CN111523568B - Antenna array fault diagnosis method based on deep neural network and radiation data compensation - Google Patents

Antenna array fault diagnosis method based on deep neural network and radiation data compensation Download PDF

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CN111523568B
CN111523568B CN202010259285.6A CN202010259285A CN111523568B CN 111523568 B CN111523568 B CN 111523568B CN 202010259285 A CN202010259285 A CN 202010259285A CN 111523568 B CN111523568 B CN 111523568B
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CN111523568A (en
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张瑛
李贞莹
果威多
周代英
冯健
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University of Electronic Science and Technology of China
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    • G01R29/08—Measuring electromagnetic field characteristics
    • G01R29/10—Radiation diagrams of antennas
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Abstract

The invention discloses an antenna array fault diagnosis method based on a deep neural network and radiation data compensation, and belongs to the field of antenna signal processing and fault identification. Firstly, in a training stage before the application of an antenna, on a plurality of measuring points in a far field area, a probe is utilized to measure radiation data of an array under a plurality of fault scenes, then the measured data is used as the input of a neural network, the number and the positions of failure array elements are respectively used as the output, and two neural networks are trained; and secondly, in an application stage, measuring far-field radiation data of the array to be diagnosed on the same sampling point as the training stage, inputting the test data into a trained neural network, preliminarily determining the position of the most probable failed array element, and gradually determining the positions of all the probable failed array elements by using a radiation compensation method. The algorithm effectively reduces the sample data required to be collected in the training stage through a data compensation method, and is suitable for large-scale array fault diagnosis.

