Underwater active noise control method adopting virtual sensing and parameter emission technology, program, equipment and storage medium

Abstract

The invention belongs to the technical field of nonlinear acoustics and active noise control, and particularly relates to an underwater active noise control method, program and equipment adopting a virtual sensing and parameter emission technology, and a storage medium. According to the method, the auxiliary filter absorbs the information of the optimal error in the training stage to form the virtual error estimator, in the noise control process of the target noise source, an error hydrophone does not need to be arranged, signals collected by the auxiliary hydrophone are constructed into virtual error signals through the trained auxiliary filter, and the target noise source is subjected to noise control through the virtual error signals. And updating the auxiliary filter by taking the virtual error signal as a cost function, and adjusting a transmitting signal of the transmitting transducer according to the output of the updated auxiliary filter. According to the method, errors are estimated in real time through virtual sensing and closed-loop control is conducted, meanwhile, the parametric array serves as a secondary sound source, interference to the surrounding environment is reduced by means of the efficient effect of the high directivity of the parametric array in a target area, and meanwhile the low-frequency applicability is improved.

Description

Technical Field

[0001] This invention belongs to the field of nonlinear acoustics and active noise control technology, specifically relating to underwater active noise control methods, programs, devices, and storage media employing virtual sensing and parametric emission technology. Background Technology

[0002] Active noise control (ANC) has been widely studied and applied in low-frequency / narrowband noise suppression. However, the "quiet zone" of traditional local ANC is formed around the error sensor. In actual engineering, due to space constraints, complex cabling and reliability constraints, it is difficult to deploy error sensors at the target control point for a long time.

[0003] Therefore, Virtual Sensing (VS) has become a hot topic in recent years: its core is to collect observation signals at deployable locations, estimate the error signal of the target point (virtual point), and use it for closed-loop control. There are three main approaches to VS: Remote Microphone-VS (RM-VS), Auxiliary Filter-VS (AF-VS), and Relative Path-VS (RP-VS). Among the three types of routes, AF-VS uses an "auxiliary filter" to absorb the optimal controller information during the training phase, and then transforms the physical error signal into a virtual error during the control phase. It has good geometric adaptability and is easy to combine with feedforward / feedback / hybrid structures. Existing work has provided feedforward implementation and experimental verification (EDAMOTO S, SHI C, KAJIKAWA Y. Virtual sensing technique for feedforwardactive noise control[C] / / Proceedings of Meetings on Acoustics. 2016:030001.). In hybrid ANC, faster convergence and a noise reduction improvement of 10–20dB are achieved by matching the error signal design (TsujiI, Toyooka S, Kajikawa Y. A novel hybrid active noise control with auxiliary filter-based virtual sensing for mixed unwanted noise reduction[J]. AppliedAcoustics, 2025, 239:110808.). A dual auxiliary filter compensation strategy is proposed for multiple noise sources (Toyooka S, Kajikawa Y. Hybrid active noise control system for remote microphone). basedvirtual sensing with two compensation filters[J]. Applied Acoustics, 2026,241:111039.).

[0004] As a secondary source, the parametric array (PA) utilizes the nonlinearity of the medium to generate a difference-frequency secondary wave in the near field. It has characteristics such as a narrow main lobe, low side lobes, and directional delivery, which can reduce noise leakage while meeting the safety distance requirement. Tanaka et al. used PA as a secondary source to realize the real-time DSP verification of binaural feedforward ANC, proving that it can achieve effective noise reduction in factory scenarios and is easy to deploy remotely (Tanaka K, Shi C, Kajikawa Y. Binaural activenoise control using parametric array loudspeakers[J]. Applied Acoustics,2017, 116:170–176.). Wang et al. demonstrated in their study on directional single-frequency suppression that, compared to conventional dynamic sources, PAs have significant advantages in source-target distance and main lobe −3 dB width (Wang H, Zhang J, Gan L, Liu Y. Directional suppression of monotone noises with a parametric array loudspeaker[J].Applied Sciences, 2023, 13(12):6868.). Meanwhile, there are also experimental reports on feedforward systems combining virtual sensing and PAs, showing that better noise reduction can be achieved at virtual points (Edamoto S, Shi C, Kajikawa Y. Virtual sensing technique for feedforward active noise control[C] / / Proceedings of Meetings on Acoustics, 2016, 29(1):030001.).

