A method and device for estimating the azimuth angle of underwater acoustic targets

Abstract

The present application proposes a method and device for estimating the azimuth of an underwater acoustic target. The method includes: for the received signal matrix of each frequency band, the omnidirectional is divided into multiple grids, and the signal energy distribution parameters and the environmental noise energy are initialized; by minimizing the expected square error between the signal matrix and the beamforming data, the beamforming matrix and the environmental noise energy are updated, and the signal energy distribution parameters are updated using the fixed point iteration method; the update process of the signal energy distribution parameters is repeated until the iteration of the signal energy distribution parameters converges or stops when the maximum number of iterations is reached, and the target signal energy distribution estimation of each frequency band is obtained; the target signal energy distribution estimation of each frequency band is fused to obtain the final output energy. The beamforming matrix and the beam energy output are updated only in some iterations, which has efficient computing performance. In addition, the update of the beamforming matrix enables the method to adaptively discover strong interference sectors, thereby suppressing strong interference.

Description

Technical Field

[0001] The present application relates to the field of underwater sonar signal processing, and in particular to a method and device for estimating the azimuth of an underwater acoustic target. Background Art

[0002] Underwater target detection based on acoustic waves is an important means of marine application fields such as environmental perception, ocean monitoring, and intelligence collection. Passive sonar estimates the target azimuth (DOA) by observing the acoustic radiation noise in the surrounding environment. However, with the development of acoustic stealth noise reduction technology, the radiation noise level of many key targets is relatively low. Strong interference such as ships, active detection signals, and communication signals in the ocean also have a serious impact on the accuracy of azimuth estimation of weak targets, and may even cause weak targets to be undetectable. Therefore, azimuth estimation in a strong interference environment is a challenging problem.

[0003] Azimuth estimation methods in strong interference environments can be roughly divided into two categories. The first category is based on beamforming technology, which aims to achieve high gain, low sidelobes, and controllable beamwidth (directivity) to suppress the impact of strong interference sources. Typical examples include the minimum variance distortionless response (MVDR) method and the characteristic analysis based adaptive interference suppression (EAAIS) method. However, beamforming methods require a sufficient number of snapshots to achieve high resolution.

[0004] The second type of method is based on the linear representation model of the data and uses optimization techniques to restore the signal energy in all directions to achieve azimuth estimation. Typical algorithms include the single spectrum separation (SpSF) method based on the maximum entropy criterion, the least squares multi-beam resolution (MBM-SR), and the sparse Bayesian learning (SBL) method based on beam power output (BPO) (SBL-BPO). Among these methods, the beam energy-based method represented by SBL-BPO establishes an optimization model in the beam output domain rather than the original received data domain. Since the beamformer can effectively suppress strong interference and the dimension of the beam energy output is significantly lower than the dimension of the original data, the beam energy-based method has the advantages of both interference suppression and high computational efficiency. However, the existing methods require the known azimuth sector of the strong interference / target to construct the optimization model or beamforming matrix, which is difficult to predict in advance in practical applications. In addition, the single spectrum separation (SpSF) and least squares multi-beam resolution (MBM-SR) methods based on the maximum entropy criterion have high computational complexity due to the need to solve convex optimization problems, and are difficult to apply on underwater processing platforms with limited computing resources.

[0005] In addition, some high-resolution azimuth estimation methods also have high estimation accuracy in strong interference environments. For example, the sparse Bayesian learning method (SBL) can achieve weak signal detection and azimuth estimation even at a low signal-to-noise ratio, but it requires multiple iterations, and each iteration requires the calculation of matrix inversion, which is relatively complex.

[0006] In actual tasks, it is difficult to obtain the target and strong interference sectors in advance, and the above methods to combat strong interference all require the target sector as a priori input, which is difficult to meet the needs of actual applications. In addition, underwater platforms are limited by volume and power supply, and usually have limited computing resources, making the above high-complexity algorithms difficult to deploy. Summary of the invention

[0007] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0008] To this end, the first purpose of the present application is to propose a method for estimating the azimuth of an underwater acoustic target, so as to achieve rapid azimuth estimation of an underwater acoustic target under conditions of unknown target sectors and strong interference.

