CN111352086A - Unknown target identification method based on deep convolutional neural network - Google Patents

Unknown target identification method based on deep convolutional neural network Download PDF

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CN111352086A
CN111352086A CN202010152743.6A CN202010152743A CN111352086A CN 111352086 A CN111352086 A CN 111352086A CN 202010152743 A CN202010152743 A CN 202010152743A CN 111352086 A CN111352086 A CN 111352086A
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range profile
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周代英
张同梦雪
李粮余
胡晓龙
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University of Electronic Science and Technology of China
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/411—Identification of targets based on measurements of radar reflectivity
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/417—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section involving the use of neural networks

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Abstract

The invention belongs to the technical field of unknown target identification, and particularly relates to an unknown target identification method based on a deep convolutional neural network. The method comprises the steps of preprocessing one-dimensional range profile data (HRRP) obtained by a broadband radar, and reducing the amplitude sensitivity of the one-dimensional range profile; secondly, extracting features by utilizing a deep convolutional neural network; and finally, processing the identification probability of the known target data by a difference probability method to obtain a discrimination threshold, and discriminating the output vector of the neural network so as to identify the unknown target. The method introduces the discrimination threshold obtained by adopting the difference probability method, effectively describes the statistical distribution area boundary of the data set of the known target and the unknown target, and solves the problem that the conventional convolutional neural network cannot identify the unknown target.

