A method for obtaining and processing sound source positioning data set in a shallow sea environment

Abstract

The application discloses a kind of shallow sea environment under sound source positioning dataset acquisition and processing method, comprising: sending end is according to preset spiral trajectory around receiving end movement and record GPS coordinates, receiving signal is received simultaneously with receiving array posture angle, GPS coordinates, shallow sea temperature salt depth data;According to the independent sample of receiving signal matching filter result interception, calculate its SNR and signal purity;The relative position of sending end is converted into array coordinate system, the real distance and azimuth angle of sending end and receiving end are obtained;Multi-dimensional label is constructed with time alignment of multi-channel waveform data;Through the background noise of superposition time shift processing, sample balance azimuth angle distribution is amplified;According to the threshold of SNR and signal purity, sample is divided into quality, and sound source positioning dataset is output.The application effectively improves the stability of posture, azimuth angle distribution uniformity and sample quality stability.

Description

Technical Field

[0001] This invention relates to the fields of underwater acoustic signal processing and marine observation, and in particular to a method for acquiring and processing sound source localization datasets in shallow sea environments, applicable to scenarios such as underwater sound source localization, cross-media communication, and construction of marine intelligent sensing datasets. Background Technology

[0002] Sound propagation in shallow waters is significantly non-stationary in time and space due to the combined effects of limited water depth, strong reflections from the seabed and surface, multipath propagation and scattering, seasonal thermohaline transitions, and noise from ships and waves. Unlike in the deep sea, direct waves in shallow waters interfere with reflected waves from the surface and seabed, causing rapid changes in arrival delay and incident azimuth due to environmental and geometrical relationships. Traditional positioning methods relying on steady-state assumptions often experience performance degradation in engineering settings. Furthermore, actual sea trial data typically contains only raw waveforms or limited metadata, lacking fully "physically traceable" labels such as attitude angles, true azimuth angles, relative transmitter and receiver positions, and CTD profiles. This makes it difficult to support reproducible evaluation of positioning algorithms and effective training of data-driven models.

[0003] Existing publicly available datasets mostly focus on passive sonar or broadband environmental noise, and integrated localization dataset solutions encompassing "active transmission—synchronization—segmentation—labeling—enhancement—grading" are still relatively lacking. Regarding synchronization, while Linear Frequency Modulation (LFM) matched filtering can provide high detection gain in multipath environments, without array attitude compensation and accurate coordinate conversion, azimuth and range labels will still exhibit systematic biases. Furthermore, shallow-sea experiments often employ fixed or small-radius circular tracks, resulting in uneven distribution of samples in azimuth and range, introducing training bias and hindering the model's generalization ability to sparse angles or long-range scenarios. In terms of quality assessment, traditionally, only the signal-to-noise ratio (SNR) is used as a data selection criterion, making it difficult to distinguish between "strong but multipath / sidelobe-contaminated" samples and "weak but structurally clean" samples, thus limiting the stability of downstream localization and feature extraction. In summary, the industry urgently needs an engineering approach that addresses the entire process, focusing on the complex propagation mechanisms in shallow seas. Summary of the Invention

[0004] To address the shortcomings of existing technologies in shallow-sea sound source localization data acquisition, such as attitude uncertainty, inaccurate azimuth labeling, uneven angle distribution, and unstable sample quality, this invention proposes a method for acquiring and processing sound source localization datasets in shallow-sea environments. This method provides an integrated workflow from experimental design and trajectory planning to dataset output: In the acquisition phase, an array and an inertial measurement unit (IMU) are introduced at the receiving end to achieve attitude calibration, combined with a preset spiral trajectory motion to achieve omnidirectional and multi-distance coverage; In the processing phase, the receiving end uses LFM matched filtering and peak detection to achieve robust synchronization and segmentation, and combines GPS and CTD to complete coordinate transformation and environmental labeling, constructing a multi-dimensional label containing information such as distance, azimuth, SNR, and signal purity; In the data processing phase, a data augmentation method that balances structural "signal purity" indicators and azimuth constraints is proposed, and a hierarchical standard oriented towards application scenarios is established, thereby forming a reproducible, scalable, and algorithm evaluation-adaptable shallow-sea sound source localization dataset.

