Adaptive compressed sensing sound field measurement system and method

Abstract

The invention discloses a self-adaptive compressed sensing sound field measurement system and method, and belongs to the technical field of sound field measurement, and the system comprises a hydrophone group which comprises a plurality of hydrophones which are used for carrying out the stepping scanning of a sound field region; a three-dimensional scanning positioning assembly; a signal acquisition unit; and the control and processing unit is used for processing the voltage signal of the hydrophone. According to the method, most measurement resources are concentrated in an effective information area of a sound field through a self-adaption strategy, the measurement resources are distributed in a self-adaption mode according to the information amount, invalid sampling in an area without sound pressure or with low sound pressure is avoided, and the number of sampling points can be reduced by more than one order of magnitude compared with a traditional dense scanning method; an iterative threshold decreasing and local subdivision strategy is suitable for different sound beam forms, and has good universality and expandability; the method can be realized only by replacing hydrophones with different sizes and combining a mature scanning control system without developing a complex array hydrophone or a coding mask, and is low in cost, easy to popularize and simple and reliable in hardware realization.

Description

Technical Field

[0001] This invention belongs to the field of sound field measurement technology, specifically relating to an adaptive compressed sensing sound field measurement system and method. Background Technology

[0002] Sound field measurement is a key technology for evaluating and calibrating the performance of various sound source devices. In fields such as medical ultrasound, industrial non-destructive testing, and underwater acoustic detection, accurately acquiring the spatial sound pressure distribution is crucial for ensuring the effectiveness, safety, and reliability of equipment. Traditional sound field measurement methods mainly rely on a single-point hydrophone (such as a needle hydrophone) performing dense step scans in three-dimensional space. This method follows the Nyquist sampling theorem, and to ensure that no sound field details (such as sharp focal points and side lobes with high gradient changes) are missed, the scan step size usually needs to be set to 1 / 3 or even smaller than the expected sound beam diameter.

[0003] For example, for a transducer with a frequency of 5MHz, the wavelength in water is approximately 0.3mm. To ensure accuracy, the lateral scanning step size often needs to be set to 0.1mm or smaller. This point-by-point scanning method results in a massive number of sampling points, making the entire measurement process extremely time-consuming, potentially lasting hours or even days. Inefficiency has become a major bottleneck for the widespread application of high-precision sound field measurement technology. In recent years, compressed sensing (CS) theory has provided a new approach to overcoming this bottleneck. This theory states that for a sparse or compressible signal in a certain transform domain (such as Fourier transform or wavelet transform), the original signal can be perfectly reconstructed using a small number of non-adaptive linear measurements far below the requirements of the Nyquist sampling theorem. Existing research and engineering applications have introduced CS into the field of acoustic imaging to reduce the sampling rate. However, applying CS to sound field measurement faces unique challenges:

[0004] Inefficiency of non-adaptive sampling: Traditional non-adaptive CS (such as random sampling) has a fixed measurement matrix, which cannot be dynamically adjusted according to the characteristics of the sound field itself. Sound field energy is usually highly concentrated (such as the focal region), and non-adaptive sampling wastes a lot of measurement resources in irrelevant regions with no or low sound pressure.

[0005] The fixed spatial response of hydrophones: A single hydrophone has a fixed effective receiving area and frequency response. While using a small-sized hydrophone can ensure spatial resolution, its receiving sensitivity is low, resulting in a poor signal-to-noise ratio in the low sound pressure region. While using a large-sized hydrophone can improve the signal-to-noise ratio, it will blur details due to the spatial averaging effect, losing the ability to detect the fine structure of the sound field. This is a difficult contradiction to reconcile.

[0006] Therefore, existing technologies lack a sound field measurement scheme that can intelligently and adaptively allocate measurement resources while taking into account measurement efficiency, global signal-to-noise ratio, and local resolution. Summary of the Invention

[0007] In view of the above-mentioned problems in the prior art, the technical problem to be solved by the present invention is to provide an adaptive compressed sensing sound field measurement system and method, which significantly reduces the number of invalid sampling points, shortens the measurement time, and improves the ability to balance global signal-to-noise ratio and local spatial resolution while ensuring that key details such as focal point and side lobes can be distinguished.

