Anti-self-noise sound source positioning method and system based on sound array carried by unmanned aerial vehicle
By acquiring the self-noise angle spectrum of the UAV and combining it with the noise angle spectrum for anti-self-noise sound source localization, the problem of self-noise affecting the sound source localization of UAV acoustic arrays is solved, and high-accuracy detection and localization of power equipment fault discharge sound sources is achieved.
Patent Information
- Application Number
- CN202511356097.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-22
- Publication Date
- 2025-11-28
AI Technical Summary
The acoustic array carried by the UAV is affected by self-noise in sound source localization, resulting in large deviations in the localization results, making it difficult to achieve high accuracy in detecting and locating the sound source of power equipment fault discharge.
The self-noise angle spectrum of the UAV under different flight parameters is obtained through calibration. The time-domain signals of the current flight parameters and the microphone unit of the acoustic array are collected, and the signals are converted into frequency-domain signals by fast Fourier transform. The noise angle spectrum is calculated, and the self-noise angle spectrum is combined to locate the anti-self-noise source.
It effectively suppressed the influence of drone self-noise, achieved high-accuracy detection and localization of power equipment fault discharge sound sources, and improved the accuracy of sound source localization.
Smart Images
Figure CN121027995A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of sound source positioning, in particular to a sound source positioning method and system based on an unmanned aerial vehicle (UAV) mounted acoustic array and resisting self-noise. BACKGROUND
[0002] The UAV mounted acoustic array (microphone array) is the "airborne ear" of the UAV hearing, which plays an important role in aerial listening, search and rescue, aerial photography, human-computer interaction and other applications. However, unlike the ground acoustic array, the UAV hearing has more diverse noise sources, including not only external noise but also self-noise. The external noise includes wind noise in hovering or flying state, noise from the ground in the surrounding environment, etc.; the self-noise includes working noise of the propeller motor, blade rotation, etc. Sound source positioning is a problem of estimating the position of one or more sound sources relative to a reference point based on the multi-channel sound signals collected by the acoustic array. The reference point is usually related to the position of the acoustic array. In most cases, sound source positioning refers to the estimation of the direction of arrival (DoA) of the sound source, that is, the estimation of the azimuth and elevation of the sound source relative to the reference position. Sound source positioning plays an important role in speech recognition, speech enhancement, noise control, room acoustics analysis and other applications. The commonly used methods in traditional sound source positioning algorithms can be classified into the following three types: beamforming based method, high resolution spectrum estimation based method and time difference of arrival based method. However, for the UAV mounted acoustic array, the positioning result will often have a large deviation due to the influence of the self-noise of the UAV itself. Therefore, how to remove the influence of the self-noise of the UAV is the main difficulty in improving the performance of sound source positioning based on the UAV mounted acoustic array. SUMMARY
[0003] The technical problem to be solved by the present application: In view of the above problems of the prior art, the present application provides a sound source positioning method and system based on an unmanned aerial vehicle (UAV) mounted acoustic array and resisting self-noise. The present application aims to suppress the influence of the self-noise of the UAV on the sound source positioning technology and to realize high-accuracy power equipment fault discharge sound source detection and positioning using the UAV mounted acoustic array.
[0004] To solve the above technical problems, the technical scheme adopted by the present application is as follows: A sound source positioning method based on an unmanned aerial vehicle (UAV) mounted acoustic array and resisting self-noise, comprising the following steps: S1. Calibrating and obtaining the self-noise angle spectrum of the UAV under different flight parameters in the absence of external sound source interference; S2. Collecting the current flight parameters of the UAV and the time domain signals of the current frame of each microphone unit of the acoustic array; S3, use Fast Fourier Transform to convert the time-domain signal of each microphone unit in the current frame into a frequency-domain signal, and calculate the current noise angle spectrum based on the frequency-domain signals of the specified two microphone units. S4. Determine whether there is a power equipment fault discharge sound source target in the current frame based on the difference between the current noise angle spectrum and the self-noise angle spectrum under the current flight parameters of the UAV and the preset maximum deviation. If there is a power equipment fault discharge sound source in the current frame, then combine the self-noise angle spectrum of the UAV and the current noise angle spectrum to locate the anti-self-noise sound source.
