An underwater DOA estimation method based on residual fourier global information fusion network

Abstract

The application discloses an underwater DOA estimation based on a residual Fourier global information fusion network, obtains a target array pattern of an underwater array sensor, generates a simulation training data set based on a sparse covariance matrix based on the target array pattern, constructs a residual Fourier module, constructs a residual Fourier global information fusion network, inputs the simulation training data set into the residual Fourier global information fusion network based on a preset training strategy for training, obtains the trained residual Fourier global information fusion network, processes data of the target underwater array sensor, inputs the processed data into the trained residual Fourier global information fusion network, obtains an estimated spatial spectrum, and determines a direction of arrival of an underwater target based on the estimated spatial spectrum.

Description

Technical Field

[0001] The present invention relates to the technical field of marine target direction estimation, and in particular to an underwater DOA estimation method based on a residual Fourier global information fusion network. Background Art

[0002] Target direction of arrival (DOA) estimation is a frontier and hot topic in sonar detection research. It plays a crucial role in battlefield reconnaissance, underwater navigation, and ocean development. DOA estimation primarily involves collecting and processing signals from a multi-sensor array, extracting parameter information representing the target's direction, and estimating the target's angle of incidence.

[0003] Traditional methods are model-driven. Their DOA estimation performance degrades rapidly when the pre-set model differs significantly from the actual engineering environment. Furthermore, achieving a close match between the pre-set model and the actual model is difficult in complex real-world applications. Deep learning methods, however, establish a mapping between the received signal from a hydrophone array and the target's direction, thereby better capturing the relationship between the signal's internal structure and the target's direction. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems existing in the prior art, the present invention provides an underwater DOA estimation method, device, equipment and storage medium based on a residual Fourier global information fusion network to solve the problem of insufficient resolution of underwater position estimation in the prior art.

[0005] In order to achieve the above-mentioned purpose, the technical solution of the embodiment of the present invention is:

[0006] In a first aspect, an embodiment of the present invention provides an underwater DOA estimation method based on a residual Fourier global information fusion network, the method comprising:

[0007] Obtain target formation of underwater array sensors;

[0008] Based on the target formation, generating a simulation training data set based on a sparse covariance matrix;

[0009] Constructing a residual Fourier module, and based on the residual Fourier module, constructing a residual Fourier global information fusion network;

[0010] Based on a preset training strategy, the simulation training data set is input into the residual Fourier global information fusion network for training to obtain a trained residual Fourier global information fusion network;

[0011] Processing data from the target underwater array sensor and inputting the processed data into the trained residual Fourier global information fusion network to obtain an estimated spatial spectrum;

[0012] Based on the estimated spatial spectrum, the incoming wave direction of the underwater target is determined.

[0013] In a second aspect, an embodiment of the present invention provides an underwater DOA estimation device based on a residual Fourier global information fusion network, the device comprising:

[0014] An acquisition module, used to obtain the target formation of underwater array sensors;

[0015] A generation module, configured to generate a simulation training data set based on a sparse covariance matrix based on the target formation;

[0016] A construction module is used to construct a residual Fourier module, and based on the residual Fourier module, a residual Fourier global information fusion network is constructed;

[0017] A training module is used to input the simulation training data set into the residual Fourier global information fusion network for training based on a preset training strategy to obtain a trained residual Fourier global information fusion network;

[0018] a processing module, configured to process data from the target underwater array sensor and input the processed data into the trained residual Fourier global information fusion network to obtain an estimated spatial spectrum;

[0019] The determination module is configured to determine the incoming wave direction of the underwater target based on the estimated spatial spectrum.

[0020] In some embodiments, the generating module is further configured to generate a receiving signal of the underwater array sensor based on the target formation, where x = As + N; wherein A is the array manifold vector; s is the simulated real sound source signal; and N = σ 2 I is additive white Gaussian noise, σ 2 is the Gaussian white noise power;

[0021] Based on the received signal, a snapshot covariance matrix of the array is calculated, in,[·] H represents the conjugate transpose of the matrix; R represents the true covariance matrix; ΔR represents the difference between the true covariance matrix R and the snapshot covariance matrix There are errors between

[0022] The snapshot covariance matrix is ​​vectorized to obtain a snapshot covariance vector. Where vec(·) represents the matrix vectorization operation; Δy=vec(ΔR), which is the result of vectorization of the covariance matrix error ΔR; η=diag(η1,η2,…,η N ) is the signal power; [·;…;·] means vertically splicing the matrices or vectors in the brackets. represents the array manifold at the mth angle; e m represents an M×1-dimensional column vector whose mth element is 1 and the rest are 0;

[0023] Rewrite the snapshot covariance vector to obtain a rewritten snapshot covariance vector, in, is the overall error;

[0024] Determining a target pseudospectral formula based on the rewritten snapshot covariance vector;

[0025] Based on the target pseudo-spectral formula, the simulation training data set is calculated.

