A method and system for identifying end-to-end modulation types of underwater acoustic communication signals

Abstract

The present invention relates to the field of underwater acoustic signal processing, and in particular to a method and system for end-to-end modulation type recognition of underwater acoustic communication signals. The method comprises: detecting a communication signal in an underwater acoustic signal to obtain an underwater acoustic communication signal; and using a pre-established and trained end-to-end modulation type recognition model for underwater acoustic communication signals based on residual attention convolution to recognize the modulation type of the obtained underwater acoustic communication signal. The trained end-to-end modulation type recognition model for underwater acoustic communication signals based on residual attention convolution comprises a feature extraction module and a classification module. The feature extraction module is used to extract features of the underwater acoustic communication signal based on residual attention; and the classification module is used to classify the extracted features to obtain the probability that the underwater acoustic communication signal belongs to a certain modulation type. The use of a residual structure combined with a convolution module can adapt to slight changes in the signal, enhance the sampling capability of the signal, and improve the reliability of recognition.

Description

Technical Field

[0001] The present invention relates to the field of underwater acoustic signal processing, and in particular to a method and system for identifying end-to-end modulation types of underwater acoustic communication signals. Background Art

[0002] Currently, underwater acoustic communication technology has experienced unprecedented development. The modulation types of underwater acoustic communication signals are mainly frequency-modulated, phase-modulated, and spread-spectrum, characterized by low operating frequencies and short communication distances. Under the existing communication system, research on the identification of underwater acoustic communication signal modulation types in non-cooperative environments is crucial for the successful implementation of various underwater missions. However, the rapid attenuation of acoustic waves underwater, the multipath effect of seawater, and Doppler shift in the ocean all affect underwater communication signals. Underwater noise is complex and includes sources such as fish schools, ships, ocean currents, sea surface waves, seismic activity, and tidal noise, all of which pose significant challenges to communication signal processing and identification.

[0003] Currently, researchers have conducted extensive research on the modulation recognition of underwater acoustic communication signals. For example, energy detection methods are used to intercept underwater pulse signals and extract different features of the signal, such as time domain features, spectral features, and parameter features. In recent years, convolutional neural networks have demonstrated excellent performance in target classification tasks. They have been used in underwater acoustic target recognition, such as ship radiated noise classification and sonar image target recognition. They are also widely used to identify the modulation mode of underwater acoustic communication signals. Signal recognition can be effectively achieved through expert knowledge bases or classifiers based on machine learning and deep learning. However, for most research methods, the feature extraction process usually uses a single feature, which has significant limitations and hinders the high-reliability recognition of communication signals.

[0004] It can be seen that the modulation type identification of non-cooperative underwater acoustic communication signals still faces many difficulties, which also puts forward new requirements for subsequent research. How to perform robust feature extraction and then achieve fast, accurate and reliable identification of signal attributes is an important research direction. Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects of the prior art and propose a method and system for identifying the end-to-end modulation type of underwater acoustic communication signals.

[0006] To achieve the above object, the present invention is implemented through the following technical solutions:

[0007] In one aspect, a method for identifying an end-to-end modulation type of an underwater acoustic communication signal is provided, comprising:

[0008] detecting a communication signal in the underwater acoustic signal to obtain an underwater acoustic communication signal;

[0009] The modulation type of the acquired underwater acoustic communication signal is identified using a pre-established and trained end-to-end modulation type recognition model for underwater acoustic communication signals based on residual attention convolution.

[0010] Among them, the trained end-to-end modulation type recognition model of underwater acoustic communication signals based on residual attention convolution includes: feature extraction module and classification module; among them,

[0011] The feature extraction module is used to extract features of the underwater acoustic communication signal based on residual attention;

[0012] The classification module is used to classify the extracted features to obtain the probability that the underwater acoustic communication signal belongs to a certain modulation type.

[0013] In an improved end-to-end modulation type identification method for underwater acoustic communication signals, the feature extraction module includes: M pairs of RAC and DC modules consisting of M+1 residual attention convolution blocks RAC and M depth convolution blocks DC, and 1 RAC module, which are connected in sequence; wherein, the first pair of RAC and DC modules respectively extracts features of the communication signal and connects the output features through a residual structure, and the remaining pairs of RAC and DC modules are used to extract features from the output features; the 1 RAC module is used to continue to extract features from the features output by the last pair of RAC and DC modules.

