Three-dimensional sound field measurement method based on air flow inner and outer combined microphone array

Abstract

The application discloses a three-dimensional sound field measurement method based on an airflow in-out combined microphone array, and belongs to the technical field of aircraft wind tunnel test. The method comprises the following steps: arranging a test model; an outer microphone array scans the three-dimensional sound field of the model from outside the flow field, and a noise source is preliminarily identified based on a beam forming algorithm; an inner microphone array scans the three-dimensional sound field of the model from inside the flow field, and a noise source is high-precision identified based on a CLEAN-SC algorithm. The scheme firstly preliminarily identifies the distribution of the noise source of the model through a small amount of microphones to form an airflow outer array, can quickly exclude obviously irrelevant areas, and avoids high-cost operations of point-by-point fine scanning in the global range. Then, the airflow inner array is used to be close to the model surface noise source. Since the airflow inner array is close to the noise source, a small amount of microphones can realize high-precision identification of the noise source. After the cooperation of the two, a small amount of microphones can realize high-precision identification of the three-dimensional noise source of a large-scale model.

Description

TECHNICAL FIELD

[0001] The present application relates to a three-dimensional sound field measurement method based on an air flow inside and outside combined microphone array, and belongs to the technical field of aircraft wind tunnel test. BACKGROUND

[0002] Large aircraft and helicopters and other aircraft need to be evaluated by wind tunnel test to assess the noise reduction effect, obtain complex shape surface noise distribution and aerodynamic noise characteristics. The existing two-dimensional microphone array technology cannot effectively obtain the three-dimensional complex sound field of large-scale models. The main method is to use air flow outside microphone array and beam forming, CLEAN-SC algorithm to realize the identification of model noise source, but the above method has three problems: first, in order to improve the accuracy of noise identification, a large number of microphones are needed, and the cost is difficult to control; second, the air flow outside the two-dimensional array cannot measure the noise source propagating in multiple directions; third, the traditional air flow outside the array is far away from the large-scale model, and it is difficult to realize high-resolution measurement. SUMMARY

[0003] The purpose of the present application is to fully adapt to the acoustic test capability requirements of large-scale models. The present application proposes a three-dimensional sound field measurement method based on an air flow inside and outside combined microphone array. The three-dimensional distribution characteristics of the model noise source are determined by the air flow outside the array, and then the high-precision identification of the noise source is realized by using the air flow inside the array and the CLEAN-SC algorithm.

[0004] The technical scheme adopted by the present application is as follows:

[0005] The three-dimensional sound field measurement method based on the air flow inside and outside combined microphone array comprises the following steps:

[0006] Step 1, arranging the test model;

[0007] Step 2, the outer microphone array scans the three-dimensional sound field of the model from the outside of the flow field, and preliminarily identifies the noise source based on the beam forming algorithm;

[0008] Step 3, the inner microphone array scans the three-dimensional sound field of the model from the inside of the flow field, and identifies the noise source with high precision based on the CLEAN-SC algorithm.

[0009] The outer microphone array is composed of a large number of microphones to form different types of arrays, and the array is placed outside the airflow. The signals measured by different microphones are summed after time delay. Discrete scanning is performed on the model scanning surface. When the scanning point coincides with the actual noise source position, all microphone signals are enhanced. When the scanning point is not a noise source, all microphone signals are suppressed, thereby realizing the identification of the noise source. Therefore, the airflow outer microphone array noise source identification based on beamforming can preliminarily identify the distribution of the model surface noise source. The CLEAN-SC algorithm is a sparse constraint-based CLEAN algorithm. The algorithm assumes that the target signal is sparse in space, that is, it only exists in a few directions. By introducing sparse constraints, interference from non-target directions is effectively suppressed, thereby realizing high-precision identification of the noise source. The airflow inner microphone array can be placed inside the airflow, closer to the model noise source, and a small number of microphones can be used to accurately identify the surface noise source.

