Method and system for calculating sea surface convergence zone parameters based on sound propagation loss

Abstract

The application discloses a kind of sea surface convergence zone parameter calculation method and system based on sound propagation loss, method includes: propagation loss acquisition step: according to horizontal sound velocity profile variation data, seabed topography variation data and seabed sediment parameter, propagation loss data is obtained by sound propagation model;Convergence path acquisition step: based on propagation loss data, convergence path data and convergence zone quantity parameter are obtained by constant false alarm processing technique and connected domain technique;Convergence zone center point acquisition step: based on convergence path data, the position of convergence zone center point is determined by constant false alarm technique;Convergence zone boundary acquisition step: based on the gradient variation characteristics of convergence zone center point position combined with propagation loss data, the range boundary of the range boundary of convergence zone center point position is determined, and the detection and identification of underwater acoustic field convergence zone under complex marine environment are realized by the application.

Description

Technical Field

[0001] This invention relates to the field of marine sound field propagation calculation, and more specifically, to a method and system for calculating sea surface convergence parameters based on sound propagation loss. Background Technology

[0002] A convergence zone is a phenomenon where sound waves emitted from a sound source near the sea surface are refracted and reversed in the ocean, returning at a distance of approximately 50–70 km, forming a ring-shaped area of ​​high sound intensity several kilometers wide. The formation of a convergence zone is closely related to the surrounding marine environment, particularly characteristic parameters such as sea depth and acoustic duct axis depth. Shipborne sonar can utilize the convergence zone effect to detect targets tens or even hundreds of nautical miles away, which is of great significance for achieving long-range sonar detection. Since the 1960s, theoretical and experimental research on the acoustic field characteristics of convergence zones has received considerable attention. Hale was among the first to discover the existence of convergence zones in sea experiments and conducted theoretical research on them. Urick analyzed the influence of different sound source depths on deep-sea convergence zones. Fan Peiqin et al. analyzed the formation principle of convergence zones and proposed a model for calculating the distance to convergence zones.

[0003] Currently, the calculation of ocean convergence zone parameters mainly relies on convergence zone characteristic parameter models, which calculate convergence zone information such as the distance to convergence and the width of the convergence zone using parameters such as the sound velocity along the sound duct axis, the initial grazing angle of the sound ray, and the sound velocity gradient. However, these convergence zone characteristic parameter calculation models typically perform a simplistic equivalent of the complex marine environment, neglecting the influence of different distances, seabed topography, and changes in seabed sediments on the sound velocity profile. When the marine environment differs significantly from the simplistic equivalent environment, the calculated convergence zone parameters deviate considerably from the actual situation, failing to meet the accuracy requirements for propagation loss in scientific research and engineering construction.

[0004] Therefore, through dedicated research, the inventors developed a method and system for calculating sea surface convergence zone parameters based on sound propagation loss to overcome the aforementioned defects. Summary of the Invention

[0005] To address the above problems, this invention provides a method for calculating sea surface convergence zone parameters based on sound propagation loss, comprising:

[0006] Steps for obtaining propagation loss: Based on the changes in horizontal sound velocity profile, seabed topography and terrain, and seabed sediment parameters, the propagation loss data is obtained through the sound propagation model.

[0007] Convergence path acquisition steps: Based on the propagation loss data, convergence path data and convergence region quantity parameters are obtained through constant false alarm rate (CFAR) processing technology and connected component technology;

[0008] Steps for obtaining the center point of the convergence zone: Based on the convergence path data, the location of the center point of the convergence zone is determined using constant false alarm rate (CFAR) technology;

[0009] Steps for obtaining the convergence zone boundary: Based on the location of the center point of the convergence zone and the gradient change characteristics of the propagation loss data, determine the range boundaries on both sides of the center point of the convergence zone.

