Enhanced DOA estimation method and system based on multi-modal iterative fusion framework

Abstract

The invention discloses an enhanced DOA (direction of arrival) estimation method and system based on a multi-modal iterative fusion framework. The method specifically comprises the following steps: constructing a dual MIMO (multiple input multiple output) receiver H2AD-FD system model in which a full-digital FD sub-array and a heterogeneous mixed H2AD structure are combined; aiming at the FD sub-array, calculating a rough estimation value of a radiation source direction; generating a plurality of candidate angle sets by performing DOA estimation on each group in the H2AD structure; based on the rough estimation value of the radiation source direction as an initial clustering center point, inferring a true solution corresponding to each group from the candidate angle set by adopting a global maximum similarity and global minimum distance clustering method; and synthesizing two parts of true solutions through an iterative fusion algorithm: taking a radiation source direction rough estimation value as an initial clustering center, iteratively updating a weighted fusion coefficient and a clustering center point of a true solution class in the candidate solution set, and obtaining a final DOA estimation value. The phase ambiguity can be quickly eliminated, and the direction finding performance of high energy efficiency, low cost and low complexity is achieved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and particularly to an enhanced DOA estimation method and system based on a multi-modal iterative fusion framework. Background Art

[0002] Direction of Arrival (DOA) estimation is a key research area in signal processing. It can be used for positioning and tracking signal sources and has extensive applications in the fields of radar, sensor arrays, sonar, and seismic exploration. DOA estimation is the process of determining the direction information of a signal source from the signals received by multiple sensors in an array, and its main research goal is to obtain the mapping relationship of the arrival directions of the array signals. Since the accuracy of DOA estimation directly determines the performance of a wireless communication system, the research on higher-performance DOA estimation methods is crucial.

[0003] Most existing DOA estimation methods use ideal incident source angles and neglect noise, while noise always exists in actual communications. In addition, directly using the angles estimated by the most advanced DOA measurement method (root-music) or deep learning to calculate the weighting coefficients may result in certain performance losses and there is a problem of phase ambiguity. Summary of the Invention

[0004] The purpose of the present invention is to provide an enhanced DOA estimation method and system based on a multi-modal iterative fusion framework with high energy efficiency, low cost, and low complexity, and capable of quickly eliminating phase ambiguity.

[0005] To address the challenges in the above background art.

[0006] The technical solution for achieving the purpose of the present invention is: an enhanced DOA estimation method based on a multi-modal iterative fusion framework, including the following steps:

[0007] Step 1, construct a dual MIMO receiver H 2 combining a full-digital FD sub-array and a heterogeneous hybrid H 2 AD structure to form an AD-FD system model;

[0008] Step 2, for the FD sub-array, use the Root-MUSIC method to calculate a rough estimate of the radiation source direction;

[0009] Step 3, generate multiple candidate angle sets by performing DOA estimation on each group in the H 2 AD structure;

[0010] Step 4: Using the rough estimated value of the radiation source direction provided by the FD subarray as the initial clustering center, adopt the global maximum similarity (i.e., GMaxCS) and global minimum distance (i.e., GMinD) clustering methods to infer the true solution corresponding to each group from the candidate angle set.

[0011] Step 5: Synthesize the true solutions of the two parts through an iterative fusion algorithm: Use the rough estimated value of the radiation source direction calculated by the FD subarray as the initial clustering center, and iteratively update the weighted fusion coefficient and clustering center of the true solution class in the candidate solution set to obtain the final DOA estimation value.

[0012] Furthermore, in Step 1, the dual MIMO receiver H 2 The parameters of the AD-FD system model are specifically:

[0013] A far-field narrowband signal where e(t) and f c are the baseband signal and the carrier frequency respectively, and t represents time; where H 2 The AD structure includes P groups, and each group is divided into K p subarrays, and each subarray contains M p antennas, that is In the array, M 1 ≠M 2 ≠…≠M P and select the values of M 1 , M 2 ,…, M P as prime numbers;

[0014] Assume there is a transmitted signal incident from the θ direction. Then the received signal vector y F (n) of the FD subarray and the received signal vector y p (n) of the p-th group are respectively expressed as:

[0015] y F (n) = a F (θ 0 )e(n) + v F (n)(1.1)

[0016]

