CN114994657A - Radar repetition frequency change steady target identification method based on repetition frequency self-adaptive network - Google Patents
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Abstract
The invention relates to a radar repetition frequency change steady target identification method based on a repetition frequency self-adaptive network, which comprises the following steps: step 1: acquiring an echo signal to be detected, wherein the echo signal to be detected contains repetition frequency information; step 2: and inputting the echo signal to be detected and the repetition frequency information of the echo signal to be detected into the repetition frequency self-adaptive network after training to obtain a classification result. Wherein, the repetition frequency self-adaptive network is obtained by training based on a training data set and a training auxiliary data set; the repetition frequency self-adaptive network comprises a self-adaptive convolution kernel generation module and a convolution neural network module which are connected. The invention constructs a repetition frequency self-adaptive network, trains the constructed network by using echo signals and repetition frequency information thereof, adaptively adjusts the network structure according to the repetition frequency information, further learns the characteristic of better separability under the current repetition frequency condition, still has better classification effect when the radar repetition frequency changes, and has better stability.
Description
Technical Field
The invention belongs to the technical field of radars, and particularly relates to a radar repetition frequency change steady target identification method based on a repetition frequency self-adaptive network.
Background
The radar target identification is to judge the type of a target by using a radar echo signal of the target. Most of classification methods proposed in the existing documents assume that repetition frequencies of a training stage and a testing stage are unchanged, and in the actual working process of a radar, in order to solve the problems of blind speed, distance ambiguity, speed ambiguity and the like, the repetition frequencies often need to be changed, at this time, a model trained by using a training sample under the repetition frequencies is no longer suitable for a testing sample under a new repetition frequency, and the problem of reduced target classification accuracy due to model mismatch can occur. For the problem, the classification method proposed in the existing literature mainly trains different templates with training samples under different repetition frequencies respectively in a training stage, and matches the template with the same repetition frequency as the current test sample in a testing stage for testing.
For example, according to the radar target doppler image classification and identification method based on the one-dimensional convolutional neural network provided by researchers, the classification and identification method assumes that the repetition frequencies of a training stage and a testing stage are unchanged, while in the actual working process of a radar, in order to solve the problems of blind speed, distance ambiguity, speed ambiguity and the like, the repetition frequencies often need to be changed, at this time, a model trained by using a training sample under one repetition frequency is no longer suitable for a testing sample under a new repetition frequency, and the problem of reduction of target classification accuracy due to model mismatch can occur.
Based on the above, researchers have proposed a method for identifying a target with stable repetition frequency variation based on a spatial pyramid pooling network, which is used for solving the problem of radar target identification under the condition that pulse repetition frequencies in training and testing stages are not matched. According to the method, a spatial pyramid pooling layer is adopted to replace a traditional pooling layer, so that the network can extract features of fixed dimensionality from radar echo input of indefinite dimensionality, and the pulse repetition frequency is classified steadily under the condition that a new model is not trained again in a testing stage. The features extracted by the method have separability, and the radar target identification performance under the pulse repetition frequency change scene is greatly improved. However, the method only trains the network by using the echo signal, does not train the network by using the repetition frequency information of the echo signal at the same time, does not participate in the learning process of the convolution kernel parameter by using the repetition frequency information, and cannot adaptively adjust the network structure according to the repetition frequency information.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a radar repetition frequency change stable target identification method based on a repetition frequency self-adaptive network. The technical problem to be solved by the invention is realized by the following technical scheme:
the invention provides a radar repetition frequency change steady target identification method based on a repetition frequency self-adaptive network, which comprises the following steps:
step 1: acquiring an echo signal to be detected, wherein the echo signal to be detected contains repetition frequency information;
step 2: inputting the echo signal to be tested and the repetition frequency information of the echo signal to be tested into a repetition frequency self-adaptive network after training to obtain a classification result;
wherein the repetition frequency adaptive network is obtained by training based on a training data set and a training auxiliary data set; the repetition frequency self-adaptive network comprises a self-adaptive convolution kernel generation module and a convolution neural network module which are connected; the adaptive convolution kernel generation module generates a corresponding adaptive convolution kernel according to input repetition frequency information of the echo signal to be detected, and the adaptive convolution kernel is used as a convolution kernel of a first convolution unit of the convolution neural network module;
and the convolutional neural network module performs prediction classification on the input echo signal to be detected and outputs a classification result.