[0010] The above-mentioned method for calculating sea surface convergence zone parameters includes the following step:

[0011] Acquire the horizontal sound velocity profile change data, the seabed topography change data, and the seabed sediment parameters within a set range;

[0012] The propagation loss data within the set range is obtained by using the wave equation solution method based on the horizontal sound velocity profile change data, the seabed topography change data, and the seabed sediment parameters.

[0013] The above-mentioned method for calculating sea surface convergence zone parameters includes the following step:

[0014] The propagation loss data is flipped.

[0015] The above-mentioned method for calculating sea surface convergence zone parameters includes the following steps for obtaining the convergence path:

[0016] Preliminary convergence path identification steps: Initial convergence path data is obtained by performing preliminary identification on the propagation loss data using constant false alarm rate (CFAR) technology;

[0017] Final convergence path identification step: Remove discrete data from the initial convergence path data using connected component techniques to obtain the final convergence path data.

[0018] The above-mentioned method for calculating sea surface convergence zone parameters includes the following preliminary identification step for the convergence path:

[0019] The left sample unit value and the right sample unit value are obtained from the sample data units on both sides of the propagation loss data;

[0020] The constant false alarm value is obtained based on the left sample cell value and the right sample cell value;

[0021] The initial convergence path data is obtained by identifying the propagation loss data using the constant false alarm value.

[0022] The above-mentioned method for calculating sea surface convergence zone parameters includes the following step: The final identification step of the convergence path includes:

[0023] In the initial convergence path data, adjacent pixels with the same pixel value are numbered and marked.

[0024] After removing interference from discrete data by setting the number of points for numbering, the final convergence path data is obtained, and the number of convergence zones is obtained based on the final convergence path data.

[0025] The above-mentioned method for calculating sea surface convergence zone parameters includes the following step: obtaining the center point of the convergence zone.

[0026] Based on the final convergence path data, depth data at a set distance from the sea surface is extracted according to the set direction, and then averaged to obtain a one-dimensional array;

[0027] The one-dimensional array is processed by Gaussian filtering;

[0028] Based on the one-dimensional array after Gaussian filtering, the position of the point where the value of the continuous segment above the constant false alarm value is obtained by detecting the one-dimensional array using constant false alarm processing technology. This position is the center point of the convergence zone.

[0029] The above-mentioned method for calculating sea surface convergence zone parameters includes the following steps for obtaining the convergence zone boundary:

[0030] Gradient data is obtained based on the final convergence path data;

[0031] Based on the gradient data, the boundary point set of the convergence region is obtained according to the first preset condition;

[0032] The boundary point set is filtered based on the location of the center point of the convergence area using a second preset condition;

[0033] The boundaries on both sides of the center point of the convergence zone are determined based on the filtered set of boundary points.

[0034] The above-mentioned method for calculating sea surface convergence zone parameters involves reversing the propagation loss data using the following formula:

[0035] TL_Trans = -TL+C

[0036] Where TL_Trans is the propagation loss data after inversion, TL is the propagation loss data before inversion, and C is a constant.

[0037] The present invention also provides a system for calculating sea surface convergence zone parameters based on acoustic propagation loss, wherein the sea surface convergence zone parameter calculation method described in any one of the above-mentioned methods is applied, and the sea surface convergence zone parameter calculation system includes:

[0038] The propagation loss acquisition unit obtains propagation loss data based on horizontal sound velocity profile change data, seabed topography change data, and seabed sediment parameters through a sound propagation model.

[0039] The convergence path acquisition unit acquires convergence path data and convergence area quantity parameters based on the propagation loss data using constant false alarm rate (CFAR) processing technology and connected component technology.

[0040] The convergence zone center point acquisition unit determines the location of the convergence zone center point based on the convergence path data using constant false alarm rate (CFAR) technology.

[0041] The convergence zone boundary acquisition unit determines the range boundaries on both sides of the center point of the convergence zone based on the location of the center point of the convergence zone and the gradient change characteristics of the propagation loss data.

