, and the ESPRIT algorithm itself limits the estimation accuracy of signal parameters to a certain extent. Summary of the Invention

[0004] Based on this, the purpose of the present invention is to provide an L-shaped array signal processing method, system, readable storage medium and computer to at least solve the deficiencies in the above related technologies.

[0005] The present invention provides an L-shaped array signal processing method, including:

[0006] Construct an L-shaped array, and divide the L-shaped array into multiple overlapping sub-linear arrays in the X and Y directions;

[0007] Merge the data of each overlapping sub-linear array to form a sixth-order tensor, and perform tensor expansion on the sixth-order tensor to obtain a corresponding eighth-order tensor;

[0008] Recombine the eighth-order tensor to obtain a virtual array with a preset degree of freedom, and perform tensor decomposition on the virtual array to obtain the factor matrix of the virtual array;

[0009] Obtain multiple signal direction parameters from the factor matrix of the virtual array, and perform signal processing on each signal direction parameter to obtain the corresponding two-dimensional signal target azimuth angle.

[0010] Further, the L-shaped array is an M-element uniform linear array located on the X-axis and Y-axis respectively, and the element spacing is .

[0011] Further, the steps of merging the data of each of the overlapping sub-arrays to form a sixth-order tensor and performing tensor expansion on the sixth-order tensor to obtain a corresponding eighth-order tensor include:

[0012] Obtain the received signal data of each of the overlapping sub-arrays, and merge the received signal data to form a sixth-order tensor;

[0013] Perform tensor expansion on the sixth-order tensor to obtain a corresponding eighth-order tensor.

[0014] Further, the steps of recombining the eighth-order tensor to obtain a virtual array with a preset degree of freedom and performing tensor decomposition on the virtual array to obtain the factor matrices of the virtual array include:

[0015] Combine several dimensions in the eighth-order tensor one by one to obtain the received tensor of a virtual array with a preset degree of freedom;

[0016] Perform tensor decomposition on the received tensor of the virtual array to obtain three factor matrices of the received tensor of the virtual array.

[0017] Further, the three factor matrices include a first matrix, a second matrix, and a third matrix. The steps of obtaining multiple signal direction parameters from the factor matrices of the virtual array include:

[0018] Obtain a first signal direction cosine and a second signal direction cosine from the first matrix and the third matrix, where both the first signal direction cosine and the second signal direction cosine are signal polarization parameters with a first precision and no angle ambiguity;

[0019] Obtain a third signal direction cosine and a fourth signal direction cosine from the second matrix, where both the third signal direction cosine and the fourth signal direction cosine are signal parameters with a second precision and angle ambiguity, and the second precision is higher than the first precision.

[0020] Further, the steps of performing signal processing on each of the signal direction parameters to obtain a corresponding two-dimensional signal target azimuth angle include:

[0021] Use the first signal direction cosine and the second signal direction cosine as references to eliminate the angle ambiguity in the third signal direction cosine and the fourth signal direction cosine to obtain a two-dimensional signal target azimuth angle with a second precision and no angle ambiguity.

[0022] The present invention also proposes an L-shaped array signal processing system, including:

[0023] An array construction module for constructing an L-shaped array and dividing the L-shaped array into multiple overlapping sub-arrays in the X and Y directions;

[0024] A tensor processing module, configured to merge data of each of the overlapping sub-arrays to form a sixth-order tensor, and perform tensor expansion on the sixth-order tensor to obtain a corresponding eighth-order tensor;

[0025] A tensor decomposition module, configured to recombine the eighth-order tensor to obtain a virtual array with a preset degree of freedom, and perform tensor decomposition on the virtual array to obtain a factor matrix of the virtual array;

[0026] A signal processing module, configured to obtain a plurality of signal direction parameters from the factor matrix of the virtual array, and perform signal processing on each of the signal direction parameters to obtain corresponding two-dimensional signal target azimuth angles.

[0027] Further, the tensor processing module includes:

[0028] A data merging unit, configured to obtain received signal data of each of the overlapping sub-arrays, and merge the received signal data to form a sixth-order tensor;

[0029] A tensor expansion unit, configured to perform tensor expansion on the sixth-order tensor to obtain a corresponding eighth-order tensor.