[0014] In an improved end-to-end modulation type recognition method for underwater acoustic communication signals, each RAC specifically includes: a first one-dimensional convolutional layer Conv1, a first ReLU6 activation layer, a second one-dimensional convolutional layer Conv2, a one-dimensional convolutional layer Attention with a channel attention mechanism, a second ReLU6 activation layer, and a maximum pooling layer MaxPooling connected in sequence;

[0015] The Conv1 is used to use y i =w i x+b i The formula extracts features from underwater acoustic communication signals, where x is the input data of the convolution layer, and w i and b i are the output features y i The corresponding convolution kernel and bias, i is the number of convolution layers;

[0016] The first ReLU6 activation layer is used to activate the extracted features to obtain activated features;

[0017] Conv2 is used to continue to extract features from the activated features using the formula;

[0018] The Attention is used to reduce the dimensionality of the features extracted by Conv2 using global pooling, output a set of vectors with the same dimension as the number of channels, and then add two dense layers using RELU activation and Sigmoid activation respectively, using the formula To learn the weight of each channel, we can get the attention weighted feature map, where p i is the feature output by Conv2, σ i is the channel weight, is the attention-weighted feature map;

[0019] The second ReLU6 activation layer is used to activate the attention weighted features to obtain activated features;

[0020] The MaxPooling is used to downsample the output features to 1 / 1 of the original length using a pooling kernel of size l×1, thereby removing redundant information and reducing model parameters.

[0021] In an improved end-to-end modulation type identification method for underwater acoustic communication signals, each DC specifically includes: four ordinary one-dimensional convolutional layers and one ReLU6 activation layer connected in sequence;

[0022] The four common one-dimensional convolutional layers connected in sequence are used to continue extracting features from the underwater acoustic communication signal;

[0023] The ReLU6 activation layer is used to activate the input features using the formula ReLU6(x)=min(6,max(x,0)) to obtain the activated features, where x represents the activation function input value, min(·) represents the minimum value function, and max(·) represents the maximum value function.

[0024] In an improved end-to-end modulation type identification method for underwater acoustic communication signals, the classification module includes: a global average pooling layer, a fully connected layer, and a Softmax activation layer connected in sequence;

[0025] The global average pooling layer is used to perform channel-average downsampling on the extracted features;

[0026] The fully connected layer is used to generate a modulation type label of the underwater acoustic communication signal based on the downsampled features;

[0027] The Softmax activation layer is used to calculate the modulation type label of the underwater acoustic communication signal using an activation function to obtain the probability that the underwater acoustic communication signal belongs to a certain modulation type.

[0028] In an improved method for end-to-end modulation type identification of underwater acoustic communication signals, the activation function is:

[0029]

[0030] Among them, Softmax(z f ) is the probability that the underwater acoustic communication signal belongs to each modulation type, z f is the modulation type label of the output of the f-th node of the fully connected layer, k is the modulation type index of the underwater acoustic communication signal, and K is the total number of modulation types of the underwater acoustic communication signal.

[0031] In an improved method for identifying end-to-end modulation types of underwater acoustic communication signals, the end-to-end modulation type identification model for underwater acoustic communication signals is trained in the following manner:

[0032] Construct training dataset D train And the test dataset D test : Detect the communication signal in the acquired underwater acoustic signal and obtain N one-dimensional signals X1, X2, ..., X of the same length as K types of underwater acoustic communication signals. N Samples and corresponding true label vectors Y1, Y2, ..., Y N ; Randomly select N1 samples and corresponding label vectors to form the training data set D train , the remaining N-N1 samples and their corresponding label vectors constitute the test data set D test ;

[0033] Initialize the modulation type recognition model: the parameters of each network layer are Gaussian distributed G(μ,σ) with a mean of 0 and a variance of 1. 2 ) Random initialization;

[0034] Training the modulation type recognition model: Divide the training data set into batches according to the set batch size; perform one round of training on all batches of data in the model to obtain the optimized model, and then use all batches of data to perform the next round of training on the optimized model until the number of training rounds reaches the set number of epochs;

[0035] The first round of model training includes: selecting a batch from the batch data, inputting the initialized end-to-end modulation type recognition model of underwater acoustic communication signals, combining the true label vector, using the cross-entropy loss function to calculate the function loss value of the model, using the error back propagation BP algorithm to update the modulation type recognition model parameters, and using the Adam optimizer with an adaptive learning rate to optimize the model parameters, repeatedly selecting a batch of data from the training set and inputting the optimized modulation type recognition model until all batches of data are selected;

[0036] Other rounds of model training include: selecting a batch from the batch data, inputting the end-to-end modulation type recognition model of underwater acoustic communication signals obtained from the previous round of training, combining the true label vector, using the cross-entropy loss function to calculate the function loss value Loss of the model, using the error back propagation BP algorithm to update the modulation type recognition model parameters, and using the Adam optimizer with an adaptive learning rate to optimize the model parameters, repeatedly selecting a batch of data from the training set, and inputting the optimized modulation type recognition model until all batches of data are selected.