[0008] Technical Solution: To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0009] An adaptive compressed sensing sound field measurement system, comprising:

[0010] The hydrophone array includes multiple units with different spatial resolutions r1, r2, r3, r i ,.....,r n The hydrophones are denoted as: H1 hydrophone, H2 hydrophone, H3 hydrophone, H... i Hydrophone, ..., H n Hydrophones are used for step-by-step scanning of sound field regions;

[0011] A 3D scanning and positioning component is used to connect to the hydrophone and move the hydrophone.

[0012] The signal acquisition unit, including a preamplifier and a high-speed digitizer, is used to acquire and record the voltage signal of the hydrophone;

[0013] The control and processing unit, connected to the three-dimensional scanning positioning component and the signal acquisition unit, is used to process the voltage signal of the hydrophone.

[0014] Preferably, the spatial resolution of the multiple hydrophones forms a geometric progression with a scaling factor of β. β is a positive integer, and 2≤β≤4.

[0015] An adaptive compressive sensing sound field measurement method, employing the aforementioned adaptive compressive sensing sound field measurement system, includes the following steps:

[0016] S1. Use the H1 hydrophone to perform a surface scan measurement of the entire target scanning area with the first step St1 to obtain the first sound field distribution map SF1. The number of pixels is assumed to be M×N.

[0017] S2. Perform wavelet transform on the first sound field distribution map SF1 data obtained in S1 to obtain the first scale coefficient and the first detail coefficient. Determine the significant pixels in the first sound field distribution map SF1 according to the preset first threshold thr1, and subdivide the area where the significant pixels are located into spatial subdivisions according to β×β. Mark all the subdivided first test spatial points.

[0018] S3. Replace the H1 hydrophone with the H2 hydrophone, and use the second step St2 to perform a surface scan measurement on the first spatial point to be measured in S2. The data of this measurement is fused with the first sound field distribution map SF1 to obtain the second sound field distribution map SF2, with the number of pixels being βM×βN.

[0019] S4. Perform wavelet transform again on the fused second sound field distribution map SF2 to obtain the second scale coefficient and the second detail coefficient. Determine the salient pixels in the second sound field distribution map SF2 according to the second threshold thr2, thr2 = thr1 / 2. Subdivide the region where the salient pixels are located spatially according to β×β. Mark all subdivided second test spatial points and perform scanning measurements. Replace the H2 hydrophone with the H3 hydrophone with higher spatial resolution. Perform surface scanning measurements on the second test spatial points using the third step St3. Fuse the data from this measurement with the second sound field distribution map SF2 to obtain the third sound field distribution map SF3, with β pixels. 2 M×β 2 N;

[0020] S5, and so on, H i Hydrophone acquires the i-th sound field distribution map SF i The obtained sound field distribution map SF i The data undergoes wavelet transform to obtain the i-th scale coefficient and the i-th detail coefficient, and then the data is processed according to the preset i-th threshold thr. i Determine the significant pixels in the sound field distribution map, thr i = thr i-1 / 2, and spatially subdivide the region where the salient pixels are located according to β×β, mark all the i-th test spatial points in the subdivision, and H i The hydrophone was replaced with an H-type hydrophone with higher spatial resolution. i+1 The hydrophone, with a step St at the (i+1)th spatial point to be measured. i+1 Perform surface scan measurements and fuse the measured data with the i-th sound field distribution map SFi to obtain the (i+1)-th sound field distribution map SF. i+1 The number of pixels is β i M×β i N, when the spatial resolution is highest H n After the hydrophone test is completed, the measured data will be compared with the (n-1)th sound field distribution diagram SF. n-1 By merging, the nth sound field distribution map SF is obtained. n The number of pixels is β n-1 M×β n-1 N.

[0021] Preferably, in step S1, a two-dimensional scanning grid covering the entire sound field of the sound source to be measured is first planned. The H1 hydrophone moves point by point according to the two-dimensional scanning grid. The first step St1 of the H1 hydrophone remains consistent in the x and y axes. At each point, the waveform output by the H1 hydrophone is collected, and the peak-to-peak sound pressure or time waveform at that point is recorded. After completion, the first sound field distribution map SF1 is obtained.

[0022] Preferably, S2 specifically includes:

[0023] Wavelet analysis: Perform Haar basis wavelet transform on the first sound field distribution map SF1 to obtain the first scale coefficient and the first detail coefficient;

[0024] Significant region determination: Compare the absolute values ​​of the first scale coefficient and the first detail coefficient with the first threshold thr1 respectively. If either one is greater than the first threshold thr1, it means that the corresponding pixel region is significant, and these significant regions are marked.

