A method and system for estimating the operating depth of an underwater unmanned submarine vehicle

Abstract

The application discloses a working depth balancing estimation method and system of an underwater unmanned underwater vehicle, and the method comprises the following steps: A, calculating sound velocity gradient data by using temperature, salinity and depth data; B, setting simulation parameters of simulation transmission depth, propagation distance and frequency according to the sound velocity gradient data, and generating a configuration file for storing environmental parameters, transmission parameters and target parameters; C, calculating propagation loss data / datasets by using an acoustic toolbox Kraken and / or an acoustic toolbox Bellhop; and D, calculating the propagation loss data / datasets by using a weighted median algorithm to obtain an estimated value of the working depth. By efficiently utilizing the estimation method of the sound propagation path depth and combining the application of the underwater unmanned underwater vehicle, flexible target detection of multiple regions and multiple types of targets in a complex environment can be realized, and the target detection probability is improved on the basis of the traditional fixed depth arrangement of a hydrophone array for target detection.

Description

Technical Field

[0001] This application relates to underwater acoustic target identification and underwater acoustic signal processing technologies, and in particular to a method and system for equalizing the working depth of an unmanned underwater vehicle. Background Technology

[0002] Unmanned underwater vehicles (UUVs), also known as underwater unmanned motion platforms, are intelligent devices that can autonomously navigate underwater and perform various tasks without the need for human crew, relying on remote or automatic control. Due to their advantages such as strong stealth and long endurance, UUVs are widely used in both civilian and military fields. They can acquire target information by carrying diverse payloads, including acoustic and non-acoustic detection systems, and then combine this information with intelligent algorithms and data processing technology to complete the target differentiation and confirmation process, adapting to complex environments such as marine noise and low visibility.

[0003] Sound wave propagation in the ocean follows the law of sound ray curvature, determined by the temperature, salinity, and pressure gradient of the seawater, forming different sound wave propagation paths, including direct waves, seabed reflected waves, and sea surface reflected waves. Sound propagation loss is related to seawater depth; specifically, sound wave propagation in shallow waters is significantly affected by multiple reflections from the seabed and surface, resulting in high sound energy loss and severe multipath effects. However, in the deep ocean, there exists a minimum sound speed layer where sound waves experience extremely low energy loss, thus enabling propagation over long distances of thousands of kilometers. Deploying an underwater unmanned vehicle (UUV) near the deep-sea acoustic axis can maximize the capture of the acoustic propagation signal of target radiated noise; if it deviates from the acoustic axis, the sound wave energy will rapidly diffuse, significantly reducing the detection range.

[0004] Although detection algorithms are constantly being iterated and updated, with many new detection algorithms offering faster convergence speeds and higher accuracy, finding a depth that can efficiently utilize the sound propagation path is particularly important for complex sea surface, seabed, and topographical environments, as it can significantly improve the probability of target detection. Summary of the Invention

[0005] In view of this, the main objective of this application is to provide a method and system for equalizing the working depth of an underwater unmanned vehicle, which can be applied to complex marine environments to calculate an optimal depth that can efficiently utilize the sound propagation path, thereby providing auxiliary decision-making for the operation of the underwater unmanned vehicle and further improving the probability of target detection.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] A method for estimating the working depth of an underwater unmanned submersible includes the following steps:

[0008] A. Steps for calculating sound velocity gradient data using temperature, salinity, and depth data;

[0009] B. Based on the sound speed gradient data, set simulation parameters for simulated launch depth, propagation distance, and frequency for different environments and targets, and generate a configuration file for storing environmental parameters, launch parameters, and target parameters;

[0010] C. Steps for calculating propagation loss data / datasets using the acoustic toolboxes Kraken and / or Bellhop;

[0011] D. Calculate the propagation loss data / dataset using the weighted median algorithm to obtain an estimate of the working depth.

[0012] Preferably, it also includes:

[0013] The step involves selecting or adjusting the propagation loss calculation results under the corresponding working model based on different situations in step C, in order to obtain the final, better working depth estimate.

[0014] Specifically, in step A, the sound velocity gradient data is calculated using temperature, salinity, and depth data. This involves calculating the sound velocity value c using formula (1).