[0005] However, existing published works mostly focus on aeroacoustics and offline / semi-real-time implementation; for underwater environments, there is a lack of an integrated real-time system that integrates online estimation of virtual errors with PA secondary sources under unified clock and power safety constraints, and verifies stable convergence under time-varying conditions of primary sources. This is precisely the technical problem that this invention aims to solve. Summary of the Invention

[0006] The purpose of this invention is to address the problems in existing active noise control technologies, such as the difficulty in deploying error sensors at the target control point, insufficient directivity of secondary sound sources leading to energy spillover, and insufficient underwater applicability. This invention provides an underwater active noise control method, program, device, and storage medium that employs virtual sensing and parametric emission technology.

[0007] An underwater active noise control method employing virtual sensing and parametric emission technology includes the following steps:

[0008] Construct a training scenario, and deploy training noise sources, transmitting transducers, reference hydrophones, auxiliary hydrophones, and error hydrophones. The frequency of the training noise sources is the same as the frequency of the target noise sources, and the sampling frequency and update frequency are preset.

[0009] The training noise source is turned off, the transmitting transducer is driven to transmit signals, and the secondary path model from the transmitting transducer to the auxiliary hydrophone and the secondary path model from the transmitting transducer to the error hydrophone are identified online.

[0010] Turn on the training noise source, and jointly update the control filter and auxiliary filter according to the secondary path model from the transmitting transducer to the error hydrophone, so that the auxiliary filter forms a virtual error estimator during the training phase.

[0011] A transmitting transducer, a reference hydrophone, and an auxiliary hydrophone are deployed in the area where the target noise source is located, ensuring that the spatial positions of the transmitting transducer, reference hydrophone, and auxiliary hydrophone relative to the target noise source are consistent with those during the training phase. The trained auxiliary filter is used to construct a virtual error signal from the signal collected by the auxiliary hydrophone. The virtual error signal is used as a cost function to update the auxiliary filter. The transmitting signal of the transmitting transducer is adjusted according to the output of the updated auxiliary filter until the signal collected by the auxiliary hydrophone is lower than the threshold and tends to stabilize at a certain moment, thereby achieving noise control of the target noise source.

[0012] Furthermore, the preset sampling frequency Less than update frequency ,Right now During the training phase and after training, when used for noise control of the target noise source, the reference hydrophone, auxiliary hydrophone, and error hydrophone are all based on the sampling frequency. Data acquisition and transmission transducers are updated at a frequency. Transmit a signal.

[0013] Furthermore, the joint update of the control filter and auxiliary filter based on the secondary path model from the transmitting transducer to the error hydrophone specifically involves:

[0014]

[0015]

[0016] in, for The coefficient vector of the control filter at any given time; for The coefficient vector of the time-dependent auxiliary filter; To control the step size of filter coefficient updates; To update the step size of the auxiliary filter coefficients; The regularization coefficient is used. for The signal acquired by the hydrophone with timing error; , for The signal collected by the hydrophone is constantly being supplemented; , for Constantly refer to the signals collected by the hydrophone; This is the filter length; , ; This is a secondary path model from the transmitting transducer to the error hydrophone.