[0009] The second objective of the present application is to provide a device for estimating the azimuth of an underwater acoustic target.

[0010] The third objective of the present application is to provide an electronic device.

[0011] A fourth objective of the present application is to provide a computer-readable storage medium.

[0012] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a method for estimating the azimuth of an underwater acoustic target, comprising:

[0013] For each frequency band’s received signal matrix, the omnidirectional area is divided into multiple grids, and the signal energy distribution parameters and environmental noise energy are initialized;

[0014] By minimizing the expected square error between the signal matrix and the beamforming data, the beamforming matrix and the ambient noise energy are updated, and the signal energy distribution parameters are updated using a fixed point iteration method;

[0015] Repeating the updating process of the signal energy distribution parameter until the iteration of the signal energy distribution parameter converges or reaches a maximum number of iterations, stopping the iteration process, and obtaining an estimate of the target signal energy distribution in each frequency band;

[0016] The target signal energy distribution estimates of each frequency band are fused to obtain the final output energy.

[0017] Optionally, also include:

[0018] Get the received signal;

[0019] Perform Fourier transform on the received signal to obtain Received signal matrix of frequency bands ,in represents the number of elements in the hydrophone array, Indicates the number of snapshots per data frame.

[0020] Optionally, divide the full range into grid, remember is the signal energy distribution parameter, The element represents the The signal energy in the grid direction is 0 if there is no signal. is the environmental noise energy;

[0021] Will Initialize to ,Will Initialize to receive signal matrix The variance of is expressed as:

[0022]

[0023]

[0024] in, It means extracting diagonal elements. Indicates the variance, superscript represents the conjugate transpose.

[0025] Optionally, updating the beamforming matrix and the ambient noise energy by minimizing the expected square error between the signal matrix and the beamforming data includes:

[0026] Record The beamforming matrix for each frequency band is , based on the principle of updating the beamforming matrix to minimize the signal matrix With beamforming data The expected square error between them is used to express the optimization problem, and the expression is:

[0027]

[0028] in, It means to find the expected square error;

[0029] For the traditional receiving signal model , assuming the signal The mean is 0 and the covariance is Complex Gaussian distribution, noise The mean is 0 and the covariance is The optimal solution of the optimization problem is calculated by using the complex Gaussian distribution:

[0030]

[0031] Will and Use its current estimated value and Instead, we get the beamforming matrix The update formula is:

[0032]

[0033] Among them, when ,renew , otherwise it will not be updated. for Last updated The estimated value of represents the number of iterations, is the first threshold;

[0034] When the beamforming matrix When updated, the ambient noise energy is realized by calculating the posterior expectation The update expression is:

[0035]

[0036] in, , Represents the trace of the matrix.

[0037] Optionally, the adopting a fixed point iteration method to update the signal energy distribution parameter includes:

[0038]

[0039] in

[0040]

[0041]

[0042]

[0043] in, It means element-wise multiplication of two matrices. Represents the conjugate of a matrix.

[0044] Optionally, repeating the updating process of the signal energy distribution parameter until the iteration of the signal energy distribution parameter converges or reaches a maximum number of iterations, stopping the iteration process, and obtaining the target signal energy distribution estimate of each frequency band includes:

[0045] when or Reached the maximum number of iterations Stop the iteration when , and get the target signal energy distribution estimation of each frequency band ,in, express No. elements, is the second threshold.

[0046] Optionally, the estimation and fusion of target signal energy distributions of each frequency band to obtain final output energy includes:

[0047] .

[0048] in, is the final output energy, is the total number of frequency bands.