[0010] The existing acoustic test in the wind tunnel mainly uses the airflow outer microphone array and algorithms such as beamforming and CLEAN-SC to identify the model noise source. However, the above methods have three problems: to improve the noise identification accuracy, a large number of microphones are needed, and the cost is difficult to control; the two-dimensional array outside the airflow cannot measure noise sources propagating in multiple directions; the array outside the airflow is far away from the large-scale model, and it is difficult to achieve high-resolution measurement. The present scheme first identifies the distribution of the model noise source by the microphone array outside the airflow, which can quickly exclude obviously irrelevant areas. Then, the airflow inner array is used to approach the model surface noise source. Since the airflow inner array is close to the noise source, a small number of microphones can achieve high-precision identification of the noise source. After the cooperation of the two, a small number of microphones can achieve high-precision identification of the three-dimensional noise source of the large-scale model.

[0011] Alternatively, the following steps are included:

[0012] Step 1, select a 1:1 front landing gear as a test model for arrangement;

[0013] Step 2.1, arrange a set of outer microphone arrays on the side and / or top of the landing gear outside the airflow;

[0014] Step 2.2, measure the spatial position relationship between the model and the outer microphone array;

[0015] Step 2.3, calibrate the spatial position of the model and the outer microphone array using a known sound source;

[0016] Step 2.4, set the test conditions, and use the outer microphone array to scan the three-dimensional sound field of the model;

[0017] Step 2.5, determine the three-dimensional spatial distribution characteristics of the main noise source based on the beamforming algorithm;

[0018] Step 3.1, arranging a set of inner microphone arrays at the main noise source inside the wind tunnel flow field;

[0019] Step 3.2, measuring the spatial position relationship between the model and the inner microphone arrays;

[0020] Step 3.3, calibrating the spatial position of the model and the inner microphone arrays with a known sound source;

[0021] Step 3.4, setting the test conditions and using the inner microphone arrays to scan the three-dimensional sound field of the model;

[0022] Step 3.5, determining the specific position of the model surface noise source based on the CLEAN-SC algorithm.

[0023] The equal ratio model ensures that the model and the actual object have similarity in geometric shape, size and motion characteristics. The outer microphone arrays are preferably arranged on the outer landing gear side and the top of the airflow, which can improve the accuracy of the experimental results, and the noise source distribution in three-dimensional space can be analyzed from different directions. Measuring the spatial position relationship between the model and the microphone arrays provides a data basis for the final calculation results. Calibration can ensure that the measurement results of the equipment meet the expected standards, eliminate the errors of the equipment itself, and ensure the accuracy and reliability of the data. Scanning the main noise source by a set of inner microphone arrays can reduce the number of microphones, reduce the experimental cost, and reduce the burden of manual operation.

[0024] Alternatively,

[0025] In step 2.2, the spatial position relationship between the model and the outer microphone array includes the deviation of the array center coordinates relative to the model center coordinates, the distance between the array microphone plane and the model scanning surface, and the distance between the array microphone plane and the wind tunnel shear layer;

[0026] In step 3.2, the spatial position relationship between the model and the inner microphone array includes the deviation of the array center coordinates relative to the model center coordinates, and the distance between the array microphone plane and the model scanning surface.

[0027] By accurately determining the spatial position relationship, the accuracy of the finally derived noise source position is ensured. It should be noted that the airflow inner array is directly placed inside the wind tunnel flow field, so there is no need for shear layer correction.

[0028] Alternatively, in step 2.3, a loudspeaker is placed on the model surface and plays pure tone noise at a predetermined frequency. The initial position of the noise source is given by the beamforming algorithm. The deviation between the array positioning noise source position and the actual loudspeaker position is calculated. Based on the deviation, the array coordinates are corrected. After the correction is completed, the position and frequency of the loudspeaker are changed to verify whether the array positioning result is consistent with the actual loudspeaker position.

[0029] In step 3.3, a loudspeaker is placed on the model surface and plays pure tone noise at a predetermined frequency. The precise location of the noise source is given by the CLEAN-SC algorithm. The deviation between the array-positioned noise source and the actual loudspeaker position is calculated. Based on this deviation, the array coordinates are corrected. After the correction is completed, the position and frequency of the loudspeaker are changed to verify whether the array positioning result is consistent with the actual loudspeaker position.

