Orthogonal matching pursuit sound source identification method based on feedback mechanism

Abstract

The invention discloses an orthogonal matching pursuit sound source identification and positioning method based on a feedback mechanism, and the method comprises the steps: setting a measurement surface and a focusing surface according to a set distance interval, carrying out the grid division of the measurement surface and the focusing surface, and obtaining a microphone position, a focusing point and a Green function transfer matrix; obtaining the relation between the sound pressure vector measured by the microphone and the source intensity of the sound source at the focus point by using the Green function transfer matrix; and the sound source intensity of the focusing surface grid points is calculated through an orthogonal matching pursuit algorithm based on source intensity prior guidance, and the sound source is accurately identified and positioned by using the obtained sound source intensity data. According to the method, the atoms are preliminarily selected through the orthogonal matching pursuit algorithm guided by the source intensity prior information, the preliminarily selected atoms are fed back to the transfer matrix, the atoms closest to the measured sound pressure vector are obtained to serve as the finally selected atoms, the reconstruction performance of a traditional algorithm at low and medium frequencies is improved, and the method is suitable for being applied to the field of sound pressure measurement. And meanwhile, the sound source identification and positioning capability under the strong correlation between atoms of the measurement matrix is improved.

Description

Technical Field

[0001] The invention relates to the recognition and positioning of sound sources, and in particular to an orthogonal matching pursuit sound source recognition method based on a feedback mechanism. Background Art

[0002] In recent years, near-field acoustic holography and beamforming technologies have played an important role in acoustic engineering such as noise source identification and positioning, sound field separation, sound field visualization, and speech enhancement. These two technologies are not affected by the shape and size of the sound source, and can easily establish mathematical models and construct transfer matrices. The implementation of near-field acoustic holography and beamforming technologies is mostly based on traditional acoustic arrays such as planar uniform arrays, and data acquisition is based on the Nyquist sampling framework. In order to ensure that the collected signal does not have spectral aliasing, the sampling frequency must be greater than 2 times the highest frequency of the signal when using the above regular array for sound field data acquisition. In order to obtain higher recognition accuracy, the above traditional arrays require more microphones, which has high spatial complexity and high measurement cost; at the same time, a large amount of measurement data increases the difficulty of storage and transmission.

[0003] Compressed sensing technology breaks through the Nyquist sampling theorem. It uses the sparsity of the signal in the transform domain, can use a lower frequency sampling to retain all the information of the original signal and realize high-precision reconstruction of the original signal, thereby significantly reducing the number of sampling sensors and reducing the system measurement cost. In the reconstruction of the original sparse signal in compressed sensing theory, the orthogonal matching pursuit algorithm is the most commonly used reconstruction method, referred to as the OMP algorithm. The OMP algorithm has the advantages of small imaging side lobes, few ghosts, and fast calculation speed. However, the OMP algorithm has shortcomings in practical applications: the OMP algorithm and various compressed sensing greedy reconstruction algorithms are based on the absolute inner product principle to expand the atomic support set. This criterion measures the atomic correlation by measuring the cosine similarity between matrix atoms. When the sound source frequency decreases, the phase of the Green's function will change slowly with distance, which will cause the correlation between atoms to gradually increase and the two columns of atoms to gradually converge; and when the focus surface grid is too densely divided, the focus surface grid point spacing corresponding to two adjacent atoms is very small, which will also cause the two columns of atoms to have a high correlation. When the inner product of the atom corresponding to the actual sound source and the residual is smaller than the inner product value of the adjacent atom and the residual, the absolute inner product matching criterion cannot expand the correct atom to the support set, which will inevitably lead to the failure of sound source identification. In addition, for coherent sound sources with low frequencies and close distances, there will be coherence peaks, causing the inner product of the atom corresponding to the coherence peak position in the measurement matrix and the signal to even exceed the inner product of the atom corresponding to the actual sound source and the original signal, resulting in the algorithm incorrectly selecting the atom corresponding to the coherence peak position in the first iteration, causing the sound source identification to fail. Summary of the invention

[0004] The present invention is to avoid the deficiencies of the above-mentioned prior art and provide an orthogonal matching pursuit sound source identification method based on a feedback mechanism, aiming to improve the algorithm reconstruction performance in an environment with strong correlation between atoms, and to avoid the problem of low sound source identification resolution caused by the enhanced correlation between adjacent atoms in the transfer matrix due to the encryption of the focusing surface grid and the change of the signal phase at low frequency.