In one embodiment of the present invention, the adaptive convolution kernel generation module includes an input layer, a first fully-connected layer, an active layer, a second fully-connected layer, and an output layer, which are connected in sequence;
the number of the neurons of the first full connection layer is set to be 16, the number of the neurons of the second full connection layer is set to be 48, and the activation function of the activation layer is set to be a ReLU function.
In one embodiment of the present invention, the convolutional neural network module includes an input layer, a first convolution unit, a second convolution unit, a third convolution unit, a fourth convolution unit, a global adaptive average pooling layer, a full connection layer, and a SoftMax layer, which are connected in sequence;
the first convolution unit, the second convolution unit, the third convolution unit and the fourth convolution unit respectively comprise a convolution layer, a pooling layer and an activation layer which are sequentially connected, and the adaptive convolution kernel generated by the adaptive convolution kernel generation module is used as a convolution kernel of the convolution layer of the first convolution unit.
In one embodiment of the present invention, the number of convolution kernels of the convolution layers is set to 16, the sizes of the convolution kernels are set to 1 × 3, and the step sizes of convolution kernel shifting are set to 1 × 2;
the sizes of the pooling cores of the pooling layers are all set to be 1 multiplied by 2, the pooling step lengths are all set to be 1 multiplied by 2, and the pooling modes are all set to be maximum pooling;
the activation functions of the activation layers are all set as ReLU functions;
the pooling kernel size of the global adaptive average pooling layer is set to 1;
the number of the neurons of the full connection layer is set to be 16 multiplied by X, and X is the number of classification categories;
the SoftMax layer employs a SoftMax activation function for calculating probabilities that the input echo signals are classified into categories.
In an embodiment of the present invention, the training method of the repetition frequency adaptive network includes:
s1: generating a training data set; the training data set comprises a plurality of training samples, and each training sample is marked with a corresponding class label;
s2: generating a training auxiliary data set according to the training data set;
s3: and training the repetition frequency self-adaptive network by using the training data set, the training auxiliary data set and the class labels corresponding to the training samples to obtain the repetition frequency self-adaptive network after training.
In an embodiment of the present invention, the S1 includes:
s11: acquiring echo signals comprising X categories as a training data set, wherein each category comprises Y repetition frequencies, each repetition frequency comprises Z echo signals, X is more than or equal to 3, Y is more than or equal to 5, and Z is more than or equal to 1200;
s12: performing clutter suppression on each echo signal in the training data set by using a regional clear method;
s13: removing the main component of each echo signal subjected to clutter suppression by utilizing a global CLEAN method;
s14: carrying out modulo two norm normalization processing on the amplitude of each echo signal from which the main component is removed;
s15: taking each echo signal after normalization processing as a training sample;
s16: and adding a corresponding class label to each training sample to obtain the training data set.
In an embodiment of the present invention, the S2 includes:
and using the repetition frequency information of each echo signal in the training data set as the auxiliary information of the echo signal, wherein the auxiliary information of all echo signals forms the training auxiliary data set.
In an embodiment of the present invention, the S3 includes:
s31: inputting the training auxiliary data set into the adaptive convolution kernel generation module to generate a corresponding adaptive convolution kernel, wherein the adaptive convolution kernel is used as a convolution kernel of a first convolution unit of the convolution neural network module;
s32: inputting the training data set into the convolutional neural network module, and outputting a prediction classification label;
s33: calculating the loss between the prediction classification label and the class label of the training sample by using a cross entropy loss function, and then iteratively updating the network parameters by using a back propagation algorithm until the cross entropy loss function is converged to obtain the repetition frequency self-adaptive network after training.
In one embodiment of the invention, the cross entropy loss function is:
wherein H represents a cross entropy loss function, Y pre Predictive classification label, Y, representing a repetition frequency adaptive network train The class label represents a training sample in the training data set, X is 1,2, …, X represents a class number of the training sample in the training data set, X represents the total class number of the training sample in the training data set, and log represents a base-10 logarithm operation.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention discloses a radar repetition frequency change steady target identification method based on a repetition frequency self-adaptive network, which constructs the repetition frequency self-adaptive network, and adopts a global self-adaptive average pooling layer to replace the traditional pooling layer, thereby overcoming the problem that in the prior art, when echo signals with different repetition frequencies are classified, templates under the corresponding repetition frequencies need to be matched, and echo signals under the corresponding repetition frequencies are recorded to train a plurality of classifiers, so that the invention can extract the characteristics of fixed dimensionality from the echo signals with different dimensionalities, reduce the influence of the dimension change of the echo signals caused by different repetition frequencies, and realize the classification of the echo signals with different repetition frequencies by using a single classifier.