[0017] Furthermore, the method for training the auxiliary filter is specifically as follows:

[0018] Step 1.1: Initialization Initialize the coefficient vector of the control filter. With the coefficient vector of the auxiliary filter ;

[0019] The training noise source is activated, and the frequency is updated via the transmitting transducer. Transmit the initial preset signal;

[0020] Step 1.2: Obtain Constantly refer to the signals collected by the hydrophone Signals collected by auxiliary hydrophones And the signals collected by the error hydrophone ;

[0021] Step 1.3: If Then let Return to step 1.2;

[0022] Otherwise, construct a reference hydrophone acquisition signal vector. Proceed to step 1.4;

[0023] Step 1.4: Based on the secondary path model from the transmitting transducer to the error hydrophone The signal vector acquired by the reference hydrophone Construct the filter reference signal vector ;

[0024] Step 1.5: Update the coefficient vector of the control filter With the coefficient vector of the auxiliary filter ;

[0025] Step 1.6: If and If all values ​​are below the threshold and tend to stabilize, then stop the iteration and output the auxiliary filter coefficient vector. Otherwise, adjust the transmitting signal of the transmitting transducer according to the output of the updated control filter, so that... Return to step 1.2.

[0026] Furthermore, the step of using a trained auxiliary filter to construct a virtual error signal from the signal acquired by the auxiliary hydrophone specifically involves:

[0027]

[0028] in, The coefficient vector of the trained auxiliary filter; for The signal collected by the hydrophone is constantly being supplemented; , for Constantly refer to the signals collected by the hydrophone;

[0029] The step of updating the auxiliary filter using the virtual error signal as a cost function specifically involves:

[0030]

[0031] in, for The coefficient vector of the time-dependent auxiliary filter; This is the update step size for the auxiliary filter coefficients during the control phase; The regularization coefficient is used. , , This is a secondary path model from the transmitting transducer to the auxiliary hydrophone.

[0032] Furthermore, adjusting the transmission signal of the transmitting transducer according to the output of the updated auxiliary filter specifically involves:

[0033] Calculate the auxiliary filter in Real-time output of moments ;

[0034]

[0035] The auxiliary filter is Real-time output of moments After interpolation and anti-mirror low-pass filtering, the result is the same as the updated frequency. Matching sequence , This is the index of the sequence in the transmitted signal of the transmitting transducer;

[0036] Constructing a non-negative envelope Select carrier frequency With the first appearance The square root modulated signal is obtained. ;

[0037]

[0038]

[0039] in, DC bias; The modulation coefficient;

[0040] Frequency is updated by transmitting transducers. Transmit square root modulated signal .

[0041] Furthermore, the use of the trained auxiliary filter to achieve noise control for the target noise source specifically involves:

[0042] Step 2.1: Initialization The coefficient vector of the auxiliary filter during the initialization control phase. ;

[0043] Frequency is updated by transmitting transducers. Transmit the initial preset signal;

[0044] Step 2.2: Obtain Constantly refer to the signals collected by the hydrophone Signals collected by auxiliary hydrophone ;

[0045] Step 2.3: If Then let Return to step 2.2;

[0046] Otherwise, construct a reference hydrophone acquisition signal vector. Proceed to step 2.4;

[0047] Step 2.4: Based on the secondary path model from the transmitting transducer to the auxiliary hydrophone The signal vector acquired by the reference hydrophone Construct the filter reference signal vector ;

[0048] Step 2.5: Update the auxiliary filter coefficient vector during the control phase ;

[0049] Step 2.6: If If the frequency drops below the threshold and stabilizes, the iteration stops, and the frequency is continuously updated via the transmitting transducer. Transmit square root modulated signal This enables noise control of the target noise source;

[0050] Otherwise, adjust the transmission signal of the transmitting transducer according to the output of the updated auxiliary filter, and update the frequency through the transmitting transducer. Transmit square root modulated signal ,make Return to step 2.2.

[0051] A computer device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the underwater active noise control method employing virtual sensing and parametric emission technology described above.

[0052] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described underwater active noise control method employing virtual sensing and parametric emission technology.

[0053] A computer program product includes computer instructions that, when executed by a processor, implement the steps of the underwater active noise control method employing virtual sensing and parametric emission technology.