Description

Antenna array fault diagnosis method based on deep neural network and radiation data compensation
Technical Field
The invention relates to the field of antenna array signal processing and deep learning, in particular to an antenna array fault diagnosis method based on deep learning.
Background
The array antenna is a special antenna formed by more than or equal to two antenna units which are arranged according to a certain rule or randomly, and compared with a single antenna, the array antenna has many advantages, mainly comprising: high antenna gain, strong directivity, narrow beam width, and beam scanning. At present, array antennas play an indispensable role in various fields such as radar detection, mobile communication, satellite remote sensing, and the like. There is a close relationship between the radiation performance of the array antenna and the number of the radiation elements, and generally, the more the radiation elements, the better the radiation performance of the antenna. Such as in large military phased arrays, typically contain hundreds or thousands of radiating elements. However, as the array size grows, the probability of array element failure also increases. When a certain number of array elements fail, the array structure can be seriously damaged, leading to main lobe broadening, side lobe level increase, gain reduction, target detection influence and the like. In order to correct the array radiation performance degradation caused by the array element failure in time, it is important to accurately detect the number and the positions of the failed array elements.
At present, array fault diagnosis methods are mainly classified into three categories: a very near field method, a source reconstruction method, and a parametric model method. The extreme near field method is to determine the position of a failure array element by measuring the radiation near field of the array to be diagnosed; the source reconstruction method is divided into two modes of equivalent source reconstruction and excitation source reconstruction, and the difference between the two modes is that the equivalent source reconstruction is an equivalent current source reconstruction, and then the position of a failure array element is determined according to the distribution of the equivalent current source; the later directly locates the failure array element by reconstructing the excitation of the array element. The parametric model method is generally divided into two stages, first a training stage: training the parametric model using known training data; the second is an application stage: inputting the characteristic data of the array to be diagnosed into the trained parameter model to determine the position of the failed array element. In the three methods, as part of the antenna application scenes are special, and accurate near-field data is difficult to obtain, the array fault diagnosis method based on near-field measurement has higher requirements on the antenna application scenes. Compared with a source reconstruction method based on far-field measurement data, the parametric model method has the defect of slow training process, but has the advantages of high diagnosis speed and capability of realizing real-time diagnosis in an application stage.
Because the parameter model method needs a large amount of data and has a large calculation amount in the training stage, the existing array fault diagnosis method based on the parameter model only stays in the fault diagnosis of a small-scale array with a small number of failure array elements. For example, Patnaik A et al uses an artificial neural network to realize fault diagnosis of a 16-array-element linear array, Vakula D et al assumes that the number of failed array elements in the 16-array-element linear array is at most two, and designs three neural networks to realize fault diagnosis of the array. However, to meet radiation performance requirements, large-scale array applications are more widespread. Therefore, in order to utilize the advantage of rapid diagnosis by the parametric model method, it is necessary to expand the array fault diagnosis method based on the parametric model method so as to be suitable for large-scale array fault diagnosis.
Disclosure of Invention
The invention provides an antenna array fault diagnosis algorithm based on deep learning, and is suitable for antenna arrays in any shapes. The method aims to determine the number and the positions of fault array elements according to measured radiation data of an array far field on the basis of completing diagnosis model training in a training stage before antenna application. Compared with the existing array fault diagnosis algorithm based on a nonparametric model, the algorithm is not limited by the shape of the array and has higher diagnosis speed; compared with an array fault diagnosis algorithm based on a parameter model method, the algorithm needs less training samples and is suitable for large-scale array fault diagnosis.
The solution of the invention is: firstly, in a training stage before the antenna is applied, on a plurality of measuring points in a far field area, the radiation data of the array under a plurality of fault scenes are measured by using a probe, then the measured data is used as the input of a neural network, the number and the positions of failure array elements are respectively used as the output, and two neural networks are trained. And secondly, an application stage, measuring far-field radiation data of the array to be diagnosed at the same sampling point as the training stage, inputting the test data into a trained neural network, preliminarily determining the position of the most probable failed array element, and gradually determining the positions of all the probable failed array elements by using a radiation compensation method. Therefore, the technical scheme of the invention is an antenna array fault diagnosis method based on deep neural network and radiation data compensation, which comprises the following steps:
a model training stage:
step 1: measuring electric field radiation data of a far field of the array under N fault scenes by using a probe at M measuring points with different angles on a far field measuring plane; the data measured by the probe at the m-th angle is expressed by the following formula:
Figure BDA0002438677650000021
where c is the speed of light, f is the working frequency of the array, L is the total number of array elements contained in the array, xiAnd diExcitation of the ith array element and position in the array coordinate system, thetamIs the angle between the m-th measurement point and the positive z-axis of the array reference frame, nmNoise for the mth measurement point;
step 2: and (3) forming the array excitation and the measurement data of M points in the step 1 into a column vector:
Figure BDA0002438677650000022
and zn＝[zn(θ1),zn(θ2),…,zn(θM)]TExcitation vectors and patterns for the nth measurement, respectively, where T represents transposition;
constructing array failure indication vector and failure array element number label k according to excitation vectornWherein, if
Figure BDA0002438677650000023
Then the
Figure BDA0002438677650000024
If not, then the mobile phone is started,
Figure BDA0002438677650000025
knis equal to the indicator vector ynThe number of the (1) in the (1),
Figure BDA0002438677650000026
and step 3: forming a training set by the data obtained in the step 2 (z)1,k1),(z2,k2),...,(zN,kN) Since z is a complex number, it is represented as zn＝[Re(zn),Im(zn)]Wherein Re (·) represents taking the real part, and Im (·) represents taking the imaginary part; respectively carrying out standardization processing on a real part and an imaginary part of the radiation data, wherein the standardization formula is as follows:
Figure BDA0002438677650000031
wherein, mumIs the mean, σ, of the m-dimensional radiation datamVariance of the mth dimension radiation data;
and 4, step 4: training a single-label neural network f for learning the mapping relation between far-field measurement data and the number of failure array elements by using the training set1:kn＝f1([Re(zn),Im(zn)]T) (ii) a By means of neural networks f1The output result of (2) and the training set obtained in step (z)1,y1),(z2,y2),...,(zN,yN) Training a multi-label neural network f for learning the mapping relation between far-field measurement data and the positions of failed array elements2:yn＝f2([Re(zn),Im(zn)]T) (ii) a Neural network f1And f2Respectively taking the multi-class logarithmic cross entropy and the binary-class logarithmic cross entropy as loss functions;