[0005] In order to achieve the above-mentioned invention object, the present invention adopts the following technical scheme:

[0006] The orthogonal matching pursuit sound source identification method based on feedback mechanism of the present invention is characterized by:

[0007] Setting the measuring plane and the focusing plane at set distance intervals, meshing the measuring plane and the focusing plane, and obtaining the microphone position and the focusing point as well as the Green's function transfer matrix;

[0008] The Green's function transfer matrix is ​​used to obtain the relationship between the sound pressure vector measured by the microphone and the source intensity of the sound source at the focal point;

[0009] The approximate solution of the source strength of the focal point sound source is obtained by calculating the least squares method, and the approximate solution of the source strength of the focal point sound source is used as prior information to guide the screening of atoms, and the position of the largest element is found as the rough sound source position. Finally, the precise sound source position is obtained by searching near the rough sound source position.

[0010] The orthogonal matching pursuit sound source identification method based on feedback mechanism of the present invention is characterized by comprising the following steps:

[0011] Step 1: Determine the transfer matrix G between the sound pressure measured by the microphone in the measurement plane and the sound source intensity at the focal point in the focusing plane:

[0012] A measurement surface W is set in the sound field generated by K sound sources, M grid points are evenly distributed in the measurement surface W, and M microphones are arranged one by one on the M grid points to form a measurement array;

[0013] Evenly distributing N focusing points on a focusing plane T formed by a sound source calculation plane, and using the N focusing points as potential sound source positions;

[0014] The transfer matrix G between the sound source intensity at the focus point and the microphone array on the measurement surface is established based on the free-field Green's function:

[0015] Step 2: Use the transfer matrix G to establish the relationship between the source intensity of the focused sound source and the sound pressure measured by the microphone as shown in formula (1):

[0016] p=Gq+ζ (1)

[0017] In formula (1):

[0018] q represents the source intensity vector of the sound source at the focal point; p represents the sound pressure vector measured by the microphone on the measurement surface;

[0019] Let ζ represent the noise vector contained in the sound pressure vector p measured by the microphone;

[0020] Step 3: The orthogonal matching pursuit algorithm based on the feedback mechanism solves the sound source intensity vector and locates the sound source according to the following process:

[0021] Step 3.1, initialize the residual R0 = p, index set It is an empty set, and the number of iterations k = 1;

[0022] Step 3.2, perform singular value decomposition on the transfer matrix G, calculate the regularization parameter λ, and use the Tikhonov regularization method to solve the approximate solution F of the sound source intensity vector;

[0023] Step 3.3: Use the sound source intensity vector approximate solution F as prior information to guide the selection of atoms, and find the location of the maximum element according to formula (2):

[0024]

[0025] In formula (2):

[0026] i k The index of the position of the maximum element at the kth iteration;

[0027] F(x) is the source intensity at the xth index position of the source intensity vector approximate solution;

[0028] It means when x=1,2,...,N, find the value of x that makes |F(x)| reach the maximum value;

[0029] Step 3.4: Update the index set to Γ k : Γ k =Γ k-1 ∪i k

[0030] Among them, Γ k-1 is the index set of the optimal atom set that has been screened in the previous iteration;

[0031] Step 3.5: Use the index set Γ k Take the corresponding atomic composition matrix from the transfer matrix G The sound source intensity q at the corresponding position of the index set is calculated using the least squares method k for: In the formula, Denotes the transfer matrix G of Γ k The new matrix consists of columns, Denote the matrix as the transpose of;

[0032] The corresponding source strength vector where: Denote the value of the source strength vector at the position of Γ k , and the values at the remaining positions of the source strength vector are all 0;

[0033] Step 3.6. Update the residual R according to Equation (3) k as:

[0034]

[0035] Step 3.7. When the iteration number k < K, increase the value of the iteration number k by 1, return to Step 3.2, and continue the iteration; otherwise, stop the iteration and output the index Γ of the maximum element position obtained in each iteration k and the rough solution q of the source strength at the corresponding position k , which are the initially calculated sound source position and the corresponding source strength;

[0036] Step 3.8. Search for all combinations δ of the grid point coordinates near the initially selected atoms:

[0037] The coordinates of the J-th sound source position initially selected and all its adjacent grid points form a set O J , J = 1, 2,..., K;