2. The method for identifying the radar repetition frequency change steady target based on the repetition frequency adaptive network constructs the repetition frequency adaptive network, and the network comprises an adaptive convolution kernel generation module and a convolution neural network module, so that the problem that the network structure cannot be adaptively adjusted according to repetition frequency information in the prior art without participating in the learning process of the convolution kernel parameters with the repetition frequency information is solved, the repetition frequency information can participate in the learning process of the convolution kernel parameters, the network structure can be adaptively adjusted according to the repetition frequency information of echo signals, the characteristic of better separability under the current repetition frequency condition is learned, the method still has a better classification effect when the radar repetition frequency changes, and the method has better steady performance.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following preferred embodiments are specifically described below with reference to the accompanying drawings.
Drawings
Fig. 1 is a schematic diagram of a method for identifying a radar repetition frequency variation robust target based on an repetition frequency adaptive network according to an embodiment of the present invention;
fig. 2 is a block diagram of a repetition frequency adaptive network according to an embodiment of the present invention;
fig. 3 is a diagram of a simulation experiment result provided by the embodiment of the present invention.
Detailed Description
In order to further explain the technical means and effects of the present invention adopted to achieve the predetermined object of the invention, a method for identifying a radar repetition frequency variation robust target based on a repetition frequency adaptive network according to the present invention is described in detail below with reference to the accompanying drawings and the detailed description.
The foregoing and other technical matters, features and effects of the present invention will be apparent from the following detailed description of the embodiments, which is to be read in connection with the accompanying drawings. The technical means and effects of the present invention adopted to achieve the predetermined purpose can be more deeply and specifically understood through the description of the specific embodiments, however, the attached drawings are provided for reference and description only and are not used for limiting the technical scheme of the present invention.
Example one
Referring to fig. 1, fig. 1 is a schematic diagram of a method for identifying a robust target for radar repetition frequency variation based on an repetition frequency adaptive network according to an embodiment of the present invention, where as shown in the figure, the method for identifying a robust target for radar repetition frequency variation based on an repetition frequency adaptive network according to the embodiment includes:
step 1: acquiring an echo signal to be detected, wherein the echo signal to be detected contains repetition frequency information;
step 2: and inputting the echo signal to be detected and the repetition frequency information of the echo signal to be detected into the repetition frequency self-adaptive network after training to obtain a classification result.
Wherein, the repetition frequency self-adaptive network is obtained by training based on a training data set and a training auxiliary data set; the repetition frequency self-adaptive network comprises a self-adaptive convolution kernel generation module and a convolution neural network module which are connected, wherein the self-adaptive convolution kernel generation module generates a corresponding self-adaptive convolution kernel according to input repetition frequency information of an echo signal to be detected, and the self-adaptive convolution kernel is used as a convolution kernel of a first convolution unit of the convolution neural network module; and the convolutional neural network module performs prediction classification on the input echo signal to be detected and outputs a classification result.
Further, please refer to fig. 2 in combination, fig. 2 is a block diagram of a repetition frequency adaptive network according to an embodiment of the present invention. In this embodiment, the adaptive convolution kernel generation module includes an input layer, a first full-connection layer, an activation layer, a second full-connection layer, and an output layer, which are connected in sequence;
the number of neurons of the first full connection layer is set to be 16, the number of neurons of the second full connection layer is set to be 48, and the activation function of the activation layer is set to be the ReLU function.
In this embodiment, the convolutional neural network module includes an input layer, a first convolution unit, a second convolution unit, a third convolution unit, a fourth convolution unit, a global adaptive average pooling layer, a full connection layer, and a SoftMax layer, which are connected in sequence;
the first convolution unit, the second convolution unit, the third convolution unit and the fourth convolution unit respectively comprise convolution layers, a pooling layer and an activation layer which are sequentially connected, and the self-adaptive convolution kernel generated by the self-adaptive convolution kernel generation module is used as a convolution kernel of the convolution layer of the first convolution unit.
That is, in this embodiment, the convolutional neural network module is a 15-layer convolutional neural network module, and includes an input layer, a first convolutional layer, a first pooling layer, a first active layer, a second convolutional layer, a second pooling layer, a second active layer, a third convolutional layer, a third pooling layer, a third active layer, a fourth convolutional layer, a fourth pooling layer, a fourth active layer, a global adaptive average pooling layer, a fully-connected layer, and a SoftMax layer, which are connected in sequence.
