Method and system for performing DOA estimation by using antenna array in partition

Abstract

The invention provides a method and system for carrying out DOA estimation by using an antenna array in a partition mode, and the method comprises the steps: 1, dividing the whole antenna array into a plurality of sub-arrays, and enabling each sub-array to independently receive a signal; 2, each sub-array independently collects the received signals, and corresponding filtering and noise reduction preprocessing is carried out; 3, performing independent direction estimation on the signal of each sub-array by using a MUSIC algorithm to obtain a plurality of preliminary DOA values; and step 4, performing comprehensive processing on the DOA preliminary estimation result of each sub-array through a weighted average or joint optimization fusion algorithm to obtain a final DOA estimation value. According to the method, through partition use of the antenna array, the calculation amount of angle estimation of the antenna array can be effectively reduced, so that the real-time performance of DOA estimation is improved, and the positioning precision and the anti-interference capability can be improved by fusing estimation results of a plurality of sub-arrays.

Description

Technical Field

[0001] The present invention relates to the technical field of positioning and angle measurement, and in particular to a method and system for performing DOA estimation using an antenna array in a partitioned manner. Background Art

[0002] In the field of positioning angle measurement technology, an antenna system composed of multiple separate antenna units is used to produce specific radiation characteristics and directivity by precisely controlling the amplitude and phase of each antenna unit. Then, the direction of arrival (DOA) of the signal can be estimated, thereby achieving the positioning of the target. However, the traditional antenna array is usually a planar structure. Although the antenna array of this structure has the advantages of simple structure and easy manufacturing, its angle measurement range is narrow (usually -60 to 60 degrees), which to a certain extent limits its application in the field of positioning angle measurement, especially in areas where the positioning scene is highly restricted. In order to solve this problem, some solutions have been proposed in the prior art. For example, a common solution is to use an antenna array with a polyhedral structure, which can provide a wider angle measurement range, thereby improving the accuracy of positioning angle measurement. However, this solution requires more antenna units, which not only increases the complexity and cost of the system, but also increases the difficulty of signal processing. Summary of the invention

[0003] In view of the defects in the prior art, an object of the present invention is to provide a method and system for performing DOA estimation using an antenna array in partitions.

[0004] The method for performing DOA estimation using an antenna array using partitions provided by the present invention includes:

[0005] Step 1: Divide the entire antenna array into multiple sub-arrays. Each sub-array receives signals independently. The number and configuration of sub-arrays are dynamically adjusted according to the application scenario and expected accuracy requirements.

[0006] Step 2: Each subarray independently collects the received signal and performs corresponding filtering and noise reduction preprocessing;

[0007] Step 3: Use the MUSIC algorithm to independently estimate the direction of each subarray signal to obtain multiple preliminary DOA values;

[0008] Step 4: The preliminary DOA estimation results of each subarray are processed comprehensively through weighted averaging or joint optimization fusion algorithm to obtain the final DOA estimation value.

[0009] Preferably, the step 3 comprises:

[0010] The MUSIC algorithm extracts the spatial angle information of the signal from the array signal data through the orthogonality of the signal and noise and the idea of ​​subspace decomposition. By scanning all DOA angles, it finds the direction that minimizes the signal and noise subspace, thereby achieving DOA positioning.

[0011] Assume that the array antenna has M antenna units, and the array signal received by each antenna unit is expressed as:

[0012] X(t)=A(θ)S(t)+N(t)

[0013] Where S(t) is the signal vector of the K×1 dimensional original signal source, X(t) is the received signal vector received by the M×1 dimensional antenna unit, A(θ) is the M×K dimensional steering vector matrix, and N(t) represents the noise vector when the signal is incident from different directions;

[0014] The covariance matrix R of the received signal is constructed by the received signal vector X(t), and the covariance matrix R is eigen-decomposed. The expression is:

[0015] R = E[X(t)X(t) H ]

[0016]

[0017] Where X(t) H is the Hermite matrix of the received signal vector, U s is the eigenvector of the signal subspace, is the Hermite matrix of the eigenvector of the signal subspace, U n is the eigenvector of the noise subspace, is the Hermite matrix of the eigenvector of the noise subspace, Λ s and Λ n are diagonal matrices containing the signal and noise eigenvalues ​​respectively;

[0018] Using orthogonality to construct the PDDA spectrum function:

[0019]

[0020] Scan all angles. When θ is the same as the angle estimated by DOA, P(θ) reaches its maximum value, that is, the spectrum function reaches its peak position, and the current angle θ is the estimated angle of the incoming signal.