[0054] The beneficial effects of this invention are as follows:

[0055] This invention utilizes an auxiliary filter to absorb "optimal error" information during the training phase to form a virtual error estimator. During noise control of the target noise source, there is no need to deploy error hydrophones. The trained auxiliary filter constructs a virtual error signal from the signal collected by the auxiliary hydrophone, and uses this virtual error signal as a cost function to update the auxiliary filter. The transmission signal of the transmitting transducer is adjusted based on the updated output of the auxiliary filter. This invention uses virtual sensing to estimate the error in real time and implements closed-loop control. Simultaneously, a parametric array is used as a secondary sound source, leveraging its high directivity to operate efficiently within the target area, reducing interference with the surrounding environment, and increasing low-frequency applicability. Attached Figure Description

[0056] Figure 1 This is a diagram of the overall architecture of the present invention.

[0057] Figure 2 This is a schematic diagram of the layout of the test platform in an embodiment of the present invention (not to scale).

[0058] Figure 3 This is a convergence curve of the first coefficient of the auxiliary filter during the training phase in an embodiment of the present invention.

[0059] Figure 4 This is a time-domain comparison diagram of the target signal, actual signal, and cost function of the auxiliary filter in the control stage of an embodiment of the present invention.

[0060] Figure 5 This is a time-frequency diagram of the control frequency point of the error signal during the control stage in an embodiment of the present invention.

[0061] Figure 6 This is a frequency domain comparison diagram of the error signal before and after control in an embodiment of the present invention. Detailed Implementation

[0062] The present invention will now be further described with reference to the accompanying drawings.

[0063] This invention provides an underwater active noise control method employing virtual sensing and parametric emission technology, comprising the following steps:

[0064] Step 1: Construct a training scenario, deploying a training noise source, a transmitting transducer, a reference hydrophone, an auxiliary hydrophone, and an error hydrophone. The frequency of the training noise source is the same as the frequency of the target noise source, and a preset sampling frequency is used. With update frequency , ;

[0065] Step 2: Turn off the training noise source and transmit the transducer to update the frequency. Transmit signals at sampling frequency Acquire signals from the auxiliary hydrophone and the error hydrophone, and identify the secondary path model from the transmitting transducer to the auxiliary hydrophone online. And the secondary path model from the transmitting transducer to the error hydrophone. ;

[0066] Step 3: Turn on the training noise source and train the control filter and auxiliary filter;

[0067] Step 3.1: Initialization Set the filter length Initialize the control filter coefficient vector With the auxiliary filter coefficient vector ;

[0068] The training noise source is activated, and the frequency is updated via the transmitting transducer. Transmit the initial preset signal;

[0069] Step 3.2: Obtain Constantly refer to the signals collected by the hydrophone Signals collected by auxiliary hydrophones And the signals collected by the error hydrophone ;

[0070] The reference hydrophone, auxiliary hydrophone, and error hydrophone all use the sampling frequency. Acquire signals;

[0071] Step 3.3: If Then let Return to step 3.2;

[0072] Otherwise, construct a reference hydrophone acquisition signal vector. Proceed to step 3.4;

[0073] Step 3.4: Based on the secondary path model from the transmitting transducer to the error hydrophone The signal vector acquired by the reference hydrophone Construct the filter reference signal vector ;

[0074]

[0075] in, ;

[0076] Step 3.5: Update the control filter coefficient vector With the auxiliary filter coefficient vector ;

[0077]

[0078]

[0079] in, To control the step size of filter coefficient updates; To update the step size of the auxiliary filter coefficients; The regularization coefficient is used. ;

[0080] Step 3.6: If and If all values ​​are below the threshold and tend to stabilize, then stop the iteration and output the auxiliary filter coefficient vector. ;

[0081] Otherwise, proceed to step 3.7;

[0082] Step 3.7: Calculate the control filter at... Real-time output of moments ;

[0083]

[0084] Control filter in Real-time output of moments After interpolation and anti-mirror low-pass filtering, the result is the same as the updated frequency. Matching sequence , Construct a non-negative envelope for the index of the sequence in the transmitted signal of the transmitting transducer. Set carrier frequency With the first appearance Constructing a square root modulated signal ;

[0085]

[0086]

[0087] in, It uses DC bias to ensure non-negativity within the square root and controls the transmit threshold. , ; To ensure redundancy and avoid numerical fluctuations; The modulation coefficient;

[0088] Frequency is updated by transmitting transducers. Transmit square root modulated signal ,make Return to step 3.2;

[0089] Step 4: Apply the trained auxiliary filter to the underwater active noise control of the target noise source. Deploy the transmitting transducer, reference hydrophone, and auxiliary hydrophone in the area where the target noise source is located, and ensure that the spatial positions of the transmitting transducer, reference hydrophone, and auxiliary hydrophone relative to the target noise source are consistent with their positions relative to the training noise source.