It should be noted that the dimension of the echo signal is directly affected by the change of the radar repetition frequency, but the dimension of the network input signal must be kept unchanged by the existence of the full connection layer in the convolutional neural network. For the one-dimensional input problem, the dimension of an input signal is assumed to be Nx 1, wherein N is a variable quantity, and after a plurality of times of convolution and pooling, the size of an output feature map is M x 1 xL. In the case of a fixed network structure, M varies with N, and L is a feature map depth, whose value is determined by the number of convolution kernels in the last convolutional layer, which can be understood as L feature maps in total, each feature map having a size of M × 1. In this embodiment, the global adaptive average pooling layer performs average pooling on each feature map, and at this time, the output vector size is L × 1 no matter how large the feature map is, so as to solve the problem of dimension change of the input signal.
Specifically, in the present embodiment, the number of convolution kernels of each of the first convolution layer, the second convolution layer, the third convolution layer, and the fourth convolution layer is set to 16, the sizes of the convolution kernels are set to 1 × 3, and the convolution kernel shift step sizes are set to 1 × 2; the sizes of the pooling cores of the first pooling layer, the second pooling layer, the third pooling layer and the fourth pooling layer are all set to be 1 x 2, the pooling step lengths are all set to be 1 x 2, and the pooling modes are all set to be maximum pooling; the activation functions of the first, second, third and fourth activation layers (not shown in the figure) are all set as ReLU functions; the size of the pooling core of the global self-adaptive average pooling layer is set to be 1, and the size of the output vector is 16 multiplied by 1 no matter how large the feature map is; the number of the neurons of the full connection layer is set to be 16 multiplied by X, and X is the number of classification categories; the SoftMax layer employs a SoftMax activation function for calculating the probability of the incoming echo signals being classified into classes.
In this embodiment, the training method of the repetition frequency adaptive network includes:
s1: generating a training data set; the training data set comprises a plurality of training samples, and each training sample is marked with a corresponding category label;
specifically, S1 includes:
s11: acquiring echo signals comprising X categories as a training data set, wherein each category comprises Y repetition frequencies, each repetition frequency comprises Z echo signals, X is more than or equal to 3, Y is more than or equal to 5, and Z is more than or equal to 1200;
s12: performing clutter suppression on each echo signal in the training data set by using a regional CLEAN method;
specifically, the clutter suppression processing includes the steps of:
the method comprises the steps of firstly, estimating ground clutter energy in an echo signal according to radar working parameters of the extracted echo signal;
performing discrete Fourier transform on each echo signal to obtain a Doppler spectrum of the echo signal, and taking a clutter frequency spectrum range as a clutter region, wherein the clutter frequency spectrum range is determined by the type of clutter;
thirdly, reconstructing a time domain signal corresponding to the maximum Doppler amplitude in the clutter region in each echo signal by using a formula (1):
wherein, C i (t) represents the signal amplitude of the reconstructed time domain signal corresponding to the maximum Doppler amplitude in the clutter region in the ith echo signal at the time t, Y i Represents the maximum Doppler amplitude of a clutter region of the Doppler spectrum of the ith echo signal, K represents the number of points of discrete Fourier transform, exp represents exponential operation with a natural constant e as the base, j represents an imaginary unit symbol, pi represents a circumferential ratio, ξ i Is represented by the formula i Corresponding Doppler frequency, θ i Is represented by the formula Y i A corresponding phase;
fourthly, subtracting the reconstructed time domain signal from each echo signal to obtain an echo processed signal;
fifthly, calculating the energy of each echo signal after echo processing in a clutter area;
judging whether the energy of each echo signal after echo processing in the clutter region is smaller than the ground clutter energy, if so, obtaining an echo signal after clutter suppression; otherwise, the second step is executed.
