Underwater ROV passive acoustic positioning method and device

Abstract

The application discloses an underwater ROV passive acoustic positioning method and device, wherein the method comprises the following steps: obtaining multiple channel signal frames by framing the collected underwater acoustic signals; calculating a spatial spectrum to extract a peak azimuth angle and a spectrum width; taking the peak azimuth angle as a target direction, determining filter constraint parameters according to the spectrum width, and filtering to obtain an enhanced signal; obtaining a sound velocity profile, and calculating a theoretical time delay difference under each azimuth angle by ray tracing to form a mapping table; taking the peak azimuth angle as the center and a spectrum width multiplied by a coefficient as a radius to demarcate a search range, matching the measured time delay difference with the theoretical time delay difference in the search range in the mapping table, and taking the azimuth angle with the maximum matching degree as a positioning result. The application can meet the positioning requirements of high precision and high robustness in a complex marine environment.

Description

Technical Field

[0001] This invention relates to the field of underwater acoustic signal processing technology, and in particular to a passive acoustic positioning method and apparatus for underwater ROVs. Background Technology

[0002] Remotely operated vehicles (ROVs) typically require real-time location information of underwater targets when performing tasks such as subsea pipeline inspection, target search, and marine environmental monitoring. While active sonar offers high positioning accuracy, its emitted signals easily reveal its own location and consume significant energy, limiting its applicability in covert operations or long-duration endurance scenarios. Therefore, passive acoustic positioning methods based on the target's own radiated noise have become an important technical approach for autonomous monitoring by underwater ROVs.

[0003] Existing passive acoustic localization methods mainly include geometric localization based on time delay estimation and sound field matching methods based on matched field processing. Time delay estimation-based methods calculate the target's orientation by measuring the time difference between the arrival of the target signal at different hydrophones and combining this with the known geometric configuration of the hydrophone array. These methods have low computational cost and high real-time performance, making them widely used in underwater robotic platforms. Matched field processing-based methods, on the other hand, utilize underwater acoustic propagation models to pre-calculate a copy of the sound field at each grid point and then match the measured sound field with the copy field. This results in higher localization accuracy, but also higher computational cost and sensitivity to environmental parameters, typically limiting their use to offline or large-platform processing.

[0004] However, traditional time-delay estimation positioning methods still face the following technical problems in practical applications: First, these methods usually assume that sound waves propagate in a straight line and do not consider the sound ray bending effect caused by seawater sound velocity stratification, which will produce significant systematic angular deviations in thermoclines or deep-water environments; Second, under conditions of multiple sound sources or low signal-to-noise ratio, the cross-correlation function is prone to false time delay peaks, leading to incorrect orientation estimation; Third, most existing methods only output positioning results and lack quantitative evaluation of the reliability of the results, making it difficult to provide uncertainty reference information for higher-level tasks such as target tracking or behavioral decision-making.

[0005] Therefore, a passive acoustic positioning method for underwater ROVs is needed to meet the high-precision and high-robust positioning requirements in complex marine environments. Summary of the Invention

[0006] This invention provides a passive acoustic positioning method for underwater ROVs to achieve high-precision and robust underwater target location estimation in marine environments. The method includes: Underwater acoustic signals are collected by multiple hydrophones deployed on the ROV; the underwater acoustic signals are then processed into frames to obtain processed multi-channel signal frames. A multi-signal classification algorithm is used to calculate the spatial spectrum of multi-channel signal frames, and the peak azimuth angle and the spectral width corresponding to the preset value of peak drop in the spatial spectrum are extracted. Using the peak azimuth angle as the target direction, the constraint parameters of the multi-channel adaptive filter are determined based on the spectral width; the multi-channel adaptive filter is then used to filter the multi-channel signal frames to obtain the multi-channel enhanced signal. The real-time sound velocity profile is obtained, and the sound ray tracing algorithm is used to calculate the theoretical time delay difference between each hydrophone and a reference hydrophone at different azimuth angles, so as to form a mapping relationship table between azimuth angle and theoretical time delay difference vector. The search range is determined with the peak azimuth angle as the center and the spectrum width multiplied by a preset coefficient as the radius. The measured time delay difference vector extracted from the multi-channel enhanced signal is matched with the theoretical time delay difference vector in the mapping table that is within the search range. The azimuth angle corresponding to the theoretical time delay difference vector with the largest matching degree is selected as the positioning result output.

[0007] This invention also provides an underwater ROV passive acoustic positioning device for achieving high-precision and robust underwater target location estimation in a marine environment. The device includes: The signal acquisition module is used to acquire underwater acoustic signals through multiple hydrophones deployed on the ROV; and to perform frame processing on the underwater acoustic signals to obtain processed multi-channel signal frames. The spatial spectrum acquisition module is used to calculate the spatial spectrum of multi-channel signal frames using a multi-signal classification algorithm, and to extract the peak azimuth angle and the spectral width corresponding to the preset value of peak drop in the spatial spectrum. The signal enhancement module is used to determine the constraint parameters of the multi-channel adaptive filter based on the peak azimuth angle and the spectral width; the multi-channel adaptive filter is used to filter the multi-channel signal frame to obtain the multi-channel enhanced signal. The mapping table generation module is used to obtain the real-time sound speed profile. Based on the sound speed profile, the sound ray tracing algorithm is used to calculate the theoretical time delay difference between each hydrophone and a reference hydrophone at different azimuth angles, so as to form a mapping table between azimuth angle and theoretical time delay difference vector. The positioning result output module is used to determine the search range with the peak azimuth angle as the center and the spectrum width multiplied by a preset coefficient as the radius; it matches the measured time delay difference vector extracted from the multi-channel enhanced signal with the theoretical time delay difference vector in the mapping table that is within the search range, and selects the azimuth angle corresponding to the theoretical time delay difference vector with the largest matching degree as the positioning result output.

[0008] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described passive acoustic positioning method for underwater ROVs.