[0042] The advantages of this invention over existing technologies are as follows:

[0043] 1. This invention comprehensively considers the changes in sound velocity profile with the horizontal direction, the undulations of seabed geology, and the influence of changes in seabed sediments on sound field propagation. It can calculate convergence parameters in relatively complex marine environments and has strong applicability.

[0044] 2. This invention uses constant false alarm rate detection and connected component analysis to accurately identify energy convergence paths, eliminate the influence of false convergence zones, and achieve a high accuracy rate in identifying convergence zones.

[0045] 3. This invention comprehensively considers the physical characteristics of the convergence zone and calculates parameters such as the number of convergence zones, center position, and boundary through methods such as smoothing, gradient maximization, and boundary discrimination. The calculation error is small and the accuracy is high.

[0046] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description and the drawings. Attached Figure Description

[0047] 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 some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a technical roadmap of the sea surface convergence zone parameter calculation method of the present invention;

[0049] Figure 2 This is a flowchart of the sea surface convergence zone parameter calculation method of the present invention;

[0050] Figure 3 for Figure 2 Flowchart of step S2;

[0051] Figure 4 To illustrate the loss data;

[0052] Figure 5 To propagate the loss inverted data plot;

[0053] Figure 6 Schematic diagram of constant false alarm rate (CFAR) processing technology;

[0054] Figure 7 Extraction map of sound convergence path;

[0055] Figure 8 This is a schematic diagram of connected components;

[0056] Figure 9 Label the connected components in the graph;

[0057] Figure 10 The acoustic convergence path diagram after connected component analysis;

[0058] Figure 11 Plot of mean propagation loss;

[0059] Figure 12 For Gaussian window plots;

[0060] Figure 13 This is a plot of the mean propagation loss after Gaussian filtering;

[0061] Figure 14 A schematic diagram of the center point of the convergence area extracted by the constant false alarm rate (CFAR) processing technology;

[0062] Figure 15 This is the gradient plot of the Gaussian filter mean.

[0063] Figure 16 Identify and label the convergence zone;

[0064] Figure 17 To identify the convergence zone for propagation loss;

[0065] Figure 18 This is a schematic diagram of the sea surface convergence zone parameter calculation system of the present invention. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] The illustrative embodiments and descriptions of the present invention are used to explain the invention, but are not intended to limit the invention. Furthermore, elements / components using the same or similar reference numerals in the drawings and embodiments are used to represent the same or similar parts.

[0068] The terms "first," "second," "S1," "S2," etc., used in this document do not specifically refer to any order or sequence, nor are they intended to limit the invention. They are merely used to distinguish elements or operations described using the same technical terms.

[0069] The directional terms used in this article, such as up, down, left, right, front, or back, are for reference only when referring to the accompanying drawings. Therefore, the use of directional terms is for illustrative purposes and not to limit this work.

[0070] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0071] The term "and / or" as used herein includes any or all of the things mentioned.

[0072] The term "multiple" in this article includes "two" and "more than two"; the term "multiple groups" in this article includes "two groups" and "more than two groups".

[0073] The terms "approximately," "about," etc., used herein are intended to modify any quantity or error that may vary slightly, but these slight variations or errors do not change the essence of the quantity or error. Generally, the range of slight variations or errors modified by such terms may be 20% in some embodiments, 10% in others, 5% in still others, or other values. Those skilled in the art should understand that the aforementioned values ​​can be adjusted according to actual needs and are not limited thereto.

[0074] Certain terms used to describe this application will be discussed below or elsewhere in this specification to provide additional guidance to those skilled in the art in describing the application.

[0075] The formation of convergence zones primarily depends on whether the sea depth at that location exceeds the critical depth of the convergence zone, which is mainly determined by the maximum sound speed in the upper ocean. Changes in sound speed profiles and seafloor topography directly affect whether a convergence zone forms, its distance from the point of origin, and its extent. Therefore, integrating refined sound speed profiles and seafloor topographic and geological variations into the convergence zone identification process is crucial. Existing models for calculating convergence zone characteristic parameters typically employ simplistic equivalences to represent complex marine environments, which has the following drawbacks:

[0076] 1. Such models require the seabed to be equivalent to a flat bottom and to assume that the sound speed profile does not change with distance. In addition, the professional barriers are too high and the ease of use is poor.