[0017] where e(n) is the baseband signal, and are the additive white Gaussian noise (AWGN) vectors, represents the noise variance, I is the identity matrix, represents a complex vector of dimension K p ×1, represents a series of noises in the q-th group; and Denote the array manifold vector:

[0018]

[0019]

[0020] where

[0021]

[0022]

[0023] where M represents the number of antennas of the FD subarray, and N p represents the number of antennas of the subarray in the q-th group, d represents the spacing between adjacent array elements in the array antenna, and λ represents the wavelength;

[0024] The block diagonal matrix Ξ A,p is expressed as:

[0025]

[0026] where ε A,p,k is expressed as:

[0027]

[0028] where Υ p,k,m is the analog beam phase shift matrix, ε A,p,k represents the analog beamforming vector of the k-th subarray in each group, and M p represents that each subarray contains M p antennas.

[0029] Furthermore, in step 2, for the FD subarray, the Root-MUSIC method is used to calculate the rough estimate value of the radiation source direction. The specific steps are as follows:

[0030] The spatial spectrum S F (θ) corresponding to the FD subarray is expressed as:

[0031]

[0032] represents the conjugate transpose of the array manifold vector of the FD subarray, θ represents the initial input arrival angle, and U N represents the signal noise subspace;

[0033] S F (θ)'s denominator is expressed as:

[0034]

[0035] where zF is a function of θ, U N represents the noise subspace of the signal, s F (z F ) represents a 2(M F -1) degree polynomial;

[0036] The polynomial equation s(z F ) has 2(M F -1) roots, so the root closest to the unit circle represents the expected DOA direction, a rough estimate of the radiation source direction is:

[0037]

[0038] Furthermore, in step 3, multiple candidate angle sets are generated by performing DOA estimation on each group in the H 2 AD structure. The specific steps are as follows:

[0039] For the H 2 AD structure, the spatial spectrum S H2AD (θ) of the virtual antenna array is calculated as:

[0040]

[0041] where

[0042]

[0043] where r p (θ) is a constant obtained by summing all elements of each subarray, represents the array manifold vector of M q root virtual antennas, E N represents the noise subspace of the signal;

[0044] Define then:

[0045]

[0046] where is the element in the i 1 th row and i 2 th column of B;

[0047] Through Root-MUSIC, the direction corresponding to the peak is the final DOA estimate, the polynomial equation in

[0048]

[0049] where s(z) represents 2Kp A - 2nd degree polynomial, z is a function containing θ, and s(η) represents 2K p - 2nd degree polynomial;

[0050] where

[0051]

[0052]

[0053] The equation s(θ) has 2K p - 2 roots, namely

[0054] Z R = {z l , l ∈ [1, 2K p - 2]}(1.18)

[0055] η p is a function containing θ, and M p represents that each sub - array contains M p root antennas;

[0056] The DOA estimation set is expressed as:

[0057]

[0058] where

[0059]

[0060] z l represents the l - th root of the polynomial equation (15);

[0061] Therefore, the estimated value of the p - th group is:

[0062]

[0063] represents the estimation of η p , and represents the estimated value of the p - th group;

[0064] Considering the function with a period of 2π, the feasible set of the M P solutions of the p - th group is extended to:

[0065]

[0066] where

[0067]

[0068] Integrating all groups gives:

[0069]

[0070] where q p ∈ {1, 2, …, M p} is the fuzzy coefficient. Therefore, the candidate set of HAD 2 is represented as:

[0071]

[0072] Candidate set contains solutions. Each candidate set has a true solution and M p − 1 false solutions. In the case of ignoring noise, the true solution class and the false solution class are respectively represented as

[0073]

[0074]

[0075] where

[0076]

[0077] χ p,m represents a constant determined by M p and m;

[0078] The true solution class is represented as

[0079]

[0080] wherein, is the predicted true solution of the p-th group of the HAD 2 structure.