[0005] The specific technical solution is as follows:

[0006] A method for acquiring and processing sound source localization datasets in shallow sea environments includes the following steps:

[0007] S1: The receiver receives the periodically transmitted signal with synchronization header from the transmitter through the receiver array. The transmitter moves around the receiver according to a preset spiral trajectory. The receiver array attitude angle is collected synchronously, and the GPS coordinates, shallow sea temperature T, salinity S and depth D of the receiver and transmitter are recorded.

[0008] S2: Perform matched filtering on the received signal, determine the signal start point based on the maximum peak value of the output, and extract the waveforms before and after the start point within a set time window as independent samples; calculate the signal-to-noise ratio (SNR) and signal purity (P) for each independent sample. u ;

[0009] S3: Convert the GPS coordinates of the transmitter in the geographic coordinate system to the relative position in the array coordinate system, obtaining the true distance R and azimuth angle θ between the transmitter and receiver; construct a system containing R, θ, SNR, and P. u Multidimensional labels of T, S, and D are generated and time-aligned with the corresponding multi-channel waveform data.

[0010] S4: Identify the distribution differences in the number of samples under each azimuth angle. For azimuth angles where the number of samples is less than the sparsity threshold, generate new amplified samples by superimposing time-shifted background noise to obtain an enhanced dataset.

[0011] S5: Based on the thresholds of signal-to-noise ratio and signal purity, the samples in the enhanced dataset are divided into three categories: high quality, medium quality, and low quality, and a sound source localization dataset containing waveform data, multi-dimensional labels, and quality grades is output.

[0012] Furthermore, in S1, the transmitting end moves around the receiving end according to the preset spiral trajectory, specifically as follows: the transmitting end moves in a spiral motion around the center of the receiving array of the receiving end as the center. After the transmitting end completes one circle, the trajectory radius increases by a set step size to achieve sample coverage at different distances and all-round angles.

[0013] Furthermore, the independent sample truncation operation in S2 is specifically as follows: using the linear frequency modulation synchronization head signal transmitted by the transmitting end as the template signal, a conjugate convolution operation is performed on the received signal; the arrival time of each transmitted signal is determined by detecting the maximum correlation peak value of the matched filter output and a synchronization point is obtained; and a time window of a preset duration before and after the synchronization point is truncated as an independent sample segment.

[0014] Further, in S2, the signal purity of an independent sample is calculated as follows: A near-empty signal region in the independent sample is selected for noise statistical estimation, and multiple standard deviations of the noise are selected as the threshold for significant peaks. Peaks with amplitudes greater than the threshold are considered significant peaks, with the largest amplitude being designated as the main peak. The proportion of the energy in the neighborhood of the main peak corresponding to the matched filter output of each array element in the total energy of all significant peaks is calculated, and the average is taken to obtain the signal purity of the independent sample. The closer the value is to 1, the purer the main peak.

[0015] Furthermore, in step S3, the GPS coordinates of the transmitting end in the geographic coordinate system are converted into relative positions in the array coordinate system. The specific operation is as follows:

[0016] Calculate the relative vector between the transmitter and receiver in the geographic coordinate system. That is, to calculate the difference between the GPS coordinates of the transmitting end and the GPS coordinates of the receiving end;

[0017] The array attitude angles include: pitch angle, roll angle, and yaw angle; an attitude rotation matrix is ​​constructed based on the array attitude angles.

[0018] The transmitter position is transformed to the array coordinate system using the attitude rotation matrix, i.e., the inverse matrix of the attitude rotation matrix is ​​calculated. The product of these terms yields the relative position vector of the transmitting end in the array coordinate system;

[0019] The magnitude of the relative position vector corresponds to the actual distance R between the transmitter and receiver, and the included angle corresponds to the azimuth angle θ of the transmitter relative to the receiver.

[0020] Furthermore, S4 is specifically implemented through the following operations:

[0021] The number of samples in each direction is evaluated by statistical azimuth histogram. The quotient of the total number of samples and the total number of angle intervals is used as the sparsity threshold. If the number of samples in a certain angle interval is lower than the sparsity threshold, then the angle interval is a sparse interval. The samples in the sparse interval are amplified by time-shifted noise superposition to the sparsity threshold.

[0022] The time-shifted noise superposition amplification specifically involves: extracting background noise from a signal-free period and randomly applying a time shift; superimposing this noise segment onto the original sample signal to generate an amplified sample; the amplified sample inherits the multidimensional label of the original sample.

[0023] Furthermore, in S5, if a sample satisfies that the signal-to-noise ratio is greater than or equal to the upper limit threshold of the signal-to-noise ratio and the signal purity is greater than or equal to the upper limit threshold of the purity, then it is classified as a high-quality sample and included in the high-quality dataset.

