Fusion monitoring scene-oriented high-precision acoustic imaging positioning method, equipment and medium

Abstract

The invention discloses a fusion monitoring scene-oriented high-precision acoustic imaging positioning method and device and a medium, and belongs to the technical field of acoustic imaging positioning. Sound signals of multiple channels are synchronously received by using a microphone array; according to the provided sound source localization algorithm based on generalized cross-correlation and l1 norm inverse problem solving, a sound propagation model is improved, and compared with a processing mode of simply regarding a sound source as a point sound source in a conventional algorithm, sound source characteristics can be described more accurately, so that the localization accuracy is improved; a l1 norm optimization method is introduced to solve a source strength coefficient, and the sparse characteristic and noise influence of sound source radiation are fully considered. In a complex acoustic environment, the algorithm can more effectively suppress noise interference, highlight main sound sources and realize more accurate positioning, and compared with a conventional beam forming method, the method shows better recognition performance under the conditions of different frequencies, different test distances and multiple sound sources.

Description

Technical Field

[0001] This application belongs to the field of acoustic imaging positioning technology, and in particular relates to a high-precision acoustic imaging positioning method, equipment and medium for fusion monitoring scenarios. Background Technology

[0002] In the field of acoustic detection and sound source localization, visual sound source recognition technology relies on a microphone array to build a core perception architecture. This array consists of multi-channel microphone units, enabling synchronous acquisition and parallel processing of acoustic signals. By accurately measuring the sound pressure distribution on a two-dimensional holographic surface and combining it with advanced sound field reconstruction algorithms, it can reconstruct the sound field information of the virtual focusing surface of the sound source. Ultimately, it transforms the intangible acoustic information into a high-resolution visual image, forming a cross-modal collaborative perception of hearing and vision, providing a new paradigm for sound source analysis in complex scenarios.

[0003] Conventional beamforming is a commonly used spatial filtering method. Its core principle is to perform spatial filtering on the signal source through weighted summation, including time alignment and weighted summation. The aim is to increase the gain in the main lobe direction and eliminate interference, thereby enhancing the target signal from the interference signal. Generalized cross-correlation (GCC) beamforming, on the other hand, analyzes the relative delays between microphones in a microphone array. By calculating the time delay difference between the signals from each microphone, it forms the "main lobe" in the acoustic imaging spectrum based on the weighted coefficients of the beamforming, thus visually displaying the sound source location. This method uses inverse Fourier transform to calculate the GCC function, reducing computational errors.

[0004] However, for acoustic monitoring in extreme scenarios such as fusion monitoring, existing technologies have the following shortcomings: 1. Insufficient accuracy in sound source modeling: Conventional acoustic visualization algorithms, in order to simplify mathematical complexity, usually do not consider the actual size and characteristics of the sound source and generally adopt the point source assumption. This oversimplified modeling method leads to a significant deviation between the sound source model and the actual physical scene. The randomness of model selection further amplifies the recognition error, making it difficult to truly reflect the complex sound source characteristics in the fusion environment and restricting the improvement of positioning accuracy.

[0005] 2. Lack of robustness in complex environments: In complex acoustic environments such as actual fusion scenarios, problems such as strong background noise interference and multipath reflection are particularly prominent. Existing microphone array-based sound source localization technologies are insufficient in terms of algorithm robustness to meet the requirements of such scenarios. For example, conventional beamforming technology has resolution bottlenecks in the low-frequency band, and its anti-interference capability drops sharply when faced with strong noise interference, resulting in a significant deterioration in positioning accuracy, making it difficult to meet the high-precision positioning requirements of fusion monitoring.

[0006] 3. Weak System Engineering Performance: Existing testing systems generally suffer from operational complexity, requiring highly skilled operators and limiting their large-scale application in engineering practice. Furthermore, system stability heavily relies on ideal acoustic environments such as anechoic chambers, exhibiting significant performance degradation under non-ideal conditions at fusion sites. This fails to meet the real-time monitoring needs of fusion production, severely hindering the industrialization and application of this technology in the fusion field. Summary of the Invention

[0007] The purpose of this application is to overcome the problems of the prior art by disclosing a high-precision acoustic imaging positioning method, device and medium for fusion monitoring scenarios, so as to solve the problems mentioned in the background art.