[0049] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a device for estimating the azimuth of an underwater acoustic target, comprising:

[0050] Initialization module, for the received signal matrix of each frequency band, is used to divide the omnidirectional into multiple grids, and initialize the signal energy distribution parameters and environmental noise energy;

[0051] An iterative update module, used for updating the beamforming matrix and the ambient noise energy by minimizing the expected square error between the signal matrix and the beamforming data, and updating the signal energy distribution parameters by using a fixed point iteration method;

[0052] An iterative output module, used to repeat the updating process of the signal energy distribution parameter until the iteration of the signal energy distribution parameter converges or reaches a maximum number of iterations, stop the iteration process, and obtain the target signal energy distribution estimation of each frequency band;

[0053] The fusion module is used to fuse the target signal energy distribution estimates of each frequency band to obtain the final output energy.

[0054] To achieve the above-mentioned purpose, the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0055] The memory stores computer-executable instructions;

[0056] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects above.

[0057] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a computer-readable storage medium, in which computer execution instructions are stored. When the computer execution instructions are executed by a processor, they are used to implement the method as described in any one of the above-mentioned first aspects.

[0058] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0059] 1. Low computational complexity. The beamforming matrix and beam energy output are updated only in some iterations in this application, which has the effect of reducing computational complexity. This condition is met in most application scenarios.

[0060] 2. Strong interference suppression is achieved in the scenario of unknown target sector. The method in this application does not require the target sector to be known, but adaptively updates the beamforming matrix based on the current signal energy distribution estimate, thereby suppressing the impact of strong interference on weak target azimuth estimation, and improving the accuracy of weak target azimuth estimation under low signal-to-interference-noise ratio conditions when the target sector is unknown.

[0061] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0063] Figure 1 is a flow chart of a method for estimating the azimuth of an underwater acoustic target according to an embodiment of the present application;

[0064] Figure 2 is an algorithm flow chart of a method for estimating the azimuth of an underwater acoustic target according to an embodiment of the present application;

[0065] Figure 3 is a graph of the root mean square error of azimuth estimation of each algorithm under different signal-to-noise ratios shown in an embodiment of the present application;

[0066] Figure 4 is a graph of root mean square errors of azimuth estimation of various algorithms under different signal-to-interference ratios according to an embodiment of the present application;

[0067] Figure 5 is a block diagram of a device for estimating the azimuth of an underwater acoustic target according to an embodiment of the present application;

[0068] Figure 6 It is a block diagram of an electronic device. DETAILED DESCRIPTION

[0069] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0070] A method and device for estimating the azimuth of an underwater acoustic target according to an embodiment of the present application will be described below with reference to the accompanying drawings.

[0071] Figure 1 It is a flow chart of a method for estimating the azimuth of an underwater acoustic target according to an embodiment of the present application.

[0072] It should be noted that before implementing interference suppression by adaptively updating the beamforming matrix, it is first necessary to perform a short-time Fourier transform on the received signal to obtain the first Received signal matrix of frequency bands ,in represents the number of elements in the hydrophone array, Indicates the number of snapshots per data frame.

[0073] Reference below Figure 1 and Figure 2 , details the received signal matrix for each frequency band Specific steps for each treatment.

[0074] like Figure 1 As shown, the method comprises the following steps:

[0075] Step 101: for each frequency band received signal matrix, divide the entire area into a plurality of grids, and initialize the signal energy distribution parameters and the environmental noise energy.

[0076] In some embodiments of the present application, it is assumed that the full range (such as 0-180° or 0-360°) is divided into grid, remember is the signal energy distribution parameter, The element represents the The signal energy in the grid direction is 0 if there is no signal. is the ambient noise energy.

[0077] Then, if Figure 2 As shown, Initialize to ,Will Initialize to receive signal matrix The variance of is expressed as:

[0078]

[0079]

[0080] in, It means extracting diagonal elements. Indicates the variance, superscript Represents the conjugate transpose

[0081] Step 102: Update the beamforming matrix and the ambient noise energy by minimizing the expected square error between the signal matrix and the beamforming data, and update the signal energy distribution parameters by using a fixed point iteration method.

[0082] In the present application embodiment, The beamforming matrix for each frequency band is The principle of updating the beamforming matrix is ​​to minimize the signal matrix With beamforming data This means that after beamforming, the interference signal and the desired signal are separated, and the optimization problem is expressed as:

[0083]

[0084] in, It means to find the expected square error.