[0024] If a sample satisfies that the signal-to-noise ratio is less than or equal to the lower limit threshold of the signal-to-noise ratio and the signal purity is less than or equal to the lower limit threshold of the purity, then it is classified as a low-quality sample and included in the low-quality dataset.

[0025] Except for the cases mentioned above, the samples in other cases are classified as medium-quality samples and included in the medium-quality dataset.

[0026] A system for acquiring and processing sound source localization datasets in shallow sea environments, used to implement the method for acquiring and processing sound source localization datasets in shallow sea environments, includes a transmitter and a receiver.

[0027] The transmitting end is deployed on the transmitting ship and includes: a transmitting end GPS module, a transmitting controller, a signal generator, a power amplifier, and a transmitting transducer; the transmitting GPS module is used to acquire the transmitting end's own GPS data in real time, the transmitting controller is used to realize the periodic transmission of sound waves, the signal generator is used to generate a transmission signal, the power amplifier is used to amplify the transmission signal, and the transmitting transducer is used to convert the amplified signal into a sound wave signal; when the GPS data shows that the transmitting end has reached the preset trajectory transmission point, the transmitting controller sends a command to the signal generator to generate a transmission signal, which is then converted into a sound wave signal by the power amplifier and the transmitting transducer and transmitted.

[0028] The receiving end is deployed on the anchored vessel and includes: a receiving array, a preamplifier, a data acquisition module, a synchronization controller, a data storage device, a data processing module, and a sensor group. The receiving array is used to receive target acoustic signals. The array includes multiple array elements with an element spacing of less than half a wavelength to suppress ambiguity. The preamplifier is used to amplify the received signal analogically. The data acquisition module is used to convert the signal into a digital signal. The synchronization controller is used to acquire sensor data at the data acquisition time point and output the sensor data and digital signal to the data storage device for storage. The data processing module is used to read data from the data storage device and process it to obtain a sound source localization dataset containing waveform data and multi-dimensional labels.

[0029] The sensor group includes: an inertial measurement unit, a temperature, salinity, and depth (TDM) measurement module, and a receiver GPS module; the inertial measurement unit is used to detect the pitch angle, roll angle, and heading angle of the receiving array; the TDM measurement module is used to acquire shallow sea temperature, salinity, and depth data; and the receiver GPS module is used to acquire the receiver's own GPS data in real time.

[0030] A device for acquiring and processing sound source localization datasets in a shallow sea environment includes a memory and one or more processors. The memory stores executable code, and when the one or more processors execute the executable code, they are used to implement the method for acquiring and processing sound source localization datasets in a shallow sea environment.

[0031] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the method for acquiring and processing sound source localization datasets in a shallow sea environment.

[0032] The beneficial effects of this invention are:

[0033] (1) This invention provides a positioning data labeling scheme for attitude calibration and orientation correction, which significantly improves the orientation accuracy of multidimensional labels;

[0034] (2) This invention generates new amplified samples by superimposing background noise that has undergone time shifting, which effectively alleviates the problem of uneven distribution of angle samples; at the same time, it constructs a multi-dimensional labeling system that includes physical environment and signal statistical features;

[0035] (3) The present invention automatically generates a sound source localization dataset with quality grading, which is suitable for AI model training, benchmark construction and algorithm evaluation. Attached Figure Description

[0036] Figure 1 This is a flowchart of the method for acquiring and processing sound source localization datasets in a shallow sea environment, as described in this embodiment of the invention.

[0037] Figure 2This is a schematic diagram of the transmitting end spiraling around the receiving end in an embodiment of the present invention.

[0038] Figure 3 This is a schematic diagram of the significant peak determination of independent samples in an embodiment of the present invention.

[0039] Figure 4 This is a schematic diagram of sample distribution and sample enhancement at different azimuth angles in an embodiment of the present invention.

[0040] Figure 5 This is a schematic diagram of the composition of the sound source localization dataset acquisition and processing system in a shallow sea environment according to an embodiment of the present invention.