[0077] 2. This type of model ignores the influence of changes in seafloor topography and sediment on the formation of convergence zones, and can only be used to calculate cases where seafloor topography and sediment changes are relatively small.

[0078] 3. This type of model assumes that the sound velocity profile is uniformly distributed over a horizontal distance, ignoring the influence of factors such as temperature, salinity, and depth on the sound velocity profile. This results in a large deviation between the calculated results and the actual situation, and cannot meet the needs of practical applications.

[0079] In practical sonar detection applications, both the sonar and the target are generally located near the sea surface, making the calculation of the sea surface convergence zone characteristics particularly important. Most current mainstream underwater sound field calculation models consider factors such as changes in horizontal sound velocity profiles, seabed topography, and seabed sediment stratification, meticulously depicting the complex marine environment and accurately calculating propagation loss. Furthermore, propagation loss can demonstrate the attenuation of sound intensity after a certain distance of sound wave propagation, enabling the inversion of convergence zone parameter characteristics.

[0080] Please refer to Figures 1-2 , Figure 1 This is a technical roadmap of the sea surface convergence zone parameter calculation method of the present invention. Figure 2 This is a flowchart of the sea surface convergence zone parameter calculation method of the present invention. Figure 2 As shown, the present invention provides a method for calculating sea surface convergence zone parameters based on acoustic propagation loss, comprising:

[0081] Step S1 for obtaining propagation loss: Based on the horizontal sound velocity profile change data, seabed topography change data, and seabed sediment parameters, the propagation loss data is obtained through the sound propagation model.

[0082] Convergence path acquisition step S2: Based on the propagation loss data, convergence path data and convergence area quantity parameters are obtained through constant false alarm rate (CFAR) processing technology and connected component technology;

[0083] Step S3 for obtaining the center point of the convergence zone: Determine the location of the center point of the convergence zone based on the convergence path data using constant false alarm rate (CFAR) technology;

[0084] Step S4 for obtaining the boundary of the convergence region: Based on the location of the center point of the convergence region and the gradient change characteristics of the propagation loss data, determine the range boundary on both sides of the center point of the convergence region.

[0085] Based on this, the present invention comprehensively considers the changes in sound velocity profile with the horizontal direction, the undulations of seabed geology, and the influence of changes in seabed sediments on sound field propagation, and can calculate relatively complex marine environments, thereby improving the accuracy of sea surface convergence zone parameter calculation.

[0086] Among them, seabed sediment parameters include parameters related to the sound source, seabed, and seawater.

[0087] Further, please refer to Figure 4 - Figure 5 , Figure 4 To illustrate the loss data; Figure 5 The propagation loss is inverted data graph. The propagation loss acquisition step S1 includes:

[0088] Acquire the horizontal sound velocity profile change data, the seabed topography change data, and the seabed sediment parameters within a set range;

[0089] The propagation loss data within the set range is obtained by using the wave equation solving method based on the horizontal sound velocity profile change data, the seabed topography change data, and the seabed sediment parameters.

[0090] The propagation loss data is flipped.

[0091] In this embodiment, the set range is the range of the area to be calculated.

[0092] Specifically, since the mid-1970s, numerical models in computational ocean acoustics have developed rapidly, and using numerical models for calculations has become the most commonly used research method in acoustic experiments. With the continuous advancement of numerical techniques for solving wave equations, the establishment of ocean acoustic models has now reached a mature level. For the sound propagation problem in ocean media, under the assumption of ideal fluid, by simultaneously solving the continuity equation (1), the state equation (2), and the motion equation under adiabatic conditions (3), the sound wave equation (4) regarding sound pressure can be obtained.