[0038] Take one element from each of O1, O2,..., O K to form a combination H i ;

[0039] δ is all combinations of the grid point coordinates near the initially selected atoms, including all combination cases, that is:

[0040] δ = [H1, H2,..., H B

[0041] Denote the i-th combination by H i , i = 1, 2,..., B, where B is the total number of all combinations;

[0042] Step 3.9. Calculate the source strength q' at the coordinate position corresponding to each combination i , and the calculated sound pressure vector p i corresponding to each combination:

[0043] Calculate the source strength q' at the coordinate position corresponding to each combination using the least squares method according to Equation (4) i as:

[0044]

[0045] in:

[0046] q′ i represents the source intensity at the coordinate position corresponding to the i-th combination;

[0047] H represents the transfer matrix G. i The new matrix consists of columns, Representation Matrix The transpose of

[0048] The source intensity vector corresponding to each combination is calculated by formula (5):

[0049]

[0050] in:

[0051] Indicates the source intensity vector in H i The value at the position, the rest of the positions of the source intensity vector are 0;

[0052] The calculated sound pressure vector p corresponding to each combination is obtained by formula (6): i :

[0053]

[0054] Step 3.10, using the maximum likelihood estimation method to calculate and obtain a final solution of the sound source intensity, wherein the final solution of the sound source intensity includes the sound source position coordinate Pos and the sound source intensity corresponding to the sound source position;

[0055] Step 3.11 identifies and locates the sound source according to the sound source position coordinates Pos.

[0056] The orthogonal matching pursuit sound source identification method based on the feedback mechanism of the present invention is also characterized in that:

[0057] The final solution of the sound source intensity calculated in step 3.10 is: Using equation (7) and the maximum likelihood estimation method, each calculated sound pressure vector p is calculated. i The index i corresponding to the minimum value of the 2-norm of the difference between the measured sound pressure vector p min :

[0058]

[0059] in:

[0060] It means that when i=1,2,...,B, find the value that makes ||p i -p||2The i value when the minimum value is obtained;

[0061] ||p i-p||2 means calculating the sound pressure vector p i The 2-norm of the difference with the measured sound pressure vector p;

[0062] The final calculated sound source position coordinate Pos is represented by formula (8):

[0063]

[0064] The magnitude of the sound source intensity corresponding to the sound source position is

[0065] The orthogonal matching pursuit sound source identification method based on the feedback mechanism of the present invention is also characterized in that:

[0066] The transfer matrix G established in step 1 is represented by equation (9):

[0067]

[0068] The elements in the transfer matrix G represented by equation (9) are expressed as g(m,n), then g(m,n) is the transfer function between the m-th microphone and the n-th focus point, m=1, 2, ..., M, n=1, 2, ..., N;

[0069] And there are:

[0070]

[0071] Where: j is the imaginary unit, f is the frequency of the sound source;

[0072] d mn is the distance between the mth microphone and the nth focusing point.

[0073] The orthogonal matching pursuit sound source identification method based on the feedback mechanism of the present invention is also characterized in that:

[0074] The source intensity vector q of the focused sound source is: q=[SS1, SS2, ..., SS N ], SS a represents the sound source intensity of the ath focus point among N focus points, a=1, 2, ..., N;

[0075] The sound pressure vector p measured by the microphone on the measuring surface is: p=[SP1, SP2, ..., SP M ], SP b represents the measured sound pressure of the bth microphone among M microphones, b=1,2,…,M.

[0076] Traditional sound source identification algorithms can only assume that the sound source is on the grid points divided by the focal plane. In actual sound source identification, the probability that the location of the sound source is exactly on the grid points is very small, so the traditional algorithm can only find the approximate location of the sound source. The method of the present invention effectively solves this problem. The optimization algorithm searches for the position on the non-grid point near the initial solution to realize the location of the sound source that is separated from the grid point. At the same time, a new measurement matrix G needs to be constructed to solve the sound source strength at the corresponding position outside the grid point. Compared with the existing technology, the beneficial effects of the present invention are embodied in:

[0077] 1. The present invention is based on the orthogonal matching pursuit algorithm based on source strength prior, and improves the orthogonal matching pursuit algorithm to achieve accurate sound source localization.