[0041] Figure 6 This is a schematic diagram of the device for acquiring and processing sound source localization datasets in a shallow sea environment, as described in this embodiment of the invention. Detailed Implementation

[0042] The present invention will be described in detail below with reference to the accompanying drawings and preferred embodiments. The objectives and effects of the present invention will become clearer as a result. The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0043] like Figure 1 As shown, a method for acquiring and processing sound source localization datasets in a shallow sea environment includes the following steps:

[0044] S1: Data Acquisition. The raw dataset includes: received signal, attitude angle of the receiving array, GPS coordinates of the receiver and transmitter, and temperature, salinity, and depth of the shallow sea. The transmitter periodically sends a composite signal containing a synchronization header. The receiver receives the signal through the array and ensures that the start time of the transmitter and receiver is aligned according to a spatiotemporal reference. The array attitude angle is acquired through the array's built-in inertial measurement unit (IMU). The GPS coordinates of the receiver and transmitter are recorded synchronously through their respective GPS modules to achieve synchronous positioning. The temperature, salinity, and depth data of the shallow sea are measured using a CTD (Conductivity, Temperature, and Depth) meter at the receiver to achieve environmental monitoring. The transmitter moves around the receiver according to a preset spiral trajectory.

[0045] Furthermore, the receiver is arranged using a circular ultra-short baseline (USBL) array. The transmitter is located on the transmitting ship, and the receiver is located on the anchoring ship.

[0046] Specifically, the anchoring vessel maintains a fixed position or a low-speed, low-drift state, and performs an initial calibration of the array attitude at the receiving end during the initial deployment phase. During the data acquisition phase, such as... Figure 2As shown, the transmitting end moves around the receiving end according to a preset spiral trajectory. Specifically, the transmitting end moves in a spiral motion around the center of the receiving array of the receiving end. After each complete circle, the trajectory radius increases by a set step size, achieving sample coverage at different distances and omnidirectional angles, thereby ensuring a balanced distribution of collected data in terms of orientation and distance. Its planar trajectory can be represented as:

[0047]

[0048] In the formula, R0 is the initial radius, ΔR is the set step size, ω is the angular velocity, and z0 is the near-horizontal depth, which is 20-100m in this embodiment. The trajectory parameters are selected to ensure that the sample maintains the most uniform coverage possible in the [0°, 360°) azimuth and multiple distance segments.

[0049] During this process, the composite signal periodically transmitted by the transmitting end is:

[0050]

[0051] In the formula, S sync (t) is the synchronization header, S data (t) represents the positioning waveform.

[0052] The synchronization header uses LFM matched filtering, as shown in the following expression:

[0053]

[0054]

[0055] In the formula, f0 is the starting frequency, k is the frequency modulation frequency, B is the system bandwidth, and T is the frequency of adjustment. sync The duration of the synchronization header.

[0056] In this embodiment, the receiver and transmitter record GPS (WGS-84) position, velocity, and time stamp information at a frequency of 1–10 Hz, and unify them to the UTC (Coordinated Universal Time) time base. The CTD operates at 1–2 Hz or in profile mode, outputting temperature T, salinity S, depth D, and timestamp.

[0057] S2: Perform matched filtering on the received signal, determine the signal start point based on the peak value of the filtered output, and extract the waveform before and after the start point within a set time window as independent samples to complete "waveform segmentation"; calculate the signal-to-noise ratio (SNR) and signal purity for each independent sample. This is achieved through the following sub-steps:

[0058] (2.1) Perform matched filtering on the received signal of the m-th array element (m=1,2,3,...,M):

[0059]

[0060] In the formula, r m (t) represents the waveform actually received by the m-th array element. Indicates the synchronization header S sync (-t) performs conjugate convolution.

[0061] (2.2) Define the envelope Take the position of its maximum peak The synchronization point is used as the center, and symmetrical time windows before and after it are extracted as independent samples. These time windows are designed to preserve the main lobe and its adjacent early or late multipath and noise background information while ensuring initial alignment, facilitating subsequent calculations of signal-to-noise ratio, signal purity, and robust feature extraction. The expression for independent samples is as follows:

[0062]

[0063] In the formula, T p This is the half-width of the time window, and its value is empirically set to 1.5 to 2 times the signal duration to ensure redundancy.

[0064] (2.3) Calculate the signal-to-noise ratio (SNR) and signal purity for each segmented signal (i.e., independent sample). The SNR is used to characterize the strength of the received signal, and the signal purity is used to determine whether the target echo is clean and whether it is contaminated by factors such as sidelobes, multipath, and narrowband interference.

