CN116205271A - Method for generating opposite network deep learning direction finding under small sample condition - Google Patents
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Abstract
The invention discloses a method for generating a countering network deep learning direction finding under a small sample condition, relates to the technical field of radio direction finding, and solves the technical problems that in the prior art, the difficulty of constructing a complete training sample library is high, and the number of samples which can be acquired under the condition of an actual application environment is smaller than the required training sample number; the method comprises the following steps: constructing and generating an countermeasure network; the generator and the discriminator mutually resist to generate a generated sample approaching to the real distribution; preprocessing data; extracting the relative amplitude and covariance characteristics of each array element to be used as input data of CNN; realizing nonlinear feature mapping of data through convolution operation; dividing the input feature map into a plurality of non-overlapping regions; small sample CNN direction finding; the method has the advantages that the small sample condition aiming at the practical application environment is realized, the generated sample which approximates to the real sample distribution is generated by adopting the generation countermeasure network, the training sample data set is effectively expanded, and the convolution neural network direction finding model is utilized, so that the high-precision direction finding is realized.
Description
Technical Field
The invention belongs to the field of radio direction finding, relates to a neural network technology, and in particular relates to a method for generating a counter network deep learning direction finding under a small sample condition.
Background
The direction finding of the radiation source target is an electronic reconnaissance device, and the arrival direction of the target signal is estimated by utilizing the received target signal through signal processing. The method mainly comprises amplitude method direction finding, phase method direction finding, space spectrum estimation direction finding and the like for the radiation source target direction finding, and the methods are all traditional non-intelligent direction finding methods. At present, an intelligent direction finding method has become a research hot spot. The patent 'random array angle of arrival estimation method based on deep learning' proposes an array signal direction finding method based on a Convolutional Neural Network (CNN), and the method aims at any given multi-element array, and realizes rapid and high-precision direction finding of a radiation source target on the basis of deep learning by extracting characteristic information such as phase differences among array elements of the array element sampling data.
However, the number of training samples required by the CNN direction finding method is huge, because, first, the method is a direction finding method based on classification recognition, and if the required direction finding range is 360 degrees, the angular resolution is 0.1 degree, and the target number is unknown, the angular classification number is
The number of training samples is huge at this time; secondly, when the wide-band direction finding requirement is met, a sample set needs to be built for each carrier frequency to train, and training samples are obviously increased. Therefore, the method has great difficulty in constructing a complete training sample library, and the number of samples which can be acquired under the condition of the practical application environment is far smaller than the required training sample number.
Therefore, a method for generating a countering network deep learning direction finding under the condition of a small sample is provided.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides the method for generating the opposite network deep learning direction finding under the condition of the small sample, and the method for generating the opposite network deep learning direction finding under the condition of the small sample solves the problems that the difficulty in constructing a complete training sample library in the prior art is extremely high, and the number of samples which can be acquired under the condition of the practical application environment is far smaller than the required training sample number.
To achieve the above object, an embodiment according to a first aspect of the present invention proposes a method for generating a countering network deep learning direction finding under a small sample condition, including the steps of:
step one: constructing and generating an countermeasure network by using a nonlinear mapping function; wherein the generation countermeasure network comprises a generator G and a discriminator D;
step two: the generator G and the discriminator D reach Nash equilibrium after mutual antagonism and mutual promotion, and generate a generated sample, which comprises the following steps:
inputting the noise sequence into a generator to generate a generated sample conforming to the real data distribution;
inputting the generated sample and the real sample into a discriminator;
the generator G is fixed, a method of minimizing cross entropy is adopted to train the discriminator D, so that the discriminator D can effectively distinguish real samples and generate samples, and the loss function of the discriminator D is as follows:
in the method, in the process of the invention,
wherein x is a real sample, and D (x) is the probability of judging the input real sample x by the judging device D;
the generator G learns the formal data distribution P data Deception discriminant D, fixed discriminant D, rawThe generator G increases the probability that the generated sample is judged to be a true sample, and when the generated sample is inputted, the output of the discriminator D is as close to 1 as possible, and the loss function of the generator G is:
step three: preprocessing the radiation sources received by each array element to obtain sampling data; wherein the preprocessing comprises power amplification, down-conversion and data sampling processing;
step four: taking the relative amplitude and covariance characteristics of each array element as the input data of CNN;
step five: the convolution layer carries out convolution operation with input data through different convolution kernels to realize nonlinear feature mapping of the data, so that feature extraction of the data is realized;
step six: the pooling layer divides the input feature map into a plurality of non-overlapping areas;
step seven: based on generating the antagonism network, the training data set under the condition of the small sample is expanded.
