A Direction Finding Estimation Algorithm Based on DOA

Abstract

The present invention discloses a DOA-based direction-finding estimation algorithm, comprising step S1: calibrating the receiving and transmitting channels using a calibration algorithm to obtain channels with standard waveform amplitude and phase; step S2: estimating the direction of arrival (DOA) based on the waveforms transmitted by different channels, which have different reception effects; and step S3: adjusting the antenna channel based on the DOA-estimated direction of arrival angle, aligning the antenna angle with the direction of arrival angle. The DOA-based direction-finding estimation algorithm disclosed by the present invention utilizes gain compensation and amplitude and phase error correction to achieve more accurate processing, resulting in high accuracy and efficiency.

Description

Technical Field

[0001] The invention belongs to the technical field of digital direction finding, and in particular relates to a direction finding estimation algorithm based on DOA. Background Art

[0002] Direction finding estimation is a new type of direction finding technology that uses digital signal processing technology to control direct line signals and antenna data to achieve channel calibration.

[0003] However, in the DOA process, the gain of signals received by different array elements may vary to varying degrees, which may have a certain impact on the DOA (direction of arrival) estimation.

[0004] Therefore, further improvements are made to the above problems. Summary of the Invention

[0005] The main purpose of the present invention is to provide a DOA-based direction finding estimation algorithm, which makes the processing more accurate through gain compensation and amplitude and phase error correction, and has the advantages of high precision and high efficiency.

[0006] To achieve the above objectives, the present invention provides a DOA-based direction finding estimation algorithm, comprising the following steps:

[0007] Step S1: Calibrate the receiving channel and the transmitting channel through a calibration algorithm to obtain a channel with standard waveform amplitude and phase;

[0008] Step S2: The waveforms transmitted by different channels have different reception effects, and the direction of arrival (DOA) is estimated;

[0009] Step S3: The antenna channel is adjusted based on the direction of arrival angle estimated by DOA, so that the angle of the antenna is aligned with the direction of arrival angle.

[0010] As a further preferred technical solution of the above technical solution, step S1 is specifically implemented as follows:

[0011] Step S1.1: After obtaining the signal X(t) received by each array element, calculate the fast Fourier transform matrix X(n) = DFT[x1(n)] + jDFT[x2(n)] = X1(k) + jX2(k), where X(n) is the original sequence, DFT is the Fourier transform, x1(n) is the odd sequence, x2(n) is the even sequence, and X1(k) and X2(k) are the results of the Fourier transform;

[0012] Step S1.2: Read the mutual coupling parameters of the matrix X(n). If there are calculated values ​​in the workspace, use the calculated values. Otherwise, load and save the mutual coupling parameters.

[0013] Step S1.3: Calculate the standard mutual coupling reference h=H('h12') / H('h13'), where h is the standard mutual coupling parameter, and H('h12') and H('h13') are the mutual coupling coefficients stored in the corresponding key-value pairs;

[0014] Step S1.4: Calculate h1 / h2, where h1 is the mutual coupling coefficient of the first channel (channel 1), and h2 is the mutual coupling coefficient of the second channel (channel 2) (the same below);

[0015] Step S1.5: Calculate the relative amplitude deviation and phase deviation of the receiving channel (the calculation is equivalent to the standard waveform amplitude and phase channel, the same below);

[0016] Step S1.6: Calculate the relative amplitude deviation and phase deviation of the transmission channel.

[0017] As a further preferred technical solution of the above technical solution, step S1.4 is specifically implemented as the following steps:

[0018] Step S1.4.1: The array calibration process uses online calibration. Each array element transmits and the adjacent array element receives. The eight array elements transmit in turn. When one array element is transmitting, the other array elements receive simultaneously.

[0019]

[0020] Where t2 represents the complex gain of transmit channel 2, t3 represents the complex gain of transmit channel 3, r1 represents the complex gain of receive channel 1, and r4 represents the complex gain of receive channel 4.

[0021] Get h1^2 / h2^2;

[0022] Step S1.4.2:

[0023] h2r24=H('h24') / H('h13');

[0024] h2r31=H('h31') / H('h13');

[0025] h1r34=H('h34') / H('h12');

[0026] h1r21=H('h21') / H('h12');

[0027] hr=(A4(3)*A1(2)*h2r24*h2r31) / (A1(3)*A4(2)*h1r34*h1r21); h1 / h2=hr^(1 / 2) (where A4(3) is the transmission channel characteristic of channels 3 and 4).