[0021] Preferably, step 4 comprises:

[0022] For each subarray, the received signal X i Perform DOA estimation and obtain a preliminary estimate θ i , and statistically extract the received signal amplitude M of each subarrayi , calculate the weight ω of each subarray according to the received signal amplitude i , and normalize the signal amplitudes of all subarrays so that their sum is 1. The expression is:

[0023]

[0024] The initial estimated value θ of each subsystem i Statistical weighting to calculate the final estimate θ final , the expression is:

[0025]

[0026] Wherein, M is the number of antenna units.

[0027] Preferably, a cylindrical antenna is used to divide the antenna array into three sub-arrays at intervals of 120 degrees when viewed from above. Each sub-array uses the MUSIC algorithm to independently estimate the arrival direction angle of each sub-area, and then the weighted average method is used to fuse the estimation results of each sub-array, and the contribution of each sub-array in the final result is dynamically adjusted by the weight based on the incoming signal.

[0028] The system for performing DOA estimation using an antenna array in partitions provided by the present invention comprises:

[0029] Module M1: Divides the entire antenna array into multiple sub-arrays. Each sub-array receives signals independently. The number and configuration of sub-arrays are dynamically adjusted according to the application scenario and expected accuracy requirements.

[0030] Module M2: Each subarray independently collects the received signal and performs corresponding filtering and noise reduction preprocessing;

[0031] Module M3: Use the MUSIC algorithm to independently estimate the direction of each subarray signal and obtain multiple preliminary DOA values;

[0032] Module M4: The preliminary DOA estimation results of each subarray are processed comprehensively through weighted average or joint optimization fusion algorithm to obtain the final DOA estimation value.

[0033] Preferably, the module M3 comprises:

[0034] The MUSIC algorithm extracts the spatial angle information of the signal from the array signal data through the orthogonality of the signal and noise and the idea of ​​subspace decomposition. By scanning all DOA angles, it finds the direction that minimizes the signal and noise subspace, thereby achieving DOA positioning.

[0035] Assume that the array antenna has M antenna units, and the array signal received by each antenna unit is expressed as:

[0036] X(t)=A(θ)S(t)+N(t)

[0037] Where S(t) is the signal vector of the K×1 dimensional original signal source, X(t) is the received signal vector received by the M×1 dimensional antenna unit, A(θ) is the M×K dimensional steering vector matrix, and N(t) represents the noise vector when the signal is incident from different directions;

[0038] The covariance matrix R of the received signal is constructed by the received signal vector X(t), and the covariance matrix R is eigen-decomposed. The expression is:

[0039] R = E[X(t)X(t) H ]

[0040]

[0041] Where X(t) H is the Hermite matrix of the received signal vector, U s is the eigenvector of the signal subspace, is the Hermite matrix of the eigenvector of the signal subspace, U n is the eigenvector of the noise subspace, is the Hermite matrix of the eigenvector of the noise subspace, Λ s and Λ n are diagonal matrices containing the signal and noise eigenvalues ​​respectively;

[0042] Using orthogonality to construct the PDDA spectrum function:

[0043]

[0044] Scan all angles. When θ is the same as the angle estimated by DOA, P(θ) reaches its maximum value, that is, the spectrum function reaches its peak position, and the current angle θ is the estimated angle of the incoming signal.

[0045] Preferably, the module M4 comprises:

[0046] For each subarray, the received signal X i Perform DOA estimation and obtain a preliminary estimate θ i , and statistically extract the received signal amplitude M of each subarray i , calculate the weight ω of each subarray according to the received signal amplitude i , and normalize the signal amplitudes of all subarrays so that their sum is 1. The expression is:

[0047]

[0048] The initial estimated value θ of each subsystem iStatistical weighting to calculate the final estimate θ final , the expression is:

[0049]

[0050] Wherein, M is the number of antenna units.