[0025] Preferably, step S3 specifically includes: replacing the H1 hydrophone with the H2 hydrophone, where the spatial resolution of the H2 hydrophone is higher than that of the H1 hydrophone; performing surface scanning measurement on the first spatial point to be measured in step S2 using a second step St2; and ensuring that the second step St2 of the H2 hydrophone remains consistent in the x-axis and y-axis directions. The measured data is fused with the first sound field distribution map SF1 to obtain the second sound field distribution map SF2.

[0026] Preferably, in S4, the third step St3 maintains consistency in the x-axis and y-axis directions, St3 = / β, the (i+1)th step St in S5 i+1 St maintains consistency in the x and y directions i+1 = / β.

[0027] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0028] 1. By adopting an adaptive strategy, most of the measurement resources are concentrated in the effective information area of ​​the sound field. The measurement resources are adaptively allocated according to the amount of information, avoiding invalid sampling in areas with no or low sound pressure. The number of sampling points can be reduced by more than an order of magnitude compared to the traditional dense scanning method, which greatly shortens the measurement time (up to 70%-90%) and improves the measurement efficiency.

[0029] 2. Large-size hydrophones are used to complete high signal-to-noise ratio global preliminary detection, while small-size hydrophones are used to complete high-resolution local fine measurement. Multi-resolution hydrophone hierarchical measurement is carried out, taking into account both global signal-to-noise ratio and local detail resolution.

[0030] 3. The iterative threshold reduction and local subdivision strategy is applicable to different beam shapes and has good versatility and scalability.

[0031] 4. No need to develop complex array hydrophones or coding masks; it can be achieved simply by replacing hydrophones of different sizes and combining them with a mature scanning control system. It is low-cost, easy to promote, and the hardware implementation is simple and reliable. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of adaptive sampling region division in an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram of the sound field measurement of the ultrasonic transducer in an embodiment of the present invention;

[0034] Figure 3 This is a flowchart of the sound field measurement method. Detailed Implementation

[0035] The present invention will be further illustrated below with reference to specific embodiments. These embodiments are implemented based on the technical solutions of the present invention, and it should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0036] like Figure 2 As shown, an adaptive compression sensing sound field measurement system includes a hydrophone group, a three-dimensional scanning positioning component, a signal acquisition unit, and a control and processing unit.

[0037] A hydrophone array consists of n hydrophones with different spatial resolutions, where the spatial resolutions of the n hydrophones are r1, r2, r3, r4, r5, r6, r7, r8, r9, r1, r1, r2, r3, r4 ...1, r2, i ,.....,r n Let the n hydrophones be denoted as: H1 hydrophone, H2 hydrophone, H3 hydrophone, H... i Hydrophone, ..., H n Hydrophones are used to perform step-by-step scanning of a sound field region; the spatial resolution of multiple hydrophones is in the form of a geometric progression, with a scaling factor of β. β is a positive integer, 2≤β≤4, to balance global signal-to-noise ratio and local resolution. In this embodiment, the hydrophone group includes three hydrophones: H1, H2, and H3, with a scaling factor β=2. In this embodiment, the spatial resolution of H1 hydrophone is r1, r1=0.50mm, the spatial resolution of H2 hydrophone is r2, r2=0.25mm, and the spatial resolution of H3 hydrophone is r3, r3=0.125mm. The hydrophones are existing piezoelectric hydrophones. The hydrophone output can be regarded as a convolution sampling of the actual sound pressure field and the hydrophone's equivalent spatial response function, plus noise. Small-aperture hydrophones have higher spatial resolution but lower signal-to-noise ratio; large-aperture hydrophones have better signal-to-noise ratio but suffer from spatial averaging effects that lead to blurred details. This embodiment uses multi-spatial-resolution hydrophone grading measurements to achieve a balance between robustness and detail at different scales.

[0038] A 3D scanning and positioning component is used to connect to and move a hydrophone. The component includes x, y, and z-axis displacement stages and a mechanical fixture. The x, y, and z-axis displacement stages use existing three-axis displacement stages (x-axis and y-axis travel 50mm×50mm, z-axis 20mm, positioning accuracy ±0.01mm, movement speed 1mm / s). The three-axis displacement stages use high-precision stepper motors or servo motors driven by 3D guide rails, with a positioning accuracy better than 0.02mm. The mechanical fixture is connected to the x, y, and z-axis displacement stages and uses existing fixtures. The three-axis displacement stages move the hydrophone along the x, y, and z axes through the mechanical fixture. The mechanical fixture supports rapid and accurate positioning and replacement of the hydrophone.