[0028] As a further preferred technical solution of the above technical solution, step S1.5 is specifically implemented as the following steps:

[0029] Step S1.5.1: Calculate r7 / r8, where r7 is the complex gain of element 8 when it is a receiving element, including digital, analog, and antenna gains; and r8 is the complex gain of element 8 when it is a receiving element, including digital, analog, and antenna gains (the same below).

[0030] a7<-a6:t6*h1*r7*h1r67,A7(6)

[0031] a8<-a6:t6*h2*r8,A8(6)

[0032] h2r68=H('h68') / H('h13');

[0033] h1r67=H('h67') / H('h12');

[0034] r7 / r8=(A7(6)*h2r68) / (A8(6)*h1r67);

[0035] Step S1.5.2: r7 = 1, i.e., r7 is set as the reference element to calculate r8;

[0036] Step S1.5.3: Calculate r1, r3, r5, r2, r4, and r6 respectively, where:

[0037] a5<-a6,A5(6)

[0038] a7<-a6,A7(6)

[0039] r5=r7*((A5(6)*fi_x3(h1r67,8)) / (A7(6)*fi_x3(h1r56,8)));

[0040] r3=r5*((A3(4)*fi_x3(h1r45,8)) / (A5(4)*fi_x3(h1r34,8)));

[0041] r1=r3*((A1(2)*fi_x3(h1r23,8))*4 / (A3(2)*c2));

[0042] r6=r7*(A6(5)*fi_x3(h2r57,8)*c2) / (A7(5)*fi_x3(h1r56,8)*hrr);

[0043] r4=r6*((A4(5)*fi_x3(h1r56,8)) / (A6(5)*fi_x3(h1r45,8)));

[0044] r2=r4*((A2(3)*fi_x3(h1r34,8)) / (A4(3)*fi_x3(h1r23,8)*2))

[0045] (where fix_3 is a three-digit integer).

[0046] As a further preferred technical solution of the above technical solution, step S1.6 is specifically implemented as the following steps:

[0047] Step S1.6.1: Calculate t1 / t2, set t2 to 1 and set t2 as the reference element, where:

[0048]

[0049] Step S1.6.2: Calculate t4, t5, t6, t7, and t8, where:

[0050]

[0051] As a further preferred technical solution of the above technical solution, the DOA estimation in step S2 is specifically implemented as follows:

[0052] Step S2.1: Integrate a logic analyzer to collect doa data and convert i (imaginary part) and q (real part) into complex numbers;

[0053] Step S2.2: Perform amplitude normalization on the X(t) signal (in the DOA algorithm, let θ be the direction of arrival angle. The MUSIC algorithm is an estimation algorithm for the angle of arrival θ. Suppose there are D signal sources transmitting signals to an array antenna with K elements. Then the received signal of the antenna is X(t) = A(θ)S(t) + N(t), where X(t) is the signal received by each element (T represents the transposed matrix), X(t) = [x1(t), x2(t), ..., x K (t)] T , S(t) is the signal emitted by each signal source, S(t)=[s1(t),s2(t),…,s D (t)] T , N(t) is the noise signal, and its variance is σ 2 ,N(t)=[n1(t),n2(t),…,n K (t)] T );

[0054]

[0055] R2=R1 / X;

[0056] Where R1 is the covariance matrix and X is the number of matrix elements;

[0057] Step S2.3: perform the operation of step S1 on R2;

[0058] Step S2.4: Calculate the covariance matrix;

[0059]

[0060] Step S2.5: Perform eigendecomposition on R3 to obtain all its K eigenvalues ​​[λ1,λ2,…,λ K ], forming a diagonal matrix ∑, σ 2 is the noise variance, I is the identity matrix;

[0061] R3=U∑U H =AR S A H +σ 2 I;

[0062]

[0063] (The covariance matrix of the received signal is:

[0064] R=E[XX H ]=AE[SS H ]A H +E[NN H ]

[0065] =AR S A H +R N =AR S A H +σ 2 I

[0066] where R S is the correlation matrix of the signal, R N is the correlation matrix of the noise, and I is the identity matrix.