[0093]

[0094]

[0095]

[0096]

[0097] Where p represents pressure, ρ represents density, c represents the speed of sound in an ideal fluid, and u represents the vibration velocity of a fluid particle. This invention combines ocean topography data and ocean sound velocity profile data, utilizing wave equation solving methods such as ray methods (e.g., Bellhop), normal wave methods (e.g., Kraken), and parabolic equation methods (e.g., FOR3D, RAM) to solve for the propagation loss TL within this range. Figure 4 As shown. Meanwhile, to facilitate the calculation of convergence zone characteristics later, the propagation loss data TL is flipped using the following formula, turning its troughs into peaks.

[0098] TL_Trans = -TL + C(5)

[0099] Where TL_Trans represents the inverted propagation loss data. After inversion, a constant C is added to ensure that the data is calculated within the first interval. The inverted propagation loss is as follows: Figure 7 As shown.

[0100] Furthermore, please refer to Figure 3 , Figure 3 for Figure 2 The step-by-step flowchart for step S2. (See attached flowchart.) Figure 3 As shown, the convergence path acquisition step S2 includes:

[0101] Convergence path preliminary identification step S21: Initial convergence path data is obtained by performing preliminary identification on the propagation loss data using constant false alarm rate (CFAR) technology;

[0102] Final convergence path identification step S2: Remove discrete data from the initial convergence path data using connected component techniques to obtain the final convergence path data.

[0103] The preliminary identification step S21 of the convergence path includes:

[0104] The left sample unit value and the right sample unit value are obtained from the sample data units on both sides of the propagation loss data;

[0105] The constant false alarm value is obtained based on the left sample cell value and the right sample cell value;

[0106] The initial convergence path data is obtained by identifying the propagation loss data using the constant false alarm value.

[0107] Specifically, please refer to Figures 6-7 , Figure 6 Schematic diagram of constant false alarm rate (CFAR) processing technology; Figure 7 This is a sound convergence path extraction map. This invention uses constant false alarm rate (CFAR) processing technology to identify energy convergence paths. The core idea of ​​this method is to estimate the background power by averaging the sampled data within a reference window. Its basic principle is as follows: Figure 6As shown, D represents the propagation loss TL_Trans data, P region is the guard unit, i.e., the data units that are skipped and do not need to be calculated, and N is the number of data in the sample unit. The calculation formulas on the left and right sides are as follows:

[0108]

[0109]

[0110] Where x is the sample data unit near D, X is the value of the sample unit on the left, and Y is the value of the sample unit on the right. The calculation formula for Figure 7 is as follows:

[0111]

[0112] Based on the calculation principle of underwater sound field propagation loss, propagation loss is quite sensitive to fluctuations. Therefore, the constant false alarm value T of this invention is:

[0113] T = α + 7(9)

[0114] Here, α is a user-defined parameter. The following comparator is used to compare and determine whether the propagation loss data should be retained, and the underwater acoustic convergence path is extracted.

[0115]

[0116] TL_Clear represents the data after constant false alarm rate (CFAR) processing. If the propagation loss data D is greater than or equal to the T value in the comparator, the data is considered to be on the energy convergence path and is retained; otherwise, the data is set to zero. This initial identification of the energy convergence path is as follows: Figure 7 As shown.

[0117] The final identification step S22 of the convergence path includes:

[0118] In the initial convergence path data, adjacent pixels with the same pixel value are numbered and marked.

[0119] After removing interference from discrete data by setting the number of points for numbering, the final convergence path data is obtained, and the number of convergence zones is obtained based on the final convergence path data.

[0120] Specifically, please refer to Figure 8 - Figure 10 , Figure 8 This is a schematic diagram of connected components; Figure 9 Label the connected components in the graph; Figure 10 This is the acoustic convergence path diagram after connected component analysis.

[0121] Connected component analysis is an image processing operation, typically used for binary images to identify and label adjacent pixels with the same pixel value. This invention uses this analysis method to remove interference from discrete propagation loss data.