[0008] On the one hand, the objective of this application is achieved through the following technical solution: An acoustic imaging localization method for fusion monitoring scenarios, the acoustic imaging localization method comprising: S1: Acoustic signal acquisition, using a microphone array to synchronously receive the acoustic signals of each channel, while simultaneously recording the visible light image of the device under test as a background; S2: Based on the preliminary processing of generalized cross-correlation, the generalized cross-correlation beamforming algorithm is used to process the acquired acoustic signal. By calculating the time delay difference between the signals of each microphone in the microphone array, the "main lobe" in the acoustic imaging spectrum is formed according to the weighting coefficient value of beamforming, based on the output result expression containing generalized cross-correlation. S3: Based on the output expression containing generalized cross-correlation obtained in S2, establish an optimization problem-solving model and introduce... l The source strength weight vector is solved using the 1-norm minimization optimization method. S4: Image localization map generation. Through geometric registration, based on the source strength weight vector obtained in S3, the optimized sound source localization result is superimposed with the recorded visible light image to form an acoustic imaging localization map, which presents the location of the sound source.

[0009] According to a preferred embodiment, step S2, the preliminary processing based on generalized cross-correlation, includes: Assuming the sound source is located at a grid point in the virtual focusing region, let the radiated sound pressure at the grid point be... p s ( t ) = P 0 e jωt ,in P 0 represents the amplitude of the sound source. ω= 2 πf Represents angular frequency. f Indicates frequency, t represents the imaginary unit, and t represents time. Using a linear microphone array integrating M microphones for measurement, the i-th microphone at... t Time, location r i The sound pressure signal received at the location for:

[0010] In the formula, Indicates the first j In the propagation path, the signal travels from the sound source to the first... i The propagation delay of each microphone, For the first j In the path, the sound source to the first i The propagation distance of each microphone, r s and r i Representing the sound source position vector and the first... i Each microphone position vector c For the speed of sound, Indicates the first i The noise signal received by each microphone.

[0011] According to a preferred embodiment, the beamforming output signal at the j-th focal point is:

[0012] In the formula, the output signal It is the nth focal point The beamforming output signal at time t Indicates the sound from the first n The number of focal grid points reaches the first The time delay of the microphone. r n Indicates the first n The position vector of each focused grid point.

[0013] According to a preferred embodiment, the output is represented as an expression containing generalized cross-correlation:

[0014] In the formula, Let represent the cross-power spectrum of the signal received by the microphone at position (i,k). Indicates PHAT weighting. This represents the signal delay difference between the microphone and the position (i,k).

[0015] According to a preferred embodiment, in step S3, during the process of establishing the optimization problem-solving model, the output results obtained from the expression containing generalized cross-correlation are...b n Reconstructed into column vectors Then optimize the solution model of the problem:

[0016] In the formula, This represents the source strength weight vector, where x represents the source strength weight coefficient. Represents the cost function. Representation and vector Simulated features of the same dimension.

[0017] According to a preferred embodiment, in step S3... Represented as:

[0018] In the formula, the matrix A transfer matrix that depends only on the virtual focus grid points, microphone array positions, and sound speed is represented as:

[0019] In the formula, Indicates the first The microphone vector to the first The transfer coefficient of a point in a focused grid region is calculated by the following formula:

[0020] In the formula, N This indicates the total number of points in the focused grid area. Indicates the first j At the first focal point, the first i and k The measured time delay difference between the microphone pairs. Indicates the first i and k The reference delay difference between the microphone pairs. This represents a time delay difference threshold constant.

[0021] According to a preferred embodiment, Substituting into the optimization problem model, we get: .

