Sonar detection distance evaluation method and system based on OTPA sound source level estimation, and medium

Abstract

The invention discloses a sonar detection distance evaluation method based on OTPA sound source level estimation. Performing multi-factor analysis on the underwater sound wave propagation path based on a transmission path analysis OTPA method, and establishing a sound wave propagation model; state information of the underwater target is monitored in real time; collecting sound wave signal data of the underwater target, constructing a sound wave data matrix, and performing singular value decomposition (SVD) and principal component analysis (PCA) processing to obtain a sound wave signal of the underwater target; and estimating the sound source level of the underwater target, and calculating the detection distance of the underwater target. Through fusion of singular value decomposition and principal component analysis, high-precision estimation of an underwater target sound source level and evaluation of a detected distance of the underwater target sound source level are realized, and the intelligent level and the real-time response capability of a sonar detection system are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of sonar detection, and particularly relates to a sonar detection distance evaluation method and system based on an OTPA sound source level estimation and a storage medium. BACKGROUND

[0002] Most of the existing sonar detection technologies rely on traditional sound source level estimation methods, which usually estimate the sound source level by simulating the test environment and measuring the underwater target using sonar equipment to calculate the detection distance. However, the existing sonar detection technology can provide relatively reliable sound source level estimation results and is suitable for static underwater environments, but has many limitations.

[0003] The traditional method relies on a complex test environment and needs to rely on a specific experimental environment and a large number of field tests, which often requires a large amount of time and resources. Moreover, the calculation efficiency is low, and since it involves complex physical calculations and environmental modeling, the calculation efficiency of the traditional estimation method is low, which cannot meet the real-time requirements.

[0004] The existing technology cannot dynamically monitor the changes of the underwater target, and it is difficult to provide real-time sound source level estimation and detection distance of the target, which limits the accuracy of the sound source level estimation. SUMMARY

[0005] In view of the defects in the prior art, the application provides a sonar detection distance evaluation method and system based on OTPA sound source level estimation and a storage medium, which comprises the following steps:

[0006] Step 1: Based on the transfer path analysis OTPA method, the underwater sound wave propagation path is analyzed in multiple factors, and a sound wave propagation model is established based on the sound source spatial position and environmental influence factors;

[0007] Step 2: Real-time monitoring of the state information of the underwater target, the state information including the speed and direction of the underwater target;

[0008] Step 3: Collecting the sound wave signal data of the underwater target and constructing a sound wave data matrix, performing singular value decomposition (SVD) on the sound wave data matrix, and extracting the main components of the sound wave;

[0009] Step 4: Based on principal component analysis (PCA), the variation direction of the main components of the sound wave is identified, and the sound wave signal of the underwater target after filtering out redundant information is obtained;

[0010] Step 5: According to the sound wave signal of the underwater target, the sound source level of the underwater target is estimated using a model algorithm;

[0011] Step 6: Based on the sound wave propagation model, the detected distance of the underwater target is calculated according to the monitored state information and the estimated sound source level.

[0012] Step 7, continuously performing steps 1 to 6 realizes continuous optimization management of underwater target detection and evaluation.

[0013] Wherein, the propagation loss model of the sound wave in water is constructed when the distance r propagates, which is expressed as:

[0014]

[0015] Wherein, α(r') is the attenuation coefficient, which is expressed as:

[0016]

[0017] Wherein, f i (X i , r') is the i th influencing factor X i The influence function of the sound wave propagation distance, the influencing factor X i At least one of the following: temperature, salinity, reverberation;

[0018] The propagation model of the sound wave is expressed as:

[0019]

[0020] Wherein, P(r, t) is the sound pressure at a distance r, P0 is the initial sound pressure, ω is the angular frequency, k is the wave number, which is expressed as k = 2πf / c, f is the frequency.