[0039] The signal acquisition unit includes a preamplifier (40dB gain, 2.5dB noise figure, 50Ω input impedance) and a high-speed digitizer (125MS / s sampling rate, 14-bit resolution). The preamplifier is connected to the hydrophone to amplify the voltage signal, and the high-speed digitizer records waveform data, acquiring peak-to-peak sound pressure levels or time waveforms. Theoretically, recording the time waveform is best because peak-to-peak value, RMS, maximum envelope value, spectrum, and other information can be calculated from it. However, recording the time waveform may require a large amount of data. If only the peak-to-peak value is of interest in the sound field, then the peak-to-peak value can be directly acquired.

[0040] The control and processing unit uses an existing industrial control computer (Intel Core i7-12700, 32GB RAM, 2TB SSD) and is connected to the 3D scanning and positioning component, the signal acquisition unit, and the sound source under test (such as an ultrasonic transducer). The industrial control computer is connected to the 3D scanning and positioning component to control the scanning path, and is connected to the signal acquisition unit to process voltage signals and perform Haar basis wavelet transform and salient region determination on the data acquired by the signal acquisition unit.

[0041] like Figure 1 and Figure 3 This embodiment also provides an adaptive compressive sensing sound field measurement method, which includes the following steps using the above-described adaptive compressive sensing sound field measurement system:

[0042] S1. Using the H1 hydrophone, perform a surface scan measurement of the entire target scanning area with the first step St1 to obtain the first sound field distribution map SF1. Assuming the number of pixels is M×N, this step specifically includes:

[0043] Connect the H1 hydrophone to the 3D scanning and positioning component, set the first step St1 of the H1 hydrophone, and plan a two-dimensional scanning grid (x-axis and y-axis) covering the entire sound field of the sound source to be measured. The H1 hydrophone moves point by point according to the two-dimensional scanning grid, and the first step St1 of the H1 hydrophone remains consistent in the x-axis and y-axis directions. At each point, the waveform output by the H1 hydrophone is collected, and the peak-to-peak sound pressure or time waveform at that point is recorded. After completion, the first sound field distribution map SF1 is obtained.

[0044] In this embodiment, the sound source under test is a medical ultrasound transducer (unit-focused ultrasound transducer). Sound field calibration measurements are performed on this transducer. The application scenario is the sound field performance testing of ultrasound diagnostic equipment, requiring accurate acquisition of key parameters such as sound pressure distribution, focal point position, and peak sound pressure. The ultrasound transducer is installed in a water tank and driven by pulse excitation / amplifier. The three-dimensional sound field to be measured consists of many slices of two-dimensional sound field. Traditional measurement methods require dense sampling (0.1mm steps), resulting in long measurement times and difficulty in achieving both signal-to-noise ratio and resolution. The transducer parameters in this embodiment are as follows: center frequency 5MHz, diameter 10mm, focal length 15mm, pulse repetition frequency 5kHz, peak transmitted sound pressure 1MPa (in water); in this embodiment, the spatial resolution of the H1 hydrophone is r1=0.5mm (a large-diameter hydrophone has high receiving sensitivity and wide spatial response, equivalent to a natural "low-pass filter"), St1=0.25mm, and a three-axis displacement stage fixes the H1 hydrophone on the transducer focal plane (in this embodiment, the distance between the transducer focal plane and the transducer is 15mm, and the distance between the hydrophone and the transducer on the z-axis is 15mm). The two-dimensional scanning grid is located at a certain section of the sound field to be measured, and the two-dimensional scanning grid is set to 30mm×30mm (including the entire sound field range of the transducer). The scanning area is discretized into M×N grid points, forming a two-dimensional discrete sound field matrix SF1(j, k), where j=1,2,3,……,M; k=1,2,3,……,N, representing the pixel index in the x and y directions, respectively. k) represents the peak-to-peak sound pressure level (or envelope peak, RMS, time waveform, etc.) at the corresponding spatial point. An industrial control computer drives a three-axis displacement stage, moving the H1 hydrophone point-by-point along a two-dimensional scanning grid. At each scanning point, the transducer is synchronously triggered to emit an ultrasonic pulse (pulse width 100 ns), and the signal acquisition unit acquires the voltage waveform output by the hydrophone (sampling time 2 ms), recording the peak-to-peak sound pressure level (unit: V) at each point. After scanning all points on the two-dimensional scanning grid, the first sound field distribution map SF1 (i.e., the two-dimensional discrete sound field matrix) is obtained. The first sound field distribution map SF1 provides global energy distribution and coarse structural localization, and provides input for subsequent saliency determination.

