A measured data driven underwater acoustic communication sound field generation method

Abstract

The application relates to the technical field of underwater acoustic field detection, and discloses a measured data driven underwater acoustic communication sound field generation method, which aims to optimize the performance of an underwater acoustic communication system in a changeable marine environment. The method integrates real-time marine hydrological conditions such as temperature, salinity, depth and seabed topography, and uses the sound ray theory to generate and store an accurate sound field model by combining a simulation method (such as a Bellhop module) of a theoretical model, calculate a sound velocity gradient graph and a propagation loss graph, and predict the propagation path and loss of sound waves. The model can automatically adapt to various marine environments, recommend an optimal communication link, and significantly improve the efficiency and adaptability of sound field generation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of underwater acoustic field detection, and in particular to a measured data driven underwater acoustic communication sound field generation method. BACKGROUND

[0002] Sound waves are the only information carriers that can propagate over long distances in seawater media so far, and sound propagation models are the scientific foundation of underwater detection and communication. In the related art, sound transmission models mainly use ray method, Gaussian beam method, beam integral method, normal wave method and parabolic method, etc. Although these traditional sound propagation models have achieved certain success in specific application scenarios, they have high requirements for user input of complex parameters of marine environment, which is a challenge to non-professionals. At the same time, the variables in the marine environment, such as temperature, salinity, depth and changes in seabed topography, limit the accuracy of these models in practical applications.

[0003] Traditional techniques, such as the BELLHOP model, usually require users to manually input a large number of marine environmental parameters, which are often derived from theoretical models or limited measured data. This method has limitations in data accuracy and real-time performance, and it is difficult to fully reflect the dynamic changes of the marine environment, thereby affecting the accuracy of the sound field simulation.

[0004] Therefore, it is necessary to provide a measured data driven underwater acoustic communication sound field generation method to solve the above technical problems. SUMMARY

[0005] To solve the above technical problems, the present application provides a measured data driven underwater acoustic communication sound field generation method. This is a technology that cross utilizes measured data and simulation software, aiming to improve the performance of underwater acoustic communication systems in changing marine environments by integrating real-time marine hydrological conditions such as temperature, salinity, depth and seabed topography to generate and store accurate sound field models. Specifically, measured data can be used to verify and optimize key parameters of the simulation model, such as sound speed profile and propagation loss, etc.; while the simulation software can provide reasonable theoretical supplements for areas not covered by the measured data. Through interactive coupling, the two form a dynamic model generation mechanism, ensuring that the model can accurately reflect the actual characteristics of the marine environment. These models are key references when selecting communication links, and by calculating the sound speed gradient and propagation loss, the optimal communication link can be recommended to achieve automatic adaptation to various marine environments.

[0006] In the process of constructing the sound field model, the present application adopts the sound ray theory, which is a method of predicting the behavior of sound waves by tracking their propagation path in the medium. The user needs to define key environmental parameters, including sound velocity profile, water depth and seabed type, which have a direct impact on the propagation path of sound rays. The present application adopts the Bellhop module to calculate the sound velocity gradient map, which uses the Gaussian beam model to calculate the reflection, refraction and scattering of sound rays in the medium, thereby predicting the propagation path of sound waves. In addition, Bellhop also takes into account factors such as geometric diffusion, medium absorption and interface reflection to calculate the propagation loss of sound waves in the medium.

[0007] The present application provides a measured data driven underwater acoustic communication sound field generation method, which comprises the following steps:

[0008] S1, ocean hydrological information import: through sensors to collect measured ocean hydrological data, including temperature, salinity, depth and seabed topography, etc., as a supplementary input of the sound field simulation module, to ensure the accuracy and real-time of the sound field model;

[0009] S2, sound field model generation: using the imported ocean hydrological conditions, a more realistic sound field model is constructed, and the sound velocity gradient map and the propagation loss map are calculated;

[0010] S3, model storage and matching: the generated sound field model is stored, and when similar ocean hydrological conditions are encountered, the existing model can be quickly called to improve the efficiency of sound field model generation;

[0011] S4, communication link analysis, analysis of communication link: combining the sound velocity gradient and the propagation loss map, using the communication link performance analysis software, the potential communication link is analyzed to provide data support for selecting the optimal communication link;

[0012] S5, real-time update and feedback: with the acquisition of new measured data, the system will update the sound field model and link selection in real time to ensure that the system can dynamically adapt to the changes of the ocean environment and maintain the optimal state of the communication link.

[0013] Preferably, the generation of the sound field model in S2 adopts the sound ray theory to predict the behavior of sound waves by tracking their propagation path in the medium.

[0014] Preferably, in the generation step of the sound field model, the Bellhop module is used to calculate the sound velocity gradient map, and the Gaussian beam model is used to calculate the reflection, refraction and scattering of sound rays in the medium, thereby predicting the propagation path of sound waves.

[0015] Preferably, in the model storage and matching step, the system creates a database to store the historically generated sound field models in a hierarchical file structure, including environment description files, basic parameter files and simulation result files.

[0016] Preferably, in the communication link analysis step, the weight parameters are set based on the communication requirements, and the potential communication links are analyzed by using a multi-objective optimization algorithm.

[0017] Preferably, in the real-time updating and feedback step, the system periodically feeds back the link performance data and updates the sound field model and link selection in real time.

[0018] Compared with the related art, the measured data driven underwater acoustic communication sound field generation method provided by the present application has the following beneficial effects:

[0019] The present application significantly improves the accuracy and real-time performance of the sound field model by integrating real-time ocean hydrological data to supplement the sound field data in the simulation software, ensuring that the model can timely reflect the dynamic changes of the marine environment.