[0051] Preferably, a cylindrical antenna is used to divide the antenna array into three sub-arrays at intervals of 120 degrees when viewed from above. Each sub-array uses the MUSIC algorithm to independently estimate the arrival direction angle of each sub-area, and then the weighted average method is used to fuse the estimation results of each sub-array, and the contribution of each sub-array in the final result is dynamically adjusted by the weight based on the incoming signal.

[0052] According to the computer-readable storage medium storing a computer program provided by the present invention, when the computer program is executed by a processor, the steps of the method for performing DOA estimation using an antenna array using partitions are implemented.

[0053] The electronic device provided according to the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the method for performing DOA estimation using a partitioned antenna array are implemented.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] The present invention uses the partitioned use of the antenna array, which can not only effectively reduce the amount of calculation of the antenna array angle estimation, thereby improving the real-time performance of DOA estimation, but also can improve the positioning accuracy and anti-interference capability by fusing the estimation results of multiple sub-arrays. It is particularly suitable for complex environments with severe multipath effects. In experimental tests, compared with the traditional single-array DOA estimation method, the method of the present invention can obtain higher positioning accuracy in a multipath environment and significantly reduce the calculation delay. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0057] Figure 1a to Figure 1c It is a common planar array antenna structure;

[0058] Figure 2 It is a common conformal array antenna structure;

[0059] Figure 3 Schematic diagram of the cylindrical conformal antenna partitioning. DETAILED DESCRIPTION

[0060] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0061] Example 1

[0062] The present invention uses a common array antenna to measure the AOA arrival angle: by using a common array antenna, the effective radiation range of the antenna can be effectively expanded, thereby increasing the effective range of angle measurement. This antenna design can effectively solve the problem of the narrow angle measurement range of the traditional planar antenna array. Figure 1a to Figure 1c shown.

[0063] The common antenna schematic diagram is as follows Figure 2 As shown in the figure, traditional antenna arrays are usually planar and installed on a fixed plane, which limits their viewing angle. However, conformal antennas can cover a larger spatial range due to their curved design, especially when installed on a cylindrical carrier, they can achieve 360-degree coverage. Compared with planar antenna arrays that are susceptible to multipath effects, conformal antenna arrays have strong spatial diversity, good anti-multipath capabilities, and high angular measurement accuracy, making them particularly suitable for complex environments. However, conformal antennas are usually distributed on a large surface with a large number of units, which increases the data dimension and requires efficient signal processing and dimensionality reduction algorithms.

[0064] Therefore, the present invention proposes a partition-based conformal antenna signal processing algorithm. Based on planar antennas, angle estimation is usually achieved using a spatial spectrum-based method, which mainly includes algorithms such as MUSIC (Multiple Signal Classification) and SSS (Subtracting Signal Subspace). This type of algorithm mainly constructs signal and noise subspaces and uses the orthogonality of signals and noise to determine the direction of arrival of the signal; the present invention proposes a partition-based method for DOA estimation using an antenna array, which divides the antenna array into multiple independent subarrays, each subarray measures the signal independently, and combines the DOA estimation results of each subarray with a specific algorithm to improve the accuracy and stability of positioning. The method includes the following steps:

[0065] 1. Antenna array pre-partitioning: The overall antenna array is divided into multiple sub-arrays, each of which can receive signals independently. The number and configuration of sub-arrays can be dynamically adjusted according to the application scenario and the expected accuracy requirements.

[0066] 2. Signal acquisition and preprocessing: Each subarray independently acquires the received signal and performs corresponding filtering and noise reduction preprocessing to reduce the impact of environmental noise and interference on DOA estimation.

[0067] 3. Preliminary DOA estimation: Use the traditional MUSIC algorithm to independently estimate the direction of each subarray signal to obtain multiple preliminary DOA values.

[0068] The MUSIC algorithm is a high-resolution technology based on array signal processing, which is used to estimate the signal arrival angle. It extracts the spatial angle information of the signal from the array signal data through the orthogonality of the signal and the idea of ​​subspace decomposition. By scanning all possible DOA angles, the direction that minimizes the signal and noise subspace is found, thereby achieving DOA positioning.