[0009] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described passive acoustic positioning method for underwater ROVs.

[0010] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described passive acoustic positioning method for underwater ROVs.

[0011] In this embodiment of the invention, underwater acoustic signals are acquired by multiple hydrophones deployed on an ROV; the underwater acoustic signals are framed to obtain processed multi-channel signal frames; a multi-signal classification algorithm is used to calculate the spatial spectrum of the multi-channel signal frames, and the peak azimuth angle and the spectral width corresponding to the preset decibel value of the peak drop in the spatial spectrum are extracted; the constraint parameters of the multi-channel adaptive filter are determined based on the spectral width, with the peak azimuth angle as the target direction; the multi-channel adaptive filter is used to filter the multi-channel signal frames to obtain multi-channel enhanced signals; a real-time sound velocity profile is acquired, and a ray tracing algorithm is used to calculate the theoretical time delay difference between each hydrophone and a reference hydrophone at different azimuth angles based on the sound velocity profile, so as to form a mapping relationship table between azimuth angle and theoretical time delay difference vector; the search range is determined with the peak azimuth angle as the center and the spectral width multiplied by a preset coefficient as the radius; the measured time delay difference vector extracted from the multi-channel enhanced signal is matched with the theoretical time delay difference vector within the search range in the mapping relationship table, and the azimuth angle corresponding to the theoretical time delay difference vector with the largest matching degree is selected as the positioning result output. In the above process, the embodiments of the present invention acquire real-time sound velocity profiles and, based on these profiles, use a ray-tracing algorithm to calculate the theoretical time delay difference between each hydrophone and a reference hydrophone at different azimuth angles, forming a mapping table between azimuth angles and theoretical time delay difference vectors. This effectively corrects the angular deviation introduced by sound ray bending in the positioning calculation. By using a multi-signal classification algorithm to calculate the spatial spectrum of multi-channel signal frames, the peak azimuth angle and the spectral width corresponding to a preset decibel drop in the peak value of the spatial spectrum are extracted. The constraint parameters of the multi-channel adaptive filter are determined based on the peak azimuth angle as the target direction and the spectral width, and the multi-channel signal frames are filtered to obtain multi-channel enhanced signals, effectively enhancing the signal components in the target direction and suppressing interference in non-target directions. Simultaneously, a search range is determined with the peak azimuth angle as the center and the spectral width multiplied by a preset coefficient as the radius. The measured time delay difference is matched with the theoretical time delay difference vectors within this search range in the mapping table, avoiding blind searching throughout space and reducing the computational load. This meets the high-precision and high-robustness positioning requirements in complex marine environments. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart of the passive acoustic positioning method for underwater ROVs in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the extraction of the spatial spectrum peak azimuth and spectral width in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the formation of the mapping relationship table in an embodiment of the present invention; Figure 4 This is a flowchart illustrating the output of the positioning results in an embodiment of the present invention; Figure 5 A schematic diagram of the passive acoustic positioning device for underwater ROVs in an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0014] Figure 1 This is a flowchart of a passive acoustic localization method for underwater ROVs in an embodiment of the present invention. The method includes: Step 101: Underwater acoustic signals are collected by multiple hydrophones deployed on the ROV; the underwater acoustic signals are processed into frames to obtain processed multi-channel signal frames. Step 102: Calculate the spatial spectrum of the multi-channel signal frame using a multi-signal classification algorithm, and extract the peak azimuth angle and the spectral width corresponding to the preset decibel value of peak drop in the spatial spectrum. Step 103: Using the peak azimuth angle as the target direction, determine the constraint parameters of the multi-channel adaptive filter based on the spectral width; use the multi-channel adaptive filter to filter the multi-channel signal frame to obtain the multi-channel enhanced signal; Step 104: Obtain the real-time sound velocity profile. Based on the sound velocity profile, use the ray tracing algorithm to calculate the theoretical time delay difference between each hydrophone and a reference hydrophone at different azimuth angles, so as to form a mapping relationship table between azimuth angle and theoretical time delay difference vector. Step 105: Determine the search range with the peak azimuth angle as the center and the spectral width multiplied by a preset coefficient as the radius; match the measured time delay difference vector extracted from the multi-channel enhanced signal with the theoretical time delay difference vector within the search range in the mapping table, and select the azimuth angle corresponding to the theoretical time delay difference vector with the largest matching degree as the positioning result output.

[0015] Each step is explained in detail below.

[0016] In step 101, underwater acoustic signals are collected by multiple hydrophones deployed on the ROV; the underwater acoustic signals are then processed into frames to obtain processed multi-channel signal frames.

[0017] In a specific embodiment, the ROV is equipped with a receiving array consisting of M hydrophones, where M ≥ 3. The three-dimensional coordinates of each hydrophone in the ROV's coordinate system are known values. The hydrophone array acquires underwater acoustic signals in a synchronous sampling manner, with the sampling frequency exemplarily set to 48 kHz. The acquired continuous time-domain signals are first pre-amplified and then subjected to anti-aliasing filtering.

[0018] In step 102, a multi-signal classification algorithm is used to calculate the spatial spectrum of the multi-channel signal frame, and the peak azimuth angle and the spectral width corresponding to the preset decibel value of the peak drop of the spatial spectrum are extracted.

[0019] Figure 2 This is a flowchart illustrating the extraction of the peak azimuth and spectral width of the spatial spectrum in an embodiment of the present invention. In one embodiment, a multi-signal classification algorithm is used to calculate the spatial spectrum of a multi-channel signal frame, and the peak azimuth and the spectral width corresponding to a preset decibel decrease in the peak value of the spatial spectrum are extracted, including: Step 201: Estimate the covariance matrix of the multi-channel signal frame, and perform eigenvalue decomposition on the covariance matrix to obtain the signal subspace and noise subspace; Step 202: Construct a spatial spectrum based on the noise subspace, perform a peak search on the spatial spectrum within a preset angle search range, and determine the angle corresponding to the maximum peak as the peak azimuth angle. Step 203: On both sides of the maximum spectral peak, determine the first boundary angle and the second boundary angle corresponding to the preset decibel decrease in spectral value, and determine the width of the angle interval between the first angle and the second angle as the spectral width.