[0005] Optionally, step S1 includes: S1.1, Collect time-domain signals of the UAV under different flight parameters and various microphone units of the acoustic array in multiple frames under the condition of no external sound source interference; S1.2, the time-domain signal of each frame of each microphone unit is converted into a frequency-domain signal using fast Fourier transform, and the self-noise angle spectrum under different flight parameters is calculated based on the frequency-domain signals of the two specified microphone units. S1.3, average the self-noise angle spectrum of each frame under the same flight parameters to obtain the self-noise angle spectrum of the UAV under different flight parameters.
[0006] Optionally, in step S3, the time-domain signal of each microphone unit is converted into a frequency-domain signal using a fast Fourier transform, and the functional expression is as follows: ; in, For the first Frequency domain signal of each microphone unit For time, The duration of the signal frame. For the first The time-domain signal of each microphone unit in the current frame The imaginary unit, Let be the angular frequency; the functional expression for calculating the current noise angular spectrum based on the frequency domain signals of the two specified microphone units is: ; in, The current noise angular spectrum, For the acoustic delay of the two specified microphone units, For the first Frequency domain signal of each microphone unit For the first Frequency domain signal of each microphone unit The imaginary unit, ω is the angular frequency.
[0007] Optionally, in step S4, when determining whether there is a power equipment fault discharge sound source target in the current frame based on the difference between the current noise angle spectrum and the self-noise angle spectrum under the current flight parameters of the UAV and the preset maximum deviation, the following judgment condition is met: >
[0008] Then it is determined that there is a power equipment fault discharge sound source target in the current frame; where, The current noise angular spectrum, The self-noise angular spectrum of the UAV under its current flight parameters. Angular frequency, For the acoustic delay of the two specified microphone units, These are the current flight parameters of the drone. For coefficient parameters greater than 1, This is the preset maximum deviation.
[0009] Optionally, the preset maximum deviation is obtained in step S1 when calibrating and acquiring the self-noise angle spectrum of the UAV under different flight parameters without external sound source interference, and the calculation function expression of the preset maximum deviation is: ; in, The preset maximum deviation, To obtain the maximum value, This represents the mean of the self-noise angular spectrum for each frame under the same flight parameters.
[0010] Optionally, when determining whether there is a power equipment fault discharge sound source target in the current frame in step S4 based on the difference between the current noise angle spectrum and the self-noise angle spectrum under the current flight parameters of the UAV and the preset maximum deviation, it also includes judging whether the following formula holds true: ; If true, it is determined that there is no power equipment fault discharge sound source target in the current frame. The current noise angular spectrum, The calibration noise angular spectrum corresponding to the current flight parameters of the UAV. Angular frequency, For the acoustic delay of the two specified microphone units, These are the current flight parameters of the drone. For coefficient parameters less than 1, The maximum deviation is preset; and the self-noise angle spectrum of the UAV's current flight parameters is updated when there is no electrical equipment fault discharge sound source target in the current frame: ; in, The self-noise angular spectrum of the updated UAV under the current flight parameters. The self-noise angular spectrum of the UAV under its current flight parameters. For smoothing factors with values less than 1, This represents the current noise angular spectrum.
[0011] Optionally, step S4, which combines the UAV's self-noise angle spectrum with the current noise angle spectrum, involves calculating the position on the two-dimensional plane according to the following formula. Output the score at each location and generate a noise source distribution map: ; in, Position on a two-dimensional plane The score at the point, and These are the lower and upper limits of the comprehensive frequency analysis, respectively. The current noise angular spectrum, This is the current self-noise angular spectrum of the drone.
[0012] Furthermore, the present invention also provides a self-noise source localization system based on an acoustic array mounted on a UAV, comprising a microprocessor and a memory interconnected thereon, wherein the microprocessor is programmed or configured to execute the self-noise source localization method based on an acoustic array mounted on a UAV.