[0021] Wherein, the sound wave signal data of the underwater target is collected by multiple sensors to construct a sound wave data matrix, which is expressed as:

[0022]

[0023] Wherein, x ij is the sound wave signal of the j th sensor at the i th time point, m is the number of time points, n is the number of sensors;

[0024] The singular value decomposition of the sound wave data matrix is performed to obtain:

[0025] X = U∑V T

[0026] Wherein, U is an m × m orthogonal matrix, which is a left singular vector, Σ is an m × n diagonal matrix, which contains singular values σ1, σ2, …, σr, r is the rank of X, V is an n × n orthogonal matrix, which is a right singular vector.

[0027] Wherein, according to the result of singular value decomposition, the first k principal components are extracted, which is expressed as:

[0028] P k = U kΣ k

[0029] where Uk is the first k columns of U, representing the left singular vectors of the first k principal components, and Σk is the first k rows and columns of Σ, containing the first k singular values;

[0030] The extracted principal components are denoted as:

[0031] Y k = P k V T

[0032] A threshold τ is set to filter the principal components, retaining the main components and removing redundant information, denoted as:

[0033]

[0034] where H(Σk, τ) is a threshold function, denoted as:

[0035]

[0036] The main components of the acoustic wave of the underwater target are denoted as:

[0037]

[0038] where based on the extracted main components of the acoustic wave of the underwater target, its covariance matrix C is calculated, denoted as:

[0039]

[0040] where m is the number of time points;

[0041] The eigenvalue decomposition of the covariance matrix is performed to obtain the ith eigenvalue λ i and the corresponding eigenvector v i ;

[0042] According to the size of the eigenvalues, the first k eigenvalues and their corresponding eigenvectors are selected, and the total variance is calculated, denoted as:

[0043]

[0044] The proportion of the variance of the first k eigenvalues to the total variance is selected, denoted as:

[0045]

[0046] The variation direction of the main components of the acoustic wave of the underwater target is identified and a variation direction matrix D k is constructed, denoted as:

[0047] D k = [v1, v2,..., vk ]

[0048] Projecting the original data into the principal component space, a signal filtering out redundant information is obtained, and the projected acoustic signal of the underwater target is represented as:

[0049]

[0050] Wherein, the sound source level estimation equation is established based on the acoustic signal of the underwater target, and is represented as:

[0051]

[0052] Wherein, ∈ i is a noise term;

[0053] By minimizing the sum of squares of errors to estimate the sound source level Ls, the objective function J is represented as:

[0054]

[0055] Derivation of the objective function J(Ls) with respect to Ls and solution of Ls are represented as:

[0056]

[0057] Wherein, the sound pressure P(r) at a distance r is represented as:

[0058]

[0059] The definition of the detected distance R refers to the distance at which the sound pressure P(r) reaches a certain threshold Pthreshold, and is represented as:

[0060]

[0061] The present application provides a kind of based on the evaluation system of sonar detection distance of OTPA sound source level estimation, the system includes: model construction module, state information acquisition module, acoustic signal processing module, sound source level estimation module, detected distance estimation module and optimization module;

[0062] Model construction module is used to monitor the state information of underwater target in real time, and the state information includes the speed and direction of the underwater target;

[0063] State information acquisition module is used to obtain the time-frequency characteristics, modulation characteristics and statistical characteristics of the sound source signal based on short-time Fourier transform;

[0064] The sound wave signal processing module is used for collecting sound wave signal data of the underwater target and constructing a sound wave data matrix, performing singular value decomposition (SVD) on the sound wave data matrix, and extracting main components of the sound wave;

[0065] The sound source level estimation module is used for estimating the sound source level of the underwater target by using a model algorithm according to the sound wave signal of the underwater target;

[0066] The detected distance estimation module is used for calculating the detected distance of the underwater target based on the sound wave propagation model, the monitored state information, and the estimated sound source level;

[0067] The optimization module is used for continuously performing steps 1 to 6 to realize continuous optimization management of underwater target detection and evaluation.