S13: removing the main component of each echo signal subjected to clutter suppression by utilizing a global CLEAN method;
specifically, the body component removal includes the steps of:
firstly, estimating main body component energy in an echo signal according to the extracted radar working parameters of the echo signal;
secondly, performing discrete Fourier transform on each echo signal to obtain a Doppler spectrum of the echo signal;
thirdly, reconstructing a time domain signal corresponding to the main component in each echo signal according to the formula (2):
wherein, B i (t) represents the signal amplitude at time t of the main component echo signal reconstructed from the main component echo signal in the ith echo signal, R i Denotes the maximum Doppler amplitude in the Doppler spectrum of the ith echo signal, K denotes the number of points of the discrete Fourier transform, exp denotes the exponential operation with the natural constant e as the base, j denotes the imaginary unit sign, π denotes the circumferential ratio, f i Represents R i Corresponding Doppler frequency, θ i Represents R i A corresponding phase;
and fourthly, subtracting the reconstructed time domain signal from each echo signal subjected to clutter suppression to obtain an echo signal from which the main component is removed.
S14: carrying out modulo two norm normalization processing on the amplitude of each echo signal with the main component removed;
s15: taking each echo signal after normalization processing as a training sample;
s16: and adding a corresponding class label to each training sample to obtain a training data set.
S2: generating a training auxiliary data set according to the training data set;
specifically, the repetition frequency information of each echo signal in the training data set is used as the auxiliary information of the echo signal, and the auxiliary information of all echo signals constitutes the training auxiliary data set.
S3: and training the repetition frequency self-adaptive network by utilizing the training data set, the training auxiliary data set and the class labels corresponding to the training samples to obtain the repetition frequency self-adaptive network after training.
Specifically, S3 includes:
s31: inputting the training auxiliary data set into a self-adaptive convolution kernel generation module to generate a corresponding self-adaptive convolution kernel, wherein the self-adaptive convolution kernel is used as a convolution kernel of a first convolution unit of a convolution neural network module;
s32: inputting the training data set into a convolutional neural network module, and outputting a prediction classification label;
s33: and calculating the loss between the prediction classification label and the class label of the training sample by using a cross entropy loss function, and iteratively updating the network parameters by using a back propagation algorithm until the cross entropy loss function is converged to obtain the repetition frequency self-adaptive network after training.
In this embodiment, the cross entropy loss function is:
wherein H represents a cross entropy loss function, Y pre Predictive class label, Y, representing an overfrequency adaptive network train Class labels representing training samples in the training data set,x is 1,2, …, X represents the class number of the training sample in the training data set, X represents the total class number of the training sample in the training data set, and log represents the base-10 logarithm operation.
In the repetition frequency adaptive network according to the present embodiment, repetition frequency information of an echo signal is input to the adaptive convolution kernel generation module as auxiliary information, and an adaptive convolution kernel output by the network is used as a convolution kernel in the convolution neural network module. In the training process of the repetition frequency adaptive network of the embodiment, different from the conventional convolutional neural network in which the convolutional kernel parameters are updated through gradient back propagation, the repetition frequency adaptive network updates the global convolutional kernel parameters and simultaneously transmits the gradients to the adaptive convolutional kernel generation module to synchronously update the network parameters. In the repetition frequency self-adaptive network of this embodiment, the constructed network is trained by using the echo signal and the repetition frequency information thereof, so that the repetition frequency information of the echo signal can participate in the learning process of the convolution kernel parameters, and the network structure is adaptively adjusted according to the repetition frequency information of the echo signal, thereby learning the characteristic of better separability under the current repetition frequency condition.
Further, when the method of this embodiment is used to perform target identification on the echo signal to be detected, the echo signal to be detected needs to be preprocessed, specifically, the method includes: performing clutter suppression on the echo signal to be detected by using a regional CLEAN method; removing the main component of the echo signal to be detected after clutter suppression by utilizing a global CLEAN method; and carrying out the module two norm normalization processing on the amplitude of the echo signal to be detected with the main component removed. Specifically, the processing steps are similar to those of the training samples, and are not described herein again.
In this embodiment, the repetition frequency information of the echo signal to be detected and the echo signal to be detected are respectively input into the adaptive convolution kernel generation module and the convolution neural network module of the repetition frequency adaptive network after training, the probability that the echo signal to be detected is classified into each category is calculated through the SoftMax layer, and the category corresponding to the highest probability is selected as the classification result.
The method for identifying the radar repetition frequency change steady target based on the repetition frequency adaptive network constructs the repetition frequency adaptive network, and the network adopts the global adaptive average pooling layer to replace the traditional pooling layer, so that the problem that in the prior art, when echo signals with different repetition frequencies are classified, templates under the corresponding repetition frequencies need to be matched, and echo signals under the corresponding repetition frequencies need to be recorded to train a plurality of classifiers is solved, the method can extract the characteristics with fixed dimensionality from the echo signals with different dimensionalities, reduce the influence of the dimensionality change of the echo signals caused by different repetition frequencies, and realize the classification of the echo signals with different repetition frequencies by using a single classifier.