[0020] In a specific embodiment, the covariance matrix of the multi-channel signal frame is first estimated. Assuming the multi-channel signal frame contains M channels, and each channel has N sampling points, the signal frame can be represented as an M×N dimensional matrix X. The estimation formula for the covariance matrix R is R = (1 / N)·X·X ^H , where the superscript H indicates conjugate transpose.

[0021] The covariance matrix R is decomposed into M eigenvalues ​​and their corresponding eigenvectors. The eigenvalues ​​are arranged in descending order. Based on the preset number of signal sources K, the eigenvectors corresponding to the first K eigenvalues ​​are used to span the signal subspace, and the remaining (MK) eigenvectors are used to span the noise subspace.

[0022] Based on the obtained noise subspace, a spatial spectrum is generated within a preset angular search range. Let U be the eigenvector matrix corresponding to the noise subspace. n For any azimuth angle θ within the search range, the corresponding steering vector is a(θ), then the spatial spectrum value at that azimuth angle is P(θ) = 1 / [a ^H(θ) ·U n ·U n ^H ·a(θ)]. For example, the angle search range is 0° to 360°, and the search step size is 0.1°.

[0023] Perform a peak search on the generated spatial spectrum to find the largest global peak value, and determine the angle corresponding to this peak as the peak azimuth angle θ. peak The peak azimuth angle is the coarse estimate of the target direction by the MUSIC algorithm, providing prior information on the target direction for subsequent adaptive filtering and mapping table matching, effectively narrowing the subsequent positioning range.

[0024] The preset decibel level is 3dB. The first boundary angle θ_left and the second boundary angle θ_right, corresponding to the spectral value decreasing to the preset decibel level on both sides of the spectral peak, are determined. The spectral width is defined as the angular interval Δθ between these two boundary angles, equal to θ_right minus θ_left. This spectral width reflects the degree of uncertainty in target orientation estimation within the current signal frame: when the signal-to-noise ratio is high and multipath effects are weak, the spatial spectral peak is sharp and the spectral width is small, indicating a high confidence level in the orientation estimation; conversely, when the signal-to-noise ratio is low or multipath interference exists, the spatial spectral peak is broadened and the spectral width is larger, indicating greater uncertainty in the orientation estimation.

[0025] Therefore, this spectral width can serve as an effective measure of the uncertainty in coarse positioning, providing a basis for determining the constraint parameters of the subsequent adaptive filter and defining the search range for mapping relationship matching. By transferring the uncertainty of coarse positioning to subsequent processing stages, this invention can dynamically adjust the processing strategy according to the actual signal quality, thereby improving the robustness of the overall positioning method in complex marine environments.

[0026] In step 103, the peak azimuth angle is taken as the target direction, and the constraint parameters of the multi-channel adaptive filter are determined according to the spectral width; the multi-channel adaptive filter is used to filter the multi-channel signal frame to obtain the multi-channel enhanced signal.

[0027] In one embodiment, determining the constraint parameters of the multi-channel adaptive filter based on the spectral width includes: The beam main lobe width of the multi-channel adaptive filter is determined based on the spectral width, and the beam main lobe width is proportional to the spectral width.

[0028] In a specific embodiment, the peak azimuth angle θ peak The direction of incidence of the desired signal is used. Based on the geometric layout of the hydrophone array and the known three-dimensional spatial coordinates of each hydrophone in the ROV coordinate system, the steering vector a(θ) of the multi-channel adaptive filter is constructed. peak The dimension of this steering vector is equal to the number of hydrophones M, and its m-th element represents the unit power plane wave from the azimuth angle θ. peak The phase response generated on the m-th hydrophone relative to a preset reference point upon incident.

[0029] Set the passband angle range θ of the multi-channel adaptive filter based on the spectral width Δθ output in step 102. pass Specifically, the passband angle range θ pass Set to peak azimuth angle θ peak The symmetrical angle interval centered on, that is: θ pass =[θ peak -k Δθ, θ peak +k Δθ] Where k is the preset broadening factor, which can be 1.5. As can be seen from the above formula, the larger the spectral width Δθ, the wider the passband angle range θ. pass The wider the passband, the wider the equivalent beam main lobe width. Since the weighting coefficients in the subsequent solution constrain the filter to maintain a high gain within this passband, a wider passband results in a wider equivalent beam main lobe width, thus achieving a positive correlation between the equivalent beam main lobe width and the spectral width. This mapping allows the filter to adaptively adjust its spatial selectivity according to the degree of uncertainty in coarse positioning: when the spectral width is large and the coarse positioning uncertainty is high, the filter uses a wider passband to accommodate possible direction errors; when the spectral width is small and the coarse positioning confidence is high, the filter uses a narrower passband to maximize the suppression of interference from non-target directions.

[0030] Based on the constructed guide vector a(θ) peak ) and the set passband angle range θ pass A spatially constrained optimization problem is constructed to solve for the weighted coefficient vector w corresponding to each channel in a multi-channel adaptive filter. The dimension of this weighted coefficient vector w is equal to the number of hydrophones M, and its m-th element w m It is the complex weighted value applied to the signal of the m-th hydrophone channel.

[0031] One possible optimization criterion is to minimize the filter's output power in undesirable directions while ensuring unit response gain in the desired direction, and to impose constraints on response ripple in the passband and attenuation in the stopband. A second-order cone programming algorithm can be used to solve for the weighted coefficient vector w that satisfies the following constraints: Constraint 1: In the desired direction θ peak Above, the array response satisfies the conjugate transpose of w multiplied by a(θ) peak ) equals 1; Constraint 2: Within the passband angle range θ pass At any angle θ within the passband, the array response gain is higher than the first preset threshold, which is exemplarily set to negative 1dB, meaning the gain fluctuation within the passband does not exceed 1dB. Constraint 3: Within the passband angle range θ pass At any angle θ outside the range, the array response gain is lower than the second preset threshold, for example, a value of -20dB, that is, the stopband attenuation is not less than 20dB.