Description

Unknown target identification method based on deep convolutional neural network
Technical Field
The invention belongs to the technical field of unknown target identification, and particularly relates to an unknown target identification method based on a deep convolutional neural network.
Background
Since the middle of the last century, radar target identification technology has gradually developed and matured, and the target to be identified by the radar is judged mainly according to the radar target cross-sectional area (RCS) or the one-dimensional range profile (HRRP). The high-resolution one-dimensional range image is the vector sum of target scattering center echoes acquired by the broadband radar, and not only provides the geometric shape and structural characteristics of a target, but also contains more relevant information required by target identification.
In recent years, the deep learning theory is gradually mature, the convolutional neural network is widely applied to the field of radar target identification, and the convolutional neural network has the characteristics of translation insensitivity, nonlinearity and self-learning, so that a good identification effect is obtained. However, the conventional convolutional neural network needs to train a large amount of data of known targets in advance, which means that the conventional convolutional neural network can only recognize targets of the known targets (i.e. targets that have participated in training), however, in practical applications, it is impossible to acquire one-dimensional range profile data of all targets in advance, construct a complete convolutional neural network for recognition, and when the network inputs data of unknown targets (i.e. targets that have not participated in training), the data will be forcibly recognized as classes of the known targets, resulting in erroneous recognition.
Disclosure of Invention
The invention provides a radar unknown target identification method based on a deep convolutional neural network, aiming at the problems. The method is based on a conventional deep convolution neural network, the training one-dimensional range profile data of the known target is used for obtaining the identification threshold, and the statistical distribution area boundary of the known target and the unknown target data set is effectively described, so that the unknown target is identified, and the problem that the conventional neural network cannot identify the unknown target is solved.
The technical scheme of the invention is as follows: an unknown target identification method based on a deep convolutional neural network comprises the following steps:
s1, setting a single target one-dimensional range profile sample obtained by the broadband radar as x ═ x based on the target scattering center model1,x2,...,xi,...,xN]Where N is the number of distance units, xiRepresenting the amplitude of the ith distance unit, highlighting the contrast effect of the strong scattering point and other scattering points in order to reduce the influence of the amplitude sensitivity of the one-dimensional distance image on the identification performance, and carrying out β -mean value standardization on the one-dimensional distance image:
Figure BDA0002403018010000021
wherein
Figure BDA0002403018010000022
Denotes the normalized amplitude of the ith distance element, β is a constant, ExRepresenting the mean of the single range profile, β -the single one-dimensional range profile after mean normalization is
Figure BDA0002403018010000023
And S2, constructing a deep convolutional neural network model, and extracting high-dimensional features of the radar one-dimensional range profile by using a Deep Convolutional Neural Network (DCNN) improved based on AlexNet under strong supervised learning. The deep convolution neural network is formed by stacking a plurality of convolution modules, and a Dropout layer is added in the middle of the deep convolution neural network to enable part of neurons to be inactivated randomly so as to reduce training parameters and reduce the risk of model overfitting. Based on a neural network principle, a Back Propagation (BP) algorithm and a random gradient descent (SGD) algorithm are adopted for model convergence and training, as shown in FIG. 1, the deep convolution neural network has 13 layers, namely a convolution module 1, a Dropout layer 1, a convolution module 2, a convolution module 3, a Dropout layer 2, a convolution module 4, a convolution module 5, a full connection layer 1, a batch normalization layer 1, a Dropout layer 3, a full connection layer 2, a batch normalization layer 2 and a classifier; the input of the deep convolutional neural network is
Figure BDA0002403018010000024
Outputting identification label given for classifier
Figure BDA0002403018010000025
Each convolution module is composed of a convolution layer, an activation function, a batch normalization layer and a pooling layer, the minimum resolution unit of radar and the one-dimensional range profile data characteristics are considered, the size of a convolution kernel is 1 × 3, each convolution layer is provided with 64 convolution kernels, the pooling kernel is 1 × 2, and because an Exponential Linear Unit (ELU) has better differential characteristics, the conventional activation function RELU is replaced by the Exponential Linear Unit (ELU), the model fitting capability is improved, and the robustness to input change is increased.
Figure BDA0002403018010000026
S3, determining an identification threshold: the conventional convolutional neural network target identification needs to acquire characteristic parameters of all classes in training learning, and an unknown class target is forced to be identified as a certain known class in classification. In order to identify unknown types, a difference probability method is introduced to obtain an identification threshold;
in the learning phase of the deep convolutional neural network, the probability output p of the ith one-dimensional distance image obtained from the classifier belonging to the jth known classijThe probability vector corresponding to each one-dimensional range profile is pi＝[pi1,pi2,...,piN]Where N is the number of known classes in the probability vector piTo choose the maximum value pmAnd the sub-maximum psmObtaining the difference probability vd＝pm-psm；
Inputting single one-dimensional range profiles of different types of targets, wherein each type of target can obtain a difference vector:
Figure BDA0002403018010000027
wherein d ismIs the difference vector for the mth class of objects,
Figure BDA0002403018010000031
the difference probability of the ith one-dimensional range profile of the mth type target is shown, and the superscript T represents a transpose symbol;
the difference vector d of all known targetsmCalculating a histogram of the difference probabilities in the image, and selecting one difference probability from the histogram of the difference probabilities as an identification threshold tau according to the predetermined correct discrimination rate of the known target;
s4, unknown target identification:
inputting the obtained single one-dimensional range profile of the unknown target into a trained deep convolutional neural network model to obtain a corresponding difference vector
Figure BDA0002403018010000032
Wherein
Figure BDA0002403018010000033
The difference probability corresponding to the ith test one-dimensional range profile data is obtained, and M represents the number of unknown target data;
will dtComparing the difference probability of the inner one-dimensional range profile with the identification threshold tau, and if the difference probability is more than or equal to the threshold
Figure BDA0002403018010000034
Identifying the ith one-dimensional range profile data as a known target; if the difference probability is less than the threshold
Figure BDA0002403018010000035
Identifying the ith one-dimensional range profile data as an unknown target, namely the identification rule is as follows:
Figure BDA0002403018010000036
wherein
Figure BDA0002403018010000037
Indicating that the ith unknown target one-dimensional range image data belongs to a known target,
Figure BDA0002403018010000038
and the ith unknown target one-dimensional range profile data belongs to the unknown target P.
The method has the advantages that the discrimination threshold obtained by adopting a difference probability method is introduced, so that the statistical distribution area boundary of the known target and the unknown target data set is effectively described, and the problem that the conventional convolutional neural network cannot identify the unknown target is solved.
Drawings
FIG. 1 is a schematic diagram of a deep convolutional neural network model structure.
Detailed Description
The effectiveness of the invention is demonstrated below in connection with the simulation example.
The experimental simulation radar parameters comprise a radar carrier frequency of 6GHz and a radar bandwidth of 400MHz, wherein the radar carrier frequency is 6GHz, and a simulation target acquires a one-dimensional range image every 0.1 DEG within a range of 0-180 DEG of AN azimuth angle at AN elevation angle of 3 DEG in the simulation scene, each type of airplane acquires 1801 one-dimensional range images, each one-dimensional range image comprises 320 range units, namely input data of each type of airplane is a one-dimensional range image matrix of 1801 ×.
In the process of training the update parameter W, the random initialization weight W ═ W1,w2,w3]And bias B ═ B1,b2,...,bN]And selecting a cross entropy loss function as a loss function and optimization parameters of an Adam optimizer with an adaptive learning rate, wherein the learning rate is initialized to 0.0001.
The identification results of the above 5 types of simulated radar target data using the conventional convolutional neural network method and the method herein are shown in table 1:
TABLE 1 recognition results of two methods on unknown targets
Figure BDA0002403018010000041
From experimental results, under the condition that three types of airplanes are randomly extracted as known targets and the other two types of airplanes are as unknown targets, the unknown targets cannot be identified by using a conventional CNN network, but the unknown targets can be well identified by the method due to the introduction of the unknown target distinguishing threshold, and the average correct identification rate of the unknown targets is over 80 percent, so that the method is verified to be effective.