[0030] Further, the tensor decomposition module includes:

[0031] A tensor combination unit, configured to combine several dimensions in the eighth-order tensor one by one to obtain a received tensor of a virtual array with a preset degree of freedom;

[0032] A tensor decomposition unit, configured to perform tensor decomposition on the received tensor of the virtual array to obtain three factor matrices of the received tensor of the virtual array.

[0033] Further, the three factor matrices include a first matrix, a second matrix, and a third matrix, and the signal processing module includes:

[0034] A first signal processing unit, configured to obtain a first signal direction cosine and a second signal direction cosine from the first matrix and the third matrix, where both the first signal direction cosine and the second signal direction cosine are signal polarization parameters with a first precision and without angle ambiguity;

[0035] A second signal processing unit, configured to obtain a third signal direction cosine and a fourth signal direction cosine from the second matrix, where both the third signal direction cosine and the fourth signal direction cosine are signal parameters with a second precision and with angle ambiguity, and the second precision is higher than the first precision.

[0036] Further, the signal processing module further includes:

[0037] A third signal processing unit, configured to use the first signal direction cosine and the second signal direction cosine as references to eliminate the angle ambiguity in the third signal direction cosine and the fourth signal direction cosine, so as to obtain a two-dimensional target azimuth angle of the signal with a second precision and without angle ambiguity.

[0038] The present invention also provides a readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned L-shaped array signal processing method is implemented.

[0039] The present invention also provides a computer, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the above-mentioned L-shaped array signal processing method is implemented.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows: using tensors to process the L-shaped array, fully exploiting the structural information contained in the array signals to improve the potential of the array degrees of freedom and parameter estimation accuracy; studying and processing the structure and data of the array signals with the help of tensor algebra theory, having strong detection stability, good system stability, and high resolution ability; solving the problem of angle ambiguity in the parameter estimation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flowchart of the L-shaped array signal processing method in the first embodiment of the present invention;

[0042] Figure 2 is Figure 1 a detailed flowchart of step S102 in

[0043] Figure 3 is Figure 1 a detailed flowchart of step S103 in

[0044] Figure 4 is Figure 1 a detailed flowchart of step S104 in

[0045] Figure 5 is a structural block diagram of the L-shaped array signal processing system in the second embodiment of the present invention;

[0046] Figure 6 is a structural block diagram of the computer in the third embodiment of the present invention.

[0047] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0049] It should be noted that when an element is referred to as being "fixedly provided on" another element, it can be directly on the other element or there may also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0051] Embodiment 1

[0052] Please refer to Figure 1 , which shows the L-shaped array signal processing method in the first embodiment of the present invention. The method specifically includes steps S101 to S104:

[0053] S101, construct an L-shaped array, and divide the L-shaped array into multiple overlapping sub-arrays in the X and Y directions;

[0054] In a specific implementation, construct an L-shaped array, which are M-element uniform linear arrays located on the X-axis and Y-axis respectively, with an element spacing of , and there are K far-field narrowband uncorrelated signals incident on the array with a two-dimensional angle of arrival , and the power is , then the array steering matrix of the th sub-array in the X-axis direction is:

[0055] ;

[0056] Furthermore, split the uniform linear array in the X direction into overlapping sub-arrays of each size of , and do the same in the Y direction, so as to obtain multiple overlapping sub-arrays.

[0057] S102, perform data merging on each of the overlapping sub-arrays to form a sixth-order tensor, and perform tensor expansion on the sixth-order tensor to obtain the corresponding eighth-order tensor;

[0058] Further, please refer to Figure 2 , the step S102 specifically includes steps S1021 to S1022:

[0059] S1021, obtain the received signal data of each of the overlapping sub-arrays, and merge the received signal data to form a sixth-order tensor;

[0060] S1022, perform tensor unfolding on the sixth-order tensor to obtain the corresponding eighth-order tensor.

[0061] In specific implementation, merge the received signal data of the overlapping sub-arrays obtained above into a sixth-order tensor ;

[0062] ;

[0063] ;

[0064] ;

[0065] Specifically, perform tensor unfolding on the sixth-order tensor to obtain an eighth-order tensor :

[0066] .

[0067] S103, recombine the eighth-order tensor to obtain a virtual array with a preset degree of freedom, and perform tensor decomposition on the virtual array to obtain the factor matrix of the virtual array;

[0068] Further, please refer to Figure 3 , the step S103 specifically includes steps S1031 to S1032:

[0069] S1031, combine several dimensions in the eighth-order tensor one by one to obtain the received tensor of a virtual array with a preset degree of freedom;

[0070] S1032, perform tensor decomposition on the received tensor of the virtual array to obtain three factor matrices of the received tensor of the virtual array.