[0032] The weighted coefficient vector w obtained by solving the above optimization problem is the constraint parameter of the multi-channel adaptive filter in the current signal frame. Through the above constraint optimization solution, the filter forms a flat high-gain passband near the target direction, while effectively suppressing interference and noise in the sidelobe direction, thereby improving the signal-to-noise ratio of subsequent time delay difference extraction.

[0033] Each element of the obtained weighted coefficient vector w is applied to the corresponding channel data of the multi-channel signal frame. Specifically, let the sampling sequence of the signal frame of the m-th channel be x. m The corresponding weighting coefficient is w m Then the enhanced signal of the m-th channel is y. m equals w m Multiply by x m , where n is the sampling point number. After performing the above weighting operation on each of the M channels, the enhanced multi-channel signal frame is obtained.

[0034] It should be noted that in this step, weighting coefficients are applied to each channel signal separately, without inter-channel summation. Therefore, the enhanced signal remains a multi-channel signal with M independent channels, and the relative phase information between each hydrophone channel is fully preserved. This design ensures that the enhancement process does not disrupt the inter-element phase relationship required for extracting the measured time delay difference in subsequent steps, allowing the adaptive filtering enhancement and time delay difference extraction stages to work together.

[0035] In step 104, a real-time sound velocity profile is obtained. Based on the sound velocity profile, a ray tracing algorithm is used to calculate the theoretical time delay difference between each hydrophone and a reference hydrophone at different azimuth angles, so as to form a mapping relationship table between azimuth angle and theoretical time delay difference vector.

[0036] Figure 3 This is a flowchart illustrating the formation of a mapping table in one embodiment of the present invention. In one embodiment, a real-time sound velocity profile is obtained, and based on the sound velocity profile, a ray tracing algorithm is used to calculate the theoretical time delay difference between each hydrophone and a reference hydrophone at different azimuth angles, thereby forming a mapping table between azimuth angles and theoretical time delay difference vectors, including: Step 301: Obtain real-time sound velocity profile data of the water area where the ROV is located; Step 302: Generate the corresponding azimuth angle within the angle range of 0° to 360° with a preset angle step size; Step 303: Using the spatial coordinates of the reference hydrophone as the starting point for ray tracing, and based on the sound speed profile data, the ray tracing algorithm is used to calculate the propagation time of the sound wave from the azimuth angle to the reference hydrophone and the propagation time to each non-reference hydrophone for each azimuth angle. The difference between the propagation time of each non-reference hydrophone and the propagation time of the reference hydrophone is calculated to obtain the theoretical time delay difference vector corresponding to the azimuth angle. Step 304: Based on each azimuth angle and the corresponding theoretical time delay difference vector, form a mapping table between the azimuth angle and the theoretical time delay difference vector.

[0037] In a specific embodiment, real-time sound velocity profile data of the water area where the ROV is located is obtained. This sound velocity profile data characterizes the relationship between sound velocity and depth.

[0038] A series of discrete azimuth angles are generated within the range of 0 to 360 degrees using a preset angle step size. For example, a step size of 1 degree generates 360 discrete azimuth angles ranging from 0 degrees, 1 degree, 2 degrees up to 359 degrees. The selection of the angle step size must balance positioning accuracy and computational cost: a smaller step size results in higher accuracy of the mapping table but longer construction time; a larger step size results in faster construction but may decrease matching accuracy. In practical applications, the step size can be preset according to the ROV's computational capabilities and task requirements.

[0039] One hydrophone is selected from multiple hydrophones as a reference hydrophone. The reference hydrophone can be any hydrophone and will remain fixed in subsequent processing. The spatial coordinates of the reference hydrophone are used as the starting point for ray tracing, and the three-dimensional coordinates of this spatial coordinates in the ROV coordinate system are known quantities.

[0040] For each azimuth angle, perform the following operations: The assumption is that the sound wave is a far-field plane wave incident from this discrete azimuth angle. In the ray tracing algorithm, the sound ray propagation path is traced in the opposite direction of this discrete azimuth angle, starting from the coordinates of the reference hydrophone. Based on the acquired sound velocity profile data, the ray tracing numerical algorithm is used to calculate the propagation trajectory and propagation time of the sound wave in the layered medium. The ray tracing algorithm is based on Snell's law, considers the change of sound velocity with depth, and can accurately calculate the bending effect of the sound ray due to refraction. The Bellhope model or its improved version can be used as the specific implementation of the ray tracing algorithm.

[0041] The propagation time of the sound wave from the incident azimuth angle to the reference hydrophone is calculated and denoted as T_ref. This propagation time represents the time required for the sound wave to travel from the far-field sound source to the reference hydrophone. For each non-reference hydrophone, the same ray tracing algorithm is used to calculate the propagation time of the sound wave from the same discrete azimuth angle to that non-reference hydrophone, denoted as T_m, where m is the index of the non-reference hydrophone.

[0042] The theoretical time delay difference τ_m = T_m - T_ref of each non-reference hydrophone is obtained by subtracting its propagation time T_m from the propagation time T_ref of the reference hydrophone. The theoretical time delay differences corresponding to all non-reference hydrophones form the theoretical time delay difference vector corresponding to the azimuth angle, i.e.: τ(θ) = [τ_1, τ_2, ..., τ_{M-1}], where M is the total number of hydrophones.

[0043] Through the above ray tracing calculations, each azimuth angle corresponds to a theoretical time delay difference vector with a dimension of M minus 1. Since the ray tracing algorithm fully considers the ray bending effect caused by the actual sound speed profile, this theoretical time delay difference vector can truly reflect the relative time relationship of sound waves arriving at each hydrophone in complex hydrological environments, effectively correcting the angular deviation introduced by the straight-line propagation assumption in traditional methods.