[0026] In some embodiments, the residual Fourier module includes at least a main path, a residual branch, and a Fourier branch; the residual Fourier module is constructed in the following manner:

[0027] The main path contains two layers of convolution kernels, and the two layers of convolution kernels are activated by the ReLU function to obtain the first output;

[0028] The residual branch performs identity mapping on the input data to obtain a second output;

[0029] The Fourier branch first passes through a layer of inverse Fourier transform, then passes through a 1×1 convolution with a ReLU function activated between the two layers of convolution kernels; finally, passes through a layer of inverse Fourier transform to obtain a third output;

[0030] The first output, the second output, and the third output are summed to obtain an output of the residual Fourier module.

[0031] In some embodiments, the residual Fourier global information fusion network includes a 25×1 convolution layer and three layers of the residual Fourier module; the main convolution kernels in the residual Fourier module are 15×1, 5×1, and 3×1, respectively, and the number of output channels is 6, 3, and 1, respectively.

[0032] In some embodiments, the apparatus further comprises: an output module, configured to increase the number of input channels from 2 to 12 by the 25×1 convolutional layer to obtain local features;

[0033] Reducing the dimension and extracting the global features of the input through the residual Fourier module;

[0034] A spatial spectrum is output based on the local features and the global features.

[0035] In some embodiments, the training module is further configured to divide the simulation training data set into a training set and a validation set using a certain ratio;

[0036] The training optimizer is determined to be Adam;

[0037] Adaptively adjust the learning rate using the ReduceLROnPlateau method;

[0038] The residual Fourier global information fusion network is trained using an early stopping training strategy, and the number of early stopping rounds is set to 90. If the validation set loss does not decrease in 90 consecutive rounds, the trained residual Fourier global information fusion network is obtained.

[0039] In some embodiments, the training module is further configured to search the estimated spatial spectrum using a preset peak detection method, and determine the vector index corresponding to the maximum peak as the incoming wave direction.

[0040] An embodiment of the present invention provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the above-mentioned underwater DOA estimation method based on the residual Fourier global information fusion network when executing the executable instructions stored in the memory.

[0041] An embodiment of the present invention provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned underwater DOA estimation method based on the residual Fourier global information fusion network.

[0042] The present invention provides an underwater DOA estimation method based on a residual Fourier global information fusion network. The method obtains a target formation of an underwater array sensor; generates a simulation training data set based on a sparse covariance matrix based on the target formation; constructs a residual Fourier module and a residual Fourier global information fusion network; inputs the simulation training data set into the residual Fourier global information fusion network for training based on a preset training strategy to obtain a trained residual Fourier global information fusion network; processes data from the target underwater array sensor and inputs the processed data into the trained residual Fourier global information fusion network to obtain an estimated spatial spectrum; and determines the incoming wave direction of the underwater target based on the estimated spatial spectrum. Thus, the present invention introduces a Fourier transform operation into the residual module to construct a residual Fourier module, which can more comprehensively extract high-frequency and low-frequency components, thereby capturing the global information of the signal. In underwater target azimuth estimation, the residual Fourier global information fusion network constructed using the residual Fourier module can more effectively estimate the incoming wave direction, improve resolution, and achieve high-resolution DOA estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 1 is a flow chart of an underwater DOA estimation method based on a residual Fourier global information fusion network provided by an embodiment of the present invention;

[0044] Figure 2 1 is a flow chart of a high-resolution underwater DOA estimation method based on a residual Fourier global information fusion network provided by an embodiment of the present invention;

[0045] Figure 3 Schematic diagram of a residual Fourier module provided by an embodiment of the present invention;

[0046] Figure 4 Schematic diagram of a residual Fourier global information fusion network provided by an embodiment of the present invention;

[0047] Figure 5 1 is a schematic diagram of peak search provided by an embodiment of the present invention;