[0085] In some embodiments of the present application, considering the traditional received signal model , assuming the signal The mean is 0 and the covariance is Complex Gaussian distribution, noise The mean is 0 and the covariance is The optimal solution to the above optimization problem is:

[0086]

[0087] Then, and Use its current estimated value and Instead, we get the beamforming matrix The update formula is:

[0088]

[0089] It should be noted that the beamforming matrix It does not need to be updated in every iteration.

[0090] In one embodiment of the present application, when and Estimated value at last update There are significant differences compared to When, update , otherwise it is not updated, where the superscript represents the number of iterations, is the first threshold.

[0091] It is understandable that due to The update involves high-complexity operations such as matrix inversion, and intermittent updates The computational complexity can be reduced.

[0092] In addition, when the beamforming matrix When updated, the parameters Also updated accordingly.

[0093] In one embodiment of the present application, the ambient noise energy is realized by calculating the posterior expectation The update expression is:

[0094]

[0095] in, , Represents the trace of the matrix.

[0096] In addition, the present application updates the signal energy distribution parameters according to the following fixed point iteration method: :

[0097]

[0098] in

[0099]

[0100]

[0101]

[0102] in, It means element-wise multiplication of two matrices. Represents the conjugate of a matrix.

[0103] Step 103, repeating the updating process of the signal energy distribution parameters until the iteration of the signal energy distribution parameters converges or reaches the maximum number of iterations, stopping the iteration process, and obtaining the target signal energy distribution estimation of each frequency band.

[0104] In the embodiment of the present application, multiple iterations are performed according to the steps shown in step 102. When the iterations converge, that is, or Reached the maximum number of iterations When the iteration is stopped, the target signal energy distribution estimation of each frequency band can be obtained. ,in, express No. elements, is the second threshold.

[0105] Step 104: The target signal energy distribution estimates of each frequency band are merged to obtain the final output energy.

[0106] In the embodiment of the present application, the target signal energy distribution estimates of all frequency bands are finally fused and summed to obtain the total output energy, which is expressed as:

[0107]

[0108] in, is the final output energy, is the total number of frequency bands.

[0109] In addition, the present invention compares the azimuth estimation performance of the proposed method with that of the traditional method under low signal-to-noise ratio and low signal-to-interference ratio conditions through simulation experiments. The hydrophone array used in the simulation includes 32 array elements. The root mean square error curves of azimuth estimation under various signal-to-noise / interference ratios obtained by simulation are shown in Figure 2. Figure 3 and Figure 4 shown.

[0110] The abbreviations in the legend mean as follows: single spectrum separation (SpSF) method based on maximum entropy criterion, least squares multi-beam resolution (MBM-SR), sparse Bayesian learning (SBL) method, and sparse Bayesian learning method based on beam power output (BPO) (SBL-BPO).

[0111] Reference Figure 3 and Figure 4 It can be seen that the method in the present invention has an estimation error similar to that of sparse Bayesian learning under low signal-to-noise ratio and low signal-to-interference ratio, and the error is smaller than that of other reference methods, which reflects the superiority of the method in the present invention in azimuth estimation accuracy.

[0112] Secondly, the present invention counts the running time of each method to compare the computational complexity of different methods, as shown in Table 1.

[0113] Table 1 Average running time of each method in the simulation experiment (unit: s)

[0114]

[0115] As shown in Table 1, compared with sparse Bayesian learning, the running time of the method in the present invention is significantly reduced, and the azimuth estimation accuracy of the two methods is comparable, which reflects the superiority of the method in the present invention in terms of computational efficiency. Although the SBL-BPO method has a shorter running time, its azimuth estimation error is too large to meet the actual application requirements.

[0116] In summary, compared with the traditional method, the method proposed in the present invention has the following performance advantages:

[0117] 1. Low computational complexity.