[0015] (1)

[0016] in: For temperature, Salinity For depth, The calculated speed of sound.

[0017] Wherein: Step B involves setting simulation parameters for simulated launch depth, propagation distance, and frequency based on sound velocity gradient data for different environments and targets, and generating a configuration file for storing environmental parameters, launch parameters, and target parameters, including:

[0018] The steps for setting the launch depth are as follows: In the scenario of marine acoustic detection and countermeasures, the different launch depths are determined by the characteristics of the submersible itself, mission requirements, and marine environmental constraints, based on the working depth of different target types.

[0019] The steps for setting the propagation distance are as follows: Referencing the correlation between the distance of interest centered on the underwater unmanned vehicle and the launch depth, set the propagation distance parameters; and...

[0020] The steps for setting frequencies are as follows: different types of frequencies are set according to the type of surface ship and its electrical system.

[0021] Furthermore, it also includes:

[0022] The calculated sound speed data and standard parameter settings are combined to form an .env file for calculating propagation loss.

[0023] Specifically, propagation loss calculations are performed using the Kraken and / or Bellhop acoustic toolkits, including:

[0024] Using Kraken, an acoustic toolkit based on wave acoustics theory, the sound wave channel consisting of seawater, seabed, and sea surface in the ocean is regarded as a layered waveguide. The propagation of sound waves in the waveguide is decomposed into several independent simple sine waves, and the total sound field is the superposition of all simple sine waves. Each simple sine wave corresponds to a specific propagation mode, cutoff frequency, and attenuation coefficient, making it suitable for deep-sea environments.

[0025] Using the Bellhop acoustic toolbox based on geometric acoustics theory, sound waves are treated as discrete rays. By tracing the reflection and refraction paths of the rays at the sound speed gradient, sea surface, and seabed, the superposition of sound intensity at the receiving point is calculated, which is suitable for target detection.

[0026] After obtaining the .env file with the above settings parameters, run the acoustic toolboxes Kraken and Bellhop respectively to obtain .shd files calculated by the two working models. The .shd file contains the propagation loss calculated by the working model. By parsing the .shd file, the propagation loss data / datasets calculated by the two working models can be obtained.

[0027] Specifically, step D involves using the weighted median algorithm to calculate the propagation loss data / dataset to obtain an estimate of the working depth. The specific process includes:

[0028] D1: Based on the propagation loss dataset calculated in step C Corresponding weight level ;

[0029] D2: Sort the dataset and weights to obtain the sorted dataset. The corresponding weighted sorting is ;

[0030] The total weight W is calculated using formula (2):

[0031] (2)

[0032] The cumulative weight S is calculated using formula (3). k The cumulative weights of the sorted data are obtained by calculating them sequentially:

[0033] (3)

[0034] D3: Determine the position of the weighted median and find the smallest positive integer. , making ,at this time:

[0035] like and The weighted median is Otherwise, the weighted median is .

[0036] After step D, the process also includes: selecting or adjusting the results of the corresponding model according to different situations to obtain the final, better working depth estimate.

[0037] A system for estimating the operating depth of an underwater unmanned submersible includes: a temperature, salinity, and depth data processing subsystem, a simulation parameter setting subsystem, a propagation loss calculation subsystem, and a weighted median calculation subsystem; wherein:

[0038] The temperature, salinity, and depth data processing subsystem obtains sound velocity gradient data by performing preliminary processing / calculation on temperature T, salinity S, and depth z data.

[0039] The simulation parameter setting subsystem is used to set simulation parameters such as simulated emission depth and frequency for different environments and targets based on sound velocity gradient data, in order to generate .env files;

[0040] The propagation loss calculation subsystem is used to set / select different working models corresponding to various acoustic wave propagation theories based on different marine environments and detection scenarios, and to obtain the propagation loss dataset for the corresponding model; and,

[0041] The weighted median calculation subsystem uses the weighted median algorithm to calculate the propagation loss dataset and obtain the corresponding estimated working depth of the underwater unmanned submersible.

[0042] Preferably, it also includes:

[0043] The working depth optimization subsystem is used to select or adjust the results of the corresponding model according to different situations in order to obtain the final better working depth estimate.