[0122] There are generally two definitions for connected regions: 4-adjacency and 8-adjacency, such as... Figure 8 As shown, annotations are performed on the data TL_Clear after constant false alarm rate (CFAR) processing. Points with four neighboring nodes are labeled with a unified number, such as... Figure 9 As shown, and filtered according to the following formula.

[0123]

[0124] Where TL_Filter represents the data after removing discrete data, i represents the connected component label, and N represents the number of points set. If the number is greater than N, the data within that connected component is retained; if the number is less than N, the data within that connected component is considered discrete data interference and is set to zero. The final convergence path data after extraction is as follows: Figure 10 As shown, the number of convergence zones within the current range can be determined based on the convergence path data.

[0125] Furthermore, step S3, which involves obtaining the center point of the convergence zone, includes:

[0126] Based on the final convergence path data, depth data at a set distance from the sea surface is extracted according to the set direction, and then averaged to obtain a one-dimensional array;

[0127] The one-dimensional array is processed by Gaussian filtering;

[0128] Based on the one-dimensional array after Gaussian filtering, the position of the point where the value of the continuous segment above the constant false alarm value is obtained by detecting the one-dimensional array using constant false alarm processing technology. This position is the center point of the convergence zone.

[0129] Specifically, please refer to Figure 11 - Figure 14 , Figure 11 Plot of mean propagation loss; Figure 12 For Gaussian window plots; Figure 13 This is a plot of the mean propagation loss after Gaussian filtering; Figure 14 A schematic diagram of the center point of the convergence zone extracted for constant false alarm processing technology.

[0130] This invention uses constant false alarm rate (CFAR) processing technology to extract sea surface data based on TL_Filter data, filters the boundary point set that may be a convergence zone, and combines the location information of the center point of the convergence zone to determine the location and width of the convergence zone.

[0131] Since this invention calculates parameters for the sea surface convergence zone, based on TL_Filter, data at a depth of 200 meters from the sea surface in the vertical direction are extracted and averaged to represent the magnitude of the propagation loss signal at that distance, resulting in a one-dimensional array temoTL, as shown below. Figure 11 As shown. Since no convergence zone is generated within 30 kilometers of the sound source, and the data fluctuation within 30 kilometers is large, it will interfere with the calculation process of convergence zone features. Therefore, the data within 30 kilometers was removed.

[0132] The one-dimensional array tempTL is then smoothed using Gaussian filtering. A Gaussian window is obtained based on the width L and the width factor α, as shown in the following formula:

[0133]

[0134] Where -(L-1) / 2≤n≤(L-1) / 2, σ=(L-1) / (2α), the calculated Gaussian window is as follows: Figure 12 As shown.

[0135] The formula for filtering and smoothing is as follows:

[0136] TL_S(n)=w(1)*tempTL(n)+w(2)*tempTL(n-1)+…+w(n)*tempTL(1) (13)

[0137] Where w is a Gaussian window, TL_S is the smoothed data, and TL_S is as follows: Figure 13 As shown.

[0138] The constant false alarm rate (CFAR) technique is used again to detect the TL_S data, such as... Figure 14 As shown, the location of the point with the maximum value of the continuous segment above the constant false alarm value T is the center point of the convergence zone, and is recorded as CenterV.

[0139] In this embodiment, the preferred implementation is to set the direction as vertical and the distance from the sea surface as 200 meters.

[0140] Furthermore, step S4, which involves obtaining the boundary of the convergence region, includes:

[0141] Gradient data is obtained based on the final convergence path data;

[0142] Based on the gradient data, the boundary point set of the convergence region is obtained according to the first preset condition;

[0143] The boundary point set is filtered based on the location of the center point of the convergence area using a second preset condition;

[0144] The boundaries on both sides of the center point of the convergence zone are determined based on the filtered set of boundary points.