The method for identifying the radar repetition frequency change steady target based on the repetition frequency adaptive network constructs the repetition frequency adaptive network, and the network comprises an adaptive convolution kernel generation module and a convolution neural network module, so that the problem that the network structure cannot be adaptively adjusted according to repetition frequency information in the prior art without participating in the learning process of the convolution kernel parameters with the repetition frequency information is solved, the repetition frequency information can be participated in the learning process of the convolution kernel parameters, the network structure can be adaptively adjusted according to the repetition frequency information of echo signals, the characteristic of better separability under the current repetition frequency condition is learned, the method still has a better classification effect when the radar repetition frequency changes, and the method has better steady performance.
Example two
The present embodiment further illustrates, through a simulation experiment, an effect of the method for identifying a radar repetition frequency variation robust target based on the repetition frequency adaptive network according to the first embodiment.
1. Simulation experiment conditions are as follows:
the hardware platform of the simulation experiment of this embodiment is: the processor is Intel (R) core (TM) i7-7700 CPU @3.60GHZ 3.60GHZ, the main frequency is 2.00GHz, and the memory is 16 GB.
The software platform of the simulation experiment of the embodiment is as follows: windows 10 operating system and python 3.7.
The data used in the simulation experiment of this embodiment are three types of 12-type airplanes, including a helicopter, a propeller, and a jet, generated by electromagnetic simulation software CST, and 4 types of three types of airplanes, and the specific rotor physical parameters are as shown in the following table:
TABLE 1 aircraft rotor physical parameter table
| Target model of airplane | Number of blades | L 1 (m) | L 2 (m) | Rotating speed (r/min) |
| |
4 | 0 | 5.5 | 383 |
| Helicopter rice-17 | 5 | 0 | 10.645 | 185 |
| |
3 | 0 | 5.345 | 394 |
| Helicopter bell 212 | 2 | 0 | 7.315 | 324 |
| |
6 | 0.28 | 1.905 | 950 |
| Propeller L-420 | 5 | 0.12 | 1.15 | 1650 |
| Propeller L- |
4 | 0.23 | 1.675 | 1150 |
| |
3 | 0.23 | 1.18 | 1690 |
| Jet plane A | 30 | 0.3 | 1.0 | 3000 |
| Jet B | 38 | 0.38 | 1.1 | 3520 |
| Jet C | 27 | 0.18 | 0.51 | 8615 |
| Jet D | 33 | 0.2 | 0.6 | 5000 |
The training data set radar echo signal used in the simulation experiment of this embodiment is generated under the radar operating frequency of 10GHz, the dwell time of 100ms, and the pulse repetition frequency of 2KHz, 3KHz, 4KHz, 5KHz, 6KHz, 7KHz, 8KHz, 9KHz, 10KHz respectively. Wherein each type of aircraft contains 300 radar echo signals respectively under every pulse repetition frequency, and every type of aircraft contains 2700 radar echo signals promptly, and every type of aircraft contains 10800 radar echo signals, and the training data set contains 32400 radar echo signals altogether. Gaussian white noise with a signal-to-noise ratio of 10dB was added to the training data set in the experiment, where the signal-to-noise ratio was defined as the signal-to-noise ratio of the micromotion component to the noise.
The test data set radar echo signal used in the simulation experiment of this embodiment is generated at a radar operating frequency of 10GHz and a dwell time of 100 ms. Divide into 9 different test data sets of group according to pulse repetition frequency's difference, 9 groups test data set's pulse repetition frequency is 2KHz, 3KHz, 4KHz, 5KHz, 6KHz, 7KHz, 8KHz, 9KHz, 10KHz respectively, wherein contains 1200 radar echo signals in every group subtest data set, and test data set contains 10800 radar echo signals altogether promptly. Gaussian white noise with a signal-to-noise ratio of 10dB was added to the training data set in the experiment, where the signal-to-noise ratio was defined as the signal-to-noise ratio of the micromotion component to the noise.
2. Simulation content and result analysis
In the simulation experiment of this embodiment, the method of the first embodiment, the support vector machine SVM classification method, and the convolutional neural network classification method are adopted to classify the test data sets respectively, so as to obtain classification results.