[0037] In an improved method for end-to-end modulation type identification of underwater acoustic communication signals, the Loss calculation formula is:

[0038]

[0039] Among them, N1 represents the number of training set samples, K is the total number of underwater acoustic communication signal modulation types, k is the modulation type index, j is the sample index, q(x j,k ) represents the sample x j,k The predicted modulation type, p(x j,k ) represents the sample x j,k is the probability of the current predicted modulation type.

[0040] On the other hand, a modulation type identification system based on the above-mentioned end-to-end modulation type identification method for underwater acoustic communication signals is provided, comprising:

[0041] An acquisition module, configured to detect the communication signal in the underwater acoustic signal and obtain the underwater acoustic communication signal;

[0042] The recognition module uses a pre-established and trained end-to-end modulation type recognition model for underwater acoustic communication signals based on residual attention convolution to identify the modulation type of the obtained underwater acoustic communication signal; wherein the trained end-to-end modulation type recognition model for underwater acoustic communication signals based on residual attention convolution specifically includes: a feature extraction module and a classification module; the feature extraction module is used to extract features of the underwater acoustic communication signal based on residual attention; the classification module is used to classify the extracted features to obtain the probability that the underwater acoustic communication signal belongs to a certain modulation type.

[0043] In an improved end-to-end modulation type recognition system for underwater acoustic communication signals, the feature extraction module includes: M pairs of RAC and DC modules consisting of M+1 residual attention convolution blocks RAC and M depth convolution blocks DC, and 1 RAC module, which are connected in sequence; wherein, the first pair of RAC and DC modules respectively extracts features of the communication signal and connects the output features through a residual structure, and the remaining pairs of RAC and DC modules are used to extract features from the output features; the 1 RAC module is used to continue to extract features from the features output by the last pair of RAC and DC modules.

[0044] Compared with the prior art, the present invention has the following advantages:

[0045] 1. Automatically identify multiple types of underwater acoustic communication signals using intelligent methods. Convolutional neural networks, a key signal processing tool, can automatically learn effective features from training data for different tasks. Using a residual structure combined with a convolutional module, they can adapt to subtle signal variations, enhance signal sampling capabilities, and improve the reliability of modulation type recognition for underwater acoustic communication signals.

[0046] 2. The model uses a channel attention mechanism. This mechanism automatically captures the importance of each feature channel, assigns different weight coefficients to each channel, and strengthens the important features obtained during feature extraction, improving the model's ability to represent features.

[0047] 3. The model has fewer parameters and is relatively lightweight. The model was designed with the number of parameters and computational cost in mind, using fewer network layers. A residual structure is used to connect the convolutional layers within the module, resulting in a less complex model and better adaptability in underwater scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flowchart of the overall process of the end-to-end modulation type identification method for underwater acoustic communication signals of the present invention;

[0049] Figure 2 Schematic diagram of the structure of the deep residual attention module RAC of the present invention;

[0050] Figure 3 This is a diagram of the end-to-end modulation recognition model of underwater acoustic communication signals based on residual attention convolution of the present invention. DETAILED DESCRIPTION

[0051] The present invention provides a method for identifying an end-to-end modulation type of an underwater acoustic communication signal, comprising:

[0052] detecting a communication signal in the underwater acoustic signal to obtain an underwater acoustic communication signal;

[0053] The modulation type of the acquired underwater acoustic communication signal is identified using a pre-established and trained end-to-end modulation type recognition model for underwater acoustic communication signals based on residual attention convolution.

[0054] Among them, the trained end-to-end modulation type recognition model of underwater acoustic communication signals based on residual attention convolution includes: feature extraction module and classification module; among them,

[0055] The feature extraction module is used to extract features of the underwater acoustic communication signal based on residual attention;

[0056] The classification module is used to classify the extracted features to obtain the probability that the underwater acoustic communication signal belongs to a certain modulation type.

[0057] In an improved end-to-end modulation type identification method for underwater acoustic communication signals, the feature extraction module includes: M pairs of RAC and DC modules consisting of M+1 residual attention convolution blocks RAC and M depth convolution blocks DC, and 1 RAC module, which are connected in sequence; wherein, the first pair of RAC and DC modules respectively extracts features of the communication signal and connects the output features through a residual structure, and the remaining pairs of RAC and DC modules are used to extract features from the output features; the 1 RAC module is used to continue to extract features from the features output by the last pair of RAC and DC modules.

