CN110245608B - Underwater target identification method based on half tensor product neural network - Google Patents
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Abstract
The invention provides an underwater target identification method based on a half tensor product neural network, which is characterized in that an underwater sound signal is received through an underwater sonar sensor, and time domain and frequency domain information of the sound signal is presented in an LOFAR map through short-time Fourier transform; constructing a half tensor product neural network of the data sample by taking the LOFAR atlas sample as an input feature matrix; dividing the received underwater acoustic signals into a training set and a verification set, and inputting a half tensor product neural network for training and verification; selecting different hyper-parameters, performing model training on the half tensor product neural network by using a training set, comparing the test effect of a verification set, and determining the hyper-parameters with high test accuracy; and finally, inputting the currently acquired acoustic signal of the underwater target into the half tensor product neural network after model training, and giving a judgment result. The method can improve the underwater target recognition rate, expand application scenes and be suitable for recognizing the underwater target in the complex marine environmental noise.
Description
Technical Field
The invention belongs to the field of signal processing, and relates to methods of a neural network, acoustic signal processing, half tensor product multiplication, underwater target identification and the like.
Background
The target classification and identification has very important significance for various research fields, and the traditional target classification and identification is to artificially extract various features and then construct a classifier for classification and identification. With the rapid development of computer vision technology, the target classification and recognition technology based on deep learning is widely researched by people, and the classification and recognition effect beyond human is achieved. However, at present, research is mainly carried out in the field of computer vision such as images and videos, research on acoustic signals is also focused on voice signal processing and natural language processing, and classification and identification of various underwater targets are still carried out on the traditional method.
The conventional acoustic signal recognition algorithm generally includes a dynamic time warping technique, a support vector machine, a gaussian mixture model, a hidden markov model, and the like. The traditional method is greatly influenced by artificial feature extraction and environmental noise, and the identification models are only symbolic systems, so that the modeling capacity is reduced, the identification performance of acoustic signals in a complex environment is greatly reduced, and the classification identification rate in complex and variable marine environmental noise is not ideal enough. Artificial neural networks are a research hotspot emerging in the field of artificial intelligence in recent years. With the continuous and deep research work of the artificial neural network, great progress has been made at present.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an underwater target identification method based on a half tensor product neural network, which introduces a novel matrix operation of half tensor product multiplication into the neural network, establishes a neural network model based on a half tensor product, is applied to the processing of acoustic signals, can improve the identification rate of underwater targets, expands application scenes, and is suitable for identifying the underwater targets in complex marine environment noise.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps: receiving underwater acoustic signals including acoustic signals of underwater targets and ocean environment background noise without the underwater targets through an underwater sonar sensor; presenting time domain and frequency domain information of the acoustic signal in a LOFAR map through short-time Fourier transform; constructing a half tensor product neural network of the data sample by taking the LOFAR atlas sample as an input feature matrix; dividing the received underwater acoustic signals into a training set and a verification set, and inputting a half tensor product neural network for training and verification; selecting different hyper-parameters, performing model training on the half tensor product neural network by using a training set, comparing the test effect of a verification set, and determining the hyper-parameters with high test accuracy; and finally, inputting the currently acquired acoustic signal of the underwater target into the half tensor product neural network after model training, and giving a judgment result.
The underwater acoustic signal
In the formula, h (t) represents the impact response of an ocean channel, s (t) represents an underwater target signal, n (t) represents the noise of an ocean environment, represents convolution operation, and t is a time variable.
After the underwater sonar sensor receives underwater acoustic signals, a high-pass filter is designed to filter low-frequency-band noise, and then frame preprocessing is carried out.
The signal duration of each frame in the frame preprocessing is 1 s.
Said short-time Fourier transform
Where x (t) represents the signal, w (t) is a window function, which acts as a time-limit, e-jωtAnd plays a role of frequency limitation.
In the half-tensor product neural network described above,
in the formula yiRepresenting the eigenmatrix, x, after a half-tensor product operationiA feature matrix representing the input, WiFormula weight matrix, biRepresenting an offset term, fc() Represents the activation function of the semi-tensor layer,
representing a half tensor product operation; after the input features are subjected to feature extraction through half tensor product, the output feature graph is transmitted to a pooling layer for feature selection and information filtering, and the pooling processIs zi＝βipool(yi)+biWherein pool () represents yiCarrying out a sampling operation ofiIndicating the multiplication bias.