[0045] S2. Perform wavelet transform on the first sound field distribution map SF1 data obtained in step 1 to obtain the first scale coefficient and the first detail coefficient. Determine the salient pixels in the first sound field distribution map SF1 according to the preset first threshold thr1, and spatially subdivide the region where the salient pixels are located according to β×β. Mark all the subdivided first test spatial points. This step specifically includes:

[0046] Wavelet analysis: Perform Haar basis wavelet transform on the first sound field distribution map SF1 to obtain the first scale coefficients. and first detail coefficient Where e = 1, 2, 3; j and k are the pixel numbers, j = 1, 2, 3, ..., M; k = 1, 2, 3, ..., N;

[0047] Significant region determination: Compare the absolute values ​​of the first scale coefficient and the first detail coefficient with the first threshold thr1. If either value is greater than the first threshold thr1, the corresponding pixel (j, k) region is significant. Mark these significant regions and subdivide each significant region in a β×β manner to obtain the second round of test point set Ω. 2 .

[0048] In this embodiment, the first threshold thr1 can be adaptively set according to the noise estimation, for example, by using the median absolute deviation (MAD) of the detail coefficients to estimate the noise. Take again λ is 3-6. The spatial blocks corresponding to the coefficients that satisfy the significance criterion are marked as significant regions (regions with concentrated energy or drastic gradient changes). Each significant block is then subdivided in a 2×2 manner to obtain the second set of test points Ω. 2 .

[0049] S3. Replace the H1 hydrophone with the H2 hydrophone, and perform a surface scan measurement on the first spatial point to be measured in S2 using the second step St2. Fuse the data from this measurement with the first sound field distribution map SF1 to obtain the second sound field distribution map SF2, with a pixel count of βM×βN. This step specifically includes:

[0050] The H1 hydrophone is replaced with the H2 hydrophone. The H2 hydrophone has a higher spatial resolution than the H1 hydrophone. The first spatial point to be measured in S2 is measured by surface scanning with the second step St2. St1=βSt2. In this embodiment, β=2 and St1=2St2. The second step St2 of the H2 hydrophone is consistent in the x-axis and y-axis directions. The measured data is fused with the first sound field distribution map SF1 to obtain the second sound field distribution map SF2.

[0051] In this embodiment, the spatial resolution of the H2 hydrophone is r2 = 0.25 mm, the z-axis remains unchanged at 15 mm, and only the second set of test points Ω is considered. 2 A surface scan measurement is performed on the corresponding spatial point (the first spatial point to be measured), with a second step St2 = 0.125 mm; the second set of points to be measured is then Ω. 2 The measured points are overwritten on the two-dimensional scanning grid, and the unmeasured points (on the two-dimensional scanning grid, except for the set of points Ω in the second round) are also overwritten. 2The region (excluding the measured points) is finely discretized. The region of unmeasured points is subdivided in a β×β manner. If a pixel value of SF1 is v, then when it is divided into β×β, each pixel value becomes v / (β×β). This ensures that the sum of the subdivided pixel values ​​equals v (for example, 2×2, if a pixel value of SF1 is v, then divide the original value v by (2×2), which is v / 4, and assign the value v / 4 to the four pixels in (2×2)). Then, these assigned points without fine sampling are merged with the fine data points collected by the H2 hydrophone to obtain the second sound field distribution map SF2 with improved resolution.

[0052] S4. Perform wavelet transform again on the fused second sound field distribution map SF2 to obtain the second scale coefficient. Second detail coefficient Where e = 1, 2, 3, j and k are pixel numbers, j = 1, 2, 3, ... M; k = 1, 2, 3, ... N; the significant pixels in the second sound field distribution map SF2 are determined according to the second threshold thr2, thr2 = thr1 / β. In this embodiment, thr2 = thr1 / 2, and the area where the significant pixels are located is spatially subdivided according to β × β. All subdivided second test spatial points are marked; the H2 hydrophone is replaced with the H3 hydrophone with higher spatial resolution, and the second test spatial points are measured by surface scanning with the third step St3. The third step St3 is consistent in the x-axis and y-axis directions, St3 = / β, in this embodiment, St3= / 2, the data from this measurement is fused with the second sound field distribution map SF2 to obtain the third sound field distribution map SF3, with β pixels. 2 M×β 2 N;