[0013] Furthermore, the present invention also provides a computer-readable storage medium storing a computer program or instructions that are programmed or configured to execute the UAV-based acoustic array anti-self-noise source localization method via a processor.
[0014] In addition, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the UAV-based acoustic array anti-self-noise source localization method via a processor.
[0015] Compared with existing technologies, this invention mainly achieves the following beneficial effects: Research has found that the self-noise of UAVs is mainly contributed by propeller noise, which is closely related to flight parameters. This invention achieves real-time acquisition of UAV self-noise characteristics based on flight parameters, thereby suppressing the influence of UAV self-noise on sound source localization technology. This enables the use of acoustic arrays mounted on UAVs to achieve high-accuracy detection and localization of power equipment fault discharge sound sources. Attached Figure Description
[0016] Figure 1 This is a schematic diagram of the basic process of the method in an embodiment of the present invention.
[0017] Figure 2 This is a schematic diagram of the calibration process in step S1 of an embodiment of the present invention.
[0018] Figure 3 The following is a comparison of noise source distribution maps in the embodiments of the present invention, wherein (a) is a noise source distribution map obtained by the existing method, and (b) is a noise source distribution map obtained by the method in the embodiments of the present invention. Detailed Implementation
[0019] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings in the embodiments of the present invention.
[0020] like Figure 1 As shown, the method for locating self-noise sound sources based on an acoustic array mounted on a UAV in this embodiment includes the following steps: S1, calibrate and obtain the self-noise angle spectrum of the UAV under different flight parameters without external sound source interference, where the flight parameters include some or all of the flight speed and flight attitude. S2, collects the current flight parameters of the UAV and the time domain signal of the current frame of each microphone unit of the acoustic array; S3, use Fast Fourier Transform to convert the time-domain signal of each microphone unit in the current frame into a frequency-domain signal, and calculate the current noise angle spectrum based on the frequency-domain signals of the specified two microphone units. S4. Determine whether there is a power equipment fault discharge sound source target in the current frame based on the difference between the current noise angle spectrum and the self-noise angle spectrum under the current flight parameters of the UAV and the preset maximum deviation. If there is a power equipment fault discharge sound source in the current frame, then combine the self-noise angle spectrum of the UAV and the current noise angle spectrum to locate the anti-self-noise sound source.
[0021] like Figure 2 As shown, step S1 in this embodiment includes: S1.1, Acquire time-domain signals from multiple frames of the UAV under different flight parameters and from each microphone unit of the acoustic array, without external sound source interference; specifically, in this embodiment, flight data acquisition is performed under conditions without other sound source signals. The UAV should cover as many operating conditions as possible during flight, while simultaneously acquiring flight parameters. Time-domain signals of each frame of the microphone unit ,in , The number of array elements (number of microphone units) in the acoustic array. S1.2, the time-domain signal of each frame of each microphone unit is converted into a frequency-domain signal using Fast Fourier Transform, and the self-noise angle spectrum under different flight parameters is calculated based on the frequency-domain signals of the two specified microphone units; in this embodiment, the acquired signal is divided into 20ms frames, and the time-domain signal of each frame of each microphone unit is converted into a frequency-domain signal using Fast Fourier Transform: ; in, For the first Frequency domain signal of each microphone unit For the first The time-domain signal of each microphone unit; the functional expression for calculating the self-noise angle spectrum under different flight parameters based on the frequency-domain signals of two specified microphone units is as follows: ; in, This represents the self-noise angular spectrum of each frame under various flight parameters of the UAV. For the two specified microphone units (the first one) , The acoustic delay of each microphone unit For the first Frequency domain signal of each microphone unit For the first Frequency domain signal of each microphone unit The imaginary unit, Angular frequency; S1.3, the self-noise angle spectrum of each frame under the same flight parameters is averaged to obtain the self-noise angle spectrum of the UAV under different flight parameters. The function expression is: ; in, The self-noise angular spectrum of the UAV under different flight parameters. This represents the mean of the self-noise angular spectrum for each frame under the same flight parameters.