[0068] The present application realizes multi-factor analysis of underwater sound wave propagation paths and establishes a sound wave propagation model based on the transfer path analysis (OTPA) method; monitors the state information of the underwater target in real time; collects sound wave signal data of the underwater target and constructs a sound wave data matrix, performs singular value decomposition (SVD) and principal component analysis (PCA) processing to obtain the sound wave signal of the underwater target; estimates the sound source level of the underwater target, and calculates the detection distance of the underwater target. By fusing singular value decomposition and principal component analysis, high-precision estimation of the sound source level of the underwater target is realized, and the detected distance is evaluated, which improves the intelligent level and real-time response capability of the sonar detection system. BRIEF DESCRIPTION OF DRAWINGS

[0069] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will be more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0070] Figure 1 is a flow chart illustrating a sonar detection distance evaluation method based on OTPA sound source level estimation according to an embodiment of the present application;

[0071] Figure 2 is a structural schematic diagram of a sonar detection distance evaluation system based on OTPA sound source level estimation according to an embodiment of the present application. DETAILED DESCRIPTION

[0072] In order to make the objects, technical solutions and advantages of the present application clearer, the following further describes the present application with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0073] The terms used in the embodiments of the present application are only for the purpose of describing particular embodiments and are not intended to limit the present application. The singular forms "a", "an" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. "Plural" generally includes at least two.

[0074] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present application to describe …, these … should not be limited to these terms. These terms are only used to distinguish … from one another. For example, without departing from the scope of the embodiments of the present application, the first … can also be referred to as the second …, and similarly, the second … can also be referred to as the first ….

[0075] It should be understood that the term "and / or" used herein is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are in an "or" relationship.

[0076] Depending on the context, the word "if" as used herein can be interpreted as meaning "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if it is determined" or "if (a stated condition or event) is detected" can be interpreted as meaning "when it is determined" or "in response to determining" or "when (a stated condition or event) is detected" or "in response to detecting (a stated condition or event)".

[0077] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that a product or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such product or device. Without more limitations, the element defined by the sentence "including a …" does not exclude the presence of another identical element in the product or device including the element.

[0078] The existing sonar detection technology usually relies on complex environmental testing and large-scale data processing, has low calculation efficiency and poor adaptability in dynamic underwater environment, and still has certain limitations. Therefore, an effective method is needed to improve the accuracy of sound source level estimation and the real-time performance of detection distance evaluation, so as to solve the problems of low calculation efficiency and poor adaptability in the prior art.

[0079] As shown in Figure 1 , the present application discloses a sonar detection distance evaluation method based on OTPA sound source level estimation, which comprises:

[0080] Step 1, based on the transfer path analysis OTPA method, the underwater sound wave propagation path is analyzed by multiple factors, and the sound wave propagation model is established based on the spatial position of the sound source and the environmental influence factors.

[0081] The transfer path analysis (OTPA) method is used to analyze the underwater sound wave propagation path by multiple factors to establish a sound wave propagation model. The calculation formula of sound velocity is affected by environmental factors such as temperature, salinity and pressure. The integral method is used to establish a propagation loss model, which considers the energy loss caused by attenuation and environmental changes during the propagation of sound waves. The spatial position of the sound source (such as three-dimensional coordinates) can be used to analyze its influence on sound waves.

[0082] By comprehensively considering the influence of various environmental factors, an effective sound velocity model is established, and finally a sound wave propagation model is formed, which describes the change of sound pressure at a certain distance and time. This process lays a solid foundation for subsequent analysis of sound wave characteristics and sound source positioning.

[0083] In an embodiment, a propagation loss model of sound wave propagation in water at a distance r is constructed, which is represented as:

[0084]

[0085] Where, α(r') is the attenuation coefficient, which is represented as:

[0086]

[0087] Where, f i (X i ,r') is the influence function of the i-th influence factor X i on the sound wave propagation distance, and the influence factor X i at least includes one of the following: temperature, salinity, reverberation;

[0088] The propagation model of sound wave is represented as:

[0089]

[0090] where P(r,t) is the sound pressure at distance r, P0 is the initial sound pressure, ω is the angular frequency, k is the wave number, expressed as k = 2πf / c, and f is the frequency.