[0044] Each azimuth angle is associated with and stored with its corresponding theoretical time delay difference vector, forming a mapping table between azimuth angles and theoretical time delay difference vectors. In waters where the sound speed profile changes slowly, the mapping table can be updated every preset time interval; in waters where the sound speed profile changes drastically, the update frequency can be increased as needed. Furthermore, once this mapping table is constructed, it can be repeatedly called during ROV missions until a significant change in the sound speed profile occurs, at which point it will be updated again.

[0045] In step 105, the search range is determined with the peak azimuth angle as the center and the spectrum width multiplied by a preset coefficient as the radius; the measured time delay difference vector extracted from the multi-channel enhanced signal is matched with the theoretical time delay difference vector in the mapping table that is within the search range, and the azimuth angle corresponding to the theoretical time delay difference vector with the largest matching degree is selected as the positioning result output.

[0046] In one embodiment, the measured time delay difference vector extracted from the multi-channel enhanced signal includes: Generalized cross-correlation is performed on each signal in the multi-channel enhancement signal to obtain the measured time delay difference vector of each hydrophone relative to the reference hydrophone.

[0047] In a specific embodiment, let the channel signal corresponding to the reference hydrophone be s_ref(t), and the channel signal corresponding to the m-th non-reference hydrophone be s_m(t), where m is an integer from 1 to M-1, and M is the total number of hydrophones. A generalized cross-correlation operation is performed on each pair of signals to obtain the measured time delay difference Δτ_m between the m-th non-reference hydrophone and the reference hydrophone.

[0048] The generalized cross-correlation operation is performed in the frequency domain: First, Fourier transforms are performed on s_ref(t) and s_m(t) respectively to obtain the frequency domain signals S_ref(f) and S_m(f); the cross-power spectrum of the two is calculated, and a frequency domain weighting function is applied to enhance the sharpness of the cross-correlation peak; the inverse Fourier transform is performed on the weighted cross-power spectrum to obtain the generalized cross-correlation function R_m(τ); the time delay variable τ corresponding to the maximum value of R_m(τ) is taken as the measured time delay difference.

[0049] The measured time delay difference vector is composed of the measured time delay differences of all non-reference hydrophones, i.e., Δτ equals [Δτ_1, Δτ_2, up to Δτ_{M-1}].

[0050] By performing generalized cross-correlation on the enhanced multi-channel signal, the target direction signal component has been enhanced, and the weighting function further sharpens the cross-correlation peak, effectively suppressing spurious peaks caused by noise interference, thereby improving the accuracy and robustness of the measured time delay difference vector extraction.

[0051] Figure 4 This is a flowchart illustrating the output of the positioning result in an embodiment of the present invention. In one embodiment, the measured time delay difference vector extracted from the multi-channel enhanced signal is matched with the theoretical time delay difference vector within the search range in the mapping table. The azimuth angle corresponding to the theoretical time delay difference vector with the highest matching degree is selected as the positioning result output, including: Step 401: Extract all theoretical time delay difference vectors whose azimuth angles are within the search range from the mapping table; Step 402: For each theoretical time delay difference vector, calculate the similarity metric between the theoretical time delay difference vector and the measured time delay difference vector; Step 403: Select the theoretical time delay difference vector with the largest similarity metric value, and output the azimuth angle corresponding to the theoretical time delay difference vector as the positioning result.

[0052] In a specific embodiment, candidate vectors within the search range are extracted from the mapping table; the similarity metric (such as Euclidean distance) between each candidate vector and the measured vector is calculated; and the vector with the highest similarity is output. Thus, within the search range defined by coarse positioning, the measured time delay difference vector is compared one by one with the theoretical time delay difference vector in the mapping table, and the azimuth angle corresponding to the most similar vector is selected as the positioning result of the current frame, i.e., the single-frame positioning result and its corresponding similarity metric.

[0053] In one embodiment, selecting the theoretical time delay difference vector with the highest similarity metric value and outputting the azimuth angle corresponding to the theoretical time delay difference vector as the positioning result, further includes: The confidence score of the location result is generated based on the similarity metric value, and the magnitude of the confidence score is positively correlated with the magnitude of the similarity metric value. The confidence level is correlated with the location result in the output.

[0054] In a specific embodiment, confidence level and similarity metric are positively correlated: the better the match, the higher the confidence level. Confidence level is output in numerical form, intuitively reflecting the reliability of the current location result. Based on the output similarity metric, it is converted into a quantified confidence index and linked to the location result in the output.

[0055] In one embodiment, after selecting the theoretical time delay difference vector corresponding to the maximum similarity and outputting the azimuth angle corresponding to the theoretical time delay difference vector as the positioning result, the method further includes: A Kalman filter is established using azimuth angle and azimuth angle change rate as state variables; The positioning results are used as observations and input into a Kalman filter; the observation noise variance of the Kalman filter is adjusted according to the confidence level: the lower the confidence level, the larger the observation noise variance. The azimuth sequence filtered by the Kalman filter is output as the final positioning result.

[0056] In a specific embodiment, a state-space model of the target motion is established, with azimuth angle and azimuth angle change rate as state variables. The positioning result is used as the observation value, and the observation noise variance is dynamically adjusted using confidence level. A smooth azimuth angle sequence is obtained by recursion through Kalman filtering.

[0057] The above process involves several steps. First, within the search range, a single-frame positioning result is obtained by matching the measured time delay difference vector with the theoretical time delay difference vector. Second, a confidence score for the positioning result is generated based on the similarity metric of the match. Finally, the positioning result is used as an observation and input into a Kalman filter. The observation noise variance is adjusted according to the confidence score; the lower the confidence score, the larger the observation noise variance. This ensures that the positioning result relies more on the observed values ​​when it is reliable and more on the state prediction values ​​when the positioning result is unreliable, ultimately outputting a smooth azimuth sequence. Through this combination, this method can balance positioning accuracy and tracking stability in complex marine environments, while ensuring real-time processing through search range constraints and Kalman filter recursive calculations.