[0022] According to a preferred embodiment, in step S3, by introducing... l 1-norm minimization optimization to solve for the source strength weight vector. The objective function is as follows:

[0023] In the formula, This represents the regularization coefficient.

[0024] On the other hand, this application also discloses: An electronic device includes: a data acquisition unit; a data output unit; at least one processor; and a memory communicatively connected to the at least one processor. The data acquisition unit includes a microphone array, and is connected to the processor for data interaction; the data output unit is also connected to the processor for data interaction. The memory stores instructions that can be executed by the processor, and the processor executes the aforementioned acoustic imaging localization method by executing the instructions stored in the memory.

[0025] On the other hand, this application also discloses: A computer-readable storage medium for storing instructions that, when executed, enable the aforementioned acoustic imaging localization method.

[0026] The aforementioned main solution and its various further alternative solutions can be freely combined to form multiple solutions, all of which are solutions that can be adopted and are claimed in this application. Those skilled in the art, after understanding the solution of this application, will realize that there are many combinations based on the prior art and common general knowledge, all of which are technical solutions to be protected in this application, and will not be exhaustively listed here.

[0027] The beneficial effects of this application are: (1) Improve the accuracy of the sound propagation model: The proposed model is based on generalized cross-correlation and l The sound source localization algorithm for solving the 1-norm inverse problem improves the sound propagation model. Compared with the conventional algorithm that simply treats the sound source as a point source, it can more accurately describe the characteristics of the sound source, thereby improving the accuracy of localization.

[0028] (2) Enhance positioning performance: Introduce l The 1-norm optimization method for source strength weight vectors fully considers the sparsity characteristics of sound source radiation and the influence of noise. In complex acoustic environments, this algorithm can more effectively suppress noise interference, highlight the main sound source, and achieve more accurate localization. Compared with conventional beamforming methods, it exhibits better recognition performance under different frequencies, different test distances, and multiple sound source conditions.

[0029] (3) Optimize visualization: By using geometric registration, the optimized sound source localization results are superimposed with the visible light image to form an acoustic imaging localization map, which more intuitively displays the sound source location, making it easier for operators to observe and analyze, and improving the interactive efficiency and accuracy of acoustic diagnosis. Attached Figure Description

[0030] Figure 1This is a diagram showing the location distribution of the array microphones in this application; Figure 2 This is a simulation layout diagram of the array and sound source in this application.

[0031] Figure 3 The figure shows a simulation comparison between the method of this application and the conventional method when the test distance is 1m and the frequency is 800Hz.

[0032] Figure 4 The figure shows a simulation comparison between the method of this application and the conventional method when the test distance is 1m and the frequency is 4KHz.

[0033] Figure 5 The figure shows a simulation comparison between the method of this application and the conventional method when the test distance is 10m and the test frequency is 4kHz.

[0034] Figure 6 The figure shows a simulation comparison between the method of this application and the conventional method when the test frequency is 8kHz, the test distance is 1m, and there are 3 random sound sources.

[0035] Figure 7 This is a schematic diagram of the audio-visual fusion result of the method in this application.

[0036] Figure 8 This is a schematic diagram of the equipment used in this application. Detailed Implementation

[0037] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0038] Example 1 This embodiment discloses an acoustic imaging localization method for fusion monitoring scenarios, which includes the following steps.

[0039] Step S1: Acoustic signal acquisition. The sound signals of each channel are received synchronously using a microphone array, and the visible light image of the device under test is recorded as the background.

[0040] Step S2: Based on the preliminary processing of generalized cross-correlation, the generalized cross-correlation beamforming algorithm is used to process the acquired acoustic signal. By calculating the time delay difference between the signals of each microphone in the microphone array, the "main lobe" in the acoustic imaging spectrum is formed according to the weighting coefficient value of beamforming, based on the output result expression containing generalized cross-correlation.