[0058] In an improved end-to-end modulation type recognition method for underwater acoustic communication signals, each RAC specifically includes: a first one-dimensional convolutional layer Conv1, a first ReLU6 activation layer, a second one-dimensional convolutional layer Conv2, a one-dimensional convolutional layer Attention with a channel attention mechanism, a second ReLU6 activation layer, and a maximum pooling layer MaxPooling connected in sequence;

[0059] The Conv1 is used to use y i =w i x+b i The formula extracts features from underwater acoustic communication signals, where x is the input data of the convolution layer, and w i and b i are the output features y i The corresponding convolution kernel and bias, i is the number of convolution layers;

[0060] The first ReLU6 activation layer is used to activate the extracted features to obtain activated features;

[0061] Conv2 is used to continue to extract features from the activated features using the formula;

[0062] The Attention is used to reduce the dimensionality of the features extracted by Conv2 using global pooling, output a set of vectors with the same dimension as the number of channels, and then add two dense layers using RELU activation and Sigmoid activation respectively, using the formula To learn the weight of each channel, we can get the attention weighted feature map, where p i is the feature output by Conv2, σ i is the channel weight, is the attention-weighted feature map;

[0063] The second ReLU6 activation layer is used to activate the attention weighted features to obtain activated features;

[0064] The MaxPooling is used to downsample the output features to 1 / 1 of the original length using a pooling kernel of size l×1, thereby removing redundant information and reducing model parameters.

[0065] In an improved end-to-end modulation type identification method for underwater acoustic communication signals, each DC specifically includes: four ordinary one-dimensional convolutional layers and one ReLU6 activation layer connected in sequence;

[0066] The four common one-dimensional convolutional layers connected in sequence are used to continue extracting features from the underwater acoustic communication signal;

[0067] The ReLU6 activation layer is used to activate the input features using the formula ReLU6(x)=min(6,max(x,0)) to obtain the activated features, where x represents the activation function input value, min(·) represents the minimum value function, and max(·) represents the maximum value function.

[0068] In an improved end-to-end modulation type identification method for underwater acoustic communication signals, the classification module includes: a global average pooling layer, a fully connected layer, and a Softmax activation layer connected in sequence;

[0069] The global average pooling layer is used to perform channel-average downsampling on the extracted features;

[0070] The fully connected layer is used to generate a modulation type label of the underwater acoustic communication signal based on the downsampled features;

[0071] The Softmax activation layer is used to calculate the modulation type label of the underwater acoustic communication signal using an activation function to obtain the probability that the underwater acoustic communication signal belongs to a certain modulation type.

[0072] In an improved method for end-to-end modulation type identification of underwater acoustic communication signals, the activation function is:

[0073]

[0074] Among them, Softmax(z f ) is the probability that the underwater acoustic communication signal belongs to each modulation type, z f is the modulation type label of the output of the f-th node of the fully connected layer, k is the modulation type index of the underwater acoustic communication signal, and K is the total number of modulation types of the underwater acoustic communication signal.

[0075] In an improved method for identifying end-to-end modulation types of underwater acoustic communication signals, the end-to-end modulation type identification model for underwater acoustic communication signals is trained in the following manner:

[0076] Construct training dataset D train And the test dataset D test : Detect the communication signal in the acquired underwater acoustic signal and obtain N one-dimensional signals X1, X2, ..., X of the same length as K types of underwater acoustic communication signals. N Samples and corresponding true label vectors Y1, Y2, ..., Y N ; Randomly select N1 samples and corresponding label vectors to form the training data set D train , the remaining N-N1 samples and their corresponding label vectors constitute the test data set D test ;

[0077] Initialize the modulation type recognition model: the parameters of each network layer are Gaussian distributed G(μ,σ) with a mean of 0 and a variance of 1. 2 ) Random initialization;

[0078] Training the modulation type recognition model: Divide the training data set into batches according to the set batch size; perform one round of training on all batches of data in the model to obtain the optimized model, and then use all batches of data to perform the next round of training on the optimized model until the number of training rounds reaches the set number of epochs;

[0079] The first round of model training includes: selecting a batch from the batch data, inputting the initialized end-to-end modulation type recognition model of underwater acoustic communication signals, combining the true label vector, using the cross-entropy loss function to calculate the function loss value of the model, using the error back propagation BP algorithm to update the modulation type recognition model parameters, and using the Adam optimizer with an adaptive learning rate to optimize the model parameters, repeatedly selecting a batch of data from the training set and inputting the optimized modulation type recognition model until all batches of data are selected;

[0080] Other rounds of model training include: selecting a batch from the batch data, inputting the end-to-end modulation type recognition model of underwater acoustic communication signals obtained from the previous round of training, combining the true label vector, using the cross-entropy loss function to calculate the function loss value Loss of the model, using the error back propagation BP algorithm to update the modulation type recognition model parameters, and using the Adam optimizer with an adaptive learning rate to optimize the model parameters, repeatedly selecting a batch of data from the training set, and inputting the optimized modulation type recognition model until all batches of data are selected.