[0078] 2. The present invention searches for grid points near the sound source position obtained by the orthogonal matching pursuit algorithm based on source intensity prior to form a set of approximate solutions, and obtains the source intensity of the corresponding position by multiplying the pseudo-inverse of the matrix composed of the columns of the corresponding position of the transfer matrix with the sound pressure vector measured by each microphone, that is, the sound source intensity of each approximate solution is obtained. The transfer matrix is ​​multiplied by the sound source intensity of each approximate solution to obtain the sound pressure vector at the measurement point position of the feedback calculation, and the second norm of the difference between each feedback sound pressure vector and the sound pressure vector measured by the actual microphone is calculated. The value reflects the degree of proximity between the feedback sound pressure vector and the actual sound pressure vector. Finally, the combination with the smallest second norm of the difference is selected as the optimal solution, and its sound source intensity is the final solution. When the correlation between the atoms of the measurement matrix is ​​strong, the sound source position reconstructed by the algorithm guided by the source intensity prior will have a certain deviation from the real sound source position, and the calculated sound source position will be near the actual sound source position. The present invention further improves the reconstruction accuracy of the algorithm when the atomic correlation is strong by searching and optimizing at nearby positions, and widens the applicable frequency range of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a simplified flow chart of the method of the present invention;

[0080] Figure 2 Schematic diagram of a sound source identification model composed of a microphone array and a focusing surface in the present invention;

[0081] Figure 3 To measure the array position map;

[0082] Figure 4a This is the sound source localization effect diagram of the standard OMP algorithm when the sound source frequency is 300Hz;

[0083] Figure 4b This is a sound source localization effect diagram of the present invention when the sound source frequency is 300 Hz;

[0084] Figure 5aThis is the sound source localization effect diagram of the standard OMP algorithm when the sound source frequency is 1700Hz;

[0085] Figure 5b This is a sound source localization effect diagram of the present invention when the sound source frequency is 1700 Hz;

[0086] Figure 6a This is the sound source localization effect diagram when the grid spacing on the focal plane is 0.01m when the standard OMP algorithm is used for positioning;

[0087] Figure 6b This is a sound source localization effect diagram with a grid spacing of 0.01m on the focusing surface after adopting the present invention; Figure 7a This is the sound source localization effect diagram when the standard OMP algorithm is used; Figure 7b This is a diagram showing the effect of sound source localization after adopting the present invention. DETAILED DESCRIPTION

[0088] See also Figure 1 and Figure 2 In this embodiment, the orthogonal matching pursuit sound source identification method based on the feedback mechanism is performed in the following steps:

[0089] Step 1: Determine the transfer matrix G between the sound pressure measured by the microphone in the measurement plane and the sound source intensity at the focal point in the focusing plane:

[0090] A measurement surface W is set in the sound field generated by K sound sources, a rectangular area with a length of Lx and a width of Ly in the measurement surface W is divided into M grid points with U rows and V columns evenly distributed, and M microphones are arranged one by one on the M grid points to form a measurement array;

[0091] On the focusing plane T formed by the sound source calculation plane, a rectangular area with a length of Nx and a width of Ny in the focusing plane T is divided into N focusing points with even distribution of X1 rows and Y1 columns, and the N focusing points are used as potential sound source positions;

[0092] According to the free-field Green's function, the transfer matrix G between the source intensity of the focused sound source and the measurement surface microphone array is established as follows:

[0093]

[0094] The elements in the transfer matrix G are represented by g(m,n), then g(m,n) is the transfer function between the m-th microphone and the n-th focus point, m = 1, 2, ..., M, n = 1, 2, ..., N; and: Where j is the imaginary unit, f is the frequency of the sound source, and d mnis the distance between the mth microphone and the nth focusing point, and c is the speed of sound. Since the number of grid points M on the measurement surface is much smaller than the number of grid points N on the focusing surface, the transfer matrix G is a matrix with much fewer rows than columns, and each column of the transfer matrix G is called an atom.

[0095] Step 2: Use the transfer matrix G to establish the relationship between the source intensity of the focused sound source and the sound pressure measured by the microphone as shown in formula (1):

[0096] p=Gq+ζ (1)

[0097] In formula (1):

[0098] Let q represent the source intensity vector of the focused sound source, q = [SS1, SS2, ..., SS N ], SS a represents the sound source intensity of the ath focus point among N focus points, a=1,2,…,N;

[0099] Let p represent the sound pressure vector measured by the microphone on the measurement surface, p=[SP1,SP2,...,SP M ], SP b represents the measured sound pressure of the bth microphone among M microphones, b=1,2,…,M;

[0100] Let ζ represent the noise vector contained in the sound pressure vector p measured by the microphone, and ζ has the same dimension as p.