[0048] Figure 6 This is an introduction to the SWellEx-96 test provided by an embodiment of the present invention, where (a) is a diagram showing the locations of the test vessel and each array, and (b) is a diagram showing the actual geometry of the North Horizontal Array;

[0049] Figure 7 This is the spatial spectrum at the 44th minute of the experiment provided by the embodiment of the present invention;

[0050] Figure 8 This is a time-direction history diagram provided by an embodiment of the present invention;

[0051] Figure 91 is a schematic diagram of the structure of an underwater DOA estimation device based on a residual Fourier global information fusion network provided by an embodiment of the present invention;

[0052] Figure 10 It is a schematic diagram of the composition structure of an underwater DOA estimation device based on a residual Fourier global information fusion network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0054] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. Unless otherwise defined, all technical and scientific terms used in the embodiments of the present invention have the same meaning as commonly understood by those skilled in the art to which the embodiments of the present invention pertain. The terms used in the embodiments of the present invention are for the purpose of describing the embodiments of the present invention only and are not intended to limit the present invention.

[0055] The embodiment of the present invention provides an underwater DOA estimation method based on a residual Fourier global information fusion network, see Figure 1 , Figure 1 This is a flow chart of an underwater DOA estimation method based on a residual Fourier global information fusion network provided by an embodiment of the present invention, which is combined with Figure 1 The steps shown are explained.

[0056] Step S110: obtaining a target formation of the underwater array sensor.

[0057] In some embodiments, the target formation refers to a known formation of underwater array sensors.

[0058] Step S120: generating a simulation training data set based on a sparse covariance matrix based on the target formation.

[0059] In some embodiments, a series of simulated data containing underwater target signals is generated based on the target formation using underwater acoustic wave propagation models and noise models. This data is processed to obtain a sparse covariance matrix, which serves as the input features for the neural network. The simulated training dataset should cover a variety of possible underwater environments and target characteristics to ensure the generalization ability of the neural network.

[0060] In the present invention, a simulation training data set based on a sparse covariance matrix can be generated according to a known formation of underwater array sensors.

[0061] Step S130: construct a residual Fourier module, and based on the residual Fourier module, construct a residual Fourier global information fusion network.

[0062] In some embodiments, the residual Fourier module is a neural network structure that combines residual learning and Fourier transforms, effectively extracting frequency-domain features from signals. Based on this, a residual Fourier global information fusion network is constructed. This network achieves deep feature extraction and global information fusion of the input signal by stacking multiple residual Fourier modules.

[0063] In the present invention, the original residual module can be improved by introducing a fast Fourier transform branch to construct a residual Fourier module, and a residual Fourier global information fusion network is designed based on this module.

[0064] Step S140: Based on a preset training strategy, the simulation training data set is input into the residual Fourier global information fusion network for training to obtain a trained residual Fourier global information fusion network.

[0065] In some embodiments, a suitable training strategy (e.g., gradient descent algorithm, learning rate adjustment strategy, etc.) is employed to input the simulated training dataset into the residual Fourier global information fusion network for training. Through multiple iterations of optimization, the network is able to accurately extract information related to the target wave direction from the input features.

[0066] In the present invention, based on the simulation training data set and the residual Fourier global information fusion network in the above steps, the residual Fourier global information fusion network can be trained by using training strategies such as adaptive adjustment of learning rate and "early stopping" training to obtain a trained residual Fourier global information fusion network.

[0067] Step S150 , processing the data of the target underwater array sensor, and inputting the processed data into the trained residual Fourier global information fusion network to obtain an estimated spatial spectrum.

[0068] In some embodiments, the actual collected underwater array sensor data is preprocessed (e.g., denoising, filtering, etc.) and then input into a trained residual Fourier transform global information fusion network. The network output is an estimated spatial spectrum, which reflects the intensity distribution of underwater target signals in different directions.

[0069] Step S160: determining the incoming wave direction of the underwater target based on the estimated spatial spectrum.

[0070] In the present invention, based on the trained residual Fourier global information fusion network, the data of the real underwater array sensor is processed to obtain the network input data, which is input into the trained network to obtain a one-dimensional spatial spectrum, and then the orientation of the underwater target is estimated.