The underwater acoustic signal is divided into K sub-samples, one single sub-sample is reserved as data of a verification model, and the other K-1 sub-samples are substituted to construct a half tensor product neural network for training; and repeating the cross validation for K times, validating each subsample once, and averaging the results of the K times to be used as the performance index of the evaluation classifier.
The invention has the beneficial effects that: because the neural network model is trained by data, a determined noise model cannot be assumed, errors brought to complex and variable marine environment noise modeling are reduced, the training and operation speed is higher, the robustness is better than that of a traditional machine learning algorithm, and meanwhile, the recognition rate of an underwater sound source target can be improved. The method can be suitable for various complex marine environmental noises, and has profound significance for underwater target intelligent sensing, marine exploration, underwater abnormal target detection and intelligent marine and marine defense construction. The invention successfully applies the artificial intelligence technology to ocean information perception and underwater target detection, and promotes the application and development of the technology in the fields of intelligent ocean, ocean defense and the like.
Drawings
FIG. 1 is a general method flow diagram of the present invention;
FIG. 2 is a schematic diagram of a half tensor product neural network structure of the present invention.
Detailed Description
The present invention will be further described with reference to the following drawings and examples, which include, but are not limited to, the following examples.
The method receives the acoustic signals of the underwater target through the underwater sonar sensor, acquires a large number of acoustic signals of different underwater targets such as ships, fishing boats, speed boats and the like and various ocean environment background noises, generates an LOFAR spectrogram of the acoustic signals through short-time Fourier transform, and divides data samples into a training set and a verification set to be input into a semi-tensor product neural network for training. And combining neural network parameter adjustment to optimize the effect based on the training set and the verification set. And finally, inputting the acoustic signal of the underwater target into a half tensor product neural network to give a judgment result.
As shown in fig. 1, the present invention comprises the steps of:
the first step is as follows: underwater sonar sensor receiving signal
The underwater sonar sensor is placed in the ocean environment, and received signals x (t) are as follows:
in the formula, h (t) represents the ocean channel impact response, s (t) represents the underwater target signal, n (t) represents the ocean environment noise, represents the convolution operation, and t is a time variable. I.e. no object is received as pure marine ambient noise.
The second step is that: signal pre-processing
Because the noise level of the ocean low-frequency environment is high, a high-pass filter is designed, low-frequency band noise is filtered, then frame preprocessing is carried out, and the signal duration of each frame is 1 s. The signal framing processing can increase the number of training samples and the length of the normalized sample signal, and is ready for obtaining visual spectrogram samples with uniform sizes in the next step.
The third step: obtaining acoustic signal time frequency visual LOFAR spectrogram
The time domain and frequency domain information of the acoustic signal is presented in a LOFAR map in a short-time Fourier transform mode:
where x (t) represents the signal (here 1s long received signal samples are represented), w (t) is a window function, which acts as a time limit, e-jωtAnd plays a role of frequency limitation.
The fourth step: constructing a semi-tensor product neural network
Replacing convolution operation in conventional convolution neural network with half tensor matrix multiplication product by a kernel matrixHalf tensor product multiplication is performed directly on each layer of input. Matrix half tensor product matrix multiplication can realize multiplication of two matrixes with any dimension. The general studies are left half tensor products. For a given two matrices A ∈ Mm×nAnd B ∈ Mp×qThen its half tensor product is:
where r ═ lcm (n, p) is the least common multiple of { n, p },
is a kronecker product operation.
Half tensor stacking: the process of the entire half tensor product can be expressed as follows:
in the formula yiRepresenting the eigenmatrix, x, after a half-tensor product operationiA feature matrix representing the input (with the LOFAR spectrogram sample obtained in the third step as the input feature matrix), WiFormula weight matrix, biRepresenting an offset term, fc() A semi-tensor layer-built activation function is represented,
representing a half tensor product operation.