and (3) a model application stage:
and 5: for the test array, inIn the model training phase, far-field radiation data acquisition is performed on the same M measurement points in step 1, and test data z ═ z (θ (theta)) can be obtained1),z(θ2),...,z(θM)]TThe data is input into the neural network f trained in step 4 after being standardized1In, the number of predicted failure array elements is
Figure BDA0002438677650000032
Step 6: inputting the test data in the step 5 into the neural network f trained in the step 42In the method, array element failure probability vector p ═ p is obtained1,p2,...,pL]Wherein p isi,i∈[1,L]Indicating the possibility of the failure of the ith array element; setting u and v as superscripts of a maximum value and a second maximum value in the probability vector p respectively and considering that one array element in u and v is invalid;
and 7: the test data z ═ z (θ) in step 5 is compensated separately using u and v1),z(θ2),...,z(θM)]T；
Figure BDA0002438677650000033
For gaussian white noise, the m-th test data after compensation are respectively:
Figure RE-GDA0002544681760000034
Figure BDA0002438677650000035
and 8: order to
Figure BDA0002438677650000036
The two groups of test data compensated in the step 7 are input into the neural network f after being standardized to replace the original test data2In (1), two probability vectors p are obtained[u]＝[p[u] 1,p[u] 2,...,p[u] L]And p[v]＝[p[v] 1,p[v] 2,...,p[v] L]If p is[u]Maximum value greater than p[v]If the maximum value is less than the maximum value, judging that the u-th array element is invalid, and updating u and v; otherwise, judging that the v-th array element is invalid, and updating u and v;
and step 9: repeating the steps 7-8 until
Figure BDA0002438677650000041
The invention discloses an antenna array fault diagnosis method based on deep learning. Firstly, in a model training stage, two diagnostic networks are trained by using data acquired in a far field area under various failure scenarios. And then, in the actual application stage, acquiring data at the same sampling point as the training stage by using a probe, inputting the data into a trained model, preliminarily determining the number and the position of the failure array elements, circularly compensating the measurement data according to a data compensation formula, and sequentially judging the positions of the failure array elements. Compared with the traditional antenna diagnosis algorithm, the algorithm has the advantage of high diagnosis speed in the practical application stage. Compared with the existing array fault diagnosis method based on the parameter model, the algorithm effectively reduces the sample data required to be collected in the training stage through a data compensation method, and is suitable for large-scale array fault diagnosis.
Drawings
FIG. 1 is a block flow diagram of the present algorithm;
FIG. 2 shows the diagnosis result of the position of the failed array element of the failure array under different numbers of the failed array elements when the SNR is equal to 30 dB;
fig. 3 shows the diagnosis result of the position of the fault array element under different signal-to-noise ratios.
Detailed Description
The fault array considered in this embodiment is a uniform linear array comprising 50 array elements, with an array element spacing of 0.5 λ, where λ is the wavelength. In practice, the number of failed array elements is usually small, and therefore, it is assumed that the number of failed array elements in the array is at most 7.
A model training stage:
step 1: the embodiment is in pitchAngle theta e [ -90 DEG, 90 DEG]The amplitude and phase data of the radiation field of the fault array are measured at 2 ° intervals, and each sample data contains 91 measurement points. Working array element excitation x i1, failing array element excitation xiAnd (0) in the far field region, under the condition of determining the number of array element failures, respectively measuring the radiation data in the random failure scene of 700 times of array element positions and carrying out standardization processing.
Step 2: and (3) forming eight training sets according to the number of the invalid array elements by using all the measurement data z [ Re (z), im (z) ] in the step 1, wherein each training set comprises 700 training samples, and sequentially constructing an array invalid indication vector y and an invalid array element number label k of the sample according to the sample excitation x.
And step 3: and (4) carrying out standardization processing on the data characteristics of the training set in the step 2 by using a formula (3).
And 4, step 4: combining and randomly ordering the samples in the eight training sets in the step 3 into
Figure BDA0002438677650000042
Then dividing the training data into two training sets
Figure BDA0002438677650000051
And
Figure BDA0002438677650000052
respectively for training the neural network f1And f2Wherein f is1And f2The network hyper-parameters are shown in table 1.
And (3) a model application stage:
and 5: aiming at 2400 random failure scenes of the AUT (300 tests under the condition of determining the number of failure array elements), far-field radiation data acquisition is carried out on 91 measurement points which are the same as those in the step 1 of the model training stage, and test data Z ═ Z can be obtained1,z2,...,z2400]TThe data is input into the neural network f trained in step 3 after being standardized1In, the number of output failure array elements is
Figure BDA0002438677650000053
Step 6: inputting the test data in the step 5 into the neural network f trained in the step 32In, output array element failure probability matrix P ═ P1,p2,…p2400]TWherein p isijIndicating the possibility of failure of the jth array element in the ith test sample. By
Figure BDA0002438677650000054
Let uiAnd viRespectively the ith test sample probability vector piThe upper and lower maximum values are labeled and are designated as uiAnd viIf one array element fails, u-u is determined for all test samples1,u2,…u2400]TAnd v ═ v1,v2,…v2400]T。
And 7: the test data Z in step 5 are compensated separately using u and v.
Figure BDA0002438677650000055
And if the signal is Gaussian white noise, the m test data of the compensated i sample are respectively as follows:
Figure BDA0002438677650000056
Figure BDA0002438677650000057
and 8: order to
Figure BDA0002438677650000058
Standardizing the two groups of test data compensated in the step 7 and inputting the standardized test data to the neural network f instead of the original test data2In (1), two probability matrices P are obtained[u]＝[p1[u],p2[u],…,p2400[u]]TAnd P[v]＝[p1[v],p2[v],…,p2400[v]]T. For the ith sample, if pi[u]Maximum value of greater than pi[v]Maximum value of (1), uiUpdating u, v when each array element fails; otherwise, viAnd (5) failing each array element, and updating u and v.
And step 9: repeating the steps 7-8 until
Figure BDA0002438677650000059
The antenna array fault diagnosis algorithm based on deep learning provided by the invention is applied to a uniform linear array, the array comprises 50 array elements in total, the array element spacing is 0.5 lambda, and lambda is the wavelength. 8000 array samples were randomly generated, of which 5600 were used for training the neural network and 2400 for diagnostic testing. In the far field region, the pitch angle theta is ∈ [ -90 DEG, 90 DEG]In the range of (2), 91 measurement field points are sampled at 2 ° intervals. The noise added is complex white gaussian noise that is expected to be zero, and the signal-to-noise ratio SNR is 30 dB. The result of the accuracy of the diagnosis of the position of the failed array element of 2400 test samples is shown in fig. 2, and the above experimental process is repeated under the conditions of different signal to noise ratios, and the diagnosis result is shown in fig. 3.
Table 1: neural network f1And f2Is a hyper-parameter of
Figure BDA0002438677650000061