[0044] The underwater unmanned submersible's working depth equalization estimation method and system of the present invention have the following beneficial effects compared with the prior art:

[0045] (1) Compared with traditional target detection, which mainly relies on deploying hydrophone arrays at a fixed depth without using technical means to evaluate and measure the deployment depth, the working depth equalization estimation method of this invention can adjust the deployment depth of hydrophones according to different targets based on traditional target detection, thereby improving the target detection probability.

[0046] (2) Combined with an autonomous and flexible underwater unmanned platform, the depth of the underwater unmanned vehicle can be changed in real time according to different targets and different environments. At the same time, the depth of the detection hydrophone carried by the underwater unmanned vehicle also changes accordingly, which can realize flexible target detection of multiple areas and multiple types of targets in complex environments and improve the target detection probability. Attached Figure Description

[0047] Figure 1 This is a flowchart illustrating the working depth equalization estimation method adapted for detection-type underwater unmanned submersibles in an embodiment of the present invention.

[0048] Figure 2 This is a schematic diagram of the sound velocity gradient calculation results in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of the Kraken propagation loss calculation results in an embodiment of the present invention;

[0050] Figure 4 This is a schematic diagram of the Bellhop propagation loss results in an embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram illustrating the weighted median calculation result based on Kraken propagation loss in an embodiment of the present invention;

[0052] Figure 6 This is a schematic diagram illustrating the weighted median calculation result based on Bellhop propagation loss in an embodiment of the present invention;

[0053] Figure 7 This is a block diagram of the working depth equalization estimation system for an underwater unmanned submersible according to an embodiment of the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0055] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0056] Figure 1 This is a flowchart illustrating the working depth equalization estimation method adapted for detection-type underwater unmanned submersibles in an embodiment of the present invention.

[0057] This invention, in its embodiments, uses temperature, salinity, and depth data collected by a Conductivity Temperature Depth (CTD) system and underwater acoustic toolkits (Kraken and Bellhop) to calculate propagation loss, estimating the optimal operating depth for detection-type underwater vehicles equipped with hydrophone arrays. This method can be applied to various scenarios, including shallow and deep seas. Depending on the target of interest (various surface vessels or underwater targets), different optimal operating depths for the underwater vehicle can be calculated, thereby improving the target detection probability.

[0058] like Figure 1 As shown, this is a method for estimating the working depth of the underwater unmanned submersible.

[0059] Step 11: The steps to calculate the sound velocity gradient data using temperature T, salinity S, and depth z data.

[0060] In this embodiment of the invention, temperature, salinity, and depth data are core parameters used to describe the physical properties of seawater, and the speed of sound in seawater is highly correlated with these three parameters. Among them, temperature T dominates the change in speed of sound, followed by salinity S, and depth z (pressure) has a more significant impact in the deep sea. In addition, the core of calculating the speed of sound in seawater is based on an empirical formula for temperature T, salinity S, and depth z, and the internationally accepted high-precision formula is the Chen-Millero formula. In this embodiment of the invention, the following formula (1) is used to calculate the speed of sound:

[0061] (1)

[0062] in: For temperature, Salinity For depth, The calculated speed of sound.

[0063] Step 12: Based on the sound velocity gradient data, set parameters such as simulated launch depth, propagation distance, and frequency for different environments and targets to generate configuration files for storing environmental, launch, and target parameters, such as .env files.

[0064] In marine acoustic simulation, the .env file used to store environmental parameters, emission parameters, and target parameter configurations can be directly read and called by the simulation program. Considering the layered characteristics of sound velocity gradients, the file structure needs to be designed according to the principles of ocean current environment layering, emission parameter matching, and target characteristic adaptation.