[0090] Step 4.1: Initialization Initialization of the auxiliary filter coefficient vector during the control phase ;

[0091] Frequency is updated by transmitting transducers. Transmit the initial preset signal;

[0092] Step 4.2: Obtain Constantly refer to the signals collected by the hydrophone Signals collected by auxiliary hydrophone ;

[0093] Both the reference hydrophone and the auxiliary hydrophone use sampling frequency. Acquire signals;

[0094] Step 4.3: If Then let Return to step 4.2;

[0095] Otherwise, construct a reference hydrophone acquisition signal vector. Proceed to step 4.4;

[0096] Step 4.4: Based on the secondary path model from the transmitting transducer to the auxiliary hydrophone The signal vector acquired by the reference hydrophone Construct the filter reference signal vector ;

[0097]

[0098] in, ;

[0099] Step 4.5: Update the auxiliary filter coefficient vector during the control phase ;

[0100]

[0101] in, To update the step size of the auxiliary filter coefficients during the control phase, ;

[0102] Step 4.6: If If the frequency drops below the threshold and stabilizes, the iteration stops, and the frequency is continuously updated via the transmitting transducer. Transmit square root modulated signal ;

[0103] Otherwise, proceed to step 4.7;

[0104] Step 4.7: Calculate the auxiliary filter in Real-time output of moments ;

[0105]

[0106] The auxiliary filter is Real-time output of moments After interpolation and anti-mirror low-pass filtering, the result is the same as the updated frequency. Matching sequence , Construct a non-negative envelope for the index of the sequence in the transmitted signal of the transmitting transducer. Select carrier frequency With the first appearance The square root modulated signal is obtained. ;

[0107]

[0108]

[0109] Frequency is updated by transmitting transducers. Transmit square root modulated signal ,make Return to step 4.2.

[0110] Example 1:

[0111] This embodiment was verified in an anechoic water tank. The primary sound source was a low-frequency underwater acoustic transducer, emitting a center frequency of [missing value]. A narrowband signal of kHz, with a parametric array as the secondary source and a carrier frequency of kHz. kHz, generating a square root modulation envelope and driving transmission via a power amplifier link. The reference hydrophone and auxiliary hydrophone are respectively positioned at... Figure 2 Consistent positioning; error hydrophones are only temporarily placed at the target control point during the identification / training phase and removed during the control phase. To improve the quality and load capacity of the hydrophone signals, a conditioner is used to filter and amplify all hydrophone signals before they enter the control system, with an amplification factor of 128.

[0112] Data acquisition and output are synchronized using a unified reference clock, and the AI ​​sampling rate is [not specified]. kHz, AO update rate MHz. During the training phase, the secondary path model from the transmitting transducer to the auxiliary hydrophone is obtained. And the secondary path model from the transmitting transducer to the error hydrophone. Then, the control filter and auxiliary filter are updated using normalized FxLMS, ​​and the filter length is... Step length =0.01, =0.01; the narrowband pre-filter is of order 1024, bandwidth Hz. The convergence curve of the first coefficient of the auxiliary filter during the training phase is shown in [reference needed]. Figure 3 .