[0081] Furthermore, in step 4, based on the rough estimated value of the radiation source direction provided by the FD subarray as the initial clustering center point, using the global maximum similarity i.e., GMaxCS and the global minimum distance i.e., GMinD clustering methods, the true solution corresponding to each group is inferred from the candidate angle set. The specific steps are as follows:

[0082] When the initial DOA value and the candidate set of the p-th group are obtained, a vector is defined as:

[0083]

[0084] represents the q p -th candidate solution of the p-th group;

[0085] Then, the cosine similarity is defined as

[0086]

[0087] Therefore, by searching for the direction with the maximum similarity, the true angle is obtained

[0088]

[0089] Furthermore, in step 5, the two parts of the true solutions are synthesized by an iterative fusion algorithm, and the specific steps are as follows:

[0090] Fuse the two parts of the true solutions to obtain the optimal DOA estimate:

[0091]

[0092] where i represents the number of iterations; represents the DOA estimate value after the i-th iteration; according to the mean square error of, the problem is transformed into

[0093]

[0094] where the weighted fusion coefficient and are respectively expressed as

[0095]

[0096]

[0097] where and are respectively expressed as

[0098]

[0099]

[0100] where

[0101]

[0102]

[0103] where H represents the number of snapshots, SNR represents the signal-to-noise ratio, represents the real part of.

[0104] The present invention also provides an enhanced DOA estimation system based on a multi-modal iterative fusion framework, and this system is used to implement the enhanced DOA estimation method based on the multi-modal iterative fusion framework.

[0105] The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the enhanced DOA estimation method based on the multimodal iterative fusion framework as described above is implemented.

[0106] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps in the enhanced DOA estimation method based on the multimodal iterative fusion framework as described above are implemented.

[0107] Compared with the prior art, the present invention has the following remarkable advantages: (1) The present invention integrates the advantages of the all-digital MIMO receiver structure and the hybrid analog-digital structure, and can dynamically adjust the number of panel antennas and the number of subarrays according to actual performance and circuit cost requirements, with high flexibility; (2) The heterogeneous hybrid H 2 AD structure can eliminate phase ambiguity within one time slot, and the introduction of the all-digital FD subarray can significantly accelerate the fast clustering of positive false solution classes. Compared with the traditional hybrid structure, the proposed new structure can achieve fast elimination of the phase ambiguity problem and has the same high energy efficiency, low cost, and low complexity; (3) The present invention uses the estimated angle to iteratively calculate the weighting coefficient to implement a more practical communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0108] Figure 1 It is a flowchart of the enhanced DOA estimation method based on the multimodal iterative fusion framework provided by the present invention.

[0109] Figure 2 It is an overall structure diagram of the enhanced DOA estimation method of the multimodal iterative fusion framework provided by the present invention.

[0110] Figure 3 It is a relationship curve diagram of the mean square error and the signal-to-noise ratio of the MM-IWF-GMaxCS method of the present invention.

[0111] Figure 4 It is a relationship curve diagram of the mean square error and the signal-to-noise ratio of the MM-IWF-GMinD method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0112] In order to make the objectives, technical solutions, and advantages of the present invention more obvious, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein. Based on the embodiments of the present invention described herein, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present invention.

[0113] In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that the present invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the present invention.

[0114] It should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present invention to those skilled in the art.

[0115] Referring to Figure 1 and Figure 2 , the present invention provides an enhanced DOA estimation method based on a multi-modal iterative fusion framework for quickly eliminating a direction finding method, and the method includes the following steps:

[0116] Step 1, construct a dual MIMO receiver (H 2 AD-FD) system model combining a full-digital (FD) subarray and a heterogeneous hybrid (H 2 AD) structure;

[0117] Step 2, for the FD subarray, use the Root-MUSIC method to calculate a rough estimate of the radiation source direction;

[0118] Step 3, generate multiple candidate angle sets by performing DOA estimation on each group in the H 2 AD structure;

[0119] Step 4, based on the true solution-like initial clustering center points provided by the FD subarray, use the global maximum similarity (GMaxCS) and global minimum distance (GMinD) clustering methods to infer the true solutions corresponding to each group from the candidate angle sets;

[0120] Step 5, synthesize the true solutions of the two parts through an iterative fusion algorithm to obtain the final DOA estimate.

[0121] As a specific example, in Step 1, the parameters of the dual MIMO receiver H 2 AD-FD system model are specifically:

[0122] A far-field narrowband signal where e(t) and f c are the baseband signal and the carrier frequency respectively, and t represents time. Wherein the H 2 AD consists of P groups and each group is divided into K p subarrays, and each subarray contains M p antenna elements, that is, In this array, M 1 ≠ M 2 ≠ … ≠ M P and M is preferably selected 1 , M 2 , …, M P as the prime number.