[0067] R is a self-conjugate symmetric matrix, and the characteristic decomposition of R is:

[0068] R=U∑U H ;

[0069] Where U is the eigenvector matrix, ∑ is the diagonal matrix consisting of the eigenvalues ​​of R, expressed as

[0070]

[0071] Step S2.6: Sort the obtained M eigenvalues ​​from largest to smallest to obtain the following:

[0072] λ1≥λ2≥…λ D >λ D+1 ≥…≥λ K ;

[0073] (These eigenvalues ​​are decomposed into two matrices

[0074]

[0075]

[0076] where Σ S is a diagonal matrix composed of large eigenvalues, Σ N is a diagonal matrix composed of small eigenvalues)

[0077] Step S2.7: Calculate the eigenvectors corresponding to the eigenvalues ​​respectively. The eigenvectors corresponding to the smaller KD eigenvalues ​​in the back [e D+1 ,e D+2 ,…,e K ], thus forming the noise subspace, and then obtaining the noise subspace matrix U N =[e D+1 ,e D+2 ,…,e K ]; (the large eigenvalue corresponds to the signal subspace

[0078] U S =[e1,e2,…,e D ];

[0079] [e1,e2,…,e D ] are the eigenvalues ​​[λ1,λ2,…,λ D ] corresponding eigenvector;

[0080] The small eigenvalue corresponds to the noise subspace

[0081] U N =[e D+1 ,e D+2 ,…,e K ];

[0082] [e D+1 ,e D+2 ,…,e K ] are the eigenvalues ​​[λ D+1 ,λ D+2 ,…,λ K ] corresponding eigenvector; thus, the covariance matrix R is decomposed into signal subspace and noise subspace

[0083]

[0084] Step S2.8: The manifold vector matrix A(θ) is expressed as A(θ) = [a(θ1), a(θ2), …, a(θ D )] T ;

[0085] Step S2.9: Perform spectrum peak search based on the signal parameter range according to the following formula. The peak value is the estimated value of the direction of arrival. The formula is:

[0086]

[0087] The principle of the DOA algorithm is that under ideal conditions, the signal subspace and the noise subspace are orthogonal to each other, that is, the steering vector of the signal subspace is also orthogonal to the noise subspace, that is,

[0088] A H (θ)U N =0;

[0089] In practice, the matrix receiving the data is of finite length, that is, the maximum likelihood estimate of the data covariance matrix (that is, the covariance matrix of the sample)

[0090]

[0091] right Perform eigendecomposition to obtain the noise subspace eigenvector matrix (composed of the eigenvectors corresponding to each noise). Due to the influence of noise, the orthogonality cannot be completely established, so the minimum optimization search method is used to achieve it, that is,

[0092]

[0093] Then the spectrum estimation formula of the DOA algorithm is obtained as follows:

[0094]

[0095] To achieve the above objectives, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the DOA-based direction finding estimation algorithm when executing the program.

[0096] To achieve the above objectives, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which implements the steps of the DOA-based direction finding estimation algorithm when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1This is a flow chart of a DOA-based direction finding estimation algorithm of the present invention. DETAILED DESCRIPTION

[0098] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are for illustrative purposes only, and those skilled in the art will readily appreciate other obvious variations. The basic principles of the present invention defined in the following description may be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the present invention.

[0099] In the preferred embodiment of the present invention, those skilled in the art should note that the DOA and the like involved in the present invention may be regarded as prior art.

[0100] Preferred embodiment.

[0101] The present invention discloses a direction finding estimation algorithm based on DOA, comprising the following steps:

[0102] Step S1: Calibrate the receiving channel and the transmitting channel through a calibration algorithm to obtain a channel with standard waveform amplitude and phase;

[0103] Step S2: The waveforms transmitted by different channels have different reception effects, and the direction of arrival (DOA) is estimated;

[0104] Step S3: The antenna channel is adjusted based on the direction of arrival angle estimated by DOA, so that the angle of the antenna is aligned with the direction of arrival angle.

[0105] Specifically, step S1 is implemented as follows:

[0106] Step S1.1: After obtaining the signal X(t) received by each array element, calculate the fast Fourier transform matrix X(n) = DFT[x1(n)] + jDFT[x2(n)] = X1(k) + jX2(k), where X(n) is the original sequence, DFT is the Fourier transform, n1(n) is the odd sequence, x2(n) is the even sequence, and X1(k) and X2(k) are the results of the Fourier transform;

[0107] Step S1.2: Read the mutual coupling parameters of the matrix X(n). If there are calculated values ​​in the workspace, use the calculated values. Otherwise, load and save the mutual coupling parameters.