[0091] The transmission path analysis (OTPA) method is used to analyze the underwater sound wave propagation path with multiple factors. It can comprehensively consider temperature, salinity, pressure and other environmental factors, thus providing more accurate sound speed and propagation loss model, reflecting the complexity of the actual underwater environment.

[0092] By establishing the influence of the spatial position of the sound source, the propagation characteristics of sound waves under different conditions can be described more accurately, thereby improving the accuracy of sound source positioning. In addition, the OTPA method provides a scientific basis for subsequent analysis of sound wave propagation characteristics, signal processing and detection distance calculation through the establishment of mathematical models, enhancing the depth and breadth of underwater acoustic research.

[0093] Step 2, real-time monitoring of the state information of the underwater target, the state information including the speed and direction of the underwater target.

[0094] By deploying sonar systems and inertial measurement units, continuous collection of sound wave signals and sensor data can be achieved to identify and track the dynamic changes of underwater targets. The Doppler effect is used to analyze the changes in sound wave frequency, thereby accurately calculating the speed of the target. Filtering algorithms such as Kalman filtering are applied to process data, reduce noise interference and improve measurement accuracy.

[0095] The monitoring results are presented in a visual form, showing the motion state of the target in real time, and a threshold alarm mechanism is provided to ensure timely response to target dynamics. This process greatly enhances the understanding of the underwater environment and the ability to track targets.

[0096] Through real-time monitoring of the state information of the underwater target, the accuracy and timeliness of dynamic tracking are significantly improved. Combined with sonar systems and inertial measurement units, the speed and direction data of the target can be efficiently collected, reducing the impact of environmental noise on measurement results. In addition, the use of the Doppler effect and filtering algorithms enhances the accuracy of data processing, making the monitoring results more reliable. Real-time visual output and preset threshold alarm mechanism ensure rapid response to potential dangers or abnormal states, thereby improving the safety and efficiency of underwater operations.

[0097] Step 3, collect sound wave signal data of the underwater target and construct a sound wave data matrix, perform singular value decomposition (SVD) on the sound wave data matrix, and extract the main components of the sound wave.

[0098] The acoustic signal data of the underwater target is collected and processed, including capturing the acoustic signal emitted or reflected by the target through sensors, forming an acoustic data matrix containing multiple time points and different sensor data. Singular value decomposition technology is applied to decompose the matrix into three parts: left singular vector matrix, singular value diagonal matrix and right singular vector matrix.

[0099] Extracting principal components can effectively identify important features in acoustic signal data, significantly reducing data dimensionality while retaining the most representative information. Not only does it improve data processing efficiency and accuracy, but it also lays an important foundation for subsequent acoustic signal analysis and target recognition.

[0100] In an embodiment, multiple sensors are used to collect acoustic signal data of the underwater target to construct an acoustic data matrix, denoted as:

[0101]

[0102] where x ij is the acoustic signal of the jth sensor at the ith time point, m is the number of time points, and n is the number of sensors;

[0103] The acoustic data matrix is subjected to singular value decomposition to obtain:

[0104] X = U∑V T

[0105] where U is an m x m orthogonal matrix, is the left singular vector, ∑ is an m x n diagonal matrix containing singular values σ1, σ2, …, σr, r is the rank of X, and V is an n x n orthogonal matrix, is the right singular vector.

[0106] In an embodiment, according to the results of singular value decomposition, the first k principal components are extracted, denoted as:

[0107] P k = U k Σ k

[0108] where Uk is the first k columns of U, representing the left singular vectors of the first k principal components, and Σk is the first k rows and columns of Σ, containing the first k singular values.