[0123] Assume that there is a transmitted signal incident from the θ direction. Then, the received signal vectors of the FD subarray and the p-th group are respectively expressed as:

[0124] y F (n) = a F (θ 0 ) e(n) + v F (n), (2.1)

[0125]

[0126] where e(n) is the baseband signal, and are additive white Gaussian noise (AWGN) vectors, represents the noise variance, I is the identity matrix, represents a complex vector of dimension K p × 1, represents a series of noises in the q-th group; and represent the array manifold vectors:

[0127]

[0128]

[0129] where

[0130]

[0131]

[0132] where M represents the number of antennas of the FD subarray, N p represents the number of antennas of the subarray in the q-th group, d represents the spacing between adjacent array elements in the array, and λ represents the wavelength.

[0133] The block diagonal matrix Ξ A,p is expressed as:

[0134]

[0135] where ε A,p,k is expressed as:

[0136]

[0137] where Υ p,k,m is the analog beam phase shift matrix. ε A,p,k represents the analog beamforming vector of the k-th subarray in each group, M p represents that each subarray contains M p antenna elements.

[0138] As a specific example, in step 2, for the FD subarray, the Root-MUSIC method is used to calculate a rough estimate of the radiation source direction. The specific steps are as follows:

[0139] The spatial spectrum corresponding to the FD subarray is expressed as:

[0140]

[0141] represents the conjugate transpose of the array manifold vector of the FD subarray, θ represents the initial input arrival angle, U N represents the signal noise subspace.

[0142] S F (θ) The denominator can be expressed as:

[0143]

[0144] z F is a function containing θ, U N represents the signal noise subspace, s F (z F ) represents a 2(M F -1) degree polynomial.

[0145] The polynomial equation s(z F ) has 2(M F -1) roots. Therefore, the root closest to the unit circle represents the expected DOA direction:

[0146]

[0147] As a specific example, in step 3, by performing DOA estimation on each group in the H 2 AD structure, multiple candidate angle sets are generated. The specific steps are as follows:

[0148] For the H 2 AD array, the spatial spectrum calculation formula for the virtual antenna array is:

[0149]

[0150] where

[0151]

[0152] where r p (θ) is a constant obtained by summing all elements of each subarray, denotes M q

[0153] root virtual antenna array manifold vector, and E N denotes the noise subspace of the signal.

[0154] Define then:

[0155]

[0156] is the element in the i-th 1 row and i-th 2 column of B.

[0157] Through Root-MUSIC, the direction corresponding to the peak is the final DOA estimate. The polynomial equation in

[0158]

[0159] where s(z) represents a 2K p -2 degree polynomial, z is a function containing θ, and s(η) represents a 2K p -2 degree polynomial.

[0160] where

[0161]

[0162]

[0163] The equation s(θ) has 2K p -2 roots, that is,

[0164]

[0165] η p is a function containing θ, and M p denotes that each subarray contains M p root antennas.

[0166] The DOA estimation set can be expressed as:

[0167]

[0168] where

[0169]

[0170] zl Denotes the \(l\)th root of the polynomial equation (15).

[0171] Therefore, the estimated value of the \(p\)th group is:

[0172]

[0173] Denotes the estimation of \(\eta\) p , Denotes the estimated value of the \(p\)th group. Consider the function with a period of \(2\pi\), then the feasible set of the \(M\) P th solutions of the \(p\)th group can be generalized as

[0174]

[0175] where

[0176]

[0177] Integrating all the groups gives:

[0178]

[0179] where \(q\) p \(\in\{1,2,\ldots,M\) p \}\) is the fuzzy coefficient. Therefore, the candidate set of H2AD can be expressed as:

[0180]

[0181] From the above analysis, it can be seen that the candidate set contains solutions, and each candidate set has a true solution and \(M\) p - 1 false solutions. Therefore, eliminating false solutions is a crucial issue. In the case where the noise is negligible, the true solution class and the false solution class can be respectively expressed as

[0182]

[0183]

[0184] where

[0185]

[0186] \(\chi\) p,m denotes the constant determined by \(M\) p and \(m\).

[0187] Then, the true solution class is expressed as

[0188]

[0189] Among them, is the predicted true solution of the p-th group.

[0190] As a specific example, in step 4, based on the initial clustering center points of the true solution class provided by the FD subarray, the true solutions corresponding to each group are inferred from the candidate angle set by using the global maximum similarity (GMaxCS) and global minimum distance (GMinD) clustering methods. The specific steps are as follows:

[0191] When the initial DOA value and the candidate set of the p-th group are obtained. Define the vector:

[0192]

[0193] Indicates the q-th p candidate solution of the p-th group.