[0071] In specific implementation, combine several dimensions in the above eighth-order tensor one by one to obtain the received tensor of a virtual array with a high degree of freedom .

[0072] ;

[0073] For the received tensor of the above virtual array Perform tensor decomposition to obtain the three factor matrices of the receiving tensor of the virtual array , , :

[0074] .

[0075] S104, obtaining a plurality of signal direction parameters from the factor matrix of the virtual array, and performing signal processing on each of the signal direction parameters to obtain a corresponding signal two-dimensional target azimuth.

[0076] For further information, see Figure 4 , the three factor matrices include a first matrix, a second matrix and a third matrix, and the step S104 specifically includes steps S1041 to S1043:

[0077] S1041, obtaining first signal direction cosines and second signal direction cosines according to the first matrix and the third matrix, wherein the first signal direction cosines and the second signal direction cosines are both signal polarization parameters with a first accuracy and without angle ambiguity;

[0078] S1042, obtaining a third signal direction cosine and a fourth signal direction cosine according to the second matrix, wherein the third signal direction cosine and the fourth signal direction cosine are both signal parameters of the second precision and with angle ambiguity, and the second precision is higher than the first precision;

[0079] S1043: Using the first signal direction cosines and the second signal direction cosines as references, eliminate angle ambiguity in the third signal direction cosines and the fourth signal direction cosines to obtain a signal two-dimensional target azimuth with a second precision and without angle ambiguity.

[0080] In the specific implementation, from the above factor matrix ,and The signal polarization parameters and low-precision but angularly ambiguous signal direction cosines are obtained from and From the above factor matrix Get high-precision but angularly ambiguous signal direction cosines and .

[0081] in, ,therefore:

[0082] ;

[0083] According to the vector cross multiplication algorithm, we can get The DOA information and polarization information contained therein:

[0084] , in the above expressions, there are two sets of direction cosine u estimation values with different precisions. When the adjacent spacing between the electric dipole and the current loop in the split vector sensor is greater than half of the carrier wavelength, from the obtained direction cosine precision is higher than . Therefore, using as a reference value to eliminate the ambiguity existing in :

[0085] ;

[0086] Similarly, the direction cosine value can be obtained from the factor matrix .

[0087] Furthermore, taking the and values as references to eliminate the angle ambiguity existing in and to obtain the two-dimensional DOA of the signal with high precision and no ambiguity: :

[0088] ;

[0089] ;

[0090] When the DOA value is known, the signal polarization information can be obtained through the following formula:

[0091] , where is the DOA information of the incident signal; ; .

[0092] It should be noted that in this embodiment, the new array is composed of two sets of orthogonally distributed split electromagnetic vector sensor uniform linear arrays, and the adjacent element spacing and the adjacent spacing of the internal components of the split electromagnetic vector sensor are both greater than half of the carrier wavelength, so as to obtain a large array aperture.

[0093] In summary, the L-shaped array signal processing method in the above embodiments of the present invention uses tensors to process the L-shaped array, fully excavates the structural information contained in the array signal to improve the potential of the array degrees of freedom and parameter estimation accuracy; studies and processes the structure and data of the array signal with the help of tensor algebra theory, has strong detection stability, good system stability, and high resolution ability; solves the angle ambiguity problem existing in the parameter estimation results.

[0094] Embodiment 2

[0095] On the other hand, the present invention also proposes an L-shaped array signal processing system. Please refer to Figure 5 , which shows the L-shaped array signal processing system in the second embodiment of the present invention, including:

[0096] An array construction module 11, configured to construct an L-shaped array and divide the L-shaped array into a plurality of overlapping sub-arrays in the X and Y directions;

[0097] A tensor processing module 12, configured to merge the data of each of the overlapping sub-arrays to form a sixth-order tensor, and perform tensor expansion on the sixth-order tensor to obtain a corresponding eighth-order tensor;

[0098] Further, the tensor processing module 12 includes:

[0099] A data merging unit, configured to obtain the received signal data of each of the overlapping sub-arrays, and merge the received signal data to form a sixth-order tensor;

[0100] A tensor expansion unit, configured to perform tensor expansion on the sixth-order tensor to obtain a corresponding eighth-order tensor.