[0101] Step 3: The orthogonal matching pursuit algorithm based on the feedback mechanism solves the sound source intensity vector and locates the sound source according to the following process:

[0102] Step 3.1, initialize the residual R0 = p, index set It is an empty set, and the number of iterations k = 1;

[0103] Step 3.2, perform singular value decomposition on the transfer matrix G, calculate the regularization parameter λ, and use the Tikhonov regularization method to solve the approximate solution F of the sound source intensity vector;

[0104] Step 3.3: Use the sound source intensity vector approximate solution F as prior information to guide the selection of atoms, and find the location of the largest element according to formula (2):

[0105]

[0106] In formula (2):

[0107] i k The index of the position of the maximum element at the kth iteration;

[0108] F(x) is the source strength at the x-th index position of the approximate solution of the source strength vector;

[0109] |F(x)| represents taking the absolute value of F(x);

[0110] It means that when x = 1, 2,..., N, find the x value when |F(x)| reaches the maximum;

[0111] Step 3.4: Add the newly selected atom to the index set and update the index set to Γ k : Γ k = Γ k-1 ∪i k

[0112] where Γ k-1 is the index set of the optimal atom set selected in the previous iteration;

[0113] Step 3.5: Take out the corresponding atoms from the transfer matrix G through the index set Γ k to form a matrix Calculate the sound source strength q at the corresponding position of the index set by using the least square method k as: In the formula represents taking the Γ k columns of the transfer matrix G to form a new matrix, represents the matrix transpose;

[0114] corresponding source strength vector where: represents the value of the source strength vector at the Γ k position, and the values at the other positions of the source strength vector are all 0;

[0115] Step 3.6: Update the residual R according to Equation (3) k as:

[0116]

[0117] Step 3.7: When the number of iterations k < K, increase the value of the number of iterations k by 1, return to Step 3.2, and continue the iteration; otherwise, stop the iteration and output the index Γ of the maximum element position obtained in each iteration k and the rough solution q of the source strength at the corresponding position k , which are the initially calculated sound source position and the corresponding source strength;

[0118] Step 3.8: Search all combinations δ of the grid point coordinates near the initially selected atoms:

[0119] The coordinates of the J-th sound source position initially selected and all its adjacent grid points are the set OJ , J = 1, 2, ..., K;

[0120] From O1, O2, ..., O K Take one element from each set to form a combination H i , H i contains K different elements, each element is the index of a grid point;

[0121] δ is all combinations of the coordinates of the grid points near the atoms that are initially selected, including all combinations, namely:

[0122] δ=[H1,H2,...,H B ]

[0123] H i represents the i-th combination, i = 1, 2, ..., B, B is the total number of all combinations, equal to the product of the number of elements in each set;

[0124] Step 3.9: Calculate the source strength q′ at the corresponding coordinate position of each combination i , and the calculated sound pressure vector p corresponding to each combination i :

[0125] According to formula (4), the source intensity q′ at the coordinate position corresponding to each combination is calculated using the least squares method: i :

[0126]

[0127] in:

[0128] q' i represents the source intensity at the coordinate position corresponding to the i-th combination;

[0129] H represents the transfer matrix G. i The new matrix consists of columns, Representation Matrix The transpose of

[0130] Express request The inverse matrix of

[0131] The source intensity vector corresponding to each combination is calculated by formula (5):

[0132]

[0133] in:

[0134] Indicates the source intensity vector in H iThe value at the position, the rest of the positions of the source intensity vector are 0;

[0135] The calculated sound pressure vector p corresponding to each combination is obtained by formula (6): i :

[0136]

[0137] Step 3.10: Use the maximum likelihood estimation method to calculate the final solution of the sound source strength:

[0138] The calculated sound pressure of each combination of grid points near the rough solution has been obtained. The actual sound source position is approximated by calculating the calculated sound pressure closest to the measured sound pressure, and the maximum likelihood estimation method is used to measure the closeness of the two vectors. The maximum likelihood estimation method is used to calculate each calculated sound pressure vector p using equation (7) i The index i corresponding to the minimum value of the 2-norm of the difference between the measured sound pressure vector p min :