[0108] Step S1.3: Calculate the standard mutual coupling reference h=H('h12') / H('h13'), where h is the standard mutual coupling parameter, and H('h12') and H('h13') are the mutual coupling coefficients stored in the corresponding key-value pairs;

[0109] Step S1.4: Calculate h1 / h2, where h1 is the mutual coupling coefficient of the first channel (channel 1), and h2 is the mutual coupling coefficient of the second channel (channel 2) (the same below);

[0110] Step S1.5: Calculate the relative amplitude deviation and phase deviation of the receiving channel (the calculation is equivalent to the standard waveform amplitude and phase channel, the same below);

[0111] Step S1.6: Calculate the relative amplitude deviation and phase deviation of the transmission channel.

[0112] More specifically, step S1.4 is implemented as follows:

[0113] Step S1.4.1: The array calibration process uses online calibration. Each array element transmits and the adjacent array element receives. The eight array elements transmit in turn. When one array element is transmitting, the other array elements receive simultaneously.

[0114]

[0115] Where t2 represents the complex gain of transmit channel 2, t3 represents the complex gain of transmit channel 3, r1 represents the complex gain of receive channel 1, and r4 represents the complex gain of receive channel 4.

[0116] Get h1^2 / h2^2;

[0117] Step S1.4.2:

[0118] h2r24=H('h24') / H('h13');

[0119] h2r31=H('h31') / H('h13');

[0120] h1r34=H('h34') / H('h12');

[0121] h1r21=H('h21') / H('h12');

[0122] hr=(A4(3)*A1(2)*h2r24*h2r31) / (A1(3)*A4(2)*h1r34*h1r21); h1 / h2=hr^(1 / 2) (where A4(3) is the transmission channel characteristic of channels 3 and 4).

[0123] Furthermore, step S1.5 is specifically implemented as the following steps:

[0124] Step S1.5.1: Calculate r7 / r8, where r7 is the complex gain of element 8 when it is a receiving element, including digital, analog, and antenna gains; and r8 is the complex gain of element 8 when it is a receiving element, including digital, analog, and antenna gains (the same below).

[0125] a7<-a6:t6*h1*r7*h1r67,A7(6)

[0126] a8<-a6:t6*h2*r8,A8(6)

[0127] h2r68=H('h68') / H('h13');

[0128] h1r67=H('h67') / H('h12');

[0129] r7 / r8=(A7(6)*h2r68) / (A8(6)*h1r67);

[0130] Step S1.5.2: r7 = 1, i.e., r7 is set as the reference element to calculate r8;

[0131] Step S1.5.3: Calculate r1, r3, r5, r2, r4, and r6 respectively, where:

[0132] a5<-a6,A5(6)

[0133] a7<-a6,A7(6)

[0134] r5=r7*((A5(6)*fi_x3(h1r67,8)) / (A7(6)*fi_x3(h1r56,8)));

[0135] r3=r5*((A3(4)*fi_x3(h1r45,8)) / (A5(4)*fi_x3(h1r34,8)));

[0136] r1=r3*((A1(2)*fi_x3(h1r23,8))*4 / (A3(2)*c2));

[0137] r6=r7*(A6(5)*fi_x3(h2r57,8)*c2) / (A7(5)*fi_x3(h1r56,8)*hrr);

[0138] r4=r6*((A4(5)*fi_x3(h1r56,8)) / (A6(5)*fi_x3(h1r45,8)));

[0139] r2=r4*((A2(3)*fi_x3(h1r34,8)) / (A4(3)*fi_x3(h1r23,8)*2))

[0140] (where fix_3 is a three-digit integer).

[0141] Furthermore, step S1.6 is specifically implemented as the following steps:

[0142] Step S1.6.1: Calculate t1 / t2, set t2 to 1 and set t2 as the reference element, where:

[0143]

[0144] Step S1.6.2: Calculate t4, t5, t6, t7, and t8, where:

[0145]

[0146] Preferably, the DOA estimation in step S2 is specifically implemented as follows:

[0147] Step S2.1: Integrate a logic analyzer to collect doa data and convert i (imaginary part) and q (real part) into complex numbers;