[0109] The extracted principal components are denoted as:

[0110] Y k = P k V T

[0111] A threshold τ is set to filter the principal components, retaining the main components and removing redundant information, denoted as:

[0112]

[0113] where H(∑k, τ) is a threshold function, expressed as:

[0114]

[0115] The main components of the underwater target's acoustic wave are expressed as:

[0116]

[0117] By constructing an acoustic wave data matrix and applying singular value decomposition, the main components of the underwater target's acoustic wave signal can be effectively extracted, significantly improving the efficiency and accuracy of data analysis. Through dimensionality reduction processing of the acoustic wave signal, this method can remove redundant information and noise, focusing on the most representative features, making subsequent analysis more concise and clear. In addition, the main components provided by SVD can reveal potential patterns and trends, providing important basis for target recognition and classification.

[0118] Step 4, based on principal component analysis (PCA), identify the variation direction of the main components of the acoustic wave, and obtain the acoustic wave signal of the underwater target after filtering out redundant information.

[0119] The principal component analysis (PCA) technique is used to identify the main component variation direction of the underwater target acoustic wave signal. First, based on the main component data extracted in step 3, the covariance matrix is calculated to evaluate the correlation between different main components. By performing eigenvalue decomposition, the eigenvalues and corresponding eigenvectors are obtained, identifying the main component direction that can best explain the data variation.

[0120] Using these eigenvectors, the original acoustic wave signal is projected into a new principal component space, effectively filtering out redundant information and retaining the most representative signal features. After this processing, the resulting acoustic wave signal is more concise and clear, accurately reflecting the dynamic state of the underwater target, providing a solid data foundation for subsequent target tracking and recognition.

[0121] In one embodiment, based on the extracted main components of the underwater target's acoustic wave, the covariance matrix C is calculated, expressed as:

[0122]

[0123] where m is the number of time points;

[0124] Eigenvalue decomposition is performed on the covariance matrix to obtain the ith eigenvalue λ i and the corresponding eigenvector v i ;

[0125] According to the size of the eigenvalues, the first k eigenvalues and their corresponding eigenvectors are selected, and the total variance is calculated, denoted as:

[0126]

[0127] The proportion of the variance of the first k eigenvalues in the total variance is selected, denoted as:

[0128]

[0129] The variation direction of the main component of the acoustic wave of the underwater target is identified, and a variation direction matrix D is constructed k , denoted as:

[0130] D k = [v1, v2,..., v k ]

[0131] The original data is projected into the main component space to obtain a signal filtered of redundant information, and the projected acoustic signal of the underwater target is obtained, denoted as:

[0132]

[0133] The variation direction of the acoustic wave signal is effectively identified through principal component analysis, thereby significantly improving the data processing efficiency and signal clarity. By filtering out redundant information and retaining the most representative signal features, the analysis result is more accurate.

[0134] Not only does it reduce the data dimension and reduce the computational complexity, but it also improves the accuracy of subsequent signal processing and target recognition. Through in-depth analysis of the acoustic wave signal, the behavior patterns and dynamic changes of the underwater target can be better understood.

[0135] Step 5, according to the acoustic signal of the underwater target, using model algorithm to estimate the sound source level of the underwater target.

[0136] According to the acoustic signal of the underwater target, a model algorithm is used to estimate its sound source level. First, the acoustic signal data processed in the previous steps is used to establish a mathematical model that considers environmental factors, sound propagation loss, target characteristics, and distance variables. Then, through the sound source level calculation formula, the intensity of the acoustic signal is associated with the distance, and the actual collected acoustic data is used for parameter estimation. Using optimization algorithms and iterative methods, the sound source level of the underwater target can be accurately calculated.

[0137] In an embodiment, a sound source level estimation equation is established based on the acoustic signal of the underwater target, denoted as:

[0138]

[0139] where ∈ i is a noise term;

[0140] By minimizing the sum of squared errors, the sound source level Ls is estimated, and the objective function J is expressed as:

[0141]

[0142] The derivative of the objective function J(Ls) with respect to Ls is taken and solved for Ls, expressed as:

[0143]

[0144] By using the model algorithm to accurately estimate the sound source level of the underwater target, this process combines the actual data of the sound wave signal and environmental factors, making the estimation result more reliable and effective. By accurately calculating the sound source level, the acoustic characteristics of the target can be better understood, supporting subsequent sound source positioning and environmental monitoring. In addition, this method improves the efficiency of data analysis and reduces the uncertainty in traditional estimation methods.