[0069] Assuming that the array antenna has M antenna units, the array signal received by each antenna unit can be expressed as:

[0070] X(t)=A(θ)S(t)+N(t)

[0071] Where S(t) is the signal vector of the K×1 dimensional original signal source, X(t) is the received signal vector received by the M×1 dimensional antenna unit, A(θ) is the M×K dimensional steering vector matrix, and N(t) represents the noise vector when the signal is incident from different directions.

[0072] The covariance matrix R of the received signal is constructed by the received signal X(t), and the covariance matrix R is eigen-decomposed.

[0073] R = E[X(t)X(t) H ]

[0074]

[0075] Where X(t) H is the Hermite matrix of the received signal vector. s is the eigenvector of the signal subspace, U n is the eigenvector of the noise subspace, Λ s and Λ n are diagonal matrices containing the signal and noise eigenvalues, respectively.

[0076] Using orthogonality to construct the PDDA spectrum function:

[0077]

[0078] Scan all possible angles. When θ is the same as the angle estimated by DOA, P(θ) reaches its maximum value, that is, the spectrum function reaches its peak position. Therefore, the current angle θ is the estimated angle of the incoming signal.

[0079] 4. Estimation result fusion: The preliminary DOA estimation results of each subarray can be comprehensively processed through fusion algorithms such as weighted averaging or joint optimization to obtain the final DOA estimation value.

[0080] For each subarray, the received signal X i Perform DOA estimation and obtain a preliminary estimate θ i , and statistically extract the received signal amplitude M of each subarray i , calculate the weight ω of each subarray according to the received signal amplitude i , and normalize the signal amplitudes of all sub-arrays so that their sum is 1.

[0081]

[0082] The initial estimated value θ of each subsystem i Statistical weighting to calculate the final estimate θ final

[0083]

[0084] like Figure 3 Using a cylindrical antenna, the antenna array can be divided into three sub-arrays at intervals of 120 degrees when viewed from above. Each sub-array uses the MUSIC estimation algorithm to independently estimate the arrival direction angle of each sub-area, and then uses the weighted average method to fuse the estimation results of each sub-array, and dynamically adjusts the contribution of each sub-array in the final result based on the weight of the incoming signal.

[0085] Example 2

[0086] The present invention also provides a system for performing DOA estimation using partitioned antenna arrays. The system for performing DOA estimation using partitioned antenna arrays can be implemented by executing the process steps of the method for performing DOA estimation using partitioned antenna arrays, that is, those skilled in the art can understand the method for performing DOA estimation using partitioned antenna arrays as a preferred implementation of the system for performing DOA estimation using partitioned antenna arrays.

[0087] The system for DOA estimation using a partitioned antenna array provided by the present invention includes: module M1: dividing the overall antenna array into multiple sub-arrays, each sub-array independently receiving a signal, and dynamically adjusting the number and configuration of the sub-arrays according to the application scenario and the expected accuracy requirements; module M2: each sub-array independently collects the received signal and performs corresponding filtering and noise reduction preprocessing; module M3: using the MUSIC algorithm to independently estimate the direction of the signal of each sub-array to obtain multiple preliminary DOA values; module M4: comprehensive processing of the preliminary DOA estimation results of each sub-array through weighted averaging or joint optimization fusion algorithm to obtain the final DOA estimation value.

[0088] The module M3 includes: the MUSIC algorithm extracts the spatial angle information of the signal from the array signal data through the orthogonality of the signal and the noise and the idea of ​​subspace decomposition, and finds the direction that minimizes the signal and noise subspace by scanning all DOA angles, thereby realizing DOA positioning;

[0089] Assume that the array antenna has M antenna units, and the array signal received by each antenna unit is expressed as:

[0090] X(t)=A(θ)S(t)+N(t)

[0091] Where S(t) is the signal vector of the K×1 dimensional original signal source, X(t) is the received signal vector received by the M×1 dimensional antenna unit, A(θ) is the M×K dimensional steering vector matrix, and N(t) represents the noise vector when the signal is incident from different directions;

[0092] The covariance matrix R of the received signal is constructed by the received signal vector X(t), and the covariance matrix R is eigen-decomposed. The expression is:

[0093] R = E[X(t)X(t) H ]

[0094]

[0095] Where X(t) H is the Hermite matrix of the received signal vector, U s is the eigenvector of the signal subspace, is the Hermite matrix of the eigenvector of the signal subspace, U n is the eigenvector of the noise subspace, is the Hermite matrix of the eigenvector of the noise subspace, Λ s and Λ n are diagonal matrices containing the signal and noise eigenvalues ​​respectively;

[0096] Using orthogonality to construct the PDDA spectrum function:

[0097]

[0098] Scan all angles. When θ is the same as the angle estimated by DOA, P(θ) reaches its maximum value, that is, the spectrum function reaches its peak position, and the current angle θ is the estimated angle of the incoming signal.