Preferably, the discriminator D is a classification model.
Preferably, the output of the discriminator D is the probability that the input sample is a true sample;
when the input real sample is output as close to 1 as possible;
when the input-generated samples are outputs as close to 0 as possible.
Preferably, the convolution layer adopts a local connection mode.
Preferably, the pooling layer compresses the data through a downsampling operation.
Compared with the prior art, the invention has the beneficial effects that:
the invention creates an antagonism network by using a nonlinear mapping function construction; the generator G and the discriminator D achieve Nash equilibrium after mutual antagonism and mutual promotion, and generate a generated sample; the method has the advantages that the small sample condition aiming at the practical application environment is realized, and the generation of the generated sample approaching to the real sample distribution is generated by adopting the generation countermeasure network;
preprocessing the radiation sources received by each array element to obtain sampling data; taking the relative amplitude and covariance characteristics of each array element as the input data of CNN; the convolution layer carries out convolution operation with input data through different convolution kernels to realize nonlinear feature mapping of the data, so that feature extraction of the data is realized; the pooling layer divides the input feature map into a plurality of non-overlapping areas; expanding a training data set under a small sample condition based on the generated antagonism network; the training sample data set is effectively expanded, and then the convolutional neural network direction finding model is utilized, so that stable high-precision direction finding is realized on the basis of learning and training.
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FIG. 1 is a schematic diagram of a generation of an impedance network;
fig. 2 is a flow chart of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1-2, the method for generating the opposite network deep learning direction finding under the condition of small sample comprises the following steps:
step one: building a spanning countermeasure network
The principle of creating an antagonism network using a nonlinear mapping function construction is shown in figure 1. The generating countermeasure network is composed of a generator G and a discriminator D, wherein the generator G is used for generating a generating sample G (z) according to an input noise sequence z, and the discriminator D is a classification model and is used for judging whether the input sample is a real sample x or the generating sample G (z).
Step two: generation and countermeasure
Firstly, inputting a noise sequence z into a generator G to generate a generated sample G (z) conforming to real data distribution, then inputting the generated sample G (z) and a real sample x into a discriminator D, wherein the discriminator D outputs the probability that the input sample is the real sample, the output is as close to 1 as possible when the real sample x is input, and the output is as close to 0 as possible when the generated sample G (z) is input.
The generator G is fixed, a method for minimizing cross entropy is adopted to train the discriminator D, so that the discriminator D can effectively distinguish a real sample x and generate a sample G (z), and the loss function of the discriminator D is as follows:
in the method, in the process of the invention,
wherein x is a real sample, and D (x) is the probability of judging the input real sample x by the judging device D;
The generator G learns the formal data distribution P data To deceptively determine the discriminator D, the discriminator D is fixed, the generator G increases the probability that the generated sample G (z) is determined to be a true sample, and when the generated sample G (z) is input, the output of the discriminator D is as close to 1 as possible, and the loss function of the generator G is:
the generator G and the arbiter D are opposed to each other and promote each other, and in the continuous countermeasure, they reach nash equalization, and finally, a pseudo-spurious generated sample G (z) can be generated.
Step three: data preprocessing
The data preprocessing includes power amplification, down-conversion and data sampling processing.
Assuming that the number of receiving array antenna array elements is L, the number of incident signals is M, and the vector x (t) = [ x ] for array output 1 (t)，x 2 (t)，…，x L (t)] T The representation is:
wherein a= [ a ] 1 ，a 1 ，…，a M ]Is an array response matrix corresponding to M incident signals,
is the i-th signal incident angle theta i Corresponding array response vectors.
And carrying out power amplification, down-conversion and data sampling processing on the radiation sources received by each array element to obtain sampling data.
Step four: generating CNN input data
In general, each signal is independent, and the covariance matrix of the array output is:
R＝E[x(t)x H (t)]＝AE[s(t)s H (t)]A H +E[v(t)v H (t)]＝AR s A H +R v
wherein R is s And R is v Representing the signal covariance matrix and the noise covariance matrix, respectively.