[0194] Then, the cosine similarity can be defined as

[0195]

[0196] Therefore, by searching for the direction with the maximum similarity, the true angle

[0197]

[0198] As a specific example, in step 5, the two parts of the true solutions are synthesized by the iterative fusion algorithm to obtain the final DOA estimation value. The specific steps are as follows:

[0199] Fuse the two parts of the true solutions to obtain the optimal DOA estimation:

[0200]

[0201] Among them i represents the number of iterations. Represents the DOA estimation value after the i-th iteration.

[0202] According to the mean square error of, the problem is transformed into:

[0203]

[0204] Among them, the weighted fusion coefficients and are respectively expressed as

[0205]

[0206]

[0207] Among them and are respectively represented as

[0208]

[0209]

[0210]

[0211] Among them

[0212]

[0213]

[0214] H represents the number of snapshots, SNR represents the signal-to-noise ratio, represents the real part of

[0215] Figure 3 and Figure 4 plots the curves of the RMSE of the proposed method and the signal-to-noise ratio with the corresponding Cramer-Rao lower bound (CRLB) as the performance benchmark. It can be seen from the figure that the proposed method can quickly select the true solution in the group and approach or even reach the corresponding Cramer-Rao lower bound, achieving better DOA estimation performance.

[0216] The present invention also provides an enhanced DOA estimation system based on a multi-modal iterative fusion framework, which is used to implement the enhanced DOA estimation method based on the multi-modal iterative fusion framework.

[0217] The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the enhanced DOA estimation method based on the multi-modal iterative fusion framework.

[0218] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the steps in the enhanced DOA estimation method based on the multi-modal iterative fusion framework.

[0219] As described above, the above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

[0220] It should be understood that, in order to streamline the present invention and assist those skilled in the art in understanding various aspects of the present invention, in the above description of the exemplary embodiments of the present invention, various features of the present invention are sometimes described in a single embodiment or with reference to a single figure. However, the present invention should not be construed as meaning that the features included in the exemplary embodiments are all essential technical features of the claims of this patent.

Claims

1. An enhanced DOA estimation method based on a multimodal iterative fusion framework, characterized in that: The following steps are involved: Step 1: Construct all-digital FD subarray and heterogeneous hybrid H 2 Dual MIMO receiver combined with AD structure 2 AD-FD system model; Step 2: For the FD subarray, use the Root-MUSIC method to calculate a rough estimate of the radiation source direction; Step 3: By 2 Each group in the AD structure performs DOA estimation to generate multiple candidate angle sets; Step 4: Based on the rough estimate of the radiation source direction provided by the FD subarray as the initial cluster center point, the global maximum similarity (GMaxCS) and the global minimum distance (GMinD) clustering method are used to infer the true solution corresponding to each group from the candidate angle set; Step 5: Synthesize the two parts of the true solution through an iterative fusion algorithm: the rough estimate of the radiation source direction calculated by the FD subarray is used as the initial cluster center, and the weighted fusion coefficient and cluster center point of the true solution class in the candidate solution set are iteratively updated to obtain the final DOA estimate.

2. The enhanced DOA estimation method based on a multimodal iterative fusion framework according to claim 1, characterized in that: In step 1, the dual MIMO receiver H 2 The parameters of the AD-FD system model are as follows: A far-field narrowband signal Where e(t) and f c are the baseband signal and carrier frequency respectively, and t represents time; where H 2 The AD structure consists of P groups, and each group is divided into K p subarrays, each containing M p Antenna, that is In the array, M1≠M2≠…≠M P And choose M1,M2,…,M P The value of is considered a prime number; Assuming there is a transmitted signal incident from the direction of θ, the received signal vector y of the FD subarray is F (n) and the received signal vector y of the pth group p (n) are respectively expressed as: the F (n)=a F (θ0)and(n)+v F (n) (1.1) Where, e(n) is the baseband signal, and is the additive white Gaussian noise AWGN vector, represents the noise variance, I is the identity matrix, Indicates that the dimension is K p ×1 complex vector, v1(n),v2(n),…, represents a series of noises in the qth group; and Represents an array manifold vector: in Where M represents the number of antennas in the FD subarray, N p represents the number of antennas in the subarray in the qth group, d represents the spacing between adjacent array elements in the array antenna, and λ represents the wavelength; Block diagonal matrix Ξ A,p It is expressed as: Among them, ε A,p,k It is expressed as: Among them p,k,m is the simulated beam phase shift matrix, ε A,p,k represents the simulated beamforming vector of the kth subarray in each group, M p Indicates that each subarray contains M p Antenna.