[0099] The module M4 comprises: receiving a signal X of each subarray i Perform DOA estimation and obtain a preliminary estimate θ i , and statistically extract the received signal amplitude M of each subarray i , calculate the weight ω of each subarray according to the received signal amplitude i , and normalize the signal amplitudes of all subarrays so that their sum is 1. The expression is:

[0100]

[0101] The initial estimated value θ of each subsystem i Statistical weighting to calculate the final estimate θ final , the expression is:

[0102]

[0103] Wherein, M is the number of antenna units.

[0104] Using a cylindrical antenna, the antenna array is divided into three sub-arrays at intervals of 120 degrees when viewed from above. Each sub-array uses the MUSIC algorithm to independently estimate the arrival direction angle of each sub-area. The estimated results of each sub-array are then fused using the weighted average method, and the contribution of each sub-array in the final result is dynamically adjusted based on the weight of the incoming signal.

[0105] Those skilled in the art know that, in addition to implementing the system, device and its various modules provided by the present invention in a purely computer-readable program code, it is entirely possible to implement the same program in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers and embedded microcontrollers by logically programming the method steps. Therefore, the system, device and its various modules provided by the present invention can be considered as a hardware component, and the modules included therein for implementing various programs can also be considered as structures within the hardware component; the modules for implementing various functions can also be considered as both software programs for implementing the method and structures within the hardware component.

[0106] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for DOA estimation using a partitioned antenna array, characterized in that: include: Step 1: Divide the entire antenna array into multiple sub-arrays. Each sub-array receives signals independently. The number and configuration of sub-arrays are dynamically adjusted according to the application scenario and expected accuracy requirements. Step 2: Each subarray independently collects the received signal and performs corresponding filtering and noise reduction preprocessing; Step 3: Use the MUSIC algorithm to independently estimate the direction of each subarray signal to obtain multiple preliminary DOA values; Step 4: The preliminary DOA estimation results of each subarray are processed comprehensively through weighted averaging or joint optimization fusion algorithm to obtain the final DOA estimation value.

2. The method for DOA estimation using a partitioned antenna array according to claim 1, characterized in that: The step 3 comprises: The MUSIC algorithm extracts the spatial angle information of the signal from the array signal data through the orthogonality of the signal and noise and the idea of ​​subspace decomposition. By scanning all DOA angles, it finds the direction that minimizes the signal and noise subspace, thereby achieving DOA positioning. Assume that the array antenna has M antenna units, and the array signal received by each antenna unit is expressed as: X(t)=A(θ)S(t)+N(t) Where S(t) is the signal vector of the K×1 dimensional original signal source, X(t) is the received signal vector received by the M×1 dimensional antenna unit, A(θ) is the M×K dimensional steering vector matrix, and N(t) represents the noise vector when the signal is incident from different directions; The covariance matrix R of the received signal is constructed by the received signal vector X(t), and the covariance matrix R is eigen-decomposed. The expression is: R=E[X(t)X(t) H ] Where X(t) H is the Hermite matrix of the received signal vector, U s is the eigenvector of the signal subspace, is the Hermite matrix of the eigenvector of the signal subspace, U n is the eigenvector of the noise subspace, is the Hermite matrix of the eigenvector of the noise subspace, Λ s and Λ n are diagonal matrices containing the signal and noise eigenvalues ​​respectively; Using orthogonality to construct the PDDA spectrum function: Scan all angles. When θ is the same as the angle estimated by DOA, P(θ) reaches its maximum value, that is, the spectrum function reaches its peak position, and the current angle θ is the estimated angle of the incoming signal.