[0145] Step 6, based on the sound wave propagation model, according to the monitored state information and the estimated sound source level, the detected distance of the underwater target is calculated.

[0146] Based on the established sound wave propagation model, combined with the monitored state information of the underwater target and the estimated sound source level, the detected distance of the target is calculated. First, using the sound wave propagation model, considering the attenuation and environmental factors such as temperature, salinity and water depth during the propagation of sound waves in water. Then, the monitored target speed and direction information are combined with the sound source level, and the relevant sound wave propagation formula is applied to calculate the distance. Through the integration of these data, the detectability range of the underwater target under specific environmental conditions can be determined.

[0147] In one embodiment, the sound pressure P(r) at a distance r is expressed as:

[0148]

[0149] The detected distance R is defined as the distance at which the sound pressure P(r) reaches a certain threshold Pthreshold, expressed as:

[0150]

[0151] Through the application of the sound wave propagation model, combined with the monitoring state information and the estimation of the sound source level, the detected distance of the underwater target can be accurately calculated. Various environmental factors and target characteristics are considered comprehensively, making the result more reliable and practical. By determining the detected distance, the detectability of the target can be effectively evaluated, the monitoring strategy can be optimized, and the efficiency of monitoring and reconnaissance can be improved.

[0152] Step 7: Continuously perform steps 1 to 6 to achieve continuous optimization management of underwater target detection and evaluation.

[0153] Continuously perform steps 1 to 6 to achieve continuous optimization management of underwater target detection and evaluation. By regularly collecting and analyzing acoustic signal data, dynamically updating the acoustic wave propagation model and sound source level estimation as environmental conditions and target states change. Through continuous iteration and optimization, the monitoring strategy can be adjusted in a timely manner to improve the accuracy and efficiency of detection. In addition, this cyclic management method can adapt to different underwater environments, improve the real-time tracking ability of the target, and ensure effective task execution in complex conditions.

[0154] By updating the acoustic wave propagation model and sound source level estimation in a timely manner, the accuracy and reliability of detection are improved. Through continuous iteration, the system can identify potential anomalies, optimize monitoring strategies, and effectively improve target tracking capabilities.

[0155] The present application analyzes the underwater acoustic wave propagation path based on the transfer path analysis (OTPA) method and establishes an acoustic wave propagation model; real-time monitoring of the state information of the underwater target; collecting acoustic signal data of the underwater target and constructing an acoustic data matrix, performing singular value decomposition (SVD) and principal component analysis (PCA) to obtain the acoustic signal of the underwater target; estimating the sound source level of the underwater target, calculating the detection distance of the underwater target. By fusing singular value decomposition and principal component analysis, high-precision estimation of the sound source level of the underwater target is realized, and its detection distance is evaluated, which improves the intelligent level and real-time response ability of the sonar detection system.

[0156] Corresponding to the sonar detection distance evaluation method based on the sound source level estimation of the OTPA provided by the present application, the present application also provides a sonar detection distance evaluation system based on the sound source level estimation of the OTPA. As shown in Figure 2 The system comprises:

[0157] a model construction module, a state information acquisition module, an acoustic signal processing module, a sound source level estimation module, a detected distance estimation module and an optimization module;

[0158] The model construction module is used for real-time monitoring of the state information of the underwater target, and the state information includes the speed and direction of the underwater target;

[0159] The state information acquisition module is used for obtaining the time-frequency characteristics, modulation characteristics and statistical characteristics of the sound source signal based on short-time Fourier transform;

[0160] The sound wave signal processing module is configured to collect sound wave signal data of the underwater target and construct a sound wave data matrix, perform singular value decomposition (SVD) on the sound wave data matrix, and extract main components of the sound wave;

[0161] The sound source level estimation module is configured to estimate a sound source level of the underwater target using a model algorithm according to the sound wave signal of the underwater target;

[0162] The detected distance estimation module is configured to calculate a detected distance of the underwater target based on the sound wave propagation model, the monitored state information, and the estimated sound source level.