[0041] Preferably, step S2, based on the preliminary processing of generalized cross-correlation, includes: Assuming the sound source is located at a grid point in the virtual focusing region, let the radiated sound pressure at the grid point be... p s ( t ) = P 0 e jωt ,in P 0 represents the amplitude of the sound source. ω= 2 πf Represents angular frequency. f Indicates frequency, t represents the imaginary unit, and t represents time. Using a linear microphone array integrating M microphones for measurement, the i-th microphone at... t Time, location r i The sound pressure signal received at the location for:

[0042] In the formula, Indicates the first j In the propagation path, the signal travels from the sound source to the first... i The propagation delay of each microphone, For the first j In the path, the sound source to the first i The propagation distance of each microphone, r s and r i Representing the sound source position vector and the first... i Each microphone position vector c For the speed of sound, Indicates the first i The noise signal received by each microphone.

[0043] Furthermore, the beamforming output signal at the j-th focal point can be obtained:

[0044] In the formula, the output signal It is the nth focal point The beamforming output signal at time t Indicates the sound from the first n The number of focal grid points reaches the first The time delay of the microphone. r n Indicates the first n The position vector of each focused grid point.

[0045] Furthermore, the output can be represented as an expression containing generalized cross-correlation:

[0046] In the formula, Let represent the cross-power spectrum of the signal received by the microphone at position (i,k). Indicates PHAT weighting. This represents the signal delay difference between the microphone and the position (i,k).

[0047] Step S3: Inverse Problem Solving and Optimization: Based on the output expression containing generalized cross-correlation obtained in S2, establish an optimization problem model and introduce... l The source strong weight vector is solved by the 1-norm minimization optimization method.

[0048] In step S3, during the process of establishing the optimization problem model, the output results obtained from the expression containing generalized cross-correlation are... b n Reconstructed into column vectors Then optimize the solution model of the problem:

[0049] In the formula, This represents the source strength weight vector, where x represents the source strength weight coefficient. Represents the cost function. Representation and vector Simulated features of the same dimension.

[0050] Furthermore, in step S3, Represented as:

[0051] In the formula, the matrix A transfer matrix that depends only on the virtual focus grid points, microphone array positions, and sound speed is represented as:

[0052] In the formula, Indicates the first The microphone vector to the first The transfer coefficient of a point in a focused grid region is calculated by the following formula:

[0053] In the formula, N This indicates the total number of points in the focused grid area. Indicates the first j At the first focal point, the first i and k The measured time delay difference between the microphone pairs. Indicates the first i and k The reference delay difference between the microphone pairs. This represents a time delay difference threshold constant.

[0054] Furthermore, Substituting into the optimization problem model, we get: .

[0055] Based on the radiation characteristics of a sound source, the sound field radiated by a sound source can generally be represented by a small number of monopole sound sources, satisfying the sparsity characteristic.

[0056] Furthermore, considering the sparsity characteristics of sound source radiation and the impact of noise, this can be achieved by introducing... l 1-norm minimization optimization to solve for the source strength weight vector. The objective function is as follows:

[0057] In the formula, This represents the regularization coefficient.

[0058] Step S4: Image localization map generation. Through geometric registration, based on the source strength weight vector obtained in S3, the optimized sound source localization result is superimposed with the recorded visible light image to form an acoustic imaging localization map, which presents the sound source location.

[0059] Preferably, when performing sound source imaging visualization, in order to facilitate the comparative analysis of sound source localization performance among different beamforming algorithms, the amplitude of the calculated sound source weight coefficient is uniformly normalized.

[0060] Simulation comparison The sound source identification performance of conventional methods in the prior art and the method proposed in this application was examined using a monopole sound source.

[0061] In the simulation, the coordinates of the point sound source are set to (0,0,0); a sampling array is set up 1m away from the sound source plane, and sound pressure data is acquired using a 144-channel spiral microphone array with an array aperture of 0.5m. The microphone array positions are distributed as follows. Figure 1 As shown; the noise source scanning surface is located on the sound source surface, with a length and width of 1m each. To facilitate post-processing of the data by the computer, the noise source scanning surface is discretized into 41×41 nodes with a discretization interval of 0.025m. The simulation layout of the array and sound source is shown in [reference needed]. Figure 2 For comparison of the remaining simulation results, see the following figures. Figures 3 to 6 .