[0139]

[0140] in:

[0141] It means that when i=1,2,...,B, find the value that makes ||p i -p||2 The value of i when the minimum value is obtained; ||p i -p||2 means calculating the sound pressure vector p i The 2-norm of the difference with the measured sound pressure vector p;

[0142] The final calculated sound source position coordinate Pos is represented by formula (8):

[0143]

[0144] Pos contains K different elements, each of which is the index of a grid point. The magnitude of the sound source corresponding to the sound source position is There are also K elements, each of which corresponds to an element in Pos one by one, indicating the magnitude of the sound source at the position of the element in Pos;

[0145] 3.11 The sound source is identified and located through the final calculated sound source position coordinates Pos, and the final determined sound source position is the grid point corresponding to the element in Pos.

[0146] Simulation Example 1: Verify that compared with the standard OMP algorithm positioning method, the present invention has better reconstruction performance for low-frequency sparse sound source signals.

[0147] Simulation process: Assume that two monopole point sound sources of equal intensity are located on the focusing plane T. The size of the focusing plane T is 1m×1m. There are 441 focusing positions evenly divided into 21×21 grid points. The coordinate positions of the monopole sound sources are set to be (-0.15, 0, 0.2)m and (0.15, 0, 0.2)m in space. The measurement array is located on the grid points of the measurement plane W. The measurement plane is divided into 11×11 grid points. The 49 measurement array elements used in the measurement are distributed on the grid points, such as Figure 3 As shown. The distance between the measurement plane W and the focusing plane T is 0.2m. In order to simulate the noise in the actual measurement process, 25dB Gaussian white noise is added to the acoustic signal measured by the array. In order to quantitatively measure the reconstruction effect of the standard OMP algorithm and the improved OMP algorithm, the error definition method is given and represented by the following formula:

[0148]

[0149] Where: err is the source strength reconstruction error, q bul is the source intensity reconstruction vector, q sou is the standard source strength position vector, and ||·||2 is the vector 2-norm.

[0150] Figure 4a This is the sound source localization effect diagram of the standard OMP algorithm when the sound source frequency is 300Hz. Figure 4b This is a sound source localization effect diagram of the present invention when the sound source frequency is 300 Hz. In the image of the sound source recognition result, "*" indicates the location of the actual sound source point.

[0151] When the sound source frequency is 300Hz, the atoms corresponding to the sound source position and the adjacent atoms have strong coherence. The traditional OMP algorithm is affected by the coherence peak and selects the wrong atom. The sound source identification error is 1.556, which makes the sound source identification fail. The OMP algorithm improved by the present invention can accurately locate the position of the sound source, and the sound source identification error is 0.034. It can be seen that the sound source identification performance of the method of the present invention is better than that of the standard OMP algorithm at medium and low frequencies.

[0152] In order to compare the sound source identification performance of the standard OMP algorithm and the improved OMP algorithm at high frequencies, the sound source frequency was changed to 1700 Hz and the spatial positions of the two monopole sound sources were changed to (-0.1, 0, 0.2) m and (0.1, 0, 0.2) m, while keeping other parameters unchanged. Figure 5a The sound source localization effect diagram of the standard OMP algorithm, the sound source identification error is 0.083; Figure 5b This is a sound source localization effect diagram of the present invention when the sound source frequency is 1700 Hz, and the sound source recognition error is 0.010; it can be seen that the sound source recognition effect of the present invention at high frequencies is slightly better than that of the standard OMP algorithm.

[0153] Simulation Example 2: Verify that the sound source localization resolution of the method of the present invention is higher than that of the standard OMP algorithm.

[0154] Simulation process: The denser the focus plane is divided, the higher the requirement for the sound source identification resolution is. In order to compare the sound source localization resolution of the method of the present invention and the standard OMP algorithm, the focus plane is divided into a uniform grid of 101*101, and the spacing between adjacent grids is 0.01m. The sound source frequency is set to 2000Hz and the coordinate positions of the two monopole sound sources are (-0.05, 0, 0.2)m and (0.05, 0, 0.2)m respectively. The other simulation conditions are the same as simulation 1. Figure 6a This is the sound source localization effect diagram when using the standard OMP algorithm. Figure 6b The effect diagram of sound source localization after adopting the present invention. When the spacing between adjacent grids on the focal plane is 0.01m, the spacing between the focal points is reduced, resulting in the enhancement of the coherence between the atoms in each column of the corresponding transfer matrix. The standard OMP algorithm selects the atom at the coherence peak and generates a false sound source, with a sound source identification error of 1.342. However, the OMP algorithm improved by the present invention can still accurately locate the position of the sound source, with a sound source identification error of 0.024. It can be seen that the method of the present invention is significantly superior to the standard OMP algorithm in terms of sound source localization resolution.