[0065] Noise statistics are estimated by selecting an approximately empty signal region far from the main peak in the independent samples, and a threshold of 3-5 times the standard deviation of the noise is selected as the significant peak threshold. Peaks greater than this threshold are designated as significant peaks, with the largest amplitude being designated as the main peak, i.e., the main peak is a special significant peak. Figure 4 As shown, the independent sample contains three significant peaks: the main peak, significant peak 1, and significant peak 2.

[0066] Quality Analysis: The proportion of the main peak energy among all significant peak energies is selected as an indicator of signal purity. The main peak energy and significant peak energy can be expressed as:

[0067]

[0068]

[0069]

[0070] In the formula, E main,m E represents the peak energy of the m-th element. side,m Ω represents the energy of all significant peaks in the m-th element except for the main peak; main The time window corresponding to the main peak, Ωp Let p be the time window corresponding to the p-th significant peak. For a set of significant peaks, Indicates all significant peaks other than the main peak; f s B represents the system sampling rate, and B represents the system bandwidth. The value of γ corresponds to the approximate main lobe width of the matched filter under ideal conditions. γ is used as an adjustable safety factor. If multipath is significant, the value of γ can be appropriately increased.

[0071] Furthermore, by fusing the multi-channel indicators, the signal purity P is obtained. u for:

[0072]

[0073] S3: Based on the attitude angle of the receiving array and the GPS coordinates, the position of the transmitting end in the geographic coordinate system is converted into the relative position in the array coordinate system to obtain the true distance and azimuth angle.

[0074] Specifically, the GPS coordinates of the receiver are: The GPS coordinates of the sending end are Then, the relative vector between the transmitter and the receiver in the geographic coordinate system is:

[0075]

[0076] The array attitude angles acquired by the IMU include pitch angle. Roll angle Heading angle Based on this, the attitude rotation matrix is ​​constructed as follows:

[0077]

[0078] Furthermore, the target position is transformed into the array coordinate system using the attitude rotation matrix:

[0079]

[0080] The true distance and azimuth between the transmitter and receiver are obtained using the following expression:

[0081]

[0082]

[0083] Construct a system containing the true distance R, azimuth angle θ, signal-to-noise ratio SNR, and signal purity P. u The multidimensional labels of temperature T, salinity S, and depth D are obtained, and they are time-aligned with the corresponding multi-channel waveform data to obtain the basic dataset.

[0084] Establish a unified label structure for each multi-channel sample:

[0085]

[0086] In the formula, t stamp The sample start time is T, S, and D, which are the interpolations of CTD at the sample start time. All labels and waveform files are saved in sequence.

[0087] S4: Perform the "Sample Distribution Analysis" operation: Based on the differences in the distribution of sample numbers at different azimuth angles, identify azimuth angles with sparse sample distribution, and generate new amplified samples at these azimuth angles by superimposing time-shifted background noise, thus obtaining an augmented dataset. The specific operations are as follows:

[0088] like Figure 4 As shown, the statistical azimuth histogram If the samples are unevenly distributed in the azimuth angle, the quotient of the total number of samples and the total number of angle intervals is used as the sparsity threshold. If the number of samples in a certain angle interval is lower than the sparsity threshold, then the angle interval is a sparsity interval. The samples in the sparsity interval are amplified by time-shifted noise superposition to the sparsity threshold.

[0089] The original sample within the sparse interval is x(t). Background noise n(t) is extracted from the empty signal period, and a random time shift τ is set to superimpose it onto the original sample signal to obtain the amplified sample. The expression is as follows:

[0090]

[0091] The amplified sample inherits the multidimensional labels of the original sample, thereby ensuring the consistency of physical meaning, and the number of samples repeatedly generated within this angle range reaches the sparsity threshold.

[0092] S5: Perform "joint threshold evaluation" on the augmented dataset based on the thresholds of signal-to-noise ratio and signal purity, divide the samples into three categories: high quality, medium quality and low quality, and output a sound source localization dataset containing waveform data, multi-dimensional labels and quality grades (i.e., realize hierarchical storage of metadata records).

[0093] The specific joint threshold evaluation rules are as follows:

[0094] If a sample satisfies both a signal-to-noise ratio (SNR) greater than or equal to the upper threshold and a signal purity greater than or equal to the upper threshold, then the sample is considered "strong and clean" and is classified as a high-quality sample, belonging to the high-quality dataset. In this embodiment, the upper threshold for SNR is set to 15 dB, and the upper threshold for purity is set to 0.85. That is, if a sample satisfies SNR ≥ 15 dB and P... u If the value is ≥0.85, it is classified as a high-quality dataset.