[0113] Upon entering the control phase, the error hydrophone is removed, and a virtual error is constructed using the auxiliary hydrophone signal and the reference signal. The control filter is updated online, and the system operates in real-time closed loop for 100 seconds. The error spectrum at the target point is measured. The peak value decreased from 103.3 dB to 92.1 dB, with a peak reduction of ≥10 dB. Figure 6 Keeping the secondary parametric matrix and sampling / update parameters unchanged, only the primary source is injected with an amplitude and phase perturbation: at 42s, a phase step of +180° is introduced; at 60s, the amplitude drops sharply to 0.7 times its initial value; and at 80s, the amplitude recovers to its initial state. The control filter continues to update online throughout the process. Test results show that the system recovers to a new steady state within 0.5s after the perturbation (e.g., ...). Figure 4 Suppression of steady-state spectral peaks maintaining ≥10dB (e.g.) Figure 5No numerical divergence was observed during the transition. This indicates that, even with the removal of the physical error hydrophone, the method used in this invention maintains robust online tracking capability under primary source amplitude and phase changes and minor environmental disturbances. No overflow / undersampling or numerical divergence occurred during the entire control period.

[0114] The invention was verified by constructing a sound-absorbing water tank test platform. The comparative results show that the invention has the following beneficial effects:

[0115] (1) Significant attenuation is achieved at the target narrowband frequency, with a spectral peak reduction of ≥10 dB under typical operating conditions.

[0116] (2) Error sensors are not deployed at the target control point during the control phase. The controller is updated through virtual error estimation closed loop, which reduces the difficulty of deployment and the risk of coupling / blocking, and improves the feasibility of the project.

[0117] (3) When the amplitude or phase of the primary source changes, the system can recover and maintain convergence in a short time (on the order of several frames), and has online adaptive tracking capability.

[0118] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An underwater active noise control method employing virtual sensing and parametric emission technology, characterized in that: Construct a training scenario, and deploy training noise sources, transmitting transducers, reference hydrophones, auxiliary hydrophones, and error hydrophones. The frequency of the training noise sources is the same as the frequency of the target noise sources, and the sampling frequency and update frequency are preset. The training noise source is turned off, the transmitting transducer is driven to transmit signals, and the secondary path model from the transmitting transducer to the auxiliary hydrophone and the secondary path model from the transmitting transducer to the error hydrophone are identified online. Turn on the training noise source, and jointly update the control filter and auxiliary filter according to the secondary path model from the transmitting transducer to the error hydrophone, so that the auxiliary filter forms a virtual error estimator during the training phase. A transmitting transducer, a reference hydrophone, and an auxiliary hydrophone are deployed in the area where the target noise source is located, ensuring that the spatial positions of the transmitting transducer, reference hydrophone, and auxiliary hydrophone relative to the target noise source are consistent with those during the training phase. The trained auxiliary filter is used to construct a virtual error signal from the signal collected by the auxiliary hydrophone. The virtual error signal is used as a cost function to update the auxiliary filter. The transmitting signal of the transmitting transducer is adjusted according to the output of the updated auxiliary filter until the signal collected by the auxiliary hydrophone is lower than the threshold and tends to stabilize at a certain moment, thereby achieving noise control of the target noise source.

2. The underwater active noise control method using virtual sensing and parametric emission technology according to claim 1, characterized in that: The preset sampling frequency Less than update frequency ,Right now During the training phase and after training, when used for noise control of the target noise source, the reference hydrophone, auxiliary hydrophone, and error hydrophone are all based on the sampling frequency. Data acquisition and transmission transducers are updated at a frequency. Transmit a signal.

3. The underwater active noise control method using virtual sensing and parametric emission technology according to claim 1, characterized in that: The joint update of the control filter and auxiliary filter based on the secondary path model from the transmitting transducer to the error hydrophone specifically involves: in, for The coefficient vector of the control filter at any given time; for The coefficient vector of the time-dependent auxiliary filter; To control the step size of filter coefficient updates; To update the step size of the auxiliary filter coefficients; The regularization coefficient is used. for The signal acquired by the hydrophone with timing error; , for The signal collected by the hydrophone is constantly being supplemented; , for Constantly refer to the signals collected by the hydrophone; This is the filter length; , ; This is a secondary path model from the transmitting transducer to the error hydrophone.