[0118] Assume the number of array elements is , the number of snapshots per frame is , the number of grid points in the angular domain is The method of the present invention is used to update the signal parameters in each iteration. The computational complexity is . Used to update the beamforming matrix , , , and noise parameters The computational complexity is .

[0119] Therefore, the average computational complexity of the present invention in each iteration is ,in represents the average frequency of updating the beamforming matrix. According to the simulation results, The empirical value of is about 0.01. In contrast, the computational complexity of each iteration of the traditional high-resolution method SBL is .when The method of the present invention has lower complexity than SBL. or 360, when When the empirical value is 0.01, or 3600 hydrophone arrays, this algorithm has the effect of reducing the computational complexity. This condition is met in most application scenarios.

[0120] 2. Strong interference suppression is achieved in scenarios where the target sector is unknown.

[0121] The method of the present invention does not require the target sector to be known, but adaptively updates the beamforming matrix based on the current signal energy distribution estimate, thereby suppressing the influence of strong interference on the weak target direction estimation, and improving the weak target direction estimation accuracy under low signal-to-interference-noise ratio conditions when the target sector is unknown.

[0122] In order to implement the above embodiment, the present application also proposes a device for estimating the azimuth angle of an underwater acoustic target.

[0123] Figure 5 1 is a block diagram of a device 10 for estimating an azimuth angle of an underwater acoustic target according to an embodiment of the present application, comprising:

[0124] Initialization module 100, for each frequency band received signal matrix, is used to divide the omnidirectional into a plurality of grids, and initialize signal energy distribution parameters and environmental noise energy;

[0125] An iterative update module 200, configured to update the beamforming matrix and the ambient noise energy by minimizing the expected square error between the signal matrix and the beamforming data, and to update the signal energy distribution parameters using a fixed point iteration method;

[0126] An iterative output module 300 is used to repeat the updating process of the signal energy distribution parameter until the iteration of the signal energy distribution parameter converges or reaches a maximum number of iterations, stop the iteration process, and obtain the target signal energy distribution estimation of each frequency band;

[0127] The fusion module 400 is used to fuse the target signal energy distribution estimates of each frequency band to obtain the final output energy.

[0128] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0129] The embodiment of the present application also provides an electronic device.

[0130] Figure 6 A schematic block diagram of an example electronic device 700 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0131] like Figure 6As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0132] A number of components in the device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0133] The computing unit 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above. For example, in some embodiments, the scheme proposed in the present application may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to perform the method shown in the present application in any other appropriate manner (e.g., by means of firmware).

[0134] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0135] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions / operations specified in the flow chart and / or block diagram. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0136] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 foregoing.

[0137] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0138] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0139] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0140] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0141] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for estimating the azimuth of an underwater acoustic target, characterized in that: include: For each frequency band’s received signal matrix, the omnidirectional area is divided into multiple grids, and the signal energy distribution parameters and environmental noise energy are initialized; By minimizing the expected square error between the signal matrix and the beamforming data, the beamforming matrix and the ambient noise energy are updated, and the signal energy distribution parameters are updated using a fixed point iteration method; Repeating the updating process of the signal energy distribution parameter until the iteration of the signal energy distribution parameter converges or reaches a maximum number of iterations, stopping the iteration process, and obtaining an estimate of the target signal energy distribution in each frequency band; The target signal energy distribution estimates of each frequency band are integrated to obtain the final output energy; Also includes: Get the received signal; Perform Fourier transform on the received signal to obtain Received signal matrix of frequency bands ,in represents the number of elements in the hydrophone array, Indicates the number of snapshots per data frame; The received signal matrix of each frequency band is divided into multiple grids in all directions, and the signal energy distribution parameters and the environmental noise energy are initialized, including: Divide the full range into grid, remember is the signal energy distribution parameter, The element represents the The signal energy in the grid direction is 0 if there is no signal. is the environmental noise energy; Will Initialize to ,Will Initialize to receive signal matrix The variance of is expressed as: in, It means extracting diagonal elements. Indicates the variance, superscript represents conjugate transpose; The updating of the beamforming matrix and the ambient noise energy by minimizing the expected square error between the signal matrix and the beamforming data comprises: Record The beamforming matrix for each frequency band is , based on the principle of updating the beamforming matrix to minimize the signal matrix With beamforming data The expected square error between them is used to express the optimization problem, and the expression is: in, It means to find the expected square error; For the traditional receiving signal model , assuming the signal The mean is 0 and the covariance is Complex Gaussian distribution, noise The mean is 0 and the covariance is The optimal solution of the optimization problem is calculated by using the complex Gaussian distribution: Will and Use its current estimated value and Instead, we get the beamforming matrix The update formula is: Among them, when ,renew , otherwise it will not be updated. for Last updated The estimated value of represents the number of iterations, is the first threshold; When the beamforming matrix When updated, the ambient noise energy is realized by calculating the posterior expectation The update expression is: in, , Represents the trace of the matrix.