[0030] Alternatively, step 2.5 may include the following steps:

[0031] Step 2.5.1: Assume there is a three-dimensional space on the surface of the test model. Discretize the three-dimensional space into a grid, and assume there is a point sound source at any grid point. Relative to any grid point in the three-dimensional space, what is the pointing vector of the external microphone array? for:

[0032]

[0033] Where T represents the transpose of the above array; Indicates the first The pointing vector of each microphone is calculated using the following formula:

[0034]

[0035] in, For the first Shear layer amplitude correction factor for each microphone This indicates the propagation distance between the sound wave scanning point and the microphone. Distance between the array center point and the scan point Indicates the delay time. j The imaginary unit, f For frequency;

[0036] Step 2.5.2: Calculate the cross-spectral density function of microphone m and microphone n on the surface of the external microphone array. ,as follows:

[0037]

[0038] in, Number of data blocks for the microphone array is the window function normalization factor, is the length of each data, is the Fourier transform of the signal received by microphone n in the kth segment of data, is the Fourier transform of the signal received by microphone m in the kth segment of data, * represents the conjugate transpose, f is the frequency;

[0039] Based on the cross-spectral density function between different microphones, an MxM cross-spectral matrix is formed, the cross-spectral matrix is as follows:

[0040]

[0041] Step 2.5.3, calculate the output power spectrum of the outer microphone array at any grid point in three-dimensional space, as follows:

[0042]

[0043] wherein, represents the pressure mean square value of sound pressure per unit bandwidth, M is the number of microphone microphones, is the MxM cross-spectral matrix, T represents transposition, is the pointing vector, divided by the number of microphones indicates that the array output power spectrum is converted to the order of a single microphone;

[0044] Step 2.5.4, based on the above beam forming method, the power spectrum of all grid nodes in three-dimensional space is obtained, and the maximum value of the power spectrum corresponds to the position of the noise source.

[0045] Alternatively, it also includes step 3.0, designing an inner microphone array, including the following steps:

[0046] Step 3.0.1, design a low self-noise airflow inner microphone array shape with good turbulent fluctuating pressure suppression effect;

[0047] Step 3.0.2, design a suitable inner microphone cavity for suppressing turbulent boundary layer fluctuating pressure, the harmonic frequency of the cylindrical cavity is:

[0048]

[0049] wherein, n is the harmonic mode, D is the diameter of the cavity, is the speed of sound propagation, is the aperture correction term, the formula is as follows:

[0050]

[0051] Wherein, k is the wave number corresponding to the harmonic frequency, a is the cavity radius.

[0052] Alternatively, it further includes step 3.0.3, covering the cavity upper surface with different mesh metal screens, and comparing in the actual test process to select the mesh size of the metal screen that best suppresses the pulsating pressure.

[0053] Alternatively, the inner microphone array adopts a streamlined design, the leading and trailing edges adopt airfoil designs respectively, and the middle part is an equal straight section.

[0054] The inner microphone cavity is conical at the upper part, the upper surface of the cavity is covered with a metal screen, and the lower cylindrical part is consistent with the diameter of the microphone for installing the microphone.

[0055] When the airflow inner microphone array is used for noise source identification measurement in the wind tunnel, the pulsating pressure of the turbulent boundary layer on the surface of the microphone array will interfere with the measurement results, and the inner microphone cavity can suppress the pulsating pressure of the turbulent boundary layer.

[0056] Alternatively, in step 3.4, the angle of the inner microphone array also needs to be adjusted, and multiple tests are performed to obtain test results at different angles.

[0057] Alternatively, in step 3.5, the following steps are included:

[0058] Step 3.5.1, obtaining a two-dimensional scanning plane noise source based on traditional beamforming technology, the position of the peak sound source , that is, the sound power at the scanning point is the maximum ; ;

[0059] Step 3.5.2, subtract the influence of the peak sound source in the sound source diagram, the formula is as follows:

[0060]

[0061] Wherein, is the power spectrum of scanning point j at the i-th iteration, is the power spectrum of scanning point j at the i-1-th iteration is the weighted vector generated by the sound source at scanning point j, * represents the conjugate transpose, is the cross-spectrum matrix of the point sound source at , and the expression of is:

[0062]

[0063] Wherein, is the steering vector related to , and is the i-1th iteration result of the maximum value of the power spectrum of the scanned surface;

[0064] The sound power not affected by the peak sound source is as follows:

[0065] ;

[0066] Step 3.5.3, the next iteration is performed;

[0067] Step 3.5.4, high-precision identification of the model surface noise source is performed through the above CLEAN-SC algorithm.