[0081] In an improved method for end-to-end modulation type identification of underwater acoustic communication signals, the Loss calculation formula is:

[0082]

[0083] Among them, N1 represents the number of training set samples, K is the total number of underwater acoustic communication signal modulation types, k is the modulation type index, j is the sample index, q(x j,k ) represents the sample x j,k The predicted modulation type, p(x j,k ) represents the sample x j,k is the probability of the current predicted modulation type.

[0084] The technical solution of the present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0085] Example

[0086] like Figure 1 As shown, an embodiment of the present invention proposes an end-to-end modulation recognition method for underwater acoustic communication signals, and its implementation includes the following steps:

[0087] Step 1: Process the acquired underwater acoustic signal, detect the communication signal in the underwater acoustic signal, and obtain different types of underwater acoustic communication signals. The specific implementation is as follows:

[0088] Step 1-1) performing communication signal detection on the acquired underwater acoustic signal, and extracting and saving the detected communication pulse signal;

[0089] Step 1-2) The extracted underwater acoustic communication signal is framed, and data is intercepted according to 4096 sampling points per frame. Two adjacent frames overlap 2048 sampling points, and the duration of each frame signal is about 0.2 seconds;

[0090] Step 1-3) Signal normalization: perform zero mean and normalization processing on each frame of data. Assuming that the signal s(n), n=1,2,…,4096, the processed sample s'(n) is

[0091]

[0092] After processing in steps 1-4), a total of N samples of K types of underwater acoustic communication signals are obtained as the data set for subsequent experiments.

[0093] Step 2: Construct an end-to-end modulation recognition model for underwater acoustic communication signals based on residual attention convolution. The specific implementation is as follows:

[0094] like Figure 3 As shown, it includes modules such as input layer, feature extraction module, classification module and output layer. The input layer InputLayer preprocesses the underwater acoustic communication signal, inputs the preprocessed underwater acoustic communication signal into the feature extraction module (Feature Extraction) for feature extraction, obtains the deep abstract features of the signal, and then generates the signal modulation type label through the classification module (Classification), and finally outputs the signal modulation type label through the output layer Output Layer; the feature extraction module includes multiple residual attention convolutions (RAC) and multiple depth convolutions (DC), among which the first RAC and the first DC respectively extract features of the communication signal, and connect the output features through the residual structure, and the output features are sequentially extracted through the remaining RACs and DCs;

[0095] The processing of the remaining RAC and DC pair features is similar to the processing of the first RAC and DC pair features, and will not be repeated here.

[0096] In the feature extraction module, there are 4 RAC units, all convolution kernels are 5×1 in size, all convolution strides are 1, and all pooling kernels are 4×1 in size. There are 3 depthwise convolution block DC units, which consist of 4 standard convolution layers and 1 ReLU activation function layer, connected by multi-convolution layer skip connections, and feature downsampling is achieved through 4 convolution kernels with a stride of 2. The convolution layers are all activated using the function ReLU6(x), and the expression is:

[0097] ReLU6(x)=min(6,max(x,0))

[0098] Where x represents the input value of the activation function, min(·) represents the minimum value function, and max(·) represents the maximum value function;

[0099] The classification module includes a global average pooling layer GAP, a fully connected layer Dense and a Softmax activation layer. The GAP layer performs channel-average downsampling on the features generated by the feature extraction module. The Dense layer generates a signal modulation type label, that is, the probability that the signal belongs to a certain category, and uses the Softmax activation function to activate the output value.

[0100] The end-to-end modulation recognition model processing process of underwater acoustic communication signals based on residual attention convolution specifically includes:

[0101] The input layer inputs the preprocessed underwater acoustic communication signal into the residual attention convolution block RAC and the depth convolution block DC in the feature extraction module;

[0102] The residual attention convolution block RAC and the depth convolution block DC respectively extract features of the communication signal, and connect the output features through the residual structure, and pass the output features through the residual attention convolution block RAC and the depth convolution block DC in turn for feature extraction;

[0103] like Figure 2 As shown in the figure, the residual attention convolution block RAC specifically includes: 2 ordinary one-dimensional convolution layers Conv1 and Conv2, 2 ReLU6 activation layers, 1 MaxPooling layer, and 1 one-dimensional convolution layer Attention with a channel attention mechanism;

[0104] The depth convolution block DC specifically includes: 4 ordinary one-dimensional convolution layers and 1 ReLU6 activation layer connected in sequence;

[0105] The feature extraction module inputs the extracted features into the global average pooling GAP layer of the classification module for downsampling and inputs them into the Dense layer;

[0106] The Dense layer generates the signal modulation type label, that is, the probability that the signal belongs to a certain category, and uses the Softmax activation function to activate the output value.