In a simulation experiment, the SVM classification method comprises the following steps: the radar micro-Doppler narrow-band target classification method is provided by the West's electronics science and technology university in the patent document' ground target classification method based on robustness time-frequency characteristics 'applied by the West's electronics science and technology university. The convolutional neural network classification method comprises the following steps: the radar micro-Doppler narrowband target classification method is proposed in a patent document 'radar target Doppler image classification identification method based on a one-dimensional convolutional neural network' applied by the Xian Ningfar electronic and electrical technology Limited company.
The method of the first embodiment is used for classifying the test data set, firstly a training data set and a training auxiliary data set are generated, then a repetition frequency self-adaptive network is constructed according to the repetition frequency self-adaptive network structure, and is trained to obtain the repetition frequency self-adaptive network after training, so that the classification test of the test data set is realized, the classification result of the test data set is obtained, and the classification accuracy of the method on the test data under different repetition frequency conditions is counted.
Classifying the test data set by using a Support Vector Machine (SVM) classification method, performing classification test on the test data set according to a classification method described in patent documents to obtain a classification result of the test data, and counting the classification accuracy of the method on the test data under different repetition frequencies.
Classifying the test data set by using a convolutional neural network classification method, performing classification test on the test data set according to the classification method described in the patent literature to obtain a classification result of the test data, and counting the classification accuracy of the method on the test data under different repetition frequencies.
The method, the SVM classification method and the convolutional neural network classification method of the first embodiment are used for plotting the classification accuracy of the test data under different repetition frequencies to obtain a curve graph, and the curve graph is a simulation experiment result graph provided by the embodiment of the invention shown in FIG. 3. In fig. 3, the abscissa represents repetition frequencies of test data, which are 2KHz, 3KHz, 4KHz, 5KHz, 6KHz, 7KHz, 8KHz, 9KHz, and 10KHz, respectively, and the ordinate represents classification accuracy of test data under different repetition frequencies.
As can be seen from FIG. 3, the classification accuracy of the method of the present invention for the test data under different repetition frequencies is higher than that of the SVM classification method and the convolutional neural network classification method, and particularly, when the repetition frequency is 2KHz, the classification accuracy of the method of the present invention for the test data is approximately 13 percentage points higher than that of the SVM classification method and approximately 7 percentage points higher than that of the SVM classification method, when the repetition frequency is 3KHz, the classification accuracy of the method of the present invention for the test data is approximately 9 percentage points higher than that of the SVM classification method and approximately 5 percentage points higher than that of the convolutional neural network classification method, when the repetition frequency is 4KHz-8KHz, the classification accuracy of the method of the present invention for the test data is approximately 5 percentage points higher than that of the SVM classification method, and approximately 1 percentage point higher than that of the convolutional neural network classification method, when the repetition frequency is 9KHz-10KHz, the classification accuracy of the method for the test data is nearly 3 percent higher than that of the SVM classification method. Therefore, the method provided by the invention obviously reduces the influence of the change of the repetition frequency on the radar micro-Doppler narrowband target classification, has a good classification effect when the radar repetition frequency changes, has good stability, and can achieve higher target classification accuracy rate than the prior art under different repetition frequencies.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that an article or device that comprises a list of elements does not include only those elements but may include other elements not expressly listed. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of additional like elements in the article or device comprising the element. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (9)
1. A radar repetition frequency change steady target identification method based on a repetition frequency self-adaptive network is characterized by comprising the following steps:
step 1: acquiring an echo signal to be detected, wherein the echo signal to be detected contains repetition frequency information;
step 2: inputting the echo signal to be tested and the repetition frequency information of the echo signal to be tested into a repetition frequency self-adaptive network after training to obtain a classification result;
wherein the repetition frequency adaptive network is obtained by training based on a training data set and a training auxiliary data set; the repetition frequency self-adaptive network comprises a self-adaptive convolution kernel generation module and a convolution neural network module which are connected; the adaptive convolution kernel generation module generates a corresponding adaptive convolution kernel according to input repetition frequency information of the echo signal to be detected, and the adaptive convolution kernel is used as a convolution kernel of a first convolution unit of the convolution neural network module;
and the convolutional neural network module performs prediction classification on the input echo signal to be detected and outputs a classification result.
2. The radar repetition frequency variation robust target identification method based on the repetition frequency adaptive network according to claim 1, characterized in that the adaptive convolution kernel generation module comprises an input layer, a first fully connected layer, an activation layer, a second fully connected layer and an output layer which are connected in sequence;
the number of the neurons of the first full connection layer is set to be 16, the number of the neurons of the second full connection layer is set to be 48, and the activation function of the activation layer is set to be a ReLU function.