[0095] If a sample satisfies both a signal-to-noise ratio (SNR) less than or equal to the lower SNR threshold and a signal purity less than or equal to the lower purity threshold, then the sample is considered "weak and dirty" and is classified as a low-quality sample, belonging to the low-quality dataset. In this embodiment, the lower SNR threshold is set to 5 dB, and the lower purity threshold is set to 0.6. That is, if a sample satisfies SNR ≤ 5 dB and P... u If the value is ≤0.6, it is classified as a low-quality dataset.

[0096] The remaining samples are classified as medium-quality samples and included in the medium-quality dataset.

[0097] The upper and lower thresholds for signal-to-noise ratio and purity can be fine-tuned based on the sea trial results to match specific carrier frequencies, bandwidths, and sea conditions.

[0098] like Figure 5 As shown, a system for acquiring and processing sound source localization datasets in a shallow sea environment includes a transmitter and a receiver.

[0099] The transmitter, located on the launching vessel, includes: a GPS module, a transmission controller, a signal generator, and an underwater acoustic communication device (including a power amplifier and a transmitting transducer). The GPS module acquires the transmitter's own GPS data in real time. The transmission controller enables periodic sound wave transmission, the signal generator generates the transmission signal, the power amplifier amplifies the transmission signal, and the transmitting transducer converts the amplified signal into an acoustic signal. When the GPS data indicates that the transmitter has reached the preset trajectory launch point, the transmission controller sends a command to the signal generator to generate the transmission signal, which is then converted into an acoustic signal by the power amplifier and the transmitting transducer before being transmitted.

[0100] The receiving end is deployed on the anchored vessel and includes: a receiving array (in this embodiment, a circular ultra-short baseline array is used), a preamplifier, a data acquisition module, a synchronization controller, a data storage device, a data processing module, and a sensor group. The circular ultra-short baseline array is used to receive target acoustic signals. This array includes M array elements, with the element spacing less than half a wavelength to suppress ambiguity. The preamplifier amplifies the received signal analogally. The data acquisition module converts the signal into a digital signal. The synchronization controller acquires the sensor data at the data acquisition time point and outputs the sensor data and digital signal to the data storage device for storage. The data processing module reads data from the data storage device and processes it to obtain a sound source localization dataset containing waveform data and multi-dimensional tags. The data acquisition module and the data storage device work together to achieve multi-channel synchronous sampling. The sensor group includes: an array-built-in IMU (used to provide the pitch, roll, and heading angles of the receiving array), a CTD measurement module (used to measure temperature T, salinity S, and water depth D), and a receiver GPS module (used to acquire the receiver's own GPS data in real time).

[0101] This invention proposes an integrated "acquisition-processing-labeling-enhancement-grading" implementation scheme for shallow-sea sound source localization. The receiving end employs a circular ultra-short baseline array, integrating IMU, GPS, and CTD to achieve attitude decoupling and environmental perception. The transmitting end moves along a spiral trajectory centered on the receiving array, covering samples from all directions and at multiple distances. The data processing module uses LFM matched filtering to achieve robust synchronization and segmentation, constructing multi-dimensional labels containing true distance, azimuth, SNR, signal purity, and temperature, salinity, and depth (TWD). The signal purity index uses the "main peak energy ratio" to characterize multipath and / or interference contamination. To address angular imbalance, an azimuth-constrained time-shift noise enhancement method is proposed; and quality grading is performed using a joint threshold of SNR and purity. This implementation ensures physical consistency of labels and controllable sample distribution, significantly improving dataset reproducibility and algorithm evaluation reliability.

[0102] Based on the above-mentioned method for acquiring and processing sound source localization datasets in shallow sea environments, the present invention also provides a device for acquiring and processing sound source localization datasets in shallow sea environments.

[0103] like Figure 6 As shown in the figure, an embodiment of the present invention provides a device for acquiring and processing sound source localization datasets in a shallow sea environment, including a memory and one or more processors. The memory stores executable code, and when the one or more processors execute the executable code, they are used to implement the method for acquiring and processing sound source localization datasets in a shallow sea environment as described in the above embodiment.