[0065] In this embodiment of the invention, the parameters that need to be set in the .env file are the emission depth, propagation distance, and frequency; the remaining parameters are set according to conventional methods. The specific setting process is as follows:

[0066] Firstly, regarding the setting of launch depth, in marine acoustic detection and countermeasure scenarios, the operating depth for different target types is determined by the characteristics of the underwater vehicle itself, mission requirements, and marine environmental constraints. Their depth ranges differ fundamentally, and the choice of depth directly affects the efficiency and stealth of acoustic detection. For example, the core characteristic of a surface vessel is that its main body is above the waterline, and its draft is the vertical height of its underwater portion. When the target is a small speedboat / patrol boat, the depth can be set to 0.5m–2.0m; if the target is a large ship, the depth can be set to 3.5m–15m. The depth setting range for various underwater targets is relatively wide. For example, for portable UUVs, it can be set to approximately 0–100m; for large autonomous underwater vehicles, the depth can be set to approximately 0–1000m. Therefore, an appropriate depth value must be selected based on the different types of targets.

[0067] Secondly, the setting of the propagation distance needs to consider the distance of interest centered on the underwater unmanned vehicle, and is also related to the launch depth. If it is a surface vessel with a high noise level, a larger propagation distance can be considered, such as 10km to 15km. If it is an underwater target with a concealed noise level, the propagation distance can be adjusted appropriately according to the situation.

[0068] Thirdly, the frequency setting also needs to consider different types of frequencies. For example, surface ships have different electrical systems due to their different origins. For instance, the electrical system of commonly used ships is 50Hz, while that of ships from other countries is 60Hz. At the same time, the diesel engines of surface ships have inherent characteristics on certain frequencies, and the same applies to underwater targets.

[0069] Therefore, in the embodiments of the present invention, the application needs to comprehensively consider the type of target being detected and set the above three parameters in conjunction with the actual situation. Finally, the calculated sound speed data and conventional parameter settings are combined to form an .env file, which is used to calculate the propagation loss mentioned in step 13.

[0070] Step 13: Calculate the propagation loss using the acoustic toolbox Kraken and / or Bellhop.

[0071] In this embodiment of the invention, the acoustic toolkits Kraken and Bellhop are based on different acoustic propagation theories and are suitable for different marine environments and detection scenarios. Wherein:

[0072] Kraken, an acoustic toolkit, is based on wave acoustics theory. It treats the ocean as a layered waveguide, i.e., a sound wave channel consisting of seawater, seabed, and sea surface. The propagation of sound waves within the waveguide can be decomposed into several independent simple sine waves, and the total sound field is the superposition of all simple sine waves. Each simple sine wave corresponds to a specific propagation mode, cutoff frequency, and attenuation coefficient, making it suitable for deep-sea environments.

[0073] The Bellhop acoustic toolbox, based on geometric acoustics theory, treats sound waves as discrete rays. By tracing the reflection and refraction paths of these rays at the sound velocity gradient, sea surface, and seabed, it calculates the superposition of sound intensity at the receiving point, making it more suitable for target detection in a wider range of environments.

[0074] After obtaining the .env file with the above settings parameters, run the acoustic toolboxes Kraken and Bellhop respectively to obtain two .shd files calculated by the working models. The .shd file contains the propagation loss calculated by the working models. By parsing the .shd file, you can obtain the propagation loss data (dataset) calculated by the two working models.

[0075] Step 14: Calculate the propagation loss data / dataset using the weighted median algorithm to obtain an estimate of the working depth.

[0076] The propagation loss dataset obtained through step 13 above Corresponding weight level .

[0077] First, sort the dataset and its weights to obtain the sorted dataset. The corresponding weighted sorting is .

[0078] The total weight W is calculated using the following formula (2):

[0079] (2)

[0080] The cumulative weight S is calculated using formula (3). k The cumulative weights of the sorted data are obtained by calculating them sequentially:

[0081] (3)

[0082] Determine the position of the weighted median:

[0083] Find the smallest positive integer , making ,at this time:

[0084] like and The weighted median is Otherwise, the weighted median is .

[0085] Preferably, it also includes:

[0086] Step 15: Select or adjust the propagation loss calculation results under the corresponding working model according to the different situations in Step 13, so as to obtain the final better working depth estimate.

[0087] In this embodiment of the invention, since the two types of models corresponding to the Bellhop and Kraken acoustic toolboxes have different underlying acoustic theories, they are suitable for different scenarios. When different parameters (frequency and depth as mentioned above) are set, the sound propagation calculation accuracy of the two types of models also differs. Therefore, it is necessary to reasonably select the model for result calculation according to the different parameters.