[0022] In step S2 of this embodiment, the current flight parameters of the UAV and the time-domain signals of the current frame of each microphone unit of the acoustic array are collected. In real-time implementation, a frame-by-frame processing method is used, typically with 20ms as one frame. Real-time acquisition of flight parameters. α and the corresponding microphone unit signal In step S3 of this embodiment, the function expression for converting the time-domain signal of each microphone unit into a frequency-domain signal using a Fast Fourier Transform is as follows: ; in, For the first Frequency domain signal of each microphone unit For time, The duration of the signal frame. For the first The time-domain signal of each microphone unit in the current frame The imaginary unit, Let be the angular frequency; the functional expression for calculating the current noise angular spectrum based on the frequency domain signals of the two specified microphone units is: ; in, The current noise angular spectrum, For the acoustic delay of the two specified microphone units, For the first Frequency domain signal of each microphone unit For the first Frequency domain signal of each microphone unit The imaginary unit, ω is the angular frequency.
[0023] In step S4 of this embodiment, when determining whether there is a power equipment fault discharge sound source target in the current frame based on the difference between the current noise angle spectrum and the self-noise angle spectrum under the current flight parameters of the UAV and the preset maximum deviation, the following judgment condition is met: ; Then it is determined that there is a power equipment fault discharge sound source target in the current frame; where, The current noise angular spectrum, The self-noise angular spectrum of the UAV under its current flight parameters. Angular frequency, For the acoustic delay of the two specified microphone units, These are the current flight parameters of the drone. The coefficient parameter is greater than 1 (it can be selected according to the size of the actual target sound source, and is generally 1.5). This is the preset maximum deviation.
[0024] In this embodiment, the preset maximum deviation is obtained in step S1 when calibrating and acquiring the self-noise angle spectrum of the UAV under different flight parameters without external sound source interference, and the calculation function expression of the preset maximum deviation is: ; in, The preset maximum deviation, To obtain the maximum value, This represents the mean of the self-noise angular spectrum for each frame under the same flight parameters.
[0025] In step S4 of this embodiment, when determining whether there is a power equipment fault discharge sound source target in the current frame based on the difference between the current noise angle spectrum and the self-noise angle spectrum under the current flight parameters of the UAV and the preset maximum deviation, it also includes judging whether the following formula is true: ; If true, it is determined that there is no power equipment fault discharge sound source target in the current frame. The current noise angular spectrum, The calibration noise angular spectrum corresponding to the current flight parameters of the UAV. Angular frequency, For the acoustic delay of the two specified microphone units, These are the current flight parameters of the drone. The coefficient parameter is less than 1 (selected according to the characteristics of the external sound source, the value needs to be less than 1, generally 0.8 is acceptable). The maximum deviation is preset; and the self-noise angle spectrum of the UAV's current flight parameters is updated when there is no electrical equipment fault discharge sound source target in the current frame: ; in, The self-noise angular spectrum of the updated UAV under the current flight parameters. The self-noise angular spectrum of the UAV under its current flight parameters. This is a smoothing factor with a value less than 1 (typically 0.98). The noise angle spectrum is updated by updating the self-noise angle spectrum under the current flight parameters of the UAV. This allows the self-noise angle spectrum under different flight parameters of the UAV to change dynamically according to the actual operation of the UAV, thereby improving the accuracy of anti-self-noise source localization by combining the self-noise angle spectrum and noise angle spectrum of the UAV.
[0026] In step S4 of this embodiment, the anti-self-noise source localization is performed by combining the self-noise angle spectrum of the UAV with the current noise angle spectrum, including calculating the position on the two-dimensional plane according to the following formula. Output the score at each location and generate a noise source distribution map: ; in, Position on a two-dimensional plane The score at the point, and These are the lower and upper limits of the comprehensive frequency analysis, respectively. The current noise angular spectrum, This represents the current self-noise angular spectrum of the drone. For more microphone pairs, the results from all microphones can be combined to obtain the final noise source distribution map.