[0101] A tensor decomposition module 13, configured to recombine the eighth-order tensor to obtain a virtual array with a preset degree of freedom, and perform tensor decomposition on the virtual array to obtain a factor matrix of the virtual array;

[0102] Further, the tensor decomposition module 13 includes:

[0103] A tensor combination unit, configured to combine several dimensions in the eighth-order tensor one by one to obtain a received tensor of a virtual array with a preset degree of freedom;

[0104] A tensor decomposition unit, configured to perform tensor decomposition on the received tensor of the virtual array to obtain three factor matrices of the received tensor of the virtual array.

[0105] A signal processing module 14, configured to obtain a plurality of signal direction parameters from the factor matrix of the virtual array, and perform signal processing on each of the signal direction parameters to obtain a corresponding two-dimensional target azimuth angle of the signal.

[0106] Further, the three factor matrices include a first matrix, a second matrix, and a third matrix, and the signal processing module 14 includes:

[0107] A first signal processing unit, configured to obtain a first signal direction cosine and a second signal direction cosine from the first matrix and the third matrix, wherein both the first signal direction cosine and the second signal direction cosine are signal polarization parameters with a first precision and without angle ambiguity;

[0108] A second signal processing unit, configured to obtain a third signal direction cosine and a fourth signal direction cosine from the second matrix, wherein both the third signal direction cosine and the fourth signal direction cosine are signal parameters with a second precision and having an angle ambiguity, and the second precision is higher than the first precision.

[0109] Further, the signal processing module 14 further includes:

[0110] A third signal processing unit, configured to use the first signal direction cosine and the second signal direction cosine as references to eliminate the angle ambiguity in the third signal direction cosine and the fourth signal direction cosine, so as to obtain a two-dimensional target azimuth angle of the signal with the second precision and without angle ambiguity.

[0111] The functions or operation steps implemented when the above-mentioned modules and units are executed are substantially the same as those in the above method embodiments, and will not be described in detail herein.

[0112] The L-shaped array signal processing system provided by the embodiments of the present invention has the same implementation principle and the same technical effects as those in the foregoing method embodiments. For a brief description, for the parts not mentioned in the system embodiments, reference may be made to the corresponding content in the foregoing method embodiments.

[0113] Embodiment III

[0114] The present invention also provides a computer. Please refer to Figure 6 , which shows the computer in the third embodiment of the present invention, including a memory 10, a processor 20, and a computer program 30 stored on the memory 10 and executable on the processor 20. When the processor 20 executes the computer program 30, the above-mentioned L-shaped array signal processing method is implemented.

[0115] Among them, the memory 10 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 10 may be an internal storage unit of the computer in some embodiments, such as the hard disk of the computer. The memory 10 may also be an external storage device in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 10 may also include both an internal storage unit of the computer and an external storage device. The memory 10 can be used not only to store application software installed on the computer and various types of data, but also to temporarily store data that has been output or will be output.

[0116] Among them, in some embodiments, the processor 20 may be an Electronic Control Unit (ECU, also known as the vehicle computer), a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips, which are used to run the program code stored in the memory 10 or process data, such as executing an access restriction program, etc.

[0117] It should be noted that Figure 6 The structure shown does not constitute a limitation on the computer. In other embodiments, the computer may include fewer or more components than shown in the figure, or combine certain components, or have a different component arrangement.

[0118] An embodiment of the present invention also provides a readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the L-shaped array signal processing method as described above is implemented.

[0119] Those skilled in the art can understand that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable storage medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable storage medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0120] More specific examples (non-exhaustive list) of computer-readable storage media include the following: an electrical connection part (electronic device) having one or more wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable storage medium can even be paper or other suitable readable storage media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other readable storage media, followed by editing, interpretation, or other appropriate processing when necessary, and then stored in a computer memory.

[0121] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0122] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0123] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the appended claims.