[0053] In this embodiment, significant pixels in the second sound field distribution map SF2 are determined based on the second threshold thr2, and the regions where the significant pixels are located are spatially subdivided according to 2×2 to obtain the third set of test points Ω. 3 (Corresponding to the second test point), the spatial resolution of the H3 hydrophone is r3=0.125mm, the third step is St3=0.0625mm, and the third test point set is Ω. 3 The measured points are overwritten on the two-dimensional scanning grid, and the unmeasured points (on the two-dimensional scanning grid, except for the set of points Ω in the third round) are also overwritten. 3The region (excluding the measured points) is finely discretized. The region of unmeasured points is subdivided in a β×β manner, for example, 2×2. Then the original value v is divided by (2×2), which is v / 4. The four pixels in (2×2) are assigned the value v / 4. Then these assigned points without fine sampling are merged with the fine data points collected by the H3 hydrophone to obtain the third sound field distribution map SF3 with improved resolution.

[0054] This embodiment only uses three hydrophones, so the third sound field distribution map SF3 has the highest resolution. If it is necessary to further improve the resolution of sound field spatial detection, hydrophones with higher spatial resolution can be used. The hydrophone group includes hydrophones H1, H2, and H3. i Hydrophone…H n The adaptive compressed sensing sound field measurement method of this embodiment also includes the following steps:

[0055] S5, and so on, H i Hydrophone acquires the i-th sound field distribution map SF i The obtained sound field distribution map SF i The data is subjected to wavelet transform to obtain the i-th scale coefficient. and the i-th detail coefficient Where i = 1, 2, 3, ..., n; j and k are the pixel indices, j = 1, 2, 3, ..., M; k = 1, 2, 3, ..., N; according to the preset i-th threshold thr i Determine the significant pixels in the sound field distribution map, thr i = thr i-1 / 2, and spatially subdivide the region where the salient pixels are located according to β×β, mark all the i-th test spatial points in the subdivision, and H i The hydrophone was replaced with an H-type hydrophone with higher spatial resolution. i+1 The hydrophone, with a step St at the (i+1)th spatial point to be measured. i+1 Perform surface scan measurement, step i+1 St i+1 St maintains consistency in the x and y directions i+1 = / β, the measured data is fused with the i-th sound field distribution map SFi to obtain the (i+1)-th sound field distribution map SF. i+1 The number of pixels is β i M×β i N, when the spatial resolution is highest H n After the hydrophone test is completed, the measured data will be compared with the (n-1)th sound field distribution diagram SF. n-1 By merging, the nth sound field distribution map SF is obtained. n The number of pixels is β n-1 M×β n-1 N.

[0056] In S5, when the area of ​​the region containing a significant pixel is lower than a preset area threshold, further scanning and measurement will stop.

[0057] The acoustic field of the transducer under test typically exhibits spatial characteristics of "concentrated energy + large flat regions + a few high-gradient structures (focal edges, side lobes, etc.)," ​​which are compressible in the wavelet domain. By identifying a small number of spatial blocks corresponding to significant coefficients in the wavelet domain and refining their sampling (similar to surface scanning measurements with a hydrophone that has higher spatial resolution), key structural information can be preserved while significantly reducing the overall sampling rate.

[0058] This embodiment uses Haar-based two-dimensional discrete wavelet transform to analyze the i-th sound field distribution map SF. i (Represents the image obtained after the i-th round of sampling, i=1,2,3,…n) Performing a one-level two-dimensional Haar decomposition yields the i-th scale coefficient. and the i-th detail coefficient Where i = 1, 2, 3, ..., n; e = 1, 2, 3; j and k are pixel numbers, j = 1, 2, 3, ... M; k = 1, 2, 3, ... N; and scale factor. Corresponding to the low-frequency approximation component, detail coefficients Includes three detail sub-bands , , These correspond to the detail components in the horizontal, vertical, and diagonal directions, respectively. In engineering implementation, the Haar transform can be equivalent to performing a "sum / difference" operation on adjacent 2×2 pixel blocks: the scale factor approximately reflects the average energy of the block, and the detail factor reflects the difference intensity along different directions within the block, thus effectively characterizing high gradient structures such as focal edges and side lobes.