[0088] For the Bellhop method, there is no strict assumption of horizontal invariance, it can handle environments with varying horizontality, the calculation is intuitive, the sound field structure is easy to understand, the wavelength of high-frequency sound waves is short, the geometric acoustics assumption holds, and the ray method can efficiently describe its propagation path. However, in low-frequency scenarios, interference and diffraction effects are ignored, resulting in insufficient accuracy and inability to accurately describe the diffraction energy of the sound shadow region. It is suitable for sound propagation calculations in high-frequency and most environments.

[0089] For Kraken, low-frequency sound waves exhibit strong waveguide effects in shallow or deep sea acoustic channels, with clear normal modes, limited number, high computational efficiency and accuracy, and accurate description of interference phenomena. However, it cannot handle horizontally changing environments (such as seabed topography and ocean currents), and the computational load in high-frequency scenarios is very large. It is suitable for low-frequency and horizontally constant environments.

[0090] In summary, different detection targets have different frequency lines and working depths, and the appropriate model should be selected based on the different characteristics of the target.

[0091] Figure 2 This is a schematic diagram of the sound velocity gradient calculation results in an embodiment of the present invention.

[0092] from Figure 2The sound speed-depth relationship shown indicates that the sound speed monotonically decreases with increasing ocean depth, decreasing by approximately 60 m / s for every 900 meters of depth, exhibiting a typical negative gradient distribution. This negative gradient distribution causes sound rays to bend towards the sea surface. This easily creates surface acoustic channels, where sound rays repeatedly reflect between the sea surface and a certain depth, enabling long-distance propagation. However, excessive bending of the sound rays can also create a sound shadow zone, thus affecting detection and communication performance.

[0093] Figure 3 This is a schematic diagram of the Kraken propagation loss calculation results in an embodiment of the present invention.

[0094] like Figure 3 As shown in the schematic diagram of the Kraken propagation loss calculation results, the sound source is set at a depth of approximately 200m and a horizontal distance of 0km (i.e., the red bright spot) as the emission point of the sound signal. In the initial stage (horizontal distance 0-1km), the sound energy is concentrated in the depth range of 200-400m, indicating that this depth layer is the main initial propagation channel of the sound signal. The sound ray generally shows an upward bending trend, which is consistent with... Figure 2 This is consistent with the negative gradient sound speed distribution. In a negative gradient environment, the sound speed decreases with increasing depth, and the sound rays bend towards the deeper sea where the sound speed is lower. At the same time, they are affected by reflection from the sea surface, forming a propagation path that bends repeatedly upwards.

[0095] Figure 4 This is a schematic diagram of the Bellhop propagation loss results in an embodiment of the present invention.

[0096] like Figure 4 As shown in the schematic diagram of the Bellhop propagation loss results, it can be seen that the sound propagation path and energy loss, etc., are related to... Figure 3 The Kraken calculation results are similar, but compared... Figure 3 The propagation loss results calculated by Kraken show clearer details of the sound ray propagation path and energy distribution. This is because Bellhop, based on ray theory, is better at showing the geometric propagation path and multipath effects of sound rays, while Kraken is better at showing modal interference and energy distribution in waveguides, making it suitable for analyzing the modal structure of long-distance propagation. Although the two models differ, under the same parameters, both verify the core propagation characteristics of upward bending of sound rays, surface channel propagation, and the existence of a sound shadow region in a negative gradient environment. This invention, through a direct comparison of the propagation loss results of the two models, allows for a better understanding of sound propagation in specific environments.

[0097] Figure 5 This is a schematic diagram illustrating the weighted median calculation result based on Kraken propagation loss in an embodiment of the present invention.

[0098] like Figure 5As shown, the weighted median calculation results based on Kraken propagation loss indicate that the globally optimal receiving depth obtained by the weighted median method is approximately 581.48m. This depth represents the optimal choice for balancing energy reception stability and attenuation after considering the propagation losses of multiple acoustic propagation paths. In practical underwater acoustic communication or detection, it can be used as the preferred deployment depth for receiving arrays.