[0071] The present invention provides an underwater DOA estimation method based on a residual Fourier global information fusion network. The method obtains a target formation of an underwater array sensor; generates a simulation training data set based on a sparse covariance matrix based on the target formation; constructs a residual Fourier module and a residual Fourier global information fusion network; inputs the simulation training data set into the residual Fourier global information fusion network for training based on a preset training strategy to obtain a trained residual Fourier global information fusion network; processes data from the target underwater array sensor and inputs the processed data into the trained residual Fourier global information fusion network to obtain an estimated spatial spectrum; and determines the incoming wave direction of the underwater target based on the estimated spatial spectrum. Thus, the present invention introduces a Fourier transform operation into the residual module to construct a residual Fourier module, which can more comprehensively extract high-frequency and low-frequency components, thereby capturing the global information of the signal. In underwater target azimuth estimation, the residual Fourier global information fusion network constructed using the residual Fourier module can more effectively estimate the incoming wave direction, improve resolution, and achieve high-resolution DOA estimation.

[0072] In some embodiments, the above step S120 can be implemented by the following steps S121 to S126:

[0073] Step S121: Based on the target formation, generate the receiving signal of the underwater array sensor, x = As + N; where A is the array manifold vector; s is the simulated real sound source signal; N = σ 2 I is additive white Gaussian noise, σ 2 is the Gaussian white noise power.

[0074] Step S122, calculating the snapshot covariance matrix of the array based on the received signal, in,[·] H represents the conjugate transpose of the matrix; R represents the true covariance matrix; ΔR represents the difference between the true covariance matrix R and the snapshot covariance matrix There are errors between.

[0075] Step S123, performing vectorization operation on the snapshot covariance matrix to obtain a snapshot covariance vector, Where vec(·) represents the matrix vectorization operation; Δy=vec(ΔR), which is the result of vectorization of the covariance matrix error ΔR; η=diag(η1,η2,…,η N ) is the signal power; [·;…;·] means vertically splicing the matrices or vectors in the brackets. represents the array manifold at the mth angle; e m Represents an M×1-dimensional column vector whose mth element is 1 and the rest are 0.

[0076] Step S124, rewrite the snapshot covariance vector to obtain a rewritten snapshot covariance vector, in, is the overall error.

[0077] Step S125 : determining a target pseudo-spectral formula based on the rewritten snapshot covariance vector.

[0078] Step S126: Calculate the simulation training data set based on the target pseudo-spectral formula.

[0079] In some embodiments, the residual Fourier module includes at least a main path, a residual branch, and a Fourier branch; the residual Fourier module is constructed in the following manner:

[0080] First, the main path contains two layers of convolution kernels, and the two layers of convolution kernels are activated by the ReLU function to obtain the first output.

[0081] Secondly, the residual branch performs identity mapping on the input data to obtain a second output.

[0082] Again, the Fourier branch first passes through a layer of inverse Fourier transform, then passes through a 1×1 convolution with a ReLU function activated between the two layers of convolution kernels; finally, it passes through a layer of inverse Fourier transform to obtain the third output.

[0083] Finally, the first output, the second output, and the third output are added to obtain the output of the residual Fourier module.

[0084] In some embodiments, the residual Fourier global information fusion network includes a 25×1 convolution layer and three layers of the residual Fourier module; the main convolution kernels in the residual Fourier module are 15×1, 5×1, and 3×1, respectively, and the number of output channels is 6, 3, and 1, respectively.

[0085] In some embodiments, the method further comprises: increasing the number of input channels from 2 to 12 by the 25×1 convolutional layer to obtain local features;

[0086] Reducing the dimension and extracting the global features of the input through the residual Fourier module;

[0087] A spatial spectrum is output based on the local features and the global features.

[0088] In some embodiments, the above step S140 can be implemented by the following steps S141 to S144:

[0089] Step S141 : Divide the simulation training data set into a training set and a validation set in a certain ratio.

[0090] Step S142: The training optimizer is determined to be Adam.

[0091] Step S143: Adaptively adjust the learning rate using the ReduceLROnPlateau method.

[0092] Step S144: Use the early stopping training strategy to train the residual Fourier global information fusion network, set the number of early stopping rounds to 90, and if the validation set loss does not decrease in 90 consecutive rounds, the trained residual Fourier global information fusion network is obtained.

[0093] In some embodiments, the above step S160 may be implemented in the following manner: searching the estimated spatial spectrum using a preset peak detection method, and determining the vector index corresponding to the maximum peak as the incoming wave direction.