[0107] Step 2-3) In the residual attention convolution block RAC, the ordinary one-dimensional convolution layer is used to extract features from the underwater communication signal. The convolution layer operation is expressed as

[0108] y i =w i x+b i

[0109] In the above formula, x is the input data of the convolutional layer, w i and b i are the output features y i Corresponding convolution kernel and bias;

[0110] The MaxPooling layer uses a pooling kernel of size l×1 to downsample features to 1 / l of their original length, removing redundant information and reducing model parameters.

[0111] In the one-dimensional convolutional layer Attention with channel attention mechanism, global pooling is first used to reduce the dimension of the features and output a set of vectors with the same dimension as the number of channels. Then, two dense layers are added using ReLU activation and Sigmoid activation respectively to learn the weights of each channel. The process is expressed as

[0112]

[0113] Where p i is the output feature map of the convolutional layer, σ i is the channel weight, is the attention-weighted feature map.

[0114] Step 2-4) The feature extraction module inputs the extracted features into the global average pooling GAP layer of the classification module for downsampling and inputs them into the Dense layer to convert the features into one dimension. The Softmax activation function is used to classify the signal and output the result, that is, the probability that the signal belongs to a certain modulation type.

[0115] Step 3: Divide the acquired underwater acoustic communication signals into training sets and validation sets. The specific implementation is as follows:

[0116] Step 3-1) After step 1) is completed, a total of N one-dimensional signals X1, X2, ..., X with the same length are obtained. N And the corresponding label vectors Y1,Y2,...,Y N ;

[0117] Step 3-2) Randomly select N1 samples and corresponding label vectors from the data set in step 3-1) to form a training data set D train , the remaining N-N1 samples and their corresponding label vectors constitute the test data set D test .

[0118] Step 4: Iteratively train the end-to-end recognition model using the underwater acoustic communication signal training set. The specific implementation is as follows:

[0119] Step 4-1) For the constructed end-to-end modulation recognition model of underwater acoustic communication signals based on residual attention convolution, the parameters of each network layer are Gaussian distributed G(μ,σ) with a mean of 0 and a variance of 1. 2 ) Random initialization;

[0120] Step 4-2) Use the Adam optimizer or the Optimizers optimizer to update the network parameters so that the error between the output signal and the label converges to the optimal level;

[0121] Step 4-3) Calculate the model's function loss value Loss through the cross entropy loss function. The calculation formula is:

[0122]

[0123] Among them, N1 represents the number of training set samples, K represents the total number of target modulation types, q(x j,k ) represents the sample x j,k The predicted modulation type, p(x j,k ) represents the sample x j,k is the probability of the current predicted modulation type.

[0124] Step 4-4) Use the Softmax classification function to convert the output of the last layer of the network, that is, the fully connected layer, into the probability Softmax (z i ), and the final recognition result is obtained. The calculation formula is:

[0125]

[0126] Among them, Softmax(z f ) is the probability that the underwater acoustic communication signal belongs to each modulation type, z f is the modulation type label output by the fth node of the fully connected layer, k is the modulation type index of the underwater acoustic communication signal, and K is the total number of modulation types of underwater acoustic communication signals. After training, the trained modulation type recognition model can be compared with other different network models, and the final classification performance of each model, including the recognition model provided by the embodiment of the present invention, can be measured using different evaluation indicators.

[0127] Step 5: Use the trained modulation type recognition model to identify the modulation type of the underwater acoustic communication signal.

[0128] Based on any of the above methods for identifying the end-to-end modulation type of underwater acoustic communication signals, an embodiment of the present invention further provides a system for identifying the end-to-end modulation type of underwater acoustic communication signals, comprising:

[0129] An acquisition module, configured to detect the communication signal in the underwater acoustic signal and obtain the underwater acoustic communication signal;

[0130] The recognition module uses a pre-established and trained end-to-end modulation type recognition model for underwater acoustic communication signals based on residual attention convolution to identify the modulation type of the obtained underwater acoustic communication signal; wherein the trained end-to-end modulation type recognition model for underwater acoustic communication signals based on residual attention convolution specifically includes: a feature extraction module and a classification module; the feature extraction module is used to extract features of the underwater acoustic communication signal based on residual attention; the classification module is used to classify the extracted features to obtain the probability that the underwater acoustic communication signal belongs to a certain modulation type.

[0131] Preferably, the feature extraction module includes: M pairs of RAC and DC modules consisting of M+1 residual attention convolution blocks RAC and M depth convolution blocks DC, and 1 RAC module, which are connected in sequence; wherein, the first pair of RAC and DC modules respectively extract features of the communication signal and connect the output features through a residual structure, and the remaining pairs of RAC and DC modules are used to extract features from the output features; the 1 RAC module is used to continue to extract features from the features output by the last pair of RAC and DC modules.