[0027] To verify the self-noise noise source localization method based on the acoustic array mounted on a UAV in this embodiment, a 32-element four-ring concentric circle array was used on the UAV. The array element radii were 0.0156 m, 0.0238 m, 0.0298 m and 0.0348 m, respectively. The frequency band was selected as 18000 Hz to 40000 Hz, the sound speed was 343 m / s, the sampling rate was 96000 Hz, the frame length was 20 ms, and the target sound source was a single point sound source. Figure 3 The following is a comparison of the noise source distribution maps in this embodiment, where (a) is the noise source distribution map obtained by the existing method, and (b) is the noise source distribution map obtained by the method of this embodiment, where the horizontal axis is the X-axis of the scan and the vertical axis is the Y-axis of the scan. Comparison Figure 3 As shown in (a) and (b) above, propeller noise significantly affects the localization results in the noise source distribution map obtained by existing methods, with propeller noise being the main contributor to the UAV's self-noise. However, in the noise source distribution map obtained using the UAV-mounted acoustic array anti-self-noise sound source localization method of this embodiment, the influence of propeller noise is effectively suppressed. Therefore, the UAV-mounted acoustic array anti-self-noise sound source localization method of this embodiment achieves real-time acquisition of UAV self-noise characteristics based on flight parameters, suppresses the influence of UAV self-noise on sound source localization technology, and enables high-accuracy detection and localization of power equipment fault discharge sound sources using the UAV-mounted acoustic array.
[0028] Furthermore, this embodiment also provides a self-noise source localization system based on an acoustic array mounted on a UAV, including a microprocessor and a memory interconnected, wherein the microprocessor is programmed or configured to execute the self-noise source localization method based on an acoustic array mounted on a UAV.
[0029] In addition, this embodiment also provides a computer-readable storage medium storing a computer program or instructions that are programmed or configured to execute the UAV-based acoustic array anti-self-noise source localization method by a processor.
[0030] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the UAV-based acoustic array anti-self-noise source localization method via a processor.
[0031] Those skilled in the art will understand that the technical solutions provided by this invention may take the form of a method, system, or computer program product. Therefore, this invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this invention may take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. 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 machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce an implementation of the flowchart... Figure 1 One or more processes and / or boxes Figure 1 The 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 operate 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 functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0032] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.
Claims
1. A method for locating self-noise sound sources based on an acoustic array mounted on a UAV, characterized in that, Includes the following steps: S1, calibrate and obtain the self-noise angle spectrum of the UAV under different flight parameters without external sound source interference; S2, collects the current flight parameters of the UAV and the time domain signal of the current frame of each microphone unit of the acoustic array; S3, use Fast Fourier Transform to convert the time-domain signal of each microphone unit in the current frame into a frequency-domain signal, and calculate the current noise angle spectrum based on the frequency-domain signals of the specified two microphone units. S4. Determine whether there is a power equipment fault discharge sound source target in the current frame based on the difference between the current noise angle spectrum and the self-noise angle spectrum under the current flight parameters of the UAV and the preset maximum deviation. If there is a power equipment fault discharge sound source in the current frame, then combine the self-noise angle spectrum of the UAV and the current noise angle spectrum to locate the anti-self-noise sound source.
2. The method for locating self-noise sound sources based on an acoustic array mounted on a UAV according to claim 1, characterized in that, Step S1 includes: S1.1, Collect time-domain signals of the UAV under different flight parameters and various microphone units of the acoustic array in multiple frames under the condition of no external sound source interference; S1.2, the time-domain signal of each frame of each microphone unit is converted into a frequency-domain signal using fast Fourier transform, and the self-noise angle spectrum under different flight parameters is calculated based on the frequency-domain signals of the two specified microphone units. S1.3, average the self-noise angle spectrum of each frame under the same flight parameters to obtain the self-noise angle spectrum of the UAV under different flight parameters.