[0104] This invention discloses a device for acquiring and processing sound source localization datasets in shallow sea environments. This device can be applied to any device with data processing capabilities, such as a computer. The device can be implemented in software, hardware, or a combination of both. Taking software implementation as an example, as a logical device, it is formed by the processor of any data processing device reading the corresponding computer program instructions from non-volatile memory into memory and executing them. From a hardware perspective, such as… Figure 6 The diagram shown is a hardware structure diagram of any device with data processing capabilities, which is part of the sound source localization dataset acquisition and processing device in a shallow sea environment according to the present invention. (Except for...) Figure 6 In addition to the processor, memory, network interface, and non-volatile memory shown, any data processing device in which the device of the present invention is located in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0105] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0106] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of the present invention according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0107] This invention also provides a computer-readable storage medium storing a program thereon, which, when executed by a processor, implements the method for acquiring and processing sound source localization datasets in shallow sea environments as described in the above embodiments.

[0108] The computer-readable storage medium can be an internal storage unit of any data-processing device in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device of any data-processing device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., mounted on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store computer programs and other programs and data required by any data-processing device, and can also be used to temporarily store data that has been output or will be output.

[0109] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0110] The above description is merely a preferred embodiment of the present invention, intended to enable those skilled in the art to understand or implement the invention, and is not intended to limit the invention. Various modifications and variations can be made to the invention by those skilled in the art, and these modifications to the embodiments will be readily apparent. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A method for acquiring and processing sound source localization datasets in a shallow sea environment, characterized in that, Includes the following steps: S1: The receiver receives the periodically transmitted signal with synchronization header from the transmitter through the receiver array. The transmitter moves around the receiver according to a preset spiral trajectory. The receiver array attitude angle is collected synchronously, and the GPS coordinates, shallow sea temperature T, salinity S and depth D of the receiver and transmitter are recorded. S2: Perform matched filtering on the received signal, determine the signal start point based on the maximum peak value of the output, and extract the waveforms before and after the start point within a set time window as independent samples; Calculate the signal-to-noise ratio (SNR) and signal purity (P) for each independent sample. u ; S3: Convert the GPS coordinates of the transmitter in the geographic coordinate system to the relative position in the array coordinate system, obtaining the true distance R and azimuth angle θ between the transmitter and receiver; construct a system containing R, θ, SNR, and P. u Multidimensional labels of T, S, and D are generated and time-aligned with the corresponding multi-channel waveform data. S4: Identify the distribution differences in the number of samples under each azimuth angle. For azimuth angles where the number of samples is less than the sparsity threshold, generate new amplified samples by superimposing time-shifted background noise to obtain an enhanced dataset. S5: Based on the thresholds of signal-to-noise ratio and signal purity, the samples in the enhanced dataset are divided into three categories: high quality, medium quality, and low quality, and a sound source localization dataset containing waveform data, multi-dimensional labels, and quality grades is output.

2. The method for acquiring and processing sound source localization datasets in shallow sea environments according to claim 1, characterized in that, In S1, the transmitting end moves around the receiving end according to the preset spiral trajectory. Specifically, the transmitting end moves in a spiral motion around the center of the receiving array of the receiving end. After the transmitting end completes one circle, the trajectory radius increases by a set step size to achieve sample coverage at different distances and all-round angles.

3. The method for acquiring and processing sound source localization datasets in shallow sea environments according to claim 1, characterized in that, The independent sample truncation operation in S2 is as follows: using the linear frequency modulation synchronization head signal transmitted by the transmitting end as the template signal, a conjugate convolution operation is performed on the received signal; the arrival time of each transmitted signal is determined by detecting the maximum correlation peak value of the matched filter output and the synchronization point is obtained; and a time window of a preset duration before and after the synchronization point is truncated as an independent sample segment.

4. The method for acquiring and processing sound source localization datasets in a shallow sea environment according to claim 1, characterized in that, In S2, the signal purity of an independent sample is calculated as follows: A near-empty signal region in the independent sample is selected for noise statistical estimation, and multiple standard deviations of the noise are selected as the threshold for significant peaks. Peaks with amplitudes greater than the threshold are considered significant peaks, with the largest amplitude being designated as the main peak. The proportion of the energy in the neighborhood of the main peak corresponding to the matched filter output of each array element in the total energy of all significant peaks is calculated, and the average is taken to obtain the signal purity of the independent sample. The closer the value is to 1, the purer the main peak.