[0099] Figure 6 This is a schematic diagram illustrating the weighted median calculation result based on Bellhop propagation loss in an embodiment of the present invention.

[0100] from Figure 6 The diagram showing the weighted median calculation results illustrates that, under the same sound velocity profile data and parameter inputs, the calculated results are comparable to... Figure 5 There are discrepancies in the weighted median calculation results based on Kraken propagation loss. The two models have different underlying theories and are applicable to different environments. Therefore, they can be used as a reference for comparative analysis to select an appropriate depth of work.

[0101] Figure 7 This is a block diagram of the working depth equalization estimation system for an underwater unmanned submersible according to an embodiment of the present invention.

[0102] like Figure 7 As shown, the operating depth equilibrium estimation system for this underwater unmanned submersible mainly includes: a temperature, salinity, and depth data processing subsystem, a simulation parameter setting subsystem, a propagation loss calculation subsystem, and a weighted median calculation subsystem. Among them:

[0103] The temperature, salinity, and depth data processing subsystem obtains sound velocity gradient data, i.e., sound velocity profile data, by performing preliminary processing / calculation on temperature (T), salinity (S), and depth (Z) data. Specifically, the corresponding sound velocity value is calculated using formula (1).

[0104] The simulation parameter setting subsystem is used to set parameters such as simulated launch depth and frequency for different environments and targets based on sound speed gradient data, in order to generate a .env file.

[0105] The propagation loss calculation subsystem is used to set / select different working models corresponding to different sound wave propagation theories based on different marine environments and detection scenarios, and to calculate the propagation loss dataset for the corresponding model.

[0106] The weighted median calculation subsystem uses a weighted median algorithm to calculate the propagation loss dataset and obtain the corresponding estimated working depth of the underwater unmanned submersible.

[0107] Furthermore, it also includes:

[0108] Working Depth Optimization Subsystem: Used to select or adjust the results under the corresponding working model according to different situations in order to obtain the final better working depth estimate.

[0109] In summary, the working depth equalization estimation method and system for adaptive detection-type underwater unmanned underwater vehicles provided by this invention, by combining autonomous and flexible detection-type underwater unmanned underwater vehicles, can achieve flexible target detection of multiple regions and multiple types of targets in complex and ever-changing marine environments.

[0110] First, sound velocity profile data is obtained by preliminary processing of the temperature, salinity, and depth data collected by the underwater unmanned vehicle. Then, based on the target category of interest and marine environmental conditions, three parameters—launch depth, propagation distance, and frequency—are set, forming a .env file. Propagation loss is then calculated using the Kraken and Bellhop acoustic model toolboxes, resulting in a .shd file. Finally, the estimated working depth is calculated using the weighted median method after parsing the .shd file. Simultaneously, the results from the two models are selected or adjusted according to different situations to obtain the final optimal depth estimate.

[0111] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for estimating the working depth of an unmanned underwater vehicle, characterized in that, include: A. Steps for calculating sound velocity gradient data using temperature, salinity, and depth data; B. Based on the sound speed gradient data, set simulation parameters for simulated launch depth, propagation distance, and frequency for different environments and targets, and generate a configuration file for storing environmental parameters, launch parameters, and target parameters; C. Steps for calculating propagation loss data / datasets using the acoustic toolboxes Kraken and / or Bellhop; D. Calculate the propagation loss data / dataset using the weighted median algorithm to obtain an estimate of the working depth.

2. The method for estimating the working depth of an underwater unmanned submersible according to claim 1, characterized in that, Also includes: The step involves selecting or adjusting the propagation loss calculation results under the corresponding working model based on different situations in step C, in order to obtain the final, better working depth estimate.

3. The method for estimating the working depth of an underwater unmanned submersible according to claim 1, characterized in that, In step A, the sound velocity gradient data is calculated using temperature, salinity, and depth data. Specifically, the sound velocity value c is calculated using formula (1): （1） in: For temperature, Salinity For depth, The calculated speed of sound.