[0094] like Figure 2 As shown, the present invention discloses a high-resolution underwater DOA estimation method based on a residual Fourier global information fusion network, which mainly includes the following four steps, which will be further explained in conjunction with specific embodiments and implementation methods. The DOA estimation method includes the following steps:

[0095] Step S1: Set the angle scanning range The number of incoming wave signals is 2, and the angle difference between the two incoming wave signals is Δθ=98, 99, 100, ..., 233. At each Δθ, the angles of the two incoming wave signals θ=[θ1, θ2] are scanned simultaneously from [0, 1, ..., 360] with a step size of 1. The specific scanning method is: in the angle scanning range Internal fixation θ1, calculation θ2=θ1+Δθ(need to ensure ), we obtain 26,588 pairs of angle samples, so there are 26,588 training data in total. At the same time, we construct a 361-dimensional vector as the label, which is set to 1 at the angle of the incoming signal and 0 at all other angles.

[0096] Since the array is placed on the seabed, the pitch angle Direction vector at pitch angle Direction angle θ i The following expression is The signal-to-noise ratio is randomly selected from SNR=[-10dB,0dB], and the signal frequency is f c=79Hz, the number of snapshots is Nsnap=256, the number of elements M=27 and other parameters, using the given real array geometry p i Construct the array manifold, the formula is as follows:

[0097]

[0098] Among them, the wave number Where c is the underwater sound speed, which is roughly taken as 1500m / s.

[0099] Use random numbers to generate a snapshot simulation signal s, and add noise to the signal at a given signal-to-noise ratio to obtain the simulated array observation signal:

[0100] x=As+N,

[0101] Where N = σ 2 I, represents the Gaussian white noise power, P s is the signal power.

[0102] Calculate the snapshot covariance matrix. At the same time, it should be noted that the snapshot covariance matrix needs to be normalized in actual application:

[0103]

[0104] The snapshot covariance matrix is ​​vectorized to obtain the snapshot covariance vector:

[0105]

[0106] Where vec(·) represents the matrix vectorization operation; Δy=vec(ΔR), which is the result of vectorization of the covariance matrix error ΔR; η=diag(η1,η2,…,η N ) is the signal power; [·;…;·] means vertically concatenating the matrices or vectors in the brackets; represents the array manifold at the mth angle; e m Represents an M×1-dimensional column vector whose mth element is 1 and the rest are 0.

[0107] Rewrite the snapshot covariance vector:

[0108]

[0109] in, is the overall error.

[0110] Calculate the pseudospectrum as input data to train the neural network:

[0111]

[0112] Step S2: Construct the residual Fourier module, such as Figure 3 As shown in Figure 1. This module has a main path and two branches, called the residual branch and the Fourier branch. The main path consists of two layers of one-dimensional convolution with large kernels, activated by a ReLU function between the two convolution layers. The residual branch directly performs an identity mapping on the input. The Fourier branch first undergoes an FFT transform, then two layers of 1×1 convolution with a ReLU activation between them, and finally an inverse FFT transform. The output of these three paths is summed to obtain the module's output. The constructed residual Fourier module is used to build a residual Fourier global information fusion network. The network is constructed by a 25×1 convolution layer and a three-layer residual Fourier module. First, a convolution layer is used to increase the dimension of the input with 2 channels to 12 channels to obtain local features. Then, the residual Fourier module is used to gradually reduce the dimension and extract the global features of the input. The convolution kernels of the main path in the three-layer residual Fourier module are 15×1, 5×1, and 3×1, respectively, and the number of output channels are 6, 3, and 1, respectively. Finally, the network uses global and local features to output the spatial spectrum, as shown in Figure 4 shown.

[0113] Step S3: Divide the training dataset into a training set and a validation set in a ratio of 8:2. The MSE loss function is selected during training. The MSE calculation expression for a batch is as follows:

[0114]

[0115] Among them, B represents the number of samples in a batch, η i represents the real spatial spectrum under the i-th sampling, η i Represents the predicted spatial spectrum under the i-th sampling; during the training process, one epoch usually contains multiple batches, and the MSE calculation expression under one epoch is as follows:

[0116]

[0117] Among them, E represents the number of batches contained in a round, represents the MSE of the e-th batch.