[0058] This invention also provides an underwater ROV passive acoustic positioning device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the underwater ROV passive acoustic positioning method, the implementation of this device can refer to the implementation of the underwater ROV passive acoustic positioning method; repeated details will not be elaborated further.

[0059] Figure 5 A schematic diagram of an underwater ROV passive acoustic positioning device in an embodiment of the present invention, the device comprising: The signal acquisition module 501 is used to acquire underwater acoustic signals through multiple hydrophones deployed on the ROV; and to perform frame processing on the underwater acoustic signals to obtain processed multi-channel signal frames. The spatial spectrum acquisition module 502 is used to calculate the spatial spectrum of multi-channel signal frames using a multi-signal classification algorithm, and extract the peak azimuth angle and the spectral width corresponding to the preset decibel value of peak drop in the spatial spectrum. The signal enhancement module 503 is used to determine the constraint parameters of the multi-channel adaptive filter based on the spectral width, with the peak azimuth angle as the target direction; and to filter the multi-channel signal frame using the multi-channel adaptive filter to obtain the multi-channel enhanced signal. The mapping relationship table generation module 504 is used to obtain the real-time sound speed profile. Based on the sound speed profile, the sound ray tracing algorithm is used to calculate the theoretical time delay difference between each hydrophone and a reference hydrophone at different azimuth angles, so as to form a mapping relationship table between azimuth angle and theoretical time delay difference vector. The positioning result output module 505 is used to determine the search range with the peak azimuth angle as the center and the spectral width multiplied by a preset coefficient as the radius; it matches the measured time delay difference vector extracted from the multi-channel enhanced signal with the theoretical time delay difference vector in the mapping table that is within the search range, and selects the azimuth angle corresponding to the theoretical time delay difference vector with the largest matching degree as the positioning result output.

[0060] In one embodiment, the spatial spectrum acquisition module 502 is further configured to: The covariance matrix of the multi-channel signal frame is estimated, and the covariance matrix is ​​decomposed into eigenvalues ​​to obtain the signal subspace and the noise subspace. A spatial spectrum is constructed based on the noise subspace. Within a preset angle search range, the spatial spectrum is searched for spectral peaks, and the angle corresponding to the maximum spectral peak is determined as the peak azimuth angle. On both sides of the maximum spectral peak, determine the first boundary angle and the second boundary angle corresponding to the preset decibel decrease in spectral value, and determine the width of the angular interval between the first angle and the second angle as the spectral width.

[0061] In one embodiment, the signal enhancement module 503 is further configured to: The beam main lobe width of the multi-channel adaptive filter is determined based on the spectral width, and the beam main lobe width is proportional to the spectral width.

[0062] In one embodiment, the mapping table generation module 504 is specifically used for: Obtain real-time sound velocity profile data of the water area where the ROV is located; Generate the corresponding azimuth angle within the 0° to 360° angle range with a preset angle step size; Using the spatial coordinates of the reference hydrophone as the starting point for ray tracing, and based on the sound speed profile data, the ray tracing algorithm is used to calculate the propagation time of the sound wave from the azimuth angle to the reference hydrophone and the propagation time to each non-reference hydrophone for each azimuth angle. The difference between the propagation time of each non-reference hydrophone and the propagation time of the reference hydrophone is calculated to obtain the theoretical time delay difference vector corresponding to the azimuth angle. Based on each azimuth angle and its corresponding theoretical time delay difference vector, a mapping table between azimuth angle and theoretical time delay difference vector is formed.

[0063] In one embodiment, the positioning result output module 505 is further configured to: Generalized cross-correlation is performed on each signal in the multi-channel enhancement signal to obtain the measured time delay difference vector of each hydrophone relative to the reference hydrophone.

[0064] In one embodiment, the positioning result output module 505 is specifically used for: Extract all theoretical time delay difference vectors whose azimuth angles fall within the search range from the mapping table; For each theoretical time delay difference vector, calculate the similarity metric between the theoretical time delay difference vector and the measured time delay difference vector; Select the theoretical time delay difference vector with the largest similarity metric value, and output the azimuth angle corresponding to the theoretical time delay difference vector as the positioning result.

[0065] In one embodiment, the positioning result output module 505 is further configured to: The confidence score of the location result is generated based on the similarity metric value, and the magnitude of the confidence score is positively correlated with the magnitude of the similarity metric value. The confidence level is correlated with the location result in the output.

[0066] In one embodiment, the positioning result output module 505 is further configured to: A Kalman filter is established using azimuth angle and azimuth angle change rate as state variables; After outputting the azimuth angle corresponding to the theoretical time delay difference vector as the positioning result, the positioning result is input into the Kalman filter as the observation value; wherein the observation noise variance of the Kalman filter is adjusted according to the confidence level: the lower the confidence level, the larger the observation noise variance; The azimuth sequence filtered by the Kalman filter is output as the final positioning result.

[0067] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described passive acoustic positioning method for underwater ROVs.

[0068] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described passive acoustic positioning method for underwater ROVs.

[0069] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described passive acoustic positioning method for underwater ROVs.