[0068] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present application are:

[0069] 1. The three-dimensional sound field measurement method based on the airflow in and out combined microphone array provided by the present application, the distribution of the model noise source is preliminarily identified by a small number of microphones forming an airflow out array, which can quickly exclude obviously irrelevant areas. Then, the airflow in array is used to approach the model surface noise source. Since the airflow in array is close to the noise source, a small number of microphones can achieve high-precision identification of the noise source. After the cooperation of the two, high-precision identification of the three-dimensional noise source of a large-scale model can be achieved by a small number of microphones.

[0070] 2. The three-dimensional sound field measurement method based on the airflow in and out combined microphone array provided by the present application has a low self-noise airflow in microphone array shape with good turbulent pulsating pressure suppression effect. When the microphone array is placed in the wind tunnel, the influence of turbulent pulsating pressure on the noise measurement result can be effectively suppressed, and high-precision results of the noise source position are obtained. BRIEF DESCRIPTION OF DRAWINGS

[0071] Figure 1 is an external microphone array arrangement diagram.

[0072] Figure 2 is a preliminary identification diagram of the noise source based on the beam forming algorithm.

[0073] Figure 3 is an internal microphone array shape diagram.

[0074] Figure 4 is an internal microphone structure diagram.

[0075] Figure 5 is a high-precision identification diagram of the noise source based on the CLEAN-SC algorithm. DETAILED DESCRIPTION

[0076] The present application will be described in detail below with reference to the accompanying drawings.

[0077] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0078] A three-dimensional sound field measurement method based on an airflow-integrated microphone array includes the following steps:

[0079] Step 1: Select a 1:1 scale front landing gear as the test model for setup. This model includes components such as fairing, main / auxiliary struts, tires, torsion bars, and lamp covers.

[0080] Step 2.1: Arrange a set of external microphone arrays on the side and top of the airflow-external landing gear; such as Figure 1 As shown, the side array is a 135-channel spiral array and the top array is a 147-channel circular array. The spatial positional relationship between the model and the array is measured. Both the side and top arrays are aligned with the center of the landing gear tires.

[0081] Step 2.2: Measure the spatial relationship between the model and the external microphone array; the spatial relationship between the model and the external microphone array includes the deviation of the array center coordinates from the model center coordinates, the distance between the array microphone plane and the model scanning plane, and the distance between the array microphone plane and the wind tunnel shear layer.

[0082] Step 2.3: Use known sound sources to calibrate the spatial position of the model and the external microphone array: Place a loudspeaker at a known position above the fairing and emit pure tone noise with a frequency of 2kHz. Use beamforming algorithm to give the preliminary position of the noise source. The positioning result is consistent with the actual position.

[0083] Step 2.4: Set the test conditions: the model angle of attack is 0°, the incoming flow velocity is 60m / s, the sampling frequency is 51.2kHz, the sampling time is 15s, and the external microphone array is used to scan the three-dimensional sound field of the model.

[0084] Step 2.5: Determine the three-dimensional spatial distribution characteristics of the main noise sources based on the beamforming algorithm:

[0085] Step 2.5.1: Discretize the three-dimensional space into a mesh. , , , The grid lengths are in the x, y, and z directions, respectively. To accurately identify the three-dimensional spatial distribution of noise sources on the landing gear model surface, the discretized three-dimensional space must include the entire landing gear model.

[0086] Assuming a 5kHz point sound source exists at any grid point, what is the pointing vector of the external microphone array relative to any grid point in three-dimensional space? for:

[0087]

[0088] Where T represents the transpose of the above array; Indicates the first The pointing vector of each microphone is calculated using the following formula:

[0089]

[0090] in, For the first Shear layer amplitude correction factor for each microphone This indicates the propagation distance between the sound wave scanning point and the microphone. Distance between the array center point and the scan point Indicates the delay time. j The imaginary unit, f For frequency;

[0091] Step 2.5.2: Calculate the cross-spectral density function of microphone m and microphone n on the surface of the external microphone array. ,as follows:

[0092]

[0093] in, Number of data blocks for the microphone array For the window function normalization factor, The length of each data point, The Fourier transform of the signal received by microphone n in the k-th data segment. This is the Fourier transform of the signal received by microphone m in the k-th data segment, where * represents the conjugate transpose. f For frequency;

[0094] Based on the cross-spectral density function between different microphones, an M×M cross-spectral matrix is ​​formed. as follows:

[0095]

[0096] Step 2.5.3: Calculate the output power spectrum of the external microphone array at any grid point in three-dimensional space, as follows:

[0097]

[0098] in, The mean square value of the pressure per unit bandwidth represents the sound pressure level. M The number of microphones in the microphone. is the cross-spectral matrix of M x M, T denotes transpose, is the pointing vector, divided by the number of microphones indicates the conversion of the array output power spectrum to the magnitude of a single microphone;

[0099] Step 2.5.4, based on the above beamforming method, the power spectrum of all grid nodes in three-dimensional space is obtained, as shown in Figure 2 The point corresponding to the maximum value of the power spectrum is the position of the 5 kHz noise source.

[0100] Step 3.0, design the inner microphone array, including the following steps:

[0101] Step 3.0.1, design the shape of the inner microphone array in the airflow with low self-noise and good turbulence fluctuation pressure suppression effect;

[0102] Step 3.0.2, design a suitable inner microphone cavity for suppressing the fluctuating pressure of the turbulent boundary layer, the harmonic frequency of the cylindrical cavity is

[0103]

[0104] where n is the harmonic mode, D is the diameter of the cavity, is the speed of sound, is the aperture correction term, and the formula is as follows:

[0105]

[0106] where k is the wave number corresponding to the harmonic frequency, and a is the radius of the cavity.

[0107] Step 3.0.3, cover the upper surface of the cavity with different mesh metal screens, such as 100 mesh, 200 mesh, and 300 mesh, and compare them during actual testing to select the mesh size that best suppresses the fluctuating pressure.

[0108] Finally, the inner microphone array is obtained:

[0109] The shape of the inner microphone array adopts a streamlined design, the leading and trailing edges adopt airfoil designs respectively, and the middle part is a straight section, as shown in Figure 3 .

[0110] The upper part of the inner microphone cavity is conical, the upper surface of the cavity is covered with a metal screen, and the lower part of the cylindrical cavity is consistent with the diameter of the microphone for installing the microphone, as shown in Figure 4 .

[0111] Step 3.1, arrange a group of inner microphone arrays at the main noise source obtained in step 2.5;

[0112] ​Step 3.2, measure the spatial relationship between the model and the outer microphone array; the spatial relationship between the model and the inner microphone array includes the deviation of the array center coordinates relative to the model center coordinates, and the distance between the array microphone plane and the model scanning plane;

[0113] Step 3.3, calibrate the spatial position of the model and the inner microphone array using a known sound source: place a loudspeaker at a known position above the fairing, and emit pure tone noise at a frequency of 2 kHz, give the accurate position of the noise source through the CLEAN-SC algorithm, calculate the deviation between the array positioning noise source position and the actual loudspeaker position, and correct the array coordinates based on the deviation. After the correction is completed, change the position and sound frequency of the loudspeaker, and finally the positioning result is consistent with the actual position;

[0114] Step 3.4, set the test working condition, adjust the angle of the inner microphone array, and use the inner microphone array to scan the model three-dimensional sound field multiple times to obtain the test results at different angles;

[0115] Step 3.5, determine the specific position of the model surface noise source based on the CLEAN-SC algorithm;

[0116] including the following steps:

[0117] Step 3.5.1, obtain the 5 kHz sound source map of the two-dimensional scanning plane based on the traditional beamforming technology, and determine the position of the peak sound source , that is, the sound power at the scanning point is the maximum value ;

[0118] Step 3.5.2, subtract the influence of the peak sound source in the sound source map, the formula is as follows:

[0119]

[0120] wherein, is the power spectrum of scanning point j at the i-th iteration, is the power spectrum of scanning point j at the i-1-th iteration is the weighted vector generated by the sound source at scanning point j, * represents the conjugate transpose, is the cross-spectral matrix of the point sound source at , the expression of is as follows:

[0121]

[0122] wherein, is the steering vector related to , and The sound power not affected by the peak sound source is as follows for the i-1th iteration result of the maximum value of the scanning surface power spectrum:

[0123] ;

[0124] Step 3.5.3, performing the next iteration;

[0125] Step 3.5.4, performing high-precision identification of the 5 kHz noise source on the model surface by the above CLEAN-SC algorithm, and the identification result is as shown in Figure 5

[0126] The above only describes the preferred embodiments of the present application and is not intended to limit the present application, and the present application extends to any new features or any new combinations disclosed in the present specification, and any modifications, equivalent replacements and improvements, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application. It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the details of the technical features not disclosed in the present embodiments can be understood according to the specific manner of the above terms in the embodiments of the present application by those skilled in the art, and the present disclosure embodiments do not specifically limit the specific manner.​

Claims

1. A method for measuring a three-dimensional sound field based on an array of internal and external combined microphones in a gas flow, characterized in that, The method comprises the following steps: Step 1, selecting a 1:1 landing gear as a test model for arrangement; Step 2, an outer microphone array is arranged outside the flow field to scan the three-dimensional sound field of the test model, and a noise source is preliminarily identified based on a beam forming algorithm: Step 2.1, an outer microphone array is arranged on the side and / or top of the landing gear outside the airflow; Step 2.2, the spatial positional relationship between the test model and the outer microphone array is measured; Step 2.3, the spatial position of the test model and the outer microphone array is calibrated by using a known sound source; Step 2.4, a test working condition is set, and the three-dimensional sound field of the test model is scanned by using the outer microphone array; Step 2.5, the three-dimensional spatial distribution characteristics of the main noise source are determined based on the beam forming algorithm; Step 3, an inner microphone array is arranged inside the flow field to scan the three-dimensional sound field of the test model, and a noise source is identified with high precision based on a CLEAN-SC algorithm: Step 3.0, designing an inner microphone array, comprising the following steps: Step 3.0.1, designing an inner microphone array with low self-noise and good turbulence pulsating pressure suppression effect; Step 3.0.2, Designing a suitable inner microphone cavity for suppressing turbulent boundary layer fluctuating pressure, cylindrical cavity harmonic frequencies is: where n is the harmonic mode, D is the cavity diameter, is the speed of sound propagation, is the aperture correction term, which is given by the formula wherein k is the wave number corresponding to the harmonic frequency, and a is the cavity radius; Step 3.1, an inner microphone array is arranged at the main noise source inside the wind tunnel flow field; Step 3.2, the spatial positional relationship between the test model and the inner microphone array is measured; Step 3.3, the spatial position of the test model and the inner microphone array is calibrated by using a known sound source; Step 3.4, a test working condition is set, and the three-dimensional sound field of the test model is scanned by using the inner microphone array; Step 3.5, the specific position of the surface noise source of the test model is determined based on the CLEAN-SC algorithm.

2. The three-dimensional sound field measurement method of claim 1, wherein In step 2.2, the spatial positional relationship between the test model and the outer microphone array includes the deviation of the center coordinates of the outer microphone array relative to the center coordinates of the test model, the distance between the microphone plane of the outer microphone array and the scanning surface of the test model, and the distance between the microphone plane of the outer microphone array and the shear layer of the wind tunnel; In step 3.2, the spatial positional relationship between the test model and the inner microphone array includes the deviation of the center coordinates of the inner microphone array relative to the center coordinates of the test model, and the distance between the microphone plane of the inner microphone array and the scanning surface of the test model.

3. The method of three-dimensional sound field measurement of claim 1, wherein, In step 2.3, a loudspeaker is arranged on the surface of the test model, and a pure tone noise of a predetermined frequency is played, the preliminary position of the noise source is preliminarily given by the beam forming algorithm, the deviation between the position of the noise source positioned by the outer microphone array and the actual position of the loudspeaker is calculated, the coordinates of the outer microphone array are corrected based on the position deviation, after the correction is completed, the position and sound frequency of the loudspeaker are changed, and whether the positioning result of the outer microphone array is consistent with the actual position of the loudspeaker is verified. In step 3.3, a loudspeaker is arranged on the surface of the test model, and a pure tone noise of a predetermined frequency is played. The CLEAN-SC algorithm is used to give the accurate position of the noise source, and the deviation between the position of the internal microphone array and the actual position of the loudspeaker is calculated. Based on the position deviation, the coordinates of the internal microphone array are corrected. After the correction, the position of the loudspeaker and the sound frequency are changed, and whether the positioning result of the internal microphone array is consistent with the actual position of the loudspeaker is verified.