[0118] Adam was selected as the training optimizer; the learning rate was adaptively adjusted using the ReduceLROnPlateau method; and the residual Fourier global information fusion network was trained using the "early stopping" training strategy, with the number of "early stopping" rounds set to 90. This indicates that if the validation set loss does not decrease after 90 consecutive rounds before reaching the preset maximum number of rounds, the neural network is considered trained and training is stopped. The model with the minimum validation set loss is selected as the optimal model for use. The output of the residual Fourier global information fusion network is the estimated spatial spectrum.

[0119] Step S4: Use 65 minutes of data from the North Horizontal Array during the S59 event as the network input, with a sampling frequency of fs = 3267.8 Hz. During data processing, set the FFT count to 4096, the overlap rate to 50%, the frequency point to fc = 79 Hz, and the number of snapshots Nsnap = 10. Construct the covariance matrix and calculate the input pseudospectrum of the neural network:

[0120]

[0121] The pseudo-spectral data is input into the trained network to obtain the estimated one-dimensional spatial spectrum, such as Figure 7 The one-dimensional spatial spectrum estimated by the network within 65 minutes is spliced ​​by row (time dimension) to obtain the time orientation history diagram, as shown in Figure 8 For the one-dimensional spatial spectrum in each time dimension, the angle of the incoming wave signal in each time dimension is obtained by setting the number of incoming wave signals in the peak search method.

[0122] Figure 9 FIG. 1 is a schematic diagram showing the structure of an underwater DOA estimation device based on a residual Fourier global information fusion network according to an embodiment of the present invention. Figure 9 As shown, an underwater DOA estimation device 900 based on a residual Fourier global information fusion network includes: an acquisition module 901 for obtaining a target formation of an underwater array sensor; a generation module 902 for generating a simulation training data set based on a sparse covariance matrix based on the target formation; a construction module 903 for constructing a residual Fourier module and, based on the residual Fourier module, constructing a residual Fourier global information fusion network; a training module 904 for inputting the simulation training data set into the residual Fourier global information fusion network for training based on a preset training strategy to obtain a trained residual Fourier global information fusion network; a processing module 905 for processing data from the target underwater array sensor and inputting the processed data into the trained residual Fourier global information fusion network to obtain an estimated spatial spectrum; and a determination module 906 for determining the incoming wave direction of the underwater target based on the estimated spatial spectrum.

[0123] In some embodiments, the generating module is further configured to generate a received signal of the underwater array sensor based on the target array pattern, x = As + N, wherein A is an array manifold vector, s is a simulated real sound source signal, N = σ 2 I is an additive white Gaussian noise, σ 2 is a Gaussian white noise power, and based on the received signal, a snapshot covariance matrix of the array is calculated, wherein [·] H represents a conjugate transpose of a matrix, R represents a real covariance matrix, ΔR represents an error existing between the real covariance matrix R and the snapshot covariance matrix , the snapshot covariance matrix is vectorized to obtain a snapshot covariance vector, wherein vec(·) represents a matrix vectorization operation, Δy = vec(ΔR) is a result obtained by vectorizing the covariance matrix error ΔR, and η = diag(η1, η2, …, η N ) is a signal power; [·; …; ·] represents vertically splicing matrices or vectors in the brackets, represents an array manifold at the mth angle; e m represents an Mx1 column vector whose mth element is 1 and other elements are 0, and the snapshot covariance vector is rewritten to obtain a rewritten snapshot covariance vector, wherein is a total error, based on the rewritten snapshot covariance vector, a target pseudospectrum formula is determined, and based on the target pseudospectrum formula, the simulated training data set is calculated.

[0124] In some embodiments, the residual Fourier module at least includes a main path, a residual branch, and a Fourier branch; the residual Fourier module is constructed in the following manner: the main path contains two layers of convolution kernels, the two layers of convolution kernels are activated by a ReLU function in the middle to obtain a first output; the residual branch performs an identity mapping on the input data to obtain a second output; the Fourier branch first passes through a layer of inverse Fourier transform, and then passes through a 1x1 convolution with the ReLU function activated in the middle of the two layers of convolution kernels; finally, a layer of inverse Fourier transform is performed to obtain a third output; the first output, the second output, and the third output are summed to obtain the output of the residual Fourier module.

[0125] In some embodiments, the residual Fourier global information fusion network includes a 25×1 convolution layer and three layers of the residual Fourier module; the main convolution kernels in the residual Fourier module are 15×1, 5×1, and 3×1, respectively, and the number of output channels is 6, 3, and 1, respectively.