[0070] In this embodiment of the invention, underwater acoustic signals are acquired by multiple hydrophones deployed on an ROV; the underwater acoustic signals are framed to obtain processed multi-channel signal frames; a multi-signal classification algorithm is used to calculate the spatial spectrum of the multi-channel signal frames, and the peak azimuth angle and the spectral width corresponding to the preset decibel value of the peak drop in the spatial spectrum are extracted; the constraint parameters of the multi-channel adaptive filter are determined based on the spectral width, with the peak azimuth angle as the target direction; the multi-channel adaptive filter is used to filter the multi-channel signal frames to obtain multi-channel enhanced signals; a real-time sound velocity profile is acquired, and a ray tracing algorithm is used to calculate the theoretical time delay difference between each hydrophone and a reference hydrophone at different azimuth angles based on the sound velocity profile, so as to form a mapping relationship table between azimuth angle and theoretical time delay difference vector; the search range is determined with the peak azimuth angle as the center and the spectral width multiplied by a preset coefficient as the radius; the measured time delay difference vector extracted from the multi-channel enhanced signal is matched with the theoretical time delay difference vector within the search range in the mapping relationship table, and the azimuth angle corresponding to the theoretical time delay difference vector with the largest matching degree is selected as the positioning result output. In the above process, the embodiments of the present invention acquire real-time sound velocity profiles and, based on these profiles, use a ray-tracing algorithm to calculate the theoretical time delay difference between each hydrophone and a reference hydrophone at different azimuth angles, forming a mapping table between azimuth angles and theoretical time delay difference vectors. This effectively corrects the angular deviation introduced by sound ray bending in the positioning calculation. By using a multi-signal classification algorithm to calculate the spatial spectrum of multi-channel signal frames, the peak azimuth angle and the spectral width corresponding to a preset decibel drop in the peak value of the spatial spectrum are extracted. The constraint parameters of the multi-channel adaptive filter are determined based on the peak azimuth angle as the target direction and the spectral width, and the multi-channel signal frames are filtered to obtain multi-channel enhanced signals, effectively enhancing the signal components in the target direction and suppressing interference in non-target directions. Simultaneously, a search range is determined with the peak azimuth angle as the center and the spectral width multiplied by a preset coefficient as the radius. The measured time delay difference is matched with the theoretical time delay difference vectors within this search range in the mapping table, avoiding blind searching throughout space and reducing the computational load. This meets the high-precision and high-robustness positioning requirements in complex marine environments.

[0071] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0075] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. 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. A passive acoustic positioning method for underwater ROVs, characterized in that, include: Underwater acoustic signals were collected by multiple hydrophones deployed on the ROV; The underwater acoustic signal is segmented into frames to obtain the processed multi-channel signal frames. A multi-signal classification algorithm is used to calculate the spatial spectrum of multi-channel signal frames, and the peak azimuth angle and the spectral width corresponding to the preset value of peak drop in the spatial spectrum are extracted. Using the peak azimuth angle as the target direction, the constraint parameters of the multi-channel adaptive filter are determined based on the spectral width; the multi-channel adaptive filter is then used to filter the multi-channel signal frames to obtain the multi-channel enhanced signal. The real-time sound velocity profile is obtained, and the sound ray tracing algorithm is used to calculate the theoretical time delay difference between each hydrophone and a reference hydrophone at different azimuth angles, so as to form a mapping relationship table between azimuth angle and theoretical time delay difference vector. The search range is determined with the peak azimuth angle as the center and the spectrum width multiplied by a preset coefficient as the radius. The measured time delay difference vector extracted from the multi-channel enhanced signal is matched with the theoretical time delay difference vector in the mapping table that is within the search range. The azimuth angle corresponding to the theoretical time delay difference vector with the largest matching degree is selected as the positioning result output.

2. The method as described in claim 1, characterized in that, A multi-signal classification algorithm is used to calculate the spatial spectrum of multi-channel signal frames, and the peak azimuth angle and the spectral width corresponding to the preset peak drop value of the spatial spectrum are extracted, including: The covariance matrix of the multi-channel signal frame is estimated, and the covariance matrix is ​​decomposed into eigenvalues ​​to obtain the signal subspace and the noise subspace. A spatial spectrum is constructed based on the noise subspace. Within a preset angle search range, the spatial spectrum is searched for spectral peaks, and the angle corresponding to the maximum spectral peak is determined as the peak azimuth angle. On both sides of the maximum spectral peak, determine the first boundary angle and the second boundary angle corresponding to the preset decibel decrease in spectral value, and determine the width of the angular interval between the first angle and the second angle as the spectral width.

3. The method as described in claim 1, characterized in that, The constraint parameters of the multi-channel adaptive filter are determined based on the spectral width, including: The beam main lobe width of the multi-channel adaptive filter is determined based on the spectral width, and the beam main lobe width is proportional to the spectral width.

4. The method as described in claim 1, characterized in that, Real-time sound velocity profiles are obtained. Based on these profiles, a ray tracing algorithm is used to calculate the theoretical time delay difference between each hydrophone and a reference hydrophone at different azimuth angles, forming a mapping table between azimuth angles and theoretical time delay difference vectors, including: Obtain real-time sound velocity profile data of the water area where the ROV is located; Generate the corresponding azimuth angle within the 0° to 360° angle range with a preset angle step size; Using the spatial coordinates of the reference hydrophone as the starting point for ray tracing, and based on the sound speed profile data, the ray tracing algorithm is used to calculate the propagation time of the sound wave from the azimuth angle to the reference hydrophone and the propagation time to each non-reference hydrophone for each azimuth angle. The difference between the propagation time of each non-reference hydrophone and the propagation time of the reference hydrophone is calculated to obtain the theoretical time delay difference vector corresponding to the azimuth angle. Based on each azimuth angle and its corresponding theoretical time delay difference vector, a mapping table between azimuth angle and theoretical time delay difference vector is formed.

5. The method as described in claim 1, characterized in that, The measured time delay difference vector extracted from the multi-channel enhanced signal includes: Generalized cross-correlation is performed on each signal in the multi-channel enhancement signal to obtain the measured time delay difference vector of each hydrophone relative to the reference hydrophone.

6. The method as described in claim 1, characterized in that, The measured time delay difference vector extracted from the multi-channel enhanced signal is matched with the theoretical time delay difference vector within the search range in the mapping table. The azimuth angle corresponding to the theoretical time delay difference vector with the highest matching degree is selected as the positioning result output, including: Extract all theoretical time delay difference vectors whose azimuth angles fall within the search range from the mapping table; For each theoretical time delay difference vector, calculate the similarity metric between the theoretical time delay difference vector and the measured time delay difference vector; Select the theoretical time delay difference vector with the highest similarity metric value, and output the azimuth angle corresponding to the theoretical time delay difference vector as the positioning result.