[0059] When determining saliency, the absolute values ​​of the scaling coefficient and the detail coefficients in each direction are compared with the i-th threshold. i Compare; when , , , If any corresponding coefficient exceeds the i-th threshold thr i When the threshold is reached, the spatial block corresponding to the coefficient is determined to be a salient region, and it is further subdivided into β×β spatial subdivisions to generate the next set of test points. The threshold can decrease with each iteration (e.g., thri = thr). i-1 / 2), to gradually capture detailed information from strong to weak.

[0060] Analysis of the results in this embodiment:

[0061] 1. Sampling Efficiency: In typical focused beam scenarios, significant areas are often concentrated in the focal point and its nearby side lobes, occupying a much smaller area than the entire domain. Therefore, the actual number of sampling points in local densification rounds is usually significantly lower than the number of points for "scanning the entire domain at the highest resolution." This shortens the measurement time while maintaining the clarity of the focal point and side lobe structure. In this embodiment, the total number of sampling points is only 12.09% of the traditional method (in this embodiment, when the two-dimensional scanning grid is set to 30mm×30mm, the spatial resolution of the H3 hydrophone is 0.125mm, and the third step is 0.0625mm, so the traditional method is 30*30 / 0.0625 / 0.0625=230400, while the actual number of sampling points in this embodiment is 27855). The number of sampling points is reduced by more than an order of magnitude, and the measurement time is shortened by more than 70%, significantly improving efficiency.

[0062] 2. Measurement accuracy: The peak sound pressure measurement error at the focal point is ±2%, which meets the accuracy requirements for medical ultrasound transducer calibration (allowable error ±5%), verifying the reliability of the data fusion and compressed sensing reconstruction algorithm.

[0063] 3. Adaptability: No need to preset the transducer sound field model, it can dynamically locate significant areas through real-time wavelet analysis, which is applicable to the complex sound field of this 5MHz transducer and has strong universality.

[0064] Example 2

[0065] The difference between this method and the adaptive compressed sensing sound field measurement method in Example 1 is that:

[0066] (1) Object and area: In this embodiment, the sound source to be tested is a 128-element linear array transducer with a center frequency of 7.5 MHz, which generates a scanning beam or multi-focus sound beam in water. The transverse-axial section related to imaging (the transducer emitting surface is the xy plane, the z direction is perpendicular to this plane, the origin of the coordinate is set at the center of the linear array transducer, and the transverse-axial section here is a plane parallel to the transducer emitting surface) is selected as the scanning plane, and the two-dimensional scanning grid is set to 40 mm × 10 mm;

[0067] (2) Process: When performing wavelet transform and extracting multiple salient regions (e.g., high gradient regions at multiple focal positions or beam boundaries) according to thresholds; the salient regions are subdivided and local encryption measurements are performed (surface scanning measurements are performed using hydrophones with higher spatial resolution). Since the linear array beam may present multiple separate energy concentration areas in space, this invention can simultaneously allocate measurement resources to multiple salient regions to achieve "multi-region parallel encryption";

[0068] (3) Stopping Criteria: Sampling can be stopped once the preset resolution limit has been reached; or, sampling can be stopped if there are no significant regions after wavelet analysis and significant region judgment in the intermediate sound field distribution map, such as SF3. In fact, when wavelet analysis and significant region judgment are performed on SF2, further sampling can also be automatically stopped when no significant regions are found. Sampling can be stopped once either of the two conditions is met.

[0069] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An adaptive compressed sensing sound field measurement system, characterized in that, include: The hydrophone array includes multiple units with different spatial resolutions r1, r2, r3, r i ,.....,r n The hydrophones are denoted as: H1 hydrophone, H2 hydrophone, H3 hydrophone, H... i Hydrophone, ..., H n Hydrophones are used for step-by-step scanning of sound field regions; A 3D scanning and positioning component is used to connect to the hydrophone and move the hydrophone. The signal acquisition unit, including a preamplifier and a high-speed digitizer, is used to acquire and record the voltage signal of the hydrophone; The control and processing unit, connected to the three-dimensional scanning positioning component and the signal acquisition unit, is used to process the voltage signal of the hydrophone.

2. The adaptive compressed sensing sound field measurement system according to claim 1, characterized in that, The spatial resolution of multiple hydrophones follows a geometric progression with a scaling factor of β. β is a positive integer, and 2≤β≤4.