[0163] The optimization module is configured to continuously perform steps 1 to 6 to achieve continuous optimization management of detection and evaluation of the underwater target.

[0164] It should be noted that the computer readable medium described above in the disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of computer readable storage media can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the disclosure, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the disclosure, the computer readable signal medium can include a data signal propagating in a baseband or as a part of a carrier wave, carrying computer readable program code. Such a propagating data signal can take various forms, including but not limited to electromagnetic signals, optical signals or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or apparatus. The program code contained on the computer readable medium can be transmitted by any suitable medium, including but not limited to electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0165] The computer readable medium described above can be contained in the electronic device described above; or can exist separately and not be assembled into the electronic device.

[0166] Computer program code for carrying out operations of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0167] The computer program instructions can also be loaded onto a computer or other programmable information processing apparatus to cause a series of operations to be performed on the computer or other programmable information processing apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable information processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0168] The units described in the embodiments of the present disclosure can be implemented by hardware, software, or a combination of hardware and software. Among them, the name of the unit does not constitute a limitation on the unit itself in some cases.

[0169] The above introduces the preferred embodiment of the present application, which aims to make the spirit of the present application clearer and easier to understand, and is not intended to limit the present application. Any modification, replacement, improvement made within the spirit and principle of the present application shall be included in the protection scope of the appended claims of the present application.

Claims

1. A method for evaluating the detection distance of a sonar based on the estimation of the sound source level of an underwater target using the OTPA method, comprising: Step 1: analyzing the underwater sound wave propagation path based on the OTPA method using the transfer path analysis method, and establishing a sound wave propagation model based on the spatial position of the sound source and environmental factors; Step 2: monitoring the state information of the underwater target in real time, wherein the state information includes the speed and direction of the underwater target; Step 3: collecting the sound wave signal data of the underwater target and constructing a sound wave data matrix, performing singular value decomposition (SVD) on the sound wave data matrix, and extracting the main components of the sound wave; Step 4: identifying the variation direction of the main components of the sound wave based on principal component analysis (PCA), and obtaining the sound wave signal of the underwater target after filtering out redundant information; Step 5: estimating the sound source level of the underwater target based on the sound wave signal of the underwater target using a model algorithm; Step 6: calculating the detected distance of the underwater target based on the sound wave propagation model and the monitored state information and the estimated sound source level; and Step 7: continuously performing steps 1 to 6 to achieve continuous optimization management of the detection and evaluation of the underwater target. 2.The method of claim 1, wherein a propagation loss model of the sound wave in water at a distance r is constructed and expressed as: wherein a is an attenuation coefficient expressed as: a (r') = a0e-a1r', and a0and a1are constants; and a propagation model of the sound wave is expressed as: P (r, t) = P0e-j (k r-ωt), wherein P (r, t) is the sound pressure at a distance r, P0is the initial sound pressure, ω is the angular frequency, k is the wave number expressed as k = 2πf / c, f is the frequency, and c is the speed of sound in water. 3.The method of claim 1, wherein a plurality of sensors are used to collect the sound wave signal data of the underwater target to construct a sound wave data matrix expressed as: X = [x1, x2, …, xm], wherein xi is the sound wave signal data collected by the ith sensor, and m is the number of sensors; and singular value decomposition is performed on the sound wave data matrix to obtain: X = UΣVH, wherein U is an m × m orthogonal matrix, is the left singular vector, Σ is an m × n diagonal matrix containing singular values σ1, σ2, …, σr, r is the rank of X, and V is an n × n orthogonal matrix, is the right singular vector. 4.The method of claim 3, wherein the first k principal components are extracted based on the results of the singular value decomposition and expressed as: UkΣkVH, wherein Uk is the first k columns of U, representing the left singular vector of the first k principal components, and Σk is the first k rows and columns of Σ, containing the first k singular values; and the principal components are extracted and expressed as: Y = UkΣkVH; a threshold value τ is set to filter the principal components, retain the main components, and remove redundant information, and expressed as: Y = H (Σk, τ), wherein H (Σk, τ) is a threshold function expressed as: H (Σk, τ) = {UkΣkVH, if Σk≥τ, 0, otherwise} ; and the main components of the sound wave of the underwater target are expressed as: Y = UkΣkVH. 5.The method of claim 1, wherein the covariance matrix C of the main components of the sound wave of the underwater target is calculated based on the extracted main components and expressed as: C = E (YYH), wherein m is the number of time points; the first k eigenvalues and their corresponding eigenvectors are selected based on the size of the eigenvalues, and the total variance is calculated and expressed as: σ2 = tr (C). ​ ​ ​ ​ ​ ​ wherein f i (X i is the i-th influencing factor X i a function of the sound wave propagation distance, said influencing factor X i at least one of: temperature, salinity, reverberation; ​ ​ ​ ​ wherein x ij is the sound wave signal of the jth sensor at the ith time point, m is the number of time points, and n is the number of sensors. ​ X = U∑V T ​ ​ ​ P k = U k ∑ k ​ ​ Y k = P k V T ​ ​ ​ ​ ​ ​ Eigenvalue decomposition of the covariance matrix yields the ith eigenvalue λ i and the corresponding eigenvector v i ; ​ The proportion of the variance of the first k characteristic values to the total variance is selected, denoted as: identifying a variation direction of a main component of an acoustic wave of the underwater target and constructing a variation direction matrix D k is expressed as: D k = [v1, v2,..., v k ] The original data is projected into the principal component space to obtain a signal filtered of redundant information, and the projected acoustic signal of the underwater target is obtained, denoted as:

6. The method of claim 1, wherein the model is an OTPA model. An acoustic source level estimation equation is established based on the acoustic signal of the underwater target, denoted as: where ∈ i is a noise term; The acoustic source level Ls is estimated by minimizing the sum of squares of errors, and the objective function J is denoted as: The derivative of the objective function J(Ls) with respect to Ls is taken and Ls is solved, denoted as:

7. The method of claim 1, wherein the model is an OTPA model. The sound pressure P(r) at a distance r is denoted as: The detected distance R is defined as the distance at which the sound pressure P(r) reaches a certain threshold Pthreshold, denoted as:

8. An apparatus for evaluating the detection range of a sonar based on the level estimation of an OTPA sound source, comprising: The model construction module, the state information acquisition module, the acoustic signal processing module, the acoustic source level estimation module, the detected distance estimation module, and the optimization module; The model construction module is used to monitor the state information of the underwater target in real time, and the state information includes the speed and direction of the underwater target; The state information acquisition module is used to obtain the time-frequency characteristics, modulation characteristics, and statistical characteristics of the acoustic source signal based on short-time Fourier transform; The acoustic signal processing module is used to collect acoustic signal data of the underwater target and construct an acoustic data matrix, perform singular value decomposition (SVD) on the acoustic data matrix, extract acoustic main components, and identify the variation direction of the acoustic main components based on principal component analysis (PCA) to obtain the acoustic signal of the underwater target filtered of redundant information; The acoustic source level estimation module is used to estimate the acoustic source level of the underwater target using a model algorithm based on the acoustic signal of the underwater target; The detected distance estimation module is used to calculate the detected distance of the underwater target based on the acoustic propagation model and the monitored state information and estimated acoustic source level; The optimization module is used to continuously perform steps 1 to 6 to achieve continuous optimization management of underwater target detection and evaluation.

9. A system for evaluating the detection distance of a sonar based on OTPA acoustic source level estimation, comprising: at least one processor; and at least one memory including computer program code, wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus to perform the method of any one of claims 1-6.

10. A computer-readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement the method of any one of claims 1-6.

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