7. The method as described in claim 6, characterized in that, Selecting the theoretical time delay difference vector with the highest similarity metric value, and outputting the azimuth angle corresponding to the theoretical time delay difference vector as the positioning result, also includes: The confidence score of the location result is generated based on the similarity metric value, and the magnitude of the confidence score is positively correlated with the magnitude of the similarity metric value. The confidence level is correlated with the localization result in the output.

8. The method as described in claim 7, characterized in that, After selecting the theoretical time delay difference vector corresponding to the maximum similarity, and outputting the azimuth angle corresponding to the theoretical time delay difference vector as the positioning result, the method further includes: A Kalman filter is established using azimuth angle and azimuth angle change rate as state variables; The positioning results are used as observations and input into a Kalman filter; the observation noise variance of the Kalman filter is adjusted according to the confidence level: the lower the confidence level, the larger the observation noise variance. The azimuth sequence filtered by the Kalman filter is output as the final positioning result.

9. A passive acoustic positioning device for underwater ROVs, characterized in that, include: The signal acquisition module is used to acquire underwater acoustic signals through multiple hydrophones deployed on the ROV; The underwater acoustic signal is segmented into frames to obtain the processed multi-channel signal frames. The spatial spectrum acquisition module is used to calculate the spatial spectrum of multi-channel signal frames using a multi-signal classification algorithm, and to extract the peak azimuth angle and the spectral width corresponding to the preset value of peak drop in the spatial spectrum. The signal enhancement module is used to determine the constraint parameters of the multi-channel adaptive filter based on the peak azimuth angle and the spectral width; the multi-channel adaptive filter is used to filter the multi-channel signal frame to obtain the multi-channel enhanced signal. The mapping table generation module is used to obtain the real-time sound speed profile. Based on the sound speed profile, the sound ray tracing algorithm is used to calculate the theoretical time delay difference between each hydrophone and a reference hydrophone at different azimuth angles, so as to form a mapping table between azimuth angle and theoretical time delay difference vector. The positioning result output module is used to determine the search range with the peak azimuth angle as the center and the spectrum width multiplied by a preset coefficient as the radius; it matches the measured time delay difference vector extracted from the multi-channel enhanced signal with the theoretical time delay difference vector in the mapping table that is within the search range, and selects the azimuth angle corresponding to the theoretical time delay difference vector with the largest matching degree as the positioning result output.

10. The apparatus as claimed in claim 9, characterized in that, The spatial spectrum acquisition module is also used for: The covariance matrix of the multi-channel signal frame is estimated, and the covariance matrix is ​​decomposed into eigenvalues ​​to obtain the signal subspace and the noise subspace. A spatial spectrum is constructed based on the noise subspace. Within a preset angle search range, the spatial spectrum is searched for spectral peaks, and the angle corresponding to the maximum spectral peak is determined as the peak azimuth angle. On both sides of the maximum spectral peak, determine the first boundary angle and the second boundary angle corresponding to the preset decibel decrease in spectral value, and determine the width of the angular interval between the first angle and the second angle as the spectral width.

11. The apparatus as claimed in claim 9, characterized in that, The signal enhancement module is also used for: The beam main lobe width of the multi-channel adaptive filter is determined based on the spectral width, and the beam main lobe width is proportional to the spectral width.

12. The apparatus as claimed in claim 9, characterized in that, The mapping relationship table generation module is specifically used for: Obtain real-time sound velocity profile data of the water area where the ROV is located; Generate the corresponding azimuth angle within the 0° to 360° angle range with a preset angle step size; Using the spatial coordinates of the reference hydrophone as the starting point for ray tracing, and based on the sound speed profile data, the ray tracing algorithm is used to calculate the propagation time of the sound wave from the azimuth angle to the reference hydrophone and the propagation time to each non-reference hydrophone for each azimuth angle. The difference between the propagation time of each non-reference hydrophone and the propagation time of the reference hydrophone is calculated to obtain the theoretical time delay difference vector corresponding to the azimuth angle. Based on each azimuth angle and its corresponding theoretical time delay difference vector, a mapping table between azimuth angle and theoretical time delay difference vector is formed.

13. The apparatus as claimed in claim 9, characterized in that, The location result output module is also used for: Generalized cross-correlation is performed on each signal in the multi-channel enhancement signal to obtain the measured time delay difference vector of each hydrophone relative to the reference hydrophone.

14. The apparatus as claimed in claim 9, characterized in that, The location result output module is specifically used for: Extract all theoretical time delay difference vectors whose azimuth angles fall within the search range from the mapping table; For each theoretical time delay difference vector, calculate the similarity metric between the theoretical time delay difference vector and the measured time delay difference vector; Select the theoretical time delay difference vector with the highest similarity metric value, and output the azimuth angle corresponding to the theoretical time delay difference vector as the positioning result.

15. The apparatus as claimed in claim 14, characterized in that, The location result output module is also used for: The confidence score of the location result is generated based on the similarity metric value, and the magnitude of the confidence score is positively correlated with the magnitude of the similarity metric value. The confidence level is correlated with the localization result in the output.

16. The apparatus as claimed in claim 15, characterized in that, The location result output module is also used for: A Kalman filter is established using azimuth angle and azimuth angle change rate as state variables; After outputting the azimuth angle corresponding to the theoretical time delay difference vector as the positioning result, the positioning result is input into the Kalman filter as the observation value; wherein the observation noise variance of the Kalman filter is adjusted according to the confidence level: the lower the confidence level, the larger the observation noise variance; The azimuth sequence filtered by the Kalman filter is output as the final positioning result.

17. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 8.

18. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 8.

19. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 8.

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