[0062] This application presents a high-precision acoustic imaging positioning method that utilizes a microphone array to simultaneously receive sound signals from multiple channels; the proposed method is based on generalized cross-correlation and... lA sound source localization algorithm for solving the 1-norm inverse problem improves the sound propagation model. Compared to conventional algorithms that simply treat the sound source as a point source, this algorithm more accurately describes the sound source characteristics, thereby improving the accuracy of localization. It also introduces... l The 1-norm optimization method solves for the source intensity coefficients, fully considering the sparsity characteristics of sound source radiation and the influence of noise. In complex acoustic environments, this algorithm can more effectively suppress noise interference, highlight the main sound source, and achieve more accurate localization. Compared with conventional beamforming methods, it exhibits better recognition performance under different frequencies, different test distances, and multiple sound source conditions.

[0063] Example 2 like Figure 8 As shown, based on Embodiment 1, this embodiment also discloses an electronic device, including: a data acquisition unit; a data output unit; at least one processor; and a memory communicatively connected to the at least one processor; The data acquisition unit includes a microphone array and is connected to the processor for data interaction; the data output unit is connected to the processor for data interaction; the specific connection medium between the functional units is not limited in this embodiment of the invention.

[0064] Figure 8 The example used is the connection between the processor and memory via a bus. The bus... Figure 8 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. Buses can be divided into address buses, data buses, control buses, etc., but for ease of representation, [the specific bus type is not shown here]. Figure 8 The processor is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, a processor can also be called a controller; there are no restrictions on the name.

[0065] In this embodiment, the memory stores instructions executable by the at least one processor. By executing the instructions stored in the memory, the at least one processor performs the method described in Embodiment 1. The processor can implement... Figure 8 The functions of each module in the device shown.

[0066] The processor is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory and calling data stored in memory, it can monitor the device's various functions and process data, thereby enabling overall monitoring of the device.

[0067] In an alternative design, the processor may include one or more processing units. The processor may integrate an application processor and a modem processor, wherein the application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. In some embodiments, the processor and memory may be implemented on the same chip; in some embodiments, they may also be implemented separately on separate chips.

[0068] The processor can be a general-purpose processor, such as a CPU, digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the acoustic imaging localization method for fusion monitoring scenarios disclosed in the embodiments of this invention can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0069] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory can include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memory, magnetic disk, optical disk, etc. Memory is any other medium capable of carrying or storing desired program code having an instruction or data structure form and accessible by a computer, but is not limited thereto. In embodiments of the present invention, memory can also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.

[0070] By designing and programming the processor, the code corresponding to the acoustic imaging localization method for fusion monitoring scenarios described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute the steps of the method described in the foregoing embodiments during runtime. How to design and program the processor is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0071] Example 3 Based on Embodiment 1, this embodiment also discloses: a computer-readable storage medium for storing instructions that, when executed, cause the method described in Embodiment 1 to be implemented.

[0072] In some alternative embodiments, the present invention also provides that various aspects of an acoustic imaging localization method for fusion monitoring scenarios can also be implemented as a program product, which includes program code. When the program product is run on a device, the program code is used to cause the control device to perform the steps in an acoustic imaging localization method for fusion monitoring scenarios according to various exemplary embodiments of the present invention as described above.