[0148] Step S2.2: Perform amplitude normalization on the X(t) signal (in the DOA algorithm, let θ be the direction of arrival angle. The MUSIC algorithm is an estimation algorithm for the angle of arrival θ. Suppose there are D signal sources transmitting signals to an array antenna with K elements. Then the received signal of the antenna is X(t) = A(θ)S(t) + N(t), where X(t) is the signal received by each element (T represents the transposed matrix), X(t) = [x1(t), x2(t), ..., x K (t)] T , S(t) is the signal emitted by each signal source, S(t)=[s1(t),s2(t),…,s D (t)] T , N(t) is the noise signal, and its variance is σ 2 ,N(t)=[n1(t),n2(t),…,n K (t)] T );

[0149]

[0150] R2=R1 / X;

[0151] Where R1 is the covariance matrix and X is the number of matrix elements;

[0152] Step S2.3: perform the operation of step S1 on R2;

[0153] Step S2.4: Calculate the covariance matrix;

[0154]

[0155] Step S2.5: Perform eigendecomposition on R3 to obtain all its K eigenvalues ​​[λ1,λ2,…,λ K ], forming a diagonal matrix ∑, σ 2 is the noise variance, I is the identity matrix;

[0156] R3=U∑U H =AR S A H +σ 2 I;

[0157]

[0158] (The covariance matrix of the received signal is:

[0159] R=E[XX H ]=AE[SS H ]A H +E[NN H ]

[0160] =AR S A H +R N =AR S A H +σ 2 I

[0161] where R S is the correlation matrix of the signal, R N is the correlation matrix of the noise, and I is the identity matrix.

[0162] E is a self-conjugate symmetric matrix, and the characteristic decomposition of E is:

[0163] E=U∑U H ;

[0164] Where U is the eigenvector matrix, ∑ is the diagonal matrix consisting of the eigenvalues ​​of R, expressed as

[0165]

[0166] Step S2.6: Sort the obtained M eigenvalues ​​from largest to smallest to obtain the following:

[0167] λ1≥λ2≥…λ D >λ D+1 ≥…≥λ K ;

[0168] (These eigenvalues ​​are decomposed into two matrices

[0169]

[0170] where Σ S is a diagonal matrix composed of large eigenvalues, Σ N is a diagonal matrix composed of small eigenvalues)

[0171] Step S2.7: Calculate the eigenvectors corresponding to the eigenvalues ​​respectively. The eigenvectors corresponding to the smaller KD eigenvalues ​​in the back [e D+1 ,e D+2 ,…,e K ], thus forming the noise subspace, and then obtaining the noise subspace matrix U N =[e D+1 ,e D+2 ,…,e K ]; (the large eigenvalue corresponds to the signal subspace

[0172] U S =[e1,e2,…,e D ];

[0173] [e1,e2,…,e D ] are the eigenvalues ​​[λ1,λ2,…,λ D ] corresponding eigenvector;

[0174] The small eigenvalue corresponds to the noise subspace

[0175] U N =[e D+1 ,e D+2 ,…,e K ];

[0176] [e D+1 ,e D+2 ,…,e K ] are the eigenvalues ​​[λ D+1 ,λ D+2 ,…,λ K ] corresponding eigenvector; thus, the covariance matrix R is decomposed into signal subspace and noise subspace

[0177]

[0178] Step S2.8: The manifold vector matrix A(θ) is expressed as A(θ) = [a(θ1), a(θ2), …, a(θ D )] T ;

[0179] Step S2.9: Perform spectrum peak search based on the signal parameter range according to the following formula. The peak value is the estimated value of the direction of arrival. The formula is:

[0180]

[0181] The principle of the DOA algorithm is that under ideal conditions, the signal subspace and the noise subspace are orthogonal to each other, that is, the steering vector of the signal subspace is also orthogonal to the noise subspace, that is,

[0182] A H (θ)U N =0;

[0183] In practice, the matrix receiving the data is of finite length, that is, the maximum likelihood estimate of the data covariance matrix (that is, the covariance matrix of the sample)

[0184]

[0185] right Perform eigendecomposition to obtain the noise subspace eigenvector matrix (composed of the eigenvectors corresponding to each noise). Due to the influence of noise, the orthogonality cannot be completely established, so the minimum optimization search method is used to achieve it, that is,

[0186]

[0187] Then the spectrum estimation formula of the DOA algorithm is obtained as follows:

[0188]

[0189] The present invention also discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the DOA-based direction finding estimation algorithm when executing the program.

[0190] The present invention also discloses a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the DOA-based direction finding estimation algorithm are implemented.