4. The underwater active noise control method using virtual sensing and parametric emission technology according to claim 3, characterized in that: The method for training the auxiliary filter is as follows: Step 1.1: Initialization Initialize the coefficient vector of the control filter. With the coefficient vector of the auxiliary filter ; The training noise source is activated, and the frequency is updated via the transmitting transducer. Transmit the initial preset signal; Step 1.2: Obtain Constantly refer to the signals collected by the hydrophone Signals collected by auxiliary hydrophones And the signals collected by the error hydrophone ; Step 1.3: If Then let Return to step 1.2; Otherwise, construct a reference hydrophone acquisition signal vector. Proceed to step 1.4; Step 1.4: Based on the secondary path model from the transmitting transducer to the error hydrophone The signal vector acquired by the reference hydrophone Construct the filter reference signal vector ; Step 1.5: Update the coefficient vector of the control filter With the coefficient vector of the auxiliary filter ; Step 1.6: If and If all values ​​are below the threshold and tend to stabilize, then stop the iteration and output the auxiliary filter coefficient vector. ; Otherwise, adjust the transmitting signal of the transmitting transducer according to the output of the updated control filter, so that... Return to step 1.

2.

5. The underwater active noise control method using virtual sensing and parametric emission technology according to claim 1, characterized in that: The process of constructing a virtual error signal from the signal acquired by the auxiliary hydrophone using a trained auxiliary filter is as follows: in, The coefficient vector of the trained auxiliary filter; for The signal collected by the hydrophone is constantly being supplemented; , for Constantly refer to the signals collected by the hydrophone; The step of updating the auxiliary filter using the virtual error signal as a cost function specifically involves: in, for The coefficient vector of the time-dependent auxiliary filter; This is the update step size for the auxiliary filter coefficients during the control phase; The regularization coefficient is used. , , This is a secondary path model from the transmitting transducer to the auxiliary hydrophone.

6. The underwater active noise control method using virtual sensing and parametric emission technology according to claim 5, characterized in that: The adjustment of the transmitting transducer's transmission signal based on the updated auxiliary filter output specifically involves: Calculate the auxiliary filter in Real-time output of moments ; The auxiliary filter is Real-time output of moments After interpolation and anti-mirror low-pass filtering, the result is the same as the updated frequency. Matching sequence , This is the index of the sequence in the transmitted signal of the transmitting transducer; Constructing a non-negative envelope Select carrier frequency With the first appearance The square root modulated signal is obtained. ; in, DC bias; The modulation coefficient; Frequency is updated by transmitting transducers. Transmit square root modulated signal .

7. The underwater active noise control method using virtual sensing and parametric emission technology according to claim 6, characterized in that: The method of using a trained auxiliary filter to control noise from the target noise source specifically involves: Step 2.1: Initialization The coefficient vector of the auxiliary filter during the initialization control phase. ; Frequency is updated by transmitting transducers. Transmit the initial preset signal; Step 2.2: Obtain Constantly refer to the signals collected by the hydrophone Signals collected by auxiliary hydrophone ; Step 2.3: If Then let Return to step 2.2; Otherwise, construct a reference hydrophone acquisition signal vector. Proceed to step 2.4; Step 2.4: Based on the secondary path model from the transmitting transducer to the auxiliary hydrophone The signal vector acquired by the reference hydrophone Construct the filter reference signal vector ; Step 2.5: Update the auxiliary filter coefficient vector during the control phase ; Step 2.6: If If the frequency drops below the threshold and stabilizes, the iteration stops, and the frequency is continuously updated via the transmitting transducer. Transmit square root modulated signal This enables noise control of the target noise source; Otherwise, adjust the transmission signal of the transmitting transducer according to the output of the updated auxiliary filter, and update the frequency through the transmitting transducer. Transmit square root modulated signal ,make Return to step 2.

2.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program implements the steps of the method according to any one of claims 1 to 7.

10. A computer program product comprising computer instructions, characterized in that: When executed by a processor, the computer instructions implement the steps of the method according to any one of claims 1 to 7.

Images