3. An adaptive compressive sensing sound field measurement method, employing the adaptive compressive sensing sound field measurement system as described in any one of claims 1 to 2, characterized in that, Includes the following steps: S1. Use the H1 hydrophone to perform a surface scan measurement of the entire target scanning area with the first step St1 to obtain the first sound field distribution map SF1. The number of pixels is assumed to be M×N. S2. Perform wavelet transform on the first sound field distribution map SF1 data obtained in S1 to obtain the first scale coefficient and the first detail coefficient. Determine the significant pixels in the first sound field distribution map SF1 according to the preset first threshold thr1, and subdivide the area where the significant pixels are located into spatial subdivisions according to β×β. Mark all the subdivided first test spatial points. S3. Replace the H1 hydrophone with the H2 hydrophone, and use the second step St2 to perform a surface scan measurement on the first spatial point to be measured in S2. The data of this measurement is fused with the first sound field distribution map SF1 to obtain the second sound field distribution map SF2, with the number of pixels being βM×βN. S4. Perform wavelet transform again on the fused second sound field distribution map SF2 to obtain the second scale coefficient and the second detail coefficient. Determine the salient pixels in the second sound field distribution map SF2 according to the second threshold thr2, thr2 = thr1 / 2. Subdivide the region where the salient pixels are located spatially according to β×β. Mark all subdivided second test spatial points and perform scanning measurements. Replace the H2 hydrophone with the H3 hydrophone with higher spatial resolution. Perform surface scanning measurements on the second test spatial points using the third step St3. Fuse the data from this measurement with the second sound field distribution map SF2 to obtain the third sound field distribution map SF3, with β pixels. 2 M×β 2 N; S5, and so on, H i Hydrophone acquires the i-th sound field distribution map SF i The obtained sound field distribution map SF i The data undergoes wavelet transform to obtain the i-th scale coefficient and the i-th detail coefficient, and then the data is processed according to the preset i-th threshold thr. i Determine the significant pixels in the sound field distribution map, thr i = thr i-1 / 2, and spatially subdivide the region where the salient pixels are located according to β×β, mark all the i-th test spatial points in the subdivision, and H i The hydrophone was replaced with an H-type hydrophone with higher spatial resolution. i+1 The hydrophone, with a step St at the (i+1)th spatial point to be measured. i+1 Perform surface scan measurements and fuse the measured data with the i-th sound field distribution map SFi to obtain the (i+1)-th sound field distribution map SF. i+1 The number of pixels is β i M×β i N, when the spatial resolution is highest H n After the hydrophone test is completed, the measured data will be compared with the (n-1)th sound field distribution diagram SF. n-1 By merging, the nth sound field distribution map SF is obtained. n The number of pixels is β n-1 M×β n- 1 N.

4. The adaptive compressed sensing sound field measurement method according to claim 3, characterized in that, In step S1, a two-dimensional scanning grid covering the entire sound field of the sound source under test is first planned. The H1 hydrophone moves point by point according to the two-dimensional scanning grid. The first step St1 of the H1 hydrophone remains consistent in the x and y axes. At each point, the waveform output by the H1 hydrophone is collected, and the peak-to-peak sound pressure or time waveform at that point is recorded. After completion, the first sound field distribution map SF1 is obtained.

5. The adaptive compressed sensing sound field measurement method according to claim 4, characterized in that, S2 specifically includes: Wavelet analysis: Perform Haar basis wavelet transform on the first sound field distribution map SF1 to obtain the first scale coefficient and the first detail coefficient; Significant region determination: Compare the absolute values ​​of the first scale coefficient and the first detail coefficient with the first threshold thr1 respectively. If either one is greater than the first threshold thr1, it means that the corresponding pixel region is significant, and these significant regions are marked.

6. The adaptive compressed sensing sound field measurement method according to claim 5, characterized in that, S3 specifically includes: replacing the H1 hydrophone with the H2 hydrophone, the H2 hydrophone having a higher spatial resolution than the H1 hydrophone; performing a surface scan measurement on the first spatial point to be measured in S2 using a second step St2; the second step St2 of the H2 hydrophone remains consistent in the x-axis and y-axis directions, St2 = / β, the measured data is fused with the first sound field distribution map SF1 to obtain the second sound field distribution map SF2.

7. The adaptive compressed sensing sound field measurement method according to claim 3, characterized in that, In S4, the third step St3 maintains consistency in the x-axis and y-axis directions, St3= / β, the (i+1)th step St in S5 i+1 St maintains consistency in the x and y directions i+1 = / β.

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