5. The method for acquiring and processing sound source localization datasets in shallow sea environments according to claim 1, characterized in that, In step S3, the GPS coordinates of the transmitting end in the geographic coordinate system are converted into relative positions in the array coordinate system. The specific operation is as follows: Calculate the relative vector between the transmitter and receiver in the geographic coordinate system. That is, to calculate the difference between the GPS coordinates of the transmitting end and the GPS coordinates of the receiving end; The array attitude angles include: pitch angle, roll angle, and yaw angle; an attitude rotation matrix is ​​constructed based on the array attitude angles. The transmitter position is transformed to the array coordinate system using the attitude rotation matrix, i.e., the inverse matrix of the attitude rotation matrix is ​​calculated. The product of these terms yields the relative position vector of the transmitting end in the array coordinate system; The magnitude of the relative position vector corresponds to the actual distance R between the transmitter and receiver, and the included angle corresponds to the azimuth angle θ of the transmitter relative to the receiver.

6. The method for acquiring and processing sound source localization datasets in a shallow sea environment according to claim 1, characterized in that, S4 is specifically implemented through the following operations: The number of samples in each direction is evaluated by statistical azimuth histogram. The quotient of the total number of samples and the total number of angle intervals is used as the sparsity threshold. If the number of samples in a certain angle interval is lower than the sparsity threshold, then the angle interval is a sparse interval. The samples in the sparse interval are amplified by time-shifted noise superposition to the sparsity threshold. The time-shifted noise superposition amplification specifically involves: extracting background noise from a signal-free period and randomly applying a time shift; superimposing this noise segment onto the original sample signal to generate an amplified sample; The amplified sample inherits the multidimensional label of the original sample.

7. The method for acquiring and processing sound source localization datasets in a shallow sea environment according to claim 1, characterized in that, In S5, if a sample satisfies that the signal-to-noise ratio is greater than or equal to the upper limit threshold of the signal-to-noise ratio and the signal purity is greater than or equal to the upper limit threshold of the purity, then it is classified as a high-quality sample and included in the high-quality dataset. If a sample satisfies that the signal-to-noise ratio is less than or equal to the lower limit threshold of the signal-to-noise ratio and the signal purity is less than or equal to the lower limit threshold of the purity, then it is classified as a low-quality sample and included in the low-quality dataset. Except for the cases mentioned above, the samples in other cases are classified as medium-quality samples and included in the medium-quality dataset.

8. A system for acquiring and processing sound source localization datasets in a shallow sea environment, used to implement the method for acquiring and processing sound source localization datasets in a shallow sea environment as described in any one of claims 1-7, characterized in that, Includes the sending end and the receiving end; The transmitter, located on the launch vessel, includes: a transmitter GPS module, a transmission controller, a signal generator, a power amplifier, and a transmission transducer. The transmitter GPS module acquires its own GPS data in real time. The transmission controller enables periodic transmission of sound waves. The signal generator generates a transmission signal. The power amplifier amplifies the transmission signal. The transmission transducer converts the amplified signal into a sound wave signal. When GPS data indicates that the transmitter has reached a preset trajectory launch point, the transmission controller sends a command to the signal generator to generate a transmission signal, which is then converted into a sound wave signal by the power amplifier and the transmission transducer before being transmitted. The receiving end is deployed on the anchored vessel and includes: a receiving array, a preamplifier, a data acquisition module, a synchronization controller, a data storage device, a data processing module, and a sensor group. The receiving array is used to receive target acoustic signals. The array includes multiple array elements with an element spacing of less than half a wavelength to suppress ambiguity. The preamplifier is used to amplify the received signal analogically. The data acquisition module is used to convert the signal into a digital signal. The synchronization controller is used to acquire sensor data at the data acquisition time point and output the sensor data and digital signal to the data storage device for storage. The data processing module is used to read data from the data storage device and process it to obtain a sound source localization dataset containing waveform data and multi-dimensional labels. The sensor group includes: an inertial measurement unit, a temperature, salinity, and depth (TDM) measurement module, and a receiver GPS module; the inertial measurement unit is used to detect the pitch angle, roll angle, and heading angle of the receiving array; the TDM measurement module is used to acquire shallow sea temperature, salinity, and depth data; and the receiver GPS module is used to acquire the receiver's own GPS data in real time.

9. A device for acquiring and processing sound source localization datasets in a shallow sea environment, characterized in that, The device includes a memory and one or more processors, wherein the memory stores executable code, and the one or more processors execute the executable code to implement the method for acquiring and processing sound source localization datasets in a shallow sea environment as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a program that, when executed by a processor, implements the method for acquiring and processing sound source localization datasets in a shallow sea environment as described in any one of claims 1-7.

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