[0155] Simulation Example 3: Verify that in an environment with four adjacent sound sources, the present invention can better identify the sound source location information than the traditional sound source localization method. Simulation process: The coordinate positions of the four monopole sound sources are set to (-0.1, 0, 0.2) m, (0.1, 0, 0.2) m, (0, 0.1, 0.2) m, (0, -0.1, 0.2) m. The sound source frequency is 1500 Hz, and the other parameters are the same as those in Simulation 1. Figure 7a This is the sound source localization effect diagram when using the standard OMP algorithm. Figure 7b This is a diagram showing the effect of sound source localization after adopting the present invention. It can be clearly seen from the figure that due to the close distance between the four sound sources and the strong coherence between the atoms, the traditional OMP algorithm is difficult to accurately distinguish the positions of each sound source in such a high coherence environment. Traditional algorithms often have positioning errors when dealing with multiple sound sources that are close to each other, and the positioning accuracy is significantly reduced. However, after adopting the improved method proposed in this article, the position of each sound source can still be accurately identified even under conditions of strong coherence. This shows that the method of the present invention has better robustness and anti-interference ability, and can effectively overcome the limitations of traditional algorithms when facing complex sound source configurations. Therefore, the method of this article not only surpasses the traditional sound source localization algorithm in terms of accuracy, but also provides stronger adaptability to application scenarios, especially when dealing with multiple sound sources that are close to each other and have large signal interference, it can significantly improve the reliability and accuracy of sound source localization.

Claims

1. An orthogonal matching pursuit sound source identification method based on feedback mechanism, characterized by: Setting the measuring plane and the focusing plane at set distance intervals, meshing the measuring plane and the focusing plane, and obtaining the microphone position and the focusing point as well as the Green's function transfer matrix; The Green's function transfer matrix is ​​used to obtain the relationship between the sound pressure vector measured by the microphone and the source intensity of the sound source at the focal point; The approximate solution of the source strength of the focal point sound source is obtained by calculating the least squares method, and the approximate solution of the source strength of the focal point sound source is used as prior information to guide the screening of atoms, and the position of the largest element is found as the rough sound source position. Finally, the precise sound source position is obtained by searching near the rough sound source position.