Claims (1)

1. An unknown target identification method based on a deep convolutional neural network is characterized by comprising the following steps:
s1, setting a single target one-dimensional range profile sample obtained by the broadband radar as x ═ x based on the target scattering center model1,x2,...,xi,…,xN]Where N is the number of distance units, xiRepresenting the magnitude of the ith range bin, the one-dimensional range profile is subjected to β -mean normalization:
Figure FDA0002403017000000011
wherein
Figure FDA0002403017000000012
Denotes the normalized amplitude of the ith distance element, β is a constant, ExRepresenting the mean of the single range profile, β -the single one-dimensional range profile after mean normalization is
Figure FDA0002403017000000013
S2, constructing a deep convolution neural network model, wherein the deep convolution neural network has 13 layers in total, namely a convolution module 1, a Dropout layer 1, a convolution module 2, a convolution module 3, a Dropout layer 2, a convolution module 4, a convolution module 5, a full connection layer 1, a batch normalization layer 1, a Dropout layer 3, a full connection layer 2, a batch normalization layer 2 and a classifier; the input of the deep convolutional neural network is
Figure FDA0002403017000000014
Outputting identification label given for classifier
Figure FDA0002403017000000015
Each convolution module is composed of convolution layers, an activation function, a batch normalization layer and a pooling layer, wherein the convolution kernel size is 1 × 3, each convolution layer has 64 convolution kernels, the pooling kernel is 1 × 2, and the activation function is:
Figure FDA0002403017000000016
s3, determining an identification threshold: in the learning phase of the deep convolutional neural network, the slave score is dividedProbability output p of ith one-dimensional distance image obtained by the classifier belonging to jth known classijThe probability vector corresponding to each one-dimensional range profile is pi＝[pi1,pi2,...,piN]Where N is the number of known classes in the probability vector piTo choose the maximum value pmAnd the sub-maximum psmObtaining the difference probability vd＝pm-psm；
Inputting single one-dimensional range profiles of different types of targets, wherein each type of target can obtain a difference vector:
Figure FDA0002403017000000017
wherein d ismIs the difference vector for the mth class of objects,
Figure FDA0002403017000000018
the difference probability of the ith one-dimensional range profile of the mth type target is shown, and the superscript T represents a transpose symbol;
the difference vector d of all known targetsmCalculating a histogram of the difference probabilities in the image, and selecting one difference probability from the histogram of the difference probabilities as an identification threshold tau according to the predetermined correct discrimination rate of the known target;
s4, unknown target identification:
inputting the obtained single one-dimensional range profile of the unknown target into a trained deep convolutional neural network model to obtain a corresponding difference vector
Figure FDA0002403017000000021
Wherein
Figure FDA0002403017000000022
The difference probability corresponding to the ith test one-dimensional range profile data is obtained, and M represents the number of unknown target data;
will dtComparing the difference probability of the inner one-dimensional range profile with the identification threshold tau, and if the difference probability is more than or equal to the threshold
Figure FDA0002403017000000023
Identifying the ith one-dimensional range profile data as a known target; if the difference probability is less than the threshold
Figure FDA0002403017000000024
Identifying the ith one-dimensional range profile data as an unknown target, namely the identification rule is as follows:
Figure FDA0002403017000000025
wherein
Figure FDA0002403017000000026
Indicating that the ith unknown target one-dimensional range image data belongs to a known target,
Figure FDA0002403017000000027
and indicating that the ith unknown target one-dimensional range profile data belongs to the unknown target.
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CN112014821A (en) * 2020-08-27 2020-12-01 电子科技大学 Unknown vehicle target identification method based on radar broadband characteristics
CN112163510A (en) * 2020-09-25 2021-01-01 电子科技大学 Human body action classification recognition method based on multi-observation variable HMM model
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CN113156416A (en) * 2021-05-17 2021-07-23 电子科技大学 Unknown target discrimination method based on multi-kernel dictionary learning
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CN114821335A (en) * 2022-05-20 2022-07-29 电子科技大学 Unknown target discrimination method based on depth feature and linear discrimination feature fusion

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