[0073] It should be noted that although several units or sub-units of the apparatus have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units. Furthermore, although the operation of the method of the invention is described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0074] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0075] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a server, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0076] Program code for performing the operations of this invention can be written using any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0077] In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0078] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the functions specified in one or more boxes. The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An acoustic imaging localization method for fusion monitoring scenarios, characterized in that, The acoustic imaging localization method includes: S1: Acoustic signal acquisition, using a microphone array to synchronously receive the acoustic signals of each channel, while simultaneously recording the visible light image of the device under test as a background. S2: Based on the preliminary processing of generalized cross-correlation, the generalized cross-correlation beamforming algorithm is used to process the acquired acoustic signal. By calculating the time delay difference between the signals of each microphone in the microphone array, the "main lobe" in the acoustic imaging spectrum is formed according to the weighting coefficient value of beamforming, based on the output result expression containing generalized cross-correlation. S3: Based on the output expression containing generalized cross-correlation obtained in S2, establish an optimization problem-solving model and introduce... l The source strength weight vector is solved using the 1-norm minimization optimization method. S4: Image localization map generation. Through geometric registration, based on the source strength weight vector obtained in S3, the optimized sound source localization result is superimposed with the recorded visible light image to form an acoustic imaging localization map, which presents the location of the sound source.

2. The acoustic imaging localization method as described in claim 1, characterized in that, Step S2, the preliminary processing based on generalized cross-correlation, includes: Assuming the sound source is located at a grid point in the virtual focusing region, let the radiated sound pressure at the grid point be... p s ( t ) = P 0 e jωt ,in P 0 represents the amplitude of the sound source. ω= 2 πf Represents angular frequency. f Indicates frequency, t represents the imaginary unit, and t represents time. Using a linear microphone array integrating M microphones for measurement, the i-th microphone at... t Time, location r i The sound pressure signal received at the location for: In the formula, Indicates the first j In the propagation path, the signal travels from the sound source to the first... i The propagation delay of each microphone, For the first j In the path, the sound source to the first i The propagation distance of each microphone, r s and r i Representing the sound source position vector and the first... i Each microphone position vector c For the speed of sound, Indicates the first i The noise signal received by each microphone.

3. The acoustic imaging localization method as described in claim 2, characterized in that, Beamforming output signal at the j-th focal point: In the formula, the output signal It is the nth focal point The beamforming output signal at time t Indicates the sound from the first n The number of focal grid points reaches the first The time delay of the microphone. r n Indicates the first n The position vector of each focused grid point.

4. The acoustic imaging localization method as described in claim 3, characterized in that, The output is represented as an expression containing generalized cross-correlation: In the formula, Let represent the cross-power spectrum of the signal received by the microphone at position (i,k). Indicates PHAT weighting. This represents the signal delay difference between the microphone and the position (i,k).

5. The acoustic imaging localization method as described in claim 4, characterized in that, In step S3, during the process of establishing the optimization problem model, the output results obtained from the expression containing generalized cross-correlation are... b n Reconstructed into column vectors Then optimize the solution model of the problem: In the formula, This represents the source strength weight vector, where x represents the source strength weight coefficient. Represents the cost function. Representation and vector Simulated features of the same dimension.

6. The acoustic imaging localization method as described in claim 5, characterized in that, In step S3, Represented as: In the formula, the matrix A transfer matrix that depends only on the virtual focus grid points, microphone array positions, and sound speed is represented as: In the formula, Indicates the first The microphone vector to the first The transfer coefficient of a point in a focused grid region is calculated by the following formula: In the formula, N This indicates the total number of points in the focused grid area. Indicates the first j At the first focal point, the first i and k The measured time delay difference between the microphone pairs. Indicates the first i and k The reference delay difference between the microphone pairs. This represents a time delay difference threshold constant.

7. The acoustic imaging localization method as described in claim 6, characterized in that, Will Substituting into the optimization problem model, we get: 。 8. The acoustic imaging localization method as described in claim 7, characterized in that, In step S3, by introducing l 1-norm minimization optimization to solve for the source strength weight vector. The objective function is as follows: In the formula, This represents the regularization coefficient.

9. An electronic device, characterized in that, include: Data acquisition unit; Data output unit; At least one processor; and a memory communicatively connected to the at least one processor; The data acquisition unit includes a microphone array, and is connected to the processor for data interaction; the data output unit is also connected to the processor for data interaction. The memory stores instructions that can be executed by the processor, and the processor executes the instructions stored in the memory to perform the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store instructions that, when executed, cause the method as described in any one of claims 1 to 8 to be implemented.

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