A pooling layer: after the input features are subjected to feature extraction by half tensor product, the output feature map is transmitted to a pooling layer for feature selection and information filtering, namely down-sampling. The process of pooling can be represented by the following formula:
zi＝βipool(yi)+bi
wherein, pool () represents sampling yi, i.e. the pooling operation; beta is aiRepresenting the multiplication offset, biA bias term is represented.
Full connection layer: the fully-connected layer is usually built on the last part of the hidden layer of the convolutional neural network, and only signals are transmitted to other fully-connected layers. The output layer is connected behind the full connection layer and outputs the classification labels by using a logic function or a normalized exponential function.
The fifth step: constructing a training set sample set
A data set acquired by an underwater sonar sensor is divided into K sub-samples, an independent sub-sample is reserved as data of a verification model, and other K-1 samples are used for substituting to construct a half tensor product neural network for training. And repeating the cross validation for K times, validating each subsample once, and averaging the results of the K times to be used as the performance index of the evaluation classifier.
And a sixth step: adjusting hyper-parameter training optimization neural network model
By selecting different hyper-parameters, model training is carried out by using a training set, and the hyper-parameters with high testing accuracy are determined by comparing the testing effects of the testing set. The hyper-parameters comprise the learning rate of the neural network, the batch size, the iteration times, the half tensor product kernel size and the activation function. The method is used for optimizing the neural network model and improving the performance and effect of network learning.
The seventh step: realize underwater target recognition classification
After the data to be detected is preprocessed, a classification result can be given through training an optimized half tensor product neural network model, and underwater target recognition and classification are achieved.
Aiming at the problems of low underwater target recognition rate and weak robustness of the traditional method, the embodiment of the invention provides an underwater target recognition method based on a half tensor product neural network, which comprises the following steps:
the first step is as follows: underwater sonar sensor receiving signal
The underwater sonar sensor is placed in the ocean environment, and received signals x (t) are as follows:
in the formula, h (t) represents the ocean channel impact response, n (t) represents the ocean environment noise, x represents the convolution operation, and t is a time variable. I.e. no object is received as pure marine ambient noise.
The second step is that: signal pre-processing
Because the noise level of the ocean low-frequency environment is high, a second-order Butterworth filter is designed, and a digital Butterworth high-pass filter is deduced through bilinear transformation. And (2) the received sound data is subjected to a second-order Butterworth high-pass filter to obtain a filtered sound signal, low-frequency-band noise is filtered, then frame division preprocessing is carried out, and the time length of each frame of signal is 1 s.
The third step: obtaining acoustic signal time frequency visual LOFAR spectrogram
The time domain and frequency domain information of the acoustic signal is presented in a LOFAR map in a short-time Fourier transform mode:
where x (t) represents the signal, w (t) is a window function functioning as a time limit, e-jωtThe window function can select a Hanning window, the number of Fourier transform points is selected to be 1024, and the length of the window is selected to be 1024.
The fourth step: constructing a semi-tensor product neural network
The convolution operation in the conventional convolutional neural network is replaced by the half tensor matrix multiplication product, and half tensor product multiplication is directly carried out on each layer of input through a kernel matrix. Matrix half tensor product matrix multiplication can realize multiplication of two matrixes with any dimension. The general studies are left half tensor products. Given two matrices A ∈ Mm×nAnd B ∈ Mp×qThen its half tensor product is:
where t ═ lcm (n, p) is the least common multiple of { n, p },
is a kronecker product operation.
Half tensor stacking: the process of the entire half tensor product can be expressed as follows:
in the formula yiRepresenting the eigenmatrix, x, after a half-tensor product operationiA feature matrix representing the input, WiFormula weight matrix, biRepresenting an offset term, fc() A semi-tensor layer-built activation function is represented,
representing a half tensor product operation.
A pooling layer: after the input features are subjected to feature extraction by half tensor product, the output feature map is transmitted to a pooling layer for feature selection and information filtering, namely down-sampling. The process of pooling can be represented by the following formula:
zi＝βipool(yi)+bi
wherein pool () representsiPerforming down-sampling operation, namely the pooling operation; beta is aiRepresenting the multiplication offset, biRepresenting the bias term.
Full connection layer: the fully-connected layer is usually built on the last part of the hidden layer of the convolutional neural network, and only signals are transmitted to other fully-connected layers. The output layer is connected behind the full connection layer and outputs the classification labels by using a logic function or a normalized exponential function.