The relative amplitude of each array element is related to the incidence direction of the signal, and the covariance matrix keeps the angle information of all targets, so that the invention takes the relative amplitude of each array element and the covariance characteristic as the input data of CNN.
Step five: convolution processing
The convolution layer primarily analyzes each small piece of data more deeply, thereby abstracting higher-level features. The convolution layer adopts a local connection mode, and carries out convolution operation on different convolution kernels and input data to realize nonlinear feature mapping on the data, so as to realize feature extraction.
Step six: pooling treatment
The pooling layer realizes data compression through downsampling operation, is used for reducing characteristic dimension after convolution and reducing the number of neurons required by a network. The pooling process divides the input feature map into a plurality of non-overlapping regions based on the translation invariant feature. And (3) carrying out pooling treatment to realize automatic extraction of the target angle feature vector.
Step seven: small sample CNN direction finding
Based on the generation of the countermeasure network, learning training under the condition of a small sample is realized. When the real data is input, the CNN direction finding model is utilized, and the distributed generation features are coupled to different space angles by the full-connection layer through pretreatment, convolution treatment and pooling treatment, so that stable high-precision direction finding is realized.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
In order to verify the performance of the method, a 9-array element uniform linear array is sampled, under the condition of different angle sample numbers (the sample number of a single angle), a patent 'random array angle of arrival estimation method based on deep learning' (hereinafter referred to as method 1) and the method of the invention are respectively applied, a direction finding statistical test is carried out, and the statistical result of the direction finding accuracy is shown in table 1.
TABLE 1 statistical results of direction finding accuracy
Experimental results show that when the number of training samples is large, the direction-finding accuracy of the two methods is high, but under the condition of small samples, the direction-finding accuracy of the method is about 3% higher than that of the method 1, and the direction-finding stability is better.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (5)
1. The method for generating the opposite network deep learning direction finding under the condition of a small sample is characterized by comprising the following steps of:
step one: constructing and generating an countermeasure network by using a nonlinear mapping function; wherein the generation countermeasure network comprises a generator G and a discriminator D;
step two: the generator G and the discriminator D reach Nash equilibrium after mutual antagonism and mutual promotion, and generate a generated sample, which comprises the following steps:
inputting the noise sequence into a generator to generate a generated sample conforming to the real data distribution;
inputting the generated sample and the real sample into a discriminator;
the generator G is fixed, a method of minimizing cross entropy is adopted to train the discriminator D, so that the discriminator D can effectively distinguish real samples and generate samples, and the loss function of the discriminator D is as follows:
in the method, in the process of the invention,
wherein x is a real sample, and D (x) is the probability of judging the input real sample x by the judging device D;
the generator G learns the formal data distribution P data Deception of the arbiter D, fixation of the arbiter D, and enlargement of the generator GWhen the generated sample is judged to be the true sample, the output of the discriminator D is as close to 1 as possible when the generated sample is input, and the loss function of the generator G is as follows:
step three: preprocessing the radiation sources received by each array element to obtain sampling data; wherein the preprocessing comprises power amplification, down-conversion and data sampling processing;
step four: taking the relative amplitude and covariance characteristics of each array element as the input data of CNN;
step five: the convolution layer carries out convolution operation with input data through different convolution kernels to realize nonlinear feature mapping of the data, so that feature extraction of the data is realized;
step six: the pooling layer divides the input feature map into a plurality of non-overlapping areas;
step seven: based on generating the antagonism network, the training data set under the condition of the small sample is expanded.
2. The method for generating a countering network deep learning direction finding under a small sample condition according to claim 1, wherein the discriminator D is a classification model.
3. The method for generating the countering network deep learning direction finding under the condition of small samples according to claim 1, wherein the output of the discriminator D is the probability that the input sample is a real sample;
when the input real sample is output as close to 1 as possible;
when the input-generated samples are outputs as close to 0 as possible.
4. The method for generating the opposite network deep learning direction finding under the condition of small samples according to claim 1, wherein the convolution layer adopts a local connection mode.
5. The small sample condition generation opposing network deep learning direction finding method of claim 1 wherein the pooling layer compresses the data by a downsampling operation.
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