[0132] The following is a further explanation of the technical effects of the present invention in conjunction with simulation experiments:

[0133] Conditions and contents of the simulation experiment:

[0134] The simulation experiment platform uses a Windows system and a Keras environment with TensorFlow as the backend to build the network. The experimental computer has an i7-10750H CPU and an RTX2070 GPU. The experimental data consists of five types of underwater acoustic communication signals: LFM, single-carrier chaotic frequency modulation, single-carrier chaotic phase modulation, multi-carrier OFDM, and single-carrier PSK. A total of 10,560 samples are used to assist in model training.

[0135] To validate the performance of the proposed method, 80% of the samples were randomly selected as the training set, and the remaining 20% ​​were used as the test set. The network cost function was categorical cross entropy, the optimizer was Adam, the batch size was set to 256, and the number of epochs was set to 100. During the iteration process, the model parameters were updated using the backpropagation (BP) algorithm and optimized using the Adam optimizer with an adaptive learning rate. The initial learning rate of the Adam optimizer was 0.001. The exponential decay rate of the first-order moment estimate was set to 0.9, and the exponential decay rate of the second-order moment estimate was set to 0.999. After 100 classification experiments, the model achieved a classification accuracy of 96.4%.

[0136] Table 1 Classification performance of different models / %

[0137]

[0138] Finally, it should be noted that the above embodiments are intended only to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions to the technical solutions of the present invention do not depart from the spirit and scope of the technical solutions of the present invention and are intended to be encompassed by the claims of the present invention.

Claims

1. A method for identifying the end-to-end modulation type of underwater acoustic communication signals, comprising: detecting a communication signal in the underwater acoustic signal to obtain an underwater acoustic communication signal; The modulation type of the acquired underwater acoustic communication signal is identified using a pre-established and trained end-to-end modulation type recognition model for underwater acoustic communication signals based on residual attention convolution. Among them, the trained end-to-end modulation type recognition model of underwater acoustic communication signals based on residual attention convolution includes: feature extraction module and classification module; among them, The feature extraction module is used to extract features of the underwater acoustic communication signal based on residual attention; The classification module is used to classify the extracted features to obtain the probability that the underwater acoustic communication signal belongs to a certain modulation type; The feature extraction module includes: M pairs of RAC and DC modules and one RAC module consisting of M+1 residual attention convolution blocks RAC and M depth convolution blocks DC, which are connected in sequence; wherein the first pair of RAC and DC modules respectively extracts features from the communication signal and connects the output features through a residual structure, and the remaining pairs of RAC and DC modules are used to extract features from the output features; the one RAC module is used to continue to extract features from the features output by the last pair of RAC and DC modules; Each RAC specifically includes: the first one-dimensional convolutional layer Conv1, the first ReLU6 activation layer, the second one-dimensional convolutional layer Conv2, the one-dimensional convolutional layer Attention with channel attention mechanism, the second ReLU6 activation layer and the maximum pooling layer MaxPooling connected in sequence; The Conv1 is used to use y i =w i x+b i The formula extracts features from underwater acoustic communication signals, where x is the input data of the convolution layer, and w is the i and b i are the output features y i The corresponding convolution kernel and bias, i is the number of convolution layers; The first ReLU6 activation layer is used to activate the extracted features to obtain activated features; Conv2 is used to continue to extract features from the activated features using the formula; The Attention is used to reduce the dimensionality of the features extracted by Conv2 using global pooling, output a set of vectors with the same dimension as the number of channels, and then add two dense layers using RELU activation and Sigmoid activation respectively, using the formula To learn the weight of each channel, we can get the attention weighted feature map, where p i is the feature output by Conv2, σ i is the channel weight, is the attention-weighted feature map; The second ReLU6 activation layer is used to activate the attention weighted features to obtain activated features; The MaxPooling is used to downsample the output features to 1 / 1 of the original length using a pooling kernel of size l×1, thereby removing redundant information and reducing model parameters.

2. The method for identifying end-to-end modulation types of underwater acoustic communication signals according to claim 1, characterized in that: Each DC specifically includes: 4 ordinary one-dimensional convolutional layers and 1 ReLU6 activation layer connected in sequence; The four common one-dimensional convolutional layers connected in sequence are used to continue extracting features from the underwater acoustic communication signal; The ReLU6 activation layer is used to activate the input features using the ReLU6(x)=min(6,max(x,0)) formula to obtain the activated features, where x represents the activation function input value, min(·) represents the minimum value function, and max(·) represents the maximum value function.