2. The orthogonal matching pursuit sound source identification method based on feedback mechanism according to claim 1 is characterized in that The steps include: Step 1: Determine the transfer matrix G between the sound pressure measured by the microphone in the measurement plane and the sound source intensity at the focal point in the focusing plane: A measurement surface W is set in the sound field generated by K sound sources, M grid points are evenly distributed in the measurement surface W, and M microphones are arranged one by one on the M grid points to form a measurement array; Evenly distributing N focusing points on a focusing plane T formed by a sound source calculation plane, and using the N focusing points as potential sound source positions; The transfer matrix G between the sound source intensity at the focus point and the microphone array on the measurement surface is established based on the free-field Green's function: Step 2: Use the transfer matrix G to establish the relationship between the source intensity of the focused sound source and the sound pressure measured by the microphone as shown in formula (1): p=Gq+ζ (1) In formula (1): q represents the source intensity vector of the sound source at the focal point; p represents the sound pressure vector measured by the microphone on the measurement surface; Let ζ represent the noise vector contained in the sound pressure vector p measured by the microphone; Step 3: The orthogonal matching pursuit algorithm based on the feedback mechanism solves the sound source intensity vector and locates the sound source according to the following process: Step 3.1, initialize the residual R0 = p, index set It is an empty set, and the number of iterations k = 1; Step 3.2, perform singular value decomposition on the transfer matrix G, calculate the regularization parameter λ, and use the Tikhonov regularization method to solve the approximate solution F of the sound source intensity vector; Step 3.3: Use the sound source intensity vector approximate solution F as prior information to guide the selection of atoms, and find the location of the maximum element according to formula (2): In formula (2): i k The index of the position of the maximum element at the kth iteration; F(x) is the source intensity at the xth index position of the source intensity vector approximate solution; It means when x=1,2,...,N, find the value of x that makes |F(x)| reach the maximum value; Step 3.4: Update the index set to Г k :G k =G k-1 ∪i k Among them, k-1 is the index set of the optimal atom set that has been screened in the previous iteration; Step 3.5: Use index set Г k Take the corresponding atomic composition matrix from the transfer matrix G The sound source intensity q at the corresponding position of the index set is calculated using the least squares method k for: In the formula, represents the transfer matrix G k The new matrix consists of columns, Representation Matrix The transpose of The corresponding source intensity vector in: Denotes the source intensity vector in Г k The value at the position, the rest of the positions of the source intensity vector are 0; Step 3.6: Update the residual R according to formula (3) k for: Step 3.7: When the iteration number k < K, increase the value of the iteration number k by 1, return to Step 3.2, and continue the iteration; otherwise, stop the iteration and output the index Г of the maximum element position obtained in each iteration k and the rough solution q of the source strength at the corresponding position k , which are the sound source position and the corresponding source strength obtained through preliminary calculation; Step 3.8, search for all combinations of grid point coordinates near the initially selected atoms δ: The coordinates of the Jth sound source position and all its adjacent grid points are initially selected as set O J , J = 1, 2, ..., K; From O1, O2, ..., O K Take one element from each set to form a combination Hi; δ is all combinations of the coordinates of the grid points near the atoms that are initially selected, including all combinations, namely: δ=[H1,H2,...,H B ] H i represents the i-th combination, i = 1, 2, ..., B, B is the total number of all combinations; Step 3.9: Calculate the source intensity q' at the corresponding coordinate position of each combination i , and the calculated sound pressure vector p corresponding to each combination i : According to formula (4), the source intensity q' at the coordinate position corresponding to each combination is calculated using the least squares method: i : in: q' i represents the source intensity at the coordinate position corresponding to the i-th combination; H represents the transfer matrix G. i The new matrix consists of columns, Representation Matrix The transpose of The source intensity vector corresponding to each combination is calculated by formula (5): in: Indicates the source intensity vector in H i The value at the position, the rest of the positions of the source intensity vector are 0; The calculated sound pressure vector p corresponding to each combination is calculated by formula (6): i : Step 3.10, using the maximum likelihood estimation method to calculate and obtain a final solution of the sound source intensity, wherein the final solution of the sound source intensity includes the sound source position coordinate Pos and the sound source intensity corresponding to the sound source position; Step 3.11 identifies and locates the sound source according to the sound source position coordinates Pos.

3. The orthogonal matching pursuit sound source identification method based on feedback mechanism according to claim 2 is characterized by: The final solution of the sound source intensity calculated in step 3.10 is: Using equation (7) and the maximum likelihood estimation method, each calculated sound pressure vector p is calculated. i The index i corresponding to the minimum value of the 2-norm of the difference between the measured sound pressure vector p min : in: It means that when i=1,2,...,B, find the value that makes ||p i -p||2The i value when the minimum value is obtained; ||p i -p||2 means calculating the sound pressure vector p i The 2-norm of the difference with the measured sound pressure vector p; Then the final calculated sound source position coordinate Pos is represented by formula (8): The magnitude of the sound source intensity corresponding to the sound source position is 4. The orthogonal matching pursuit sound source identification method based on feedback mechanism according to claim 2 is characterized by: The transfer matrix G established in step 1 is represented by equation (9): The elements in the transfer matrix G represented by equation (9) are expressed as g(m,n), then g(m,n) is the transfer function between the m-th microphone and the n-th focus point, m = 1, 2, ..., M, n = 1, 2, ..., N; And there are: Where: j is the imaginary unit, f is the frequency of the sound source; d mn is the distance between the mth microphone and the nth focusing point.

5. The orthogonal matching pursuit sound source identification method based on feedback mechanism according to claim 2 is characterized by: The source intensity vector q of the focused sound source is: q=[SS1, SS2, ..., SS N ], SS a represents the sound source intensity of the ath focus point among N focus points, a=1,2,…,N; The sound pressure vector p measured by the microphone on the measuring surface is: p=[SP1, SP2, ..., SP M ], SP b represents the measured sound pressure of the bth microphone among M microphones, b=1,2,…,M.

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