4. The method for estimating the working depth of an underwater unmanned submersible according to claim 1, characterized in that, Step B involves setting simulation parameters for launch depth, propagation distance, and frequency based on sound velocity gradient data, tailored to different environments and targets, and generating a configuration file for storing environmental, launch, and target parameters, including: The steps for setting the launch depth are as follows: In the scenario of marine acoustic detection and countermeasures, the different launch depths are determined by the characteristics of the submersible itself, mission requirements, and marine environmental constraints, based on the working depth of different target types. The steps for setting the propagation distance are as follows: Referencing the correlation between the distance of interest centered on the underwater unmanned vehicle and the launch depth, set the propagation distance parameters; and... The steps for setting frequencies are as follows: different types of frequencies are set according to the type of surface ship and its electrical system.

5. The method for estimating the working depth of an underwater unmanned submersible according to claim 4, characterized in that, Also includes: The calculated sound speed data and standard parameter settings are combined to form an .env file for calculating propagation loss.

6. The method for estimating the working depth of an underwater unmanned submersible according to claim 1 or 5, characterized in that, Propagation loss calculations are performed using the Kraken and / or Bellhop acoustic toolkits, including: Using Kraken, an acoustic toolkit based on wave acoustics theory, the sound wave channel consisting of seawater, seabed, and sea surface in the ocean is regarded as a layered waveguide. The propagation of sound waves in the waveguide is decomposed into several independent simple sine waves, and the total sound field is the superposition of all simple sine waves. Each simple sine wave corresponds to a specific propagation mode, cutoff frequency, and attenuation coefficient, making it suitable for deep-sea environments. Using the Bellhop acoustic toolbox based on geometric acoustics theory, sound waves are treated as discrete rays. By tracing the reflection and refraction paths of the rays at the sound speed gradient, sea surface, and seabed, the superposition of sound intensity at the receiving point is calculated, which is suitable for target detection. After obtaining the .env file with the above settings parameters, run the acoustic toolboxes Kraken and Bellhop respectively to obtain .shd files calculated by the two working models. The .shd file contains the propagation loss calculated by the working model. By parsing the .shd file, the propagation loss data / datasets calculated by the two working models can be obtained.

7. The method for estimating the working depth of an underwater unmanned submersible according to claim 1, characterized in that, Step D involves using the weighted median algorithm to calculate the propagation loss data / dataset to obtain an estimate of the working depth. The specific process includes: D1: Based on the propagation loss dataset calculated in step C Corresponding weight level ; D2: Sort the dataset and weights to obtain the sorted dataset. The corresponding weighted sorting is ; The total weight W is calculated using formula (2): （2） The cumulative weight S is calculated using formula (3). k The cumulative weights of the sorted data are obtained by calculating them sequentially: （3） D3: Determine the position of the weighted median and find the smallest positive integer. , making ,at this time: like and The weighted median is Otherwise, the weighted median is .

8. The method for estimating the working depth of an underwater unmanned submersible according to claim 1, characterized in that, After step D, the process also includes: selecting or adjusting the results of the corresponding model according to different situations to obtain the final, better working depth estimate.

9. A system for estimating the working depth of an underwater unmanned submersible, characterized in that, include: The system comprises a temperature, salinity, and depth data processing subsystem, a simulation parameter setting subsystem, a propagation loss calculation subsystem, and a weighted median calculation subsystem; among which: The temperature, salinity, and depth data processing subsystem obtains sound velocity gradient data by performing preliminary processing / calculation on temperature T, salinity S, and depth z data. The simulation parameter setting subsystem is used to set simulation parameters such as simulated emission depth and frequency for different environments and targets based on sound velocity gradient data, in order to generate .env files; The propagation loss calculation subsystem is used to set / select different working models corresponding to various acoustic wave propagation theories based on different marine environments and detection scenarios, and to obtain the propagation loss dataset for the corresponding model; and, The weighted median calculation subsystem uses the weighted median algorithm to calculate the propagation loss dataset and obtain the corresponding estimated working depth of the underwater unmanned submersible.

10. The underwater unmanned submersible working depth equalization estimation system according to claim 9, characterized in that, Also includes: The working depth optimization subsystem is used to select or adjust the results of the corresponding model according to different situations in order to obtain the final better working depth estimate.

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