[0126] In some embodiments, the device further includes: an output module for increasing the number of input channels from 2 to 12 by the 25×1 convolutional layer to obtain local features; reducing the dimension and extracting the global features of the input by the residual Fourier module; and outputting a spatial spectrum based on the local features and the global features.

[0127] In some embodiments, the training module is further used to divide the simulation training data set into a training set and a validation set using a certain ratio; the training optimizer is determined to be Adam; the learning rate is adaptively adjusted using the ReduceLROnPlat eau method; the residual Fourier global information fusion network is trained using an early stopping training strategy, the number of early stopping rounds is set to 90, and the validation set loss does not decrease in 90 consecutive rounds, thereby obtaining the trained residual Fourier global information fusion network.

[0128] In some embodiments, the training module is further configured to search the estimated spatial spectrum using a preset peak detection method, and determine the vector index corresponding to the maximum peak as the incoming wave direction.

[0129] In some embodiments, it should be noted that the description of the device embodiment of the present invention is similar to the description of the method embodiment described above, and has similar beneficial effects as the same method embodiment, so it is not repeated here. For technical details not disclosed in the present device embodiment, please refer to the description of the method embodiment of the present invention for understanding.

[0130] It should be noted that, in the embodiment of the present invention, if the above-mentioned underwater DOA estimation method based on the residual Fourier global information fusion network is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a terminal to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk. In this way, the embodiment of the present invention is not limited to any specific combination of hardware and software.

[0131] Correspondingly, the embodiment of the present application provides an underwater DOA estimation device based on a residual Fourier global information fusion network, Figure 10 is a constituent structure schematic diagram of the underwater DOA estimation device based on the residual Fourier global information fusion network provided by the embodiment of the present application, as Figure 10 shown, the underwater DOA estimation device based on the residual Fourier global information fusion network 1000 at least includes a processor 1001 and a computer readable storage medium 1002 configured to store executable instructions, wherein the processor 1001 generally controls the overall operation of the underwater DOA estimation device based on the residual Fourier global information fusion network. The computer readable storage medium 1002 is configured to store instructions and applications executable by the processor 1001, and can also cache data to be processed by the processor 1001 and each module in the underwater DOA estimation device based on the residual Fourier global information fusion network 1000 to be processed or processed, which can be realized by FLASH or RAM (Random Access Memory).

[0132] The embodiment of the present application provides a storage medium storing executable instructions, wherein the storage medium stores executable instructions, and when the executable instructions are executed by a processor, the processor will execute the method provided by the embodiment of the present application, for example, the method shown in Figure 1 .

[0133] In some embodiments, the storage medium can be a computer readable storage medium, for example, FRAM (Ferromagnetic Random Access Memory), ROM (Read Only Memory), PROM (Programmable Read Only Memory), EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable Programmable Read Only Memory), flash memory, magnetic surface memory, optical disc, or CD-ROM (Compact Disk-Read Only Memory) and the like memory; it can also be various devices including one or any combination of the above memories.

[0134] In some embodiments, executable instructions may be in the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0135] As examples, executable instructions may, but need not necessarily, correspond to a file in a file system, may be stored as part of a file storing other programs or data, such as one or more scripts in a Hypertext Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinating files (e.g., files storing one or more modules, subroutines, or code portions). As examples, executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located in one location, or on multiple electronic devices distributed across multiple locations and interconnected by a communication network.

[0136] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and scope of the present invention are included in the scope of protection of the present invention.

[0137] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are for description only and do not represent the advantages and disadvantages of the embodiments.

[0138] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, an element defined by the statement "comprises a..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed.

[0139] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An underwater DOA estimation method based on residual Fourier global information fusion network, characterized in that: The method comprises: Obtain target formation of underwater array sensors; Based on the target formation, generating a simulation training data set based on a sparse covariance matrix; Constructing a residual Fourier module, and based on the residual Fourier module, constructing a residual Fourier global information fusion network; Based on a preset training strategy, the simulation training data set is input into the residual Fourier global information fusion network for training to obtain a trained residual Fourier global information fusion network; Processing data from the target underwater array sensor and inputting the processed data into the trained residual Fourier global information fusion network to obtain an estimated spatial spectrum; Based on the estimated spatial spectrum, the incoming wave direction of the underwater target is determined.