3. The method for identifying end-to-end modulation types of underwater acoustic communication signals according to claim 1, characterized in that: The classification module includes: a global average pooling layer, a fully connected layer and a Softmax activation layer connected in sequence; The global average pooling layer is used to perform channel-average downsampling on the extracted features; The fully connected layer is used to generate a modulation type label of the underwater acoustic communication signal based on the downsampled features; The Softmax activation layer is used to calculate the modulation type label of the underwater acoustic communication signal using an activation function to obtain the probability that the underwater acoustic communication signal belongs to a certain modulation type.

4. The method for identifying end-to-end modulation types of underwater acoustic communication signals according to claim 3, characterized in that: The activation function is: Among them, Softmax(z f ) is the probability that the underwater acoustic communication signal belongs to each modulation type, z f is the modulation type label of the output of the f-th node of the fully connected layer, k is the modulation type index of the underwater acoustic communication signal, and K is the total number of modulation types of the underwater acoustic communication signal.

5. The method for identifying end-to-end modulation type of underwater acoustic communication signals according to claim 1, characterized in that: The underwater acoustic communication signal end-to-end modulation type recognition model is trained in the following manner: Construct training dataset D train And the test dataset D test : Detect the communication signal in the acquired underwater acoustic signal and obtain N one-dimensional signals X1, X2, ..., X of the same length as K types of underwater acoustic communication signals. N Samples and corresponding true label vectors Y1, Y2, ..., Y N ; Randomly select N1 samples and corresponding label vectors to form the training data set D train , the remaining N-N1 samples and their corresponding label vectors constitute the test data set D test ; Initialize the modulation type recognition model: the parameters of each network layer are Gaussian distributed G(μ,σ) with a mean of 0 and a variance of 1. 2 ) Random initialization; Training the modulation type recognition model: Divide the training data set into batches according to the set batch size; perform one round of training on all batches of data in the model to obtain the optimized model, and then use all batches of data to perform the next round of training on the optimized model until the number of training rounds reaches the set number of epochs; The first round of model training includes: selecting a batch from the batch data, inputting the initialized end-to-end modulation type recognition model of underwater acoustic communication signals, combining the true label vector, using the cross-entropy loss function to calculate the function loss value of the model, using the error back propagation BP algorithm to update the modulation type recognition model parameters, and using the Adam optimizer with an adaptive learning rate to optimize the model parameters, repeatedly selecting a batch of data from the training set and inputting the optimized modulation type recognition model until all batches of data are selected; Other rounds of model training include: selecting a batch from the batch data, inputting the end-to-end modulation type recognition model of underwater acoustic communication signals obtained from the previous round of training, combining the true label vector, using the cross-entropy loss function to calculate the function loss value Loss of the model, using the error back propagation BP algorithm to update the modulation type recognition model parameters, and using the Adam optimizer with an adaptive learning rate to optimize the model parameters, repeatedly selecting a batch of data from the training set, and inputting the optimized modulation type recognition model until all batches of data are selected.

6. The method for identifying end-to-end modulation types of underwater acoustic communication signals according to claim 5, characterized in that: The calculation formula of Loss is: Among them, N1 represents the number of training set samples, K is the total number of underwater acoustic communication signal modulation types, k is the modulation type index, j is the sample index, q(x j,k ) represents the sample x j,k The predicted modulation type, p(x j,k ) represents the sample x j,k is the probability of the current predicted modulation type.

7. A modulation type identification system based on the end-to-end modulation type identification method for underwater acoustic communication signals according to claim 1, characterized in that: include: An acquisition module, configured to detect the communication signal in the underwater acoustic signal and obtain the underwater acoustic communication signal; The recognition module uses a pre-established and trained end-to-end modulation type recognition model for underwater acoustic communication signals based on residual attention convolution to identify the modulation type of the obtained underwater acoustic communication signal; wherein the trained end-to-end modulation type recognition model for underwater acoustic communication signals based on residual attention convolution specifically includes: a feature extraction module and a classification module; the feature extraction module is used to extract features of the underwater acoustic communication signal based on residual attention; the classification module is used to classify the extracted features to obtain the probability that the underwater acoustic communication signal belongs to a certain modulation type.

8. The modulation type recognition system according to claim 7, characterized in that include: The feature extraction module includes: M pairs of RAC and DC modules consisting of M+1 residual attention convolution blocks RAC and M depth convolution blocks DC, and 1 RAC module, which are connected in sequence; wherein, the first pair of RAC and DC modules respectively extract features of the communication signal and connect the output features through the residual structure, and the remaining pairs of RAC and DC modules are used to extract features of the output features; the 1 RAC module is used to continue to extract features from the features output by the last pair of RAC and DC modules.

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