[0145] Specifically, please refer to Figure 15 - Figure 17 , Figure 15 This is the gradient plot of the Gaussian filter mean. Figure 16 Identify and label the convergence zone; Figure 17 To identify the convergence zone for propagation loss.

[0146] After determining the location of the center point of the convergence region, the gradient characteristics are calculated based on the TL_S data using the following formula.

[0147] kTL i =|TL_S i -TL_S i-1 |, i = 2, 3, 4.... (14)

[0148] kTL represents gradient data, and the calculation results are as follows: Figure 15 As shown. Based on this kTL data, find the data that simultaneously satisfies kTL. i kTL i+1 kTL i kTL i-1 The key points for the two conditions are recorded as borV, and this data may be the set of boundary points of the convergence region.

[0149] Since the width of the convergence zone is generally within 20 kilometers, this invention uses the position of the center point of each convergence zone in CenterV as the axis and filters the points belonging to borV within 10 kilometers on the left and right sides according to the following formula.

[0150] {leftCon∈borV|CenterV-dis<rightCon<CenterV} (15)

[0151] {rightCon∈borV|CenterV<rightCon<CenterV+dis} (16)

[0152] Where leftCon represents points within 10 kilometers to the left of the central axis that belong to borV, rightCon represents points within 10 kilometers to the right of the central axis that belong to borV, and dis is the number of points after converting the 10-kilometer step size. The location of the maximum gradient in leftCon is the left boundary of the convergence region, and the location of the maximum gradient in rightCon is the right boundary of the convergence region. The boundary detection is as follows: Figure 16 , 17 As shown, this method can accurately identify the characteristics of the convergence region, whether based on the mean propagation loss or the original propagation loss data.

[0153] Please refer to Figure 18 , Figure 18This is a schematic diagram of the sea surface convergence zone parameter calculation system of the present invention. Figure 18 As shown, the present invention provides a sea surface convergence zone parameter calculation system based on sound propagation loss, applying the sea surface convergence zone parameter calculation method described in any one of the above-mentioned methods. The sea surface convergence zone parameter calculation system includes:

[0154] The propagation loss acquisition unit 11 acquires propagation loss data based on the horizontal sound velocity profile change data, seabed topography change data, and seabed sediment parameters through the sound propagation model.

[0155] Convergence path acquisition unit 12 acquires convergence path data and convergence area quantity parameters based on the propagation loss data using constant false alarm rate processing technology and connected component technology;

[0156] The convergence zone center point acquisition unit 13 determines the location of the convergence zone center point based on the convergence path data using constant false alarm rate (CFAR) technology.

[0157] The convergence zone boundary acquisition unit 14 determines the range boundaries on both sides of the center point of the convergence zone based on the location of the center point of the convergence zone and the gradient change characteristics of the propagation loss data.

[0158] The convergence path acquisition unit 12 includes:

[0159] The convergence path preliminary identification module 121 uses constant false alarm rate (CFAR) technology to perform preliminary identification on the propagation loss data to obtain initial convergence path data.

[0160] The final convergence path identification module 122 obtains the final convergence path data by removing discrete data from the initial convergence path data using connected component technology.

[0161] In summary, this invention first comprehensively considers changes in horizontal sound velocity profile, seabed topography, and seabed sediment parameters, and uses sound propagation models such as normal modes, parabolic equations, and ray propagation to calculate propagation loss and perform data inversion; secondly, it uses constant false alarm rate (CFAR) processing and connected component techniques to identify energy convergence paths; thirdly, it uses Gaussian filtering to smooth the data and uses CFAR techniques to determine the center point of the convergence zone; finally, it combines the characteristics of propagation loss gradient changes to determine the boundary of the area on both sides of the center point of the convergence zone, thus realizing the detection and identification of underwater sound field convergence zones in complex marine environments.