3. The method for DOA estimation using a partitioned antenna array according to claim 1, characterized in that: The step 4 comprises: For each subarray, the received signal X i Perform DOA estimation and obtain a preliminary estimate θ i , and statistically extract the received signal amplitude M of each subarray i , calculate the weight ω of each subarray according to the received signal amplitude i , and normalize the signal amplitudes of all subarrays so that their sum is 1. The expression is: The initial estimated value θ of each subsystem i Statistical weighting to calculate the final estimate θ final , the expression is: Wherein, M is the number of antenna units.

4. The method for DOA estimation using a partitioned antenna array according to claim 1, characterized in that: Using a cylindrical antenna, the antenna array is divided into three sub-arrays at intervals of 120 degrees when viewed from above. Each sub-array uses the MUSIC algorithm to independently estimate the arrival direction angle of each sub-area. The estimated results of each sub-array are then fused using the weighted average method, and the contribution of each sub-array in the final result is dynamically adjusted based on the weight of the incoming signal.

5. A system for performing DOA estimation using an antenna array in a partitioned manner, characterized in that: include: Module M1: Divides the entire antenna array into multiple sub-arrays. Each sub-array receives signals independently. The number and configuration of sub-arrays are dynamically adjusted according to the application scenario and expected accuracy requirements. Module M2: Each subarray independently collects the received signal and performs corresponding filtering and noise reduction preprocessing; Module M3: Use the MUSIC algorithm to independently estimate the direction of each subarray signal and obtain multiple preliminary DOA values; Module M4: The preliminary DOA estimation results of each subarray are processed comprehensively through weighted average or joint optimization fusion algorithm to obtain the final DOA estimation value.

6. The system for performing DOA estimation using a partitioned antenna array according to claim 5, characterized in that: The module M3 comprises: The MUSIC algorithm extracts the spatial angle information of the signal from the array signal data through the orthogonality of the signal and noise and the idea of ​​subspace decomposition. By scanning all DOA angles, it finds the direction that minimizes the signal and noise subspace, thereby achieving DOA positioning. Assume that the array antenna has M antenna units, and the array signal received by each antenna unit is expressed as: X(t)=A(θ)S(t)+N(t) Where S(t) is the signal vector of the K×1 dimensional original signal source, X(t) is the received signal vector received by the M×1 dimensional antenna unit, A(θ) is the M×K dimensional steering vector matrix, and N(t) represents the noise vector when the signal is incident from different directions; The covariance matrix R of the received signal is constructed by the received signal vector X(t), and the covariance matrix R is eigen-decomposed. The expression is: R=E[X(t)X(t) H ] Where X(t) H is the Hermite matrix of the received signal vector, U s is the eigenvector of the signal subspace, is the Hermite matrix of the eigenvector of the signal subspace, U n is the eigenvector of the noise subspace, is the Hermite matrix of the eigenvector of the noise subspace, Λ s and Λ n are diagonal matrices containing the signal and noise eigenvalues ​​respectively; Using orthogonality to construct the PDDA spectrum function: Scan all angles. When θ is the same as the angle estimated by DOA, P(θ) reaches its maximum value, that is, the spectrum function reaches its peak position, and the current angle θ is the estimated angle of the incoming signal.

7. The system for performing DOA estimation using a partitioned antenna array according to claim 5, characterized in that: The module M4 comprises: For each subarray, the received signal X i Perform DOA estimation and obtain a preliminary estimate θ i , and statistically extract the received signal amplitude M of each subarray i , calculate the weight ω of each subarray according to the received signal amplitude i , and normalize the signal amplitudes of all subarrays so that their sum is 1. The expression is: The initial estimated value θ of each subsystem i Statistical weighting to calculate the final estimate θ final , the expression is: Wherein, M is the number of antenna units.

8. The system for performing DOA estimation using a partitioned antenna array according to claim 5, characterized in that: Using a cylindrical antenna, the antenna array is divided into three sub-arrays at intervals of 120 degrees when viewed from above. Each sub-array uses the MUSIC algorithm to independently estimate the arrival direction angle of each sub-area. The estimated results of each sub-array are then fused using the weighted average method, and the contribution of each sub-array in the final result is dynamically adjusted based on the weight of the incoming signal.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for DOA estimation using a partitioned antenna array according to any one of claims 1 to 4 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is executed by a processor, the steps of the method for DOA estimation using a partitioned antenna array according to any one of claims 1 to 4 are implemented.

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