Claims (1)

1. A method for diagnosing faults of an antenna array based on a deep neural network and radiation data compensation comprises the following steps:
a model training stage:
step 1: measuring electric field radiation data of a far field of the array under N fault scenes by using a probe at M measuring points with different angles on a far field measuring plane; the data measured by the probe at the m-th angle is expressed by the following formula:
Figure RE-FDA0002544681750000011
where c is the speed of light, f is the working frequency of the array, L is the total number of array elements contained in the array, xiAnd diExcitation of the ith array element and position in the array coordinate system, thetamIs the angle between the m-th measurement point and the positive z-axis of the array reference frame, nmNoise for the mth measurement point;
step 2: and (3) forming the array excitation and the measurement data of M points in the step 1 into a column vector:
Figure RE-FDA0002544681750000012
and zn＝[zn(θ1),zn(θ2),...,zn(θM)]TExcitation vectors and patterns for the nth measurement, respectively, where T represents transposition;
constructing array failure indication vector and failure array element number label k according to excitation vectornWherein, if
Figure RE-FDA0002544681750000013
Then
Figure RE-FDA0002544681750000014
If not, then,
Figure RE-FDA0002544681750000015
knis equal to the indicator vector ynThe number of the (1) in the (1),
Figure RE-FDA0002544681750000016
and step 3: forming a training set by the data obtained in the step 2 (z)1,k1),(z2,k2),...,(zN,kN) Since z is a complex number, it is represented as zn＝[Re(zn),Im(zn)]Wherein Re (·) represents taking the real part, and Im (·) represents taking the imaginary part; separately normalizing the real and imaginary components of radiation dataProcessing, the normalized formula is:
Figure RE-FDA0002544681750000017
wherein, mumIs the mean, σ, of the m-dimensional radiation datamVariance of the mth dimension radiation data;
and 4, step 4: training a single-label neural network f for learning the mapping relation between far-field measurement data and the number of failure array elements by using the training set1:kn＝f1([Re(zn),Im(zn)]T) (ii) a By means of neural networks f1The output result of (2) and the training set obtained in step (z)1,y1),(z2,y2),...,(zN,yN) Training a multi-label neural network f for learning the mapping relation between far-field measurement data and the positions of failed array elements2:yn＝f2([Re(zn),Im(zn)]T) (ii) a Neural network f1And f2Respectively taking the multi-class logarithmic cross entropy and the binary-class logarithmic cross entropy as loss functions;
and (3) a model application stage:
and 5: for the test array, far-field radiation data acquisition is performed on the same M measurement points as in step 1 of the model training phase, and test data z ═ z (θ) can be obtained1),z(θ2),...,z(θM)]TThe data is input into the neural network f trained in step 4 after being standardized1In, the number of predicted failure array elements is
Figure RE-FDA0002544681750000018
Step 6: inputting the test data in the step 5 into the neural network f trained in the step 42In the method, an array element failure probability vector p ═ p is obtained1,p2,...,pL]Wherein p isi,i∈[1,L]Indicating the possibility of the failure of the ith array element; let u and v be in the probability vector p, respectivelyThe superscript of the maximum value and the second maximum value considers that one array element in u and v is invalid;
and 7: the test data z ═ z (θ) in step 5 is compensated separately using u and v1),z(θ2),...,z(θM)]T；
Figure RE-FDA0002544681750000021
And the m-th test data after compensation is white gaussian noise, respectively:
Figure RE-FDA0002544681750000022
Figure RE-FDA0002544681750000023
and 8: order to
Figure RE-FDA0002544681750000024
The two groups of test data compensated in the step 7 are input into the neural network f after being standardized to replace the original test data2In (1), two probability vectors p are obtained[u]＝[p[u] 1,p[u] 2,...,p[u] L]And p[v]＝[p[v] 1,p[v] 2,...,p[v] L]If p is[u]Maximum value greater than p[v]If the maximum value is less than the maximum value, judging that the u-th array element is invalid, and updating u and v; otherwise, judging that the v-th array element is invalid, and updating u and v;
and step 9: repeating the steps 7-8 until
Figure RE-FDA0002544681750000025
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