[0162] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calculating sea surface convergence zone parameters based on sound propagation loss, characterized in that, include: Propagation loss acquisition steps: Based on the horizontal sound velocity profile change data, seabed topography change data, and seabed sediment parameters, the propagation loss data is obtained through the sound propagation model, and the propagation loss data is inverted. Convergence path acquisition steps: Based on the propagation loss data, convergence path data and convergence region quantity parameters are obtained through constant false alarm rate (CFAR) processing technology and connected component technology; Steps for obtaining the center point of the convergence zone: Based on the convergence path data, the location of the center point of the convergence zone is determined using constant false alarm rate (CFAR) technology; Steps for obtaining the convergence zone boundary: Based on the location of the center point of the convergence zone and the gradient change characteristics of the propagation loss data, determine the boundary of the area on both sides of the center point of the convergence zone. The convergence path acquisition step includes: Preliminary convergence path identification steps: Initial convergence path data is obtained by performing preliminary identification on the propagation loss data using constant false alarm rate (CFAR) technology; Final convergence path identification step: Remove discrete data from the initial convergence path data using connected component techniques to obtain the final convergence path data; The preliminary identification steps for the convergence path include: The left sample unit value and the right sample unit value are obtained from the sample data units on both sides of the propagation loss data; The constant false alarm value is obtained based on the left sample cell value and the right sample cell value; The propagation loss data is identified using the constant false alarm value to obtain initial convergence path data; The final identification step of the convergence path includes: In the initial convergence path data, adjacent pixels with the same pixel value are numbered and marked. After removing interference from discrete data by setting the number of points for numbering, the final convergence path data is obtained, and the number of convergence zones is obtained based on the final convergence path data.

2. The method for calculating sea surface convergence zone parameters as described in claim 1, characterized in that, The steps for obtaining propagation loss include: Acquire the horizontal sound velocity profile change data, the seabed topography change data, and the seabed sediment parameters within a set range; The propagation loss data within the set range is obtained by using the wave equation solution method based on the horizontal sound velocity profile change data, the seabed topography change data, and the seabed sediment parameters.

3. The method for calculating sea surface convergence zone parameters as described in claim 1, characterized in that, The steps for obtaining the center point of the convergence zone include: Based on the final convergence path data, data of a set depth are extracted according to the set direction, and the average is taken to obtain a one-dimensional array; The one-dimensional array is processed by Gaussian filtering; Based on the one-dimensional array after Gaussian filtering, the position of the point where the value of the continuous segment above the constant false alarm value is obtained by detecting the one-dimensional array using constant false alarm processing technology. This position is the center point of the convergence zone.

4. The method for calculating sea surface convergence zone parameters as described in claim 3, characterized in that, The steps for obtaining the convergence region boundary include: Gradient data is obtained based on the final convergence path data; Based on the gradient data, the boundary point set of the convergence region is obtained according to the first preset condition; The boundary point set is filtered based on the location of the center point of the convergence area using a second preset condition; The boundaries on both sides of the center point of the convergence zone are determined based on the filtered set of boundary points.

5. The method for calculating sea surface convergence zone parameters as described in claim 1, characterized in that, The propagation loss data is inverted using the following formula: in, This is the propagation loss data after reversal. The propagation loss data before reversal is given, and C is a constant.

6. A system for calculating sea surface convergence zone parameters based on acoustic propagation loss, characterized in that, The sea surface convergence zone parameter calculation system, using the method for calculating sea surface convergence zone parameters according to any one of claims 1-5, comprises: The propagation loss acquisition unit obtains propagation loss data based on horizontal sound velocity profile change data, seabed topography change data, and seabed sediment parameters through a sound propagation model. The convergence path acquisition unit acquires convergence path data and convergence area quantity parameters based on the propagation loss data using constant false alarm rate (CFAR) processing technology and connected component technology. The convergence zone center point acquisition unit determines the location of the convergence zone center point based on the convergence path data using constant false alarm rate (CFAR) technology. The convergence zone boundary acquisition unit determines the range boundaries on both sides of the center point of the convergence zone based on the location of the center point of the convergence zone and the gradient change characteristics of the propagation loss data.

Images