Claims

1. An L-shaped array signal processing method, characterized in that, Including: Construct an L-shaped array and divide the L-shaped array into multiple overlapping sub-arrays in the X and Y directions; Merge the data of each of the overlapping sub-arrays to form a sixth-order tensor, and perform tensor expansion on the sixth-order tensor to obtain a corresponding eighth-order tensor; Recombine the eighth-order tensor to obtain a virtual array with a preset degree of freedom, and perform tensor decomposition on the virtual array to obtain the factor matrix of the virtual array; Obtain multiple signal direction parameters from the factor matrix of the virtual array, and perform signal processing on each of the signal direction parameters to obtain the corresponding two-dimensional target azimuth angle of the signal; Among them, the step of recombining the eighth-order tensor to obtain a virtual array with a preset degree of freedom and performing tensor decomposition on the virtual array to obtain the factor matrix of the virtual array includes: Combine several dimensions in the eighth-order tensor one by one to obtain the received tensor of the virtual array with a preset degree of freedom; Perform tensor decomposition on the received tensor of the virtual array to obtain three factor matrices of the received tensor of the virtual array; Among them, the three factor matrices include a first matrix, a second matrix, and a third matrix. The step of obtaining multiple signal direction parameters from the factor matrix of the virtual array includes: Obtain a first signal direction cosine and a second signal direction cosine from the first matrix and the third matrix, where both the first signal direction cosine and the second signal direction cosine are signal polarization parameters with a first precision and no angle ambiguity; Obtain a third signal direction cosine and a fourth signal direction cosine from the second matrix, where both the third signal direction cosine and the fourth signal direction cosine are signal parameters with a second precision and angle ambiguity, and the second precision is higher than the first precision.

2. The L-shaped array signal processing method according to claim 1, wherein The L-shaped array is an M-element uniform linear array located on the X-axis and Y-axis respectively, and the element spacing is .

3. The L-shaped array signal processing method according to claim 1, wherein The step of merging the data of each of the overlapping sub-arrays to form a sixth-order tensor and performing tensor expansion on the sixth-order tensor to obtain a corresponding eighth-order tensor includes: Obtain the received signal data of each of the overlapping sub-arrays, and merge the received signal data to form a sixth-order tensor; Perform tensor expansion on the sixth-order tensor to obtain a corresponding eighth-order tensor.

4. The L-shaped array signal processing method according to claim 1, wherein The step of performing signal processing on each of the signal direction parameters to obtain the corresponding two-dimensional target azimuth angle of the signal includes: Using the first signal direction cosine and the second signal direction cosine as references, eliminate the angle ambiguity in the third signal direction cosine and the fourth signal direction cosine to obtain the two-dimensional target azimuth angle of the signal with a second precision and no angle ambiguity.

5. An L-shaped array signal processing system, characterized in that, Including: An array construction module for constructing an L-shaped array and dividing the L-shaped array into multiple overlapping sub-arrays in the X and Y directions; A tensor processing module for merging the data of each of the overlapping sub-arrays to form a sixth-order tensor and performing tensor expansion on the sixth-order tensor to obtain a corresponding eighth-order tensor; A tensor decomposition module for recombining the eighth-order tensor to obtain a virtual array with a preset degree of freedom and performing tensor decomposition on the virtual array to obtain the factor matrix of the virtual array; A signal processing module, configured to obtain a plurality of signal direction parameters from the factor matrices of the virtual array, and perform signal processing on each of the signal direction parameters to obtain corresponding two-dimensional target azimuth angles of the signals; Wherein, the tensor decomposition module includes: A tensor combination unit, configured to combine several dimensions in the octal tensor one by one to obtain a received tensor of a virtual array with a preset degree of freedom; A tensor decomposition unit, configured to perform tensor decomposition on the received tensor of the virtual array to obtain three factor matrices of the received tensor of the virtual array; Wherein, the three factor matrices include a first matrix, a second matrix, and a third matrix, and the signal processing module includes: A first signal processing unit, configured to obtain a first signal direction cosine and a second signal direction cosine from the first matrix and the third matrix, wherein both the first signal direction cosine and the second signal direction cosine are signal polarization parameters with a first precision and without angle ambiguity; A second signal processing unit, configured to obtain a third signal direction cosine and a fourth signal direction cosine from the second matrix, wherein both the third signal direction cosine and the fourth signal direction cosine are signal parameters with a second precision and with angle ambiguity, and the second precision is higher than the first precision.

6. The L-shaped array signal processing system according to claim 5, wherein The tensor processing module includes: A data merging unit, configured to obtain the received signal data of each of the overlapping sub-arrays, and merge the received signal data to form a sixth-order tensor; A tensor expansion unit, configured to perform tensor expansion on the sixth-order tensor to obtain a corresponding octal tensor.

7. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the L-shaped array signal processing method according to any one of claims 1 to 4.

8. A computer, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the L-shaped array signal processing method according to any one of claims 1 to 4.

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