[0191] It is worth mentioning that the technical features such as DOA involved in the patent application of this invention should be regarded as prior art. The specific structure, working principle and possible control method and spatial layout method of these technical features can be selected by conventional means in the field and should not be regarded as the inventive point of this patent. This patent will not be further elaborated.

[0192] For those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned embodiments, or to make equivalent replacements for some of the technical features therein. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A direction finding estimation algorithm based on DOA, characterized in that: The following steps are involved: Step S1: Calibrate the receiving channel and the transmitting channel through a calibration algorithm to obtain a channel with standard waveform amplitude and phase; Step S2: The waveforms transmitted by different channels have different reception effects, and the direction of arrival (DOA) is estimated; Step S3: Adjust the antenna channel based on the direction of arrival angle estimated by DOA, so that the antenna angle is aligned with the direction of arrival angle; Step S1 is specifically implemented as follows: Step S1.1: Get the signal received by each array element Then, calculate the matrix after fast Fourier transform ,in is the original sequence, DFT is Fourier transform, For the odd number sequence, For the even number sequence, and is the result after Fourier transform; Step S1.2: Read the matrix If there is a calculated value in the workspace, the calculated value will be used; otherwise, the mutual coupling parameters will be loaded and saved. Step S1.3: Calculate the standard mutual coupling reference , where h is the standard mutual couple parameter, and is the mutual coupling coefficient stored for the corresponding key-value pair; Step S1.4: Calculate h1 / h2, where h1 is the mutual coupling coefficient of the first channel and h2 is the mutual coupling coefficient of the second channel; Step S1.5: Calculate the relative amplitude deviation and phase deviation of the receiving channel; Step S1.6: Calculate the relative amplitude deviation and phase deviation of the transmission channel.

2. A DOA-based direction finding estimation algorithm according to claim 1, characterized in that: Step S1.4 is specifically implemented as follows: Step S1.4.1: The array calibration process uses online calibration. Each array element transmits and the adjacent array element receives. The eight array elements transmit in turn. When one array element is transmitting, the other array elements receive simultaneously. ； Where t2 represents the complex gain of transmit channel 2, t3 represents the complex gain of transmit channel 3, r1 represents the complex gain of receive channel 1, and r4 represents the complex gain of receive channel 4. get ; Step S1.4.2: ； 。 3. A DOA-based direction finding estimation algorithm according to claim 2, characterized in that: Step S1.5 is specifically implemented as follows: Step S1.5.1: Calculate r7 / r8, where r7 is the complex gain of element 8 when it is a receiving element, including digital, analog, and antenna gains; and r8 is the complex gain of element 8 when it is a receiving element, including digital, analog, and antenna gains. ； Step S1.5.2: , that is, set r7 as the reference element to calculate r8; Step S1.5.3: Calculate r1, r3, r5, r2, r4, and r6 respectively, where: 。 4. A DOA-based direction finding estimation algorithm according to claim 3, characterized in that: Step S1.6 is specifically implemented as follows: Step S1.6.1: Calculate t1 / t2, set t2 to 1 and set t2 as the reference element, where: ； Step S1.6.2: Calculate t4, t5, t6, t7, and t8, where: 。 5. A DOA-based direction finding estimation algorithm according to claim 4, characterized in that: The DOA estimation in step S2 is specifically implemented as follows: Step S2.1: Integrate a logic analyzer to collect doa data and convert i and q into complex numbers; Step S2.2: The signal is amplitude normalized; ； ； Where R1 is the covariance matrix and X is the number of matrix elements; Step S2.3: perform the operation of step S1 on R2; Step S2.4: Calculate the covariance matrix; ； Step S2.5: Perform eigendecomposition on R3 to obtain all its K eigenvalues , forming a diagonal matrix is the noise variance, is the identity matrix; ； Step S2.6: Sort the obtained M eigenvalues ​​from largest to smallest to obtain the following: ； Step S2.7: Find the eigenvectors corresponding to the eigenvalues ​​respectively, and the eigenvectors corresponding to the smaller KD eigenvalues ​​in the back , thus forming the noise subspace, and then obtaining the noise subspace matrix ; Step S2.8: Manifold Vector Matrix Expressed as ; Step S2.9: Perform spectrum peak search based on the signal parameter range according to the following formula. The peak value is the estimated value of the direction of arrival. The formula is: 。 6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the DOA-based direction finding estimation algorithm according to any one of claims 1 to 5 are implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the DOA-based direction finding estimation algorithm according to any one of claims 1 to 5 are implemented.

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