4. The method of three-dimensional sound field measurement of claim 1, wherein, In step 2.5, the following steps are included: Step 2.5.1, assuming a three-dimensional space on the surface of the test model, discretizing the three-dimensional space into a grid, and assuming a point sound source at any grid point, the pointing vector of the outward transmission microphone array relative to any grid point in the three-dimensional space is: : Wherein, T represents the transpose processing to the above array; represents the pointing vector of the first external microphone, and the calculation formula is as follows: wherein, is the shear layer amplitude correction factor for the th outer microphone, represents the propagation distance between the acoustic wave scanning point and the outer microphone, represents the distance between the center point of the outer microphone array and the scanning point, represents the delay time, j is the imaginary unit, f is the frequency; Step 2.5.2, computing the cross-spectral density function of the surface microphones m and n of the outward-facing microphone array As follows: wherein, is the number of data blocks for the outer microphone array, is a window function normalization factor, is the length of each data, is the Fourier transform of the signal received by microphone n in the kth data block, is the Fourier transform of the signal received by microphone m in the kth data block, denotes the conjugate transpose, f is the frequency; Based on the cross-spectral density function between different microphones, an MxM cross-spectral matrix is formed, the cross-spectral matrix is as follows: In step 2.5.3, the output power spectrum of the external microphone array for any grid point in three-dimensional space is calculated as follows: wherein P2denotes the pressure mean square value of the sound pressure of the unit bandwidth, M Noutdenotes the number of out-going microphones, R denotes the cross-spectral matrix of M x M, T denotes the transpose, denotes the steering vector, and Noutdenotes the number of out-going microphones, except that the steering vector is the magnitude of transforming the out-going microphone array output power spectrum to a single microphone; In step 2.5.4, based on the above beamforming method, the power spectrum of all grid nodes in three-dimensional space is obtained. The point corresponding to the maximum value of the power spectrum is the position of the noise source.

5. The method of three-dimensional sound field measurement of claim 1, wherein, Step 3.0.3 is also included, which covers different mesh wire screens on the upper surface of the cavity. In the actual test process, comparison is made, and the mesh number of the wire screen that best suppresses the pulsating pressure is selected.

6. The method of three-dimensional sound field measurement of claim 1, wherein, The shape of the internal microphone array is designed in a streamlined manner, with wing-shaped designs on the front and rear edges, and an equal straight section in the middle. The upper part of the internal microphone cavity is conical, the upper surface of the cavity is covered with a mesh wire screen, and the lower part of the cylindrical cavity is consistent with the diameter of the microphone, which is used to install the microphone.

7. The method of three-dimensional sound field measurement of claim 1, wherein, In step 3.4, the angle of the internal microphone array needs to be adjusted, and multiple tests are performed to obtain the test results at different angles.

8. The method of three-dimensional sound field measurement of claim 1, wherein, In step 3.5, the following steps are included: Step 3.5.1 Obtain a 2D scanned plane sound source map based on traditional beamforming technique, determine the position of the peak sound source , i.e. the power spectrum at the scanning point is maximum . ; In step 3.5.2, the influence of the peak sound source is subtracted from the sound source map, and the formula is as follows: wherein is the power spectrum of the scanning point j at the i-th iteration, is the power spectrum of the scanning point j at the i-1-th iteration is the weighting vector generated by the sound source at the scanning point j, denotes the conjugate transpose, is is the cross-spectral matrix of the point sources at The expression of is: wherein is a steering vector associated with a direct vector, is the i-th iteration result of the maximum value of the power spectrum of the scanning surface, and the power spectrum not affected by the peak sound source is as follows: ； In step 3.5.3, the next iteration is performed. In step 3.5.4, the CLEAN-SC algorithm is used to identify the high-precision noise source on the surface of the test model.

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