3. The method for identifying the radar repetition frequency variation robust target based on the repetition frequency adaptive network according to claim 1, wherein the convolutional neural network module comprises an input layer, a first convolution unit, a second convolution unit, a third convolution unit, a fourth convolution unit, a global adaptive average pooling layer, a full connection layer and a SoftMax layer which are connected in sequence;
the first convolution unit, the second convolution unit, the third convolution unit and the fourth convolution unit respectively comprise a convolution layer, a pooling layer and an activation layer which are sequentially connected, and the adaptive convolution kernel generated by the adaptive convolution kernel generation module is used as a convolution kernel of the convolution layer of the first convolution unit.
4. The method for identifying the radar repetition frequency variation robust target based on the repetition frequency adaptive network as claimed in claim 3, wherein the number of convolution kernels of the convolution layers is set to 16, the sizes of the convolution kernels are set to 1 x 3, and the moving step sizes of the convolution kernels are set to 1 x 2;
the sizes of the pooling cores of the pooling layers are all set to be 1 multiplied by 2, the pooling step lengths are all set to be 1 multiplied by 2, and the pooling modes are all set to be maximum pooling;
the activation functions of the activation layers are all set as ReLU functions;
the pooling kernel size of the global adaptive average pooling layer is set to 1;
the number of the neurons of the full connection layer is set to be 16 multiplied by X, and X is the number of classification categories;
the SoftMax layer employs a SoftMax activation function for calculating probabilities that the input echo signals are classified into categories.
5. The method for identifying radar repetition frequency variation robust target based on repetition frequency adaptive network according to claim 1, wherein the training method of the repetition frequency adaptive network comprises:
s1: generating a training data set; the training data set comprises a plurality of training samples, and each training sample is marked with a corresponding class label;
s2: generating a training auxiliary data set according to the training data set;
s3: and training the repetition frequency self-adaptive network by using the training data set, the training auxiliary data set and the class labels corresponding to the training samples to obtain the repetition frequency self-adaptive network after training.
6. The method for identifying radar repetition frequency variation robust target based on repetition frequency adaptive network according to claim 5, wherein said S1 comprises:
s11: acquiring echo signals containing X categories as a training data set, wherein each category contains Y repetition frequencies, each repetition frequency contains Z echo signals, X is more than or equal to 3, Y is more than or equal to 5, and Z is more than or equal to 1200;
s12: performing clutter suppression on each echo signal in the training data set by using a regional clear method;
s13: removing the main component of each echo signal subjected to clutter suppression by utilizing a global CLEAN method;
s14: carrying out modulo two norm normalization processing on the amplitude of each echo signal with the main component removed;
s15: taking each echo signal after normalization processing as a training sample;
s16: and adding a corresponding class label to each training sample to obtain the training data set.
7. The method for identifying radar repetition frequency variation robust target based on repetition frequency adaptive network according to claim 5, wherein said S2 comprises:
and using the repetition frequency information of each echo signal in the training data set as the auxiliary information of the echo signal, wherein the auxiliary information of all echo signals forms the training auxiliary data set.
8. The method for identifying radar repetition frequency variation robust target based on repetition frequency adaptive network according to claim 5, wherein said S3 comprises:
s31: inputting the training auxiliary data set into the adaptive convolution kernel generation module to generate a corresponding adaptive convolution kernel, wherein the adaptive convolution kernel is used as a convolution kernel of a first convolution unit of the convolution neural network module;
s32: inputting the training data set into the convolutional neural network module, and outputting a prediction classification label;
s33: calculating the loss between the prediction classification label and the class label of the training sample by using a cross entropy loss function, and then iteratively updating the network parameters by using a back propagation algorithm until the cross entropy loss function is converged to obtain the repetition frequency self-adaptive network after training.
9. The method for identifying radar repetition frequency variation robust targets based on repetition frequency adaptive network according to claim 8, wherein the cross entropy loss function is:
wherein H represents a cross entropy loss function, Y pre Predictive classification label, Y, representing a repetition frequency adaptive network train The class label represents a training sample in the training data set, X is 1,2, …, X represents a class serial number of the training sample in the training data set, X represents a total class number of the training sample in the training data set, and log represents a base-10 logarithm operation.
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