[0020] The present application reduces the user's demand for inputting complex data by automatically generating the sound field model, thereby simplifying the operation process; the system improves the efficiency of model generation and accelerates the construction process of the sound field model by storing and quickly calling the existing sound field model.

[0021] In addition, the present application enhances the reliability of communication link selection by calculating the sound velocity gradient and propagation loss, providing a scientific basis for selecting the optimal communication link. Finally, the system realizes dynamic adaptability, which can update the sound field model and link selection in real time as new measured data is obtained, ensuring that the communication link is always in an optimized state. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 A flowchart of a measured data driven underwater acoustic communication sound field generation method provided by the present application;

[0023] Figure 2 A sound velocity gradient graph in the present application;

[0024] Figure 3 A propagation loss graph in the present application. DETAILED DESCRIPTION

[0025] The present application will be further described below in conjunction with the drawings and embodiments.

[0026] The flowchart of a measured data driven underwater acoustic communication sound field generation method proposed by the present application is shown in Figure 1 , including the following steps:

[0027] S1, ocean hydrological information import: import the measured ocean hydrological data, including temperature, salinity, depth and seabed topography, as the input of the sound field simulation module, and combine the historical hydrological data of the sound field simulation software as a supplement to cover the vacancy area of the measured data.

[0028] S2, sound field model generation: using the imported ocean hydrological conditions, the sound speed and propagation loss are obtained using empirical formula, the sound field model is constructed, and the sound speed gradient graph and propagation loss graph are calculated.

[0029] S3, model storage and matching: the system will create a database, and the sound field model generated historically is stored in a hierarchical file structure, including environment description file (such as temperature, depth and salinity data), basic parameter file (such as sound speed profile, propagation loss), and simulation result file (such as sound speed gradient graph and propagation loss graph). When encountering similar ocean hydrological conditions, the existing model can be quickly called from the database, and the efficiency of sound field model generation is improved.

[0030] S4, communication link analysis: combined with the sound speed gradient graph (such as Figure 2 ) and the propagation loss graph (such as Figure 3 ), using the communication link performance analysis software, based on the communication demand (such as transmission rate, reliability), setting the weight parameter, using the multi-objective optimization algorithm (such as particle swarm optimization algorithm or genetic algorithm) to analyze the potential communication link, providing data support for selecting the optimal communication link.

[0031] S5, real-time update and feedback: with the acquisition of new measured data, the system will periodically feedback the link performance data (such as signal-to-noise ratio, packet loss rate), real-time update the sound field model and link selection, to ensure that the system can dynamically adapt to the changes of the ocean environment, and maintain the optimization state of the communication link.

[0032] Compared with the related art, the measured data driven underwater acoustic communication sound field generation method provided by the present application has the following beneficial effects:

[0033] The present application generates sound field model in an automated way by using advanced data processing technology and real-time ocean hydrological data, which has significant advantages compared with traditional sound field generation technology. The present application reduces the need for user to input complex data, and can more accurately reflect the changes of the ocean environment, thereby providing more reliable communication link selection and signal stability prediction.

[0034] In addition, the system can store the sound field model generated each time, including the sound speed gradient and propagation loss graph, so that when encountering similar hydrological conditions, the corresponding sound field model can be quickly generated, thereby significantly improving the efficiency and adaptability of sound field generation.

[0035] The above merely illustrates the embodiments of the present application, and is not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process conversion, or direct or indirect application in other related technical fields, which is made according to the content of the present application, shall be included in the patent protection scope of the present application.

Claims

1. A method for generating underwater acoustic communication sound fields driven by measured data, characterized in that: The following steps are involved: S1. Import of ocean hydrological information: Collect measured ocean hydrological data, including temperature, salinity, depth, and seabed topography, through sensors as supplementary input for the acoustic field simulation module to ensure the accuracy and real-time performance of the acoustic field model; S2. Acoustic field model generation: Using the imported ocean hydrological conditions, a more realistic acoustic field model is constructed, and the sound velocity gradient map and propagation loss map are calculated; S3. Model storage and matching: The generated sound field model is stored. When encountering similar ocean hydrological conditions, the existing model can be quickly called to improve the efficiency of sound field model generation; S4. Communication link analysis: Analyze the communication links using communication link performance analysis software, combining the sound velocity gradient and propagation loss diagram, to analyze potential communication links and provide data support for selecting the optimal communication link. In the communication link analysis step, weight parameters are set based on communication requirements, and a multi-objective optimization algorithm is used to analyze potential communication links. S5. Real-time update and feedback: As new measured data is acquired, the system will update the sound field model and link selection in real time to ensure that the system can dynamically adapt to changes in the marine environment and maintain the optimized state of the communication link.

2. The method for generating underwater acoustic communication sound fields driven by measured data according to claim 1, characterized in that: The generation of the sound field model in S2 adopts the sound ray theory to predict the behavior of the sound wave by tracing the propagation path of the sound wave in the medium.

3. The method for generating underwater acoustic communication sound fields driven by measured data according to claim 1, characterized in that: In the step of generating the sound field model, a Bellhop module is used to calculate the sound velocity gradient map, and a Gaussian beam model is used to calculate the reflection, refraction and scattering of the sound ray in the medium, thereby predicting the propagation path of the sound wave.

4. The method for generating underwater acoustic communication sound fields driven by measured data according to claim 1, wherein: In the model storage and matching step, the system will create a database to store the historically generated sound field models in a hierarchical file structure, including environment description files, basic parameter files and simulation result files.

5. The method for generating underwater acoustic communication sound fields driven by measured data according to claim 1, wherein: In the real-time update and feedback step, the system will regularly feed back link performance data and update the sound field model and link selection in real time.

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