2. The method according to claim 1, characterized in that The method of updating the signal energy distribution parameter by using a fixed point iteration method comprises: in in, It means element-wise multiplication of two matrices. Represents the conjugate of a matrix.

3. The method according to claim 2, characterized in that The updating process of repeating the signal energy distribution parameter until the iteration of the signal energy distribution parameter converges or reaches a maximum number of iterations, stopping the iteration process, and obtaining the target signal energy distribution estimation of each frequency band, includes: when or Reached the maximum number of iterations Stop the iteration when , and get the target signal energy distribution estimation of each frequency band ,in, express No. elements, is the second threshold.

4. The method according to claim 3, characterized in that The energy distribution estimation of the target signal in each frequency band is integrated to obtain the final output energy, including: in, is the final output energy, is the total number of frequency bands.

5. A device for estimating the azimuth of an underwater acoustic target, characterized in that: include: Initialization module, for the received signal matrix of each frequency band, is used to divide the omnidirectional into multiple grids, and initialize the signal energy distribution parameters and environmental noise energy; An iterative update module, used for updating the beamforming matrix and the ambient noise energy by minimizing the expected square error between the signal matrix and the beamforming data, and updating the signal energy distribution parameters by using a fixed point iteration method; An iterative output module, used to repeat the updating process of the signal energy distribution parameter until the iteration of the signal energy distribution parameter converges or reaches a maximum number of iterations, stop the iteration process, and obtain the target signal energy distribution estimation of each frequency band; A fusion module is used to estimate and fuse the target signal energy distribution of each frequency band to obtain the final output energy; Also includes: Get the received signal; Perform Fourier transform on the received signal to obtain Received signal matrix of frequency bands ,in represents the number of elements in the hydrophone array, Indicates the number of snapshots per data frame; The received signal matrix of each frequency band is divided into multiple grids in all directions, and the signal energy distribution parameters and the environmental noise energy are initialized, including: Divide the full range into grid, remember is the signal energy distribution parameter, The element represents the The signal energy in the grid direction is 0 if there is no signal. is the environmental noise energy; Will Initialize to ,Will Initialize to receive signal matrix The variance of is expressed as: in, It means extracting diagonal elements. Indicates the variance, superscript represents conjugate transpose; The updating of the beamforming matrix and the ambient noise energy by minimizing the expected square error between the signal matrix and the beamforming data comprises: Record The beamforming matrix for each frequency band is , based on the principle of updating the beamforming matrix to minimize the signal matrix With beamforming data The expected square error between them is used to express the optimization problem, and the expression is: in, It means to find the expected square error; For the traditional receiving signal model , assuming the signal The mean is 0 and the covariance is Complex Gaussian distribution, noise The mean is 0 and the covariance is The optimal solution of the optimization problem is calculated by using the complex Gaussian distribution: Will and Use its current estimated value and Instead, we get the beamforming matrix The update formula is: Among them, when ,renew , otherwise it will not be updated. for Last updated The estimated value of represents the number of iterations, is the first threshold; When the beamforming matrix When updated, the ambient noise energy is realized by calculating the posterior expectation The update expression is: in, , Represents the trace of the matrix.

6. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 4 when executed by a processor.

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