3. The method for locating self-noise sound sources based on an acoustic array mounted on a UAV according to claim 1, characterized in that, In step S3, the function expression for converting the time-domain signal of each microphone unit into a frequency-domain signal using a fast Fourier transform is as follows: ； in, For the first Frequency domain signal of each microphone unit For time, The duration of the signal frame. For the first The time-domain signal of each microphone unit in the current frame The imaginary unit, Let be the angular frequency; the functional expression for calculating the current noise angular spectrum based on the frequency domain signals of the two specified microphone units is: ； in, The current noise angular spectrum, For the acoustic delay of the two specified microphone units, For the first Frequency domain signal of each microphone unit For the first Frequency domain signal of each microphone unit The imaginary unit, ω is the angular frequency.
4. The method for locating self-noise sound sources based on an acoustic array mounted on a UAV according to claim 1, characterized in that, In step S4, when determining whether there is a power equipment fault discharge sound source target in the current frame based on the difference between the current noise angle spectrum and the self-noise angle spectrum under the current flight parameters of the UAV and the preset maximum deviation, the following judgment condition is met: > Then it is determined that there is a power equipment fault discharge sound source target in the current frame; where, The current noise angular spectrum, The self-noise angular spectrum of the UAV under its current flight parameters. Angular frequency, For the acoustic delay of the two specified microphone units, These are the current flight parameters of the drone. For coefficient parameters greater than 1, This is the preset maximum deviation.
5. The method for locating self-noise sound sources based on an acoustic array mounted on a UAV according to claim 4, characterized in that, The preset maximum deviation is obtained in step S1 when calibrating and acquiring the self-noise angle spectrum of the UAV under different flight parameters without external sound source interference, and the calculation function expression of the preset maximum deviation is: ； in, The preset maximum deviation, To obtain the maximum value, This represents the mean of the self-noise angular spectrum for each frame under the same flight parameters.
6. The method for locating self-noise sound sources based on an acoustic array mounted on a UAV according to claim 1, characterized in that, In step S4, when determining whether there is a power equipment fault discharge sound source target in the current frame based on the difference between the current noise angle spectrum and the self-noise angle spectrum under the current flight parameters of the UAV and the preset maximum deviation, it also includes judging whether the following formula holds true: ； If true, it is determined that there is no power equipment fault discharge sound source target in the current frame. The current noise angular spectrum, The calibration noise angular spectrum corresponding to the current flight parameters of the UAV. Angular frequency, For the acoustic delay of the two specified microphone units, These are the current flight parameters of the drone. For coefficient parameters less than 1, The maximum deviation is preset; and the self-noise angle spectrum of the UAV's current flight parameters is updated when there is no electrical equipment fault discharge sound source target in the current frame: ； in, The self-noise angular spectrum of the updated UAV under the current flight parameters. The self-noise angular spectrum of the UAV under its current flight parameters. For smoothing factors with values less than 1, This represents the current noise angular spectrum.
7. The method for locating self-noise sound sources based on an acoustic array mounted on a UAV according to claim 1, characterized in that, Step S4 involves combining the UAV's self-noise angle spectrum with the current noise angle spectrum to perform anti-self-noise source localization, including calculating the position on the two-dimensional plane according to the following formula. Output the score at each location and generate a noise source distribution map: ； in, Position on a two-dimensional plane The score at the point, and These are the lower and upper limits of the comprehensive frequency analysis, respectively. The current noise angular spectrum, This is the current self-noise angular spectrum of the drone.
8. A self-noise source localization system based on an acoustic array mounted on a UAV, comprising a microprocessor and a memory interconnected thereon, characterized in that, The microprocessor is programmed or configured to execute the method for localizing self-noise sound sources based on an acoustic array mounted on a UAV, as described in any one of claims 1 to 7.
9. A computer-readable storage medium storing a computer program or instructions, characterized in that, The computer program or instructions are programmed or configured to execute, via a processor, the method for localizing self-noise sources based on an acoustic array mounted on a UAV as described in any one of claims 1 to 7.
10. A computer program product, comprising a computer program or instructions, characterized in that, The computer program or instructions are programmed or configured to execute, via a processor, the method for localizing self-noise sources based on an acoustic array mounted on a UAV as described in any one of claims 1 to 7.