And constructing a half tensor product neural network with 5 convolutional layers, 5 pooling layers and 1 full-connection layer, wherein the number of neurons in an output layer is the number of classification categories.
The fifth step: constructing a training set sample set
Tagged data collected from an underwater sonar sensor is then segmented into K sub-sample sets, one individual sub-sample is retained as data for a verification model, and the other K-1 sample sets are used for training. And repeating the cross validation for K times, validating each subsample once, and averaging the results of the K times to be used as the performance index of the evaluation classifier.
And a sixth step: adjusting hyper-parameter training optimization neural network model
The neural network model is optimized by adjusting the learning rate, batch size, iteration times, half tensor product kernel size, activation function and other hyper-parameters of the neural network, and the performance and effect of network learning are improved. The learning rate can be selected from 0.0001-0.01, the batch size can be selected from 10-30, the iteration times are selected from 2-5, the half tensor product kernels are all selected from 3 x 3, the nonlinear excitation function is selected from a Relu function, and the pooling layer is selected from 2 x 2 average pooling.
The seventh step: realize underwater target recognition classification
After the data to be detected are preprocessed, a classification result can be given through a half tensor product neural network model which is well trained and optimized, and underwater target recognition and classification are achieved.
Claims (6)
1. An underwater target identification method based on a half tensor product neural network is characterized by comprising the following steps: receiving underwater acoustic signals including acoustic signals of underwater targets and ocean environment background noise without the underwater targets through an underwater sonar sensor; presenting time domain and frequency domain information of the acoustic signal in a LOFAR map through short-time Fourier transform; the LOFAR atlas is used as an input feature matrix, a half tensor product neural network which replaces convolution operation with half tensor products is constructed, convolution operation in a conventional convolutional neural network is replaced with half tensor matrix multiplication products, and the process of the whole half tensor product in half tensor lamination is expressed by the following formula:
in the formula yiRepresenting the eigenmatrix, x, after a half-tensor product operationiA feature matrix representing the input, WiIs a weight matrix, biRepresenting an offset term, fc() A semi-tensor layer-built activation function is represented,
expressing half tensor product operation, after the input features are subjected to feature extraction through half tensor product, the output feature graph is transmitted to a pooling layer for feature selection and information filtering, and the pooling process is zi＝βipool(yi)+biWherein pool () represents yiCarrying out a sampling operation ofiRepresents the multiply bias; dividing the received underwater acoustic signals into a training set and a verification set, and inputting a half tensor product neural network for training and verification; selecting different hyper-parameters, performing model training on the half tensor product neural network by using a training set, comparing the test effect of a verification set, and determining the hyper-parameters with high test accuracy; and finally, inputting the currently acquired acoustic signal of the underwater target into the half tensor product neural network after model training, and giving a judgment result.
2. The underwater target identification method based on the half tensor product neural network as claimed in claim 1, wherein: the underwater acoustic signal
In the formula, h (t) represents the ocean channel impact response, s (t) represents the underwater target signal, n (t) represents the ocean environment noise, represents the convolution operation, and t is a time variable.
3. The underwater target identification method based on the half tensor product neural network as claimed in claim 1, wherein: after the underwater sonar sensor receives underwater acoustic signals, a high-pass filter is designed to filter low-frequency-band noise, and then frame preprocessing is carried out.
4. The underwater target identification method based on the half tensor product neural network as claimed in claim 3, wherein: the signal duration of each frame in the frame preprocessing is 1 s.
5. The half tensor product based nerve of claim 1The underwater target identification method of the network is characterized in that: said short-time Fourier transform
Where x (t) represents the underwater acoustic signal, w (t) is a window function, which acts as a time-limit, e-jωtAnd plays a role of frequency limitation.
6. The underwater target identification method based on the half tensor product neural network as claimed in claim 1, wherein:
the underwater acoustic signal is divided into K sub-samples, an individual sub-sample is reserved as data of a verification model, and other K-1 samples are used for substituting the constructed half tensor product neural network for training; and repeating the cross validation for K times, validating each subsample once, and averaging the results of the K times to be used as the performance index of the evaluation classifier.
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