2. The method according to claim 1, characterized in that The step of generating a simulation training data set based on a sparse covariance matrix based on the target formation includes: Based on the target formation, the receiving signal of the underwater array sensor is generated, x=As+N; where A is the array manifold vector; s is the simulated real sound source signal; N=σ 2 I is additive white Gaussian noise, σ 2 is the Gaussian white noise power; Based on the received signal, a snapshot covariance matrix of the array is calculated, in,[·] H represents the conjugate transpose of the matrix; R represents the true covariance matrix; ΔR represents the difference between the true covariance matrix R and the snapshot covariance matrix There are errors between The snapshot covariance matrix is ​​vectorized to obtain a snapshot covariance vector. Where vec(·) represents the matrix vectorization operation; Δy=vec(ΔR) is the result obtained by vectorizing the covariance matrix error ΔR; η=diag(η1, η2, ..., η N ) is the signal power; [·;…;·] means vertically splicing the matrices or vectors in the brackets. represents the array manifold at the mth angle; e m represents an M×1-dimensional column vector whose mth element is 1 and the rest are 0; Rewrite the snapshot covariance vector to obtain a rewritten snapshot covariance vector, in, is the overall error; Determining a target pseudospectral formula based on the rewritten snapshot covariance vector; Based on the target pseudo-spectral formula, the simulation training data set is calculated.

3. The method according to claim 1, characterized in that The residual Fourier module at least includes a main path, a residual branch and a Fourier branch; The residual Fourier module is constructed in the following way: The main path contains two layers of convolution kernels, and the two layers of convolution kernels are activated by the ReLU function to obtain the first output; The residual branch performs identity mapping on the input data to obtain a second output; The Fourier branch first passes through a layer of inverse Fourier transform, then passes through a 1×1 convolution with a ReLU function activated between the two layers of convolution kernels; finally, passes through a layer of inverse Fourier transform to obtain a third output; The first output, the second output, and the third output are summed to obtain an output of the residual Fourier module.

4. The method according to claim 3, characterized in that The residual Fourier global information fusion network includes a 25×1 convolution layer and three residual Fourier modules; the convolution kernels of the main path in the residual Fourier module are 15×1, 5×1, and 3×1, respectively, and the number of output channels is 6, 3, and 1, respectively.

5. The method according to claim 4, characterized in that The method further comprises: The 25×1 convolutional layer increases the number of input channels from 2 to 12 to obtain local features; Reducing the dimension and extracting the global features of the input through the residual Fourier module; A spatial spectrum is output based on the local features and the global features.

6. The method according to claim 1, characterized in that The method of inputting the simulation training data set into the residual Fourier global information fusion network for training based on a preset training strategy to obtain a trained residual Fourier global information fusion network includes: Dividing the simulation training data set into a training set and a validation set using a certain ratio; The training optimizer is determined to be Adam; Adaptively adjust the learning rate using the ReduceLROnPlateau method; The residual Fourier global information fusion network is trained using an early stopping training strategy, and the number of early stopping rounds is set to 90. If the validation set loss does not decrease in 90 consecutive rounds, the trained residual Fourier global information fusion network is obtained.

7. The method according to claim 1, characterized in that The determining the incoming wave direction of the underwater target based on the estimated spatial spectrum includes: The estimated spatial spectrum is searched using a preset peak detection method, and a vector index corresponding to a maximum peak is determined as the incoming wave direction.

8. An underwater DOA estimation device based on residual Fourier global information fusion network, characterized in that: The device comprises: An acquisition module, used to obtain the target formation of underwater array sensors; A generation module, configured to generate a simulation training data set based on a sparse covariance matrix based on the target formation; A construction module is used to construct a residual Fourier module, and based on the residual Fourier module, a residual Fourier global information fusion network is constructed; A training module is used to input the simulation training data set into the residual Fourier global information fusion network for training based on a preset training strategy to obtain a trained residual Fourier global information fusion network; a processing module, configured to process data from the target underwater array sensor and input the processed data into the trained residual Fourier global information fusion network to obtain an estimated spatial spectrum; The determination module is configured to determine the incoming wave direction of the underwater target based on the estimated spatial spectrum.

9. An electronic device, characterized in that: include: a memory for storing executable instructions; The processor is configured to implement the underwater DOA estimation method based on the residual Fourier global information fusion network according to any one of claims 1 to 7 when executing the executable instructions stored in the memory.

10. A computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the underwater DOA estimation method based on the residual Fourier global information fusion network according to any one of claims 1 to 7.

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