3. The enhanced DOA estimation method based on a multimodal iterative fusion framework according to claim 2, characterized in that: In step 2, for the FD subarray, the Root-MUSIC method is used to calculate a rough estimate of the radiation source direction. The specific steps are: The spatial spectrum S corresponding to the FD subarray F (θ) is expressed as: represents the conjugate transpose of the array manifold vector of the FD subarray, θ represents the arrival angle of the initial input, and U N represents the noise subspace of the signal; S F The denominator of (θ) is expressed as: Among them, z F is a function containing θ, U N represents the noise subspace of the signal, s F (z F ) means 2(M F -1) polynomial; Polynomial equation s(z F ) has 2(M F -1) root, so the root closest to the unit circle represents the expected DOA direction, a rough estimate of the radiation source direction for:

4. The enhanced DOA estimation method based on a multimodal iterative fusion framework according to claim 3, characterized in that: In step 3, by 2 Each group in the AD structure performs DOA estimation to generate multiple candidate angle sets. The specific steps are as follows: For H 2 AD structure, spatial spectrum of virtual antenna array The calculation formula is: in Among them, r p (θ) is a constant obtained by adding all elements of each subarray, Indicates M q The array manifold vector of the root virtual antenna, E N represents the noise subspace of the signal; definition but: in, is the element in row i1 and column i2 of B; Through Root-MUSIC, The direction corresponding to the peak is the final DOA estimate. The polynomial equation in is defined as Where s(z) represents 2K p -2 degree polynomial, z is a function including θ, s(η) represents 2K p - polynomial of degree 2; in The equation s(θ) has 2K p -2 roots, i.e. WITH R ={z l ,l∈[1,2K p -2]}(1.18) η p is a function containing θ, M p Indicates that each subarray contains M p Antenna; DOA estimation set It is expressed as: in z l represents the lth root of the polynomial equation (15); Therefore, the estimated value of group p for: Represents η p The estimate, represents the estimated value of group p; Consider the function The period is 2π, then M of the pth group P The feasible set of solutions is generalized to: in The groups that integrate all of them are: where q p ∈{1,2,…,M p } is the fuzzy coefficient, so H 2 The candidate set of AD is expressed as: Candidate Set Include Solution, each candidate set There is a true solution and M p -1 pseudo solution. When the noise is ignored, the true solution class and the false solution class are expressed as in χ p,m Indicated by M p A constant determined by and m; The true solution class is represented as in, H 2 The predicted true solution of the pth group of AD structures.

5. The enhanced DOA estimation method based on a multimodal iterative fusion framework according to claim 4 is characterized in that: In step 4, based on the rough estimate of the radiation source direction provided by the FD subarray as the initial cluster center point, the global maximum similarity, i.e., GMaxCS, and the global minimum distance, i.e., GMinD clustering method, are used to infer the true solution corresponding to each group from the candidate angle set. The specific steps are as follows: When the initial DOA value is obtained and the candidate set of group p When , define the vector: It means the qth p Candidate solutions; Then, the cosine similarity is defined as Therefore, by searching for the direction with the greatest similarity, the true angle 6. The enhanced DOA estimation method based on a multimodal iterative fusion framework according to claim 5, characterized in that: In step 5, the two parts of the true solution are synthesized through an iterative fusion algorithm. The specific steps are: The optimal DOA estimation is obtained by combining the two true solutions: in, i represents the number of iterations; represents the DOA estimate after the i-th iteration; according to The mean square error is converted into The weighted fusion coefficient and Respectively expressed as in and Respectively expressed as in Where H represents the number of snapshots, SNR represents the signal-to-noise ratio, express The real part of .

7. An enhanced DOA estimation system based on a multimodal iterative fusion framework, characterized in that: The system is used to implement the enhanced DOA estimation method based on a multimodal iterative fusion framework as described in any one of claims 1 to 6.

8. A mobile terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the enhanced DOA estimation method based on a multimodal iterative fusion framework as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the enhanced DOA estimation method based on a multimodal iterative fusion framework as described in any one of claims 1 to 6 are implemented.

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