CN107607942A - Based on the large scale electromagnetic scattering of deep learning model and the Forecasting Methodology of back scattering - Google Patents

Based on the large scale electromagnetic scattering of deep learning model and the Forecasting Methodology of back scattering Download PDF

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CN107607942A
CN107607942A CN201710767562.2A CN201710767562A CN107607942A CN 107607942 A CN107607942 A CN 107607942A CN 201710767562 A CN201710767562 A CN 201710767562A CN 107607942 A CN107607942 A CN 107607942A
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CN107607942B (en
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王龙刚
贺凯
李廉林
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Peking University
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Abstract

The invention discloses a kind of based on the large scale electromagnetic scattering of deep learning model and the Forecasting Methodology of back scattering.The present invention proposes two kinds of convolutional neural networks based on physical mechanism respectively for electromagnetic scattering and Inverse Problems in Electromagnetics；Existing convolutional neural networks framework is generalized to complex field from real number field, and assigns its corresponding physical meaning；The Forecasting Methodology of scattering and the back scattering of the present invention is applied to conventional various radar systems；The convolutional neural networks of the present invention are applied to training algorithms all at present；Network is convolution operation, thus has extremely strong applicability, suitable for any large scale electromagnetic field scape.It is proposed by the invention to solve electromagnetic scattering using based on the convolutional neural networks of physical mechanism and having that computational efficiency is high during inverse Problem, the characteristics such as time delay is low, to realize that the real-time solution of large scale electromagnetic scattering and inverse Problem is laid a good foundation.

Description

Based on the large scale electromagnetic scattering of deep learning model and the Forecasting Methodology of back scattering
Technical field
The present invention relates to electromagnetic scattering and back scattering technology, and in particular to a kind of large scale electricity based on deep learning model Magnetic scattering and the Forecasting Methodology of back scattering.
Background technology
With the rapid development of economic society, large scale electromagnetic scattering and back scattering have become remote sensing, electromagnetism stealth and Key, the Basic Problems of the various fields such as medical treatment.
Over-the-horizon targeting capability is considered as the key factor that can a country win modernized war.Due to current The radar that army equips has the characteristics that operating distance is remote, detecting precision is high, all weather operations more, thus how to design high motor-driven Property, high stealth aircraft becomes the important topic of national defense industry research.In high speed stealthy aircraft design process, such as An important factor for what quick obtaining target scattering data turns into flow of research.In the traditional meter of academia and industrial quarters generally use Calculate electromagnetic method (numerical method such as such as Finite-Difference Time-Domain Method, moment method) or using based on the business simulation for calculating electromagnetic method Software obtains target scattering data.Although the advantages that these methods have technology maturation, and precision is high, when target area is larger When, then traditional calculations electromagnetic method needs to consume a large amount of computing resources, the shortcomings of calculating overlong time, has seriously tied down aircraft The progress of design.For improve computational efficiency, researcher it is also proposed various numerical approximation methods (such as Born approximation method, High-frequency approximation method etc.), these methods more ignore between target internal and target electromagnetic coupled effect, cause computational accuracy compared with It is low.
On the other hand, Inverse Problems in Electromagnetics (such as radar imagery) has nowadays been widely used in geographical science, medical science And other various military and civilian scenes.Among the past few decades, Inverse Problems in Electromagnetics is also every country research work Person's priority fields of study.Because Inverse Problems in Electromagnetics has uncomfortable fixed, the extremely challenging feature such as non-linear and pathosis, Although numerous algorithms have been proposed (such as in scientist：Back-projection algorithm, Born's iterative method, distorted born iterative method method, gene are calculated Method etc.), but still can not effectively meet the needs of engineer applied.When calculating scene increase, conventional method can meet with meter Calculation amount is big, the problem of can not meeting real-time application requirement.
In recent years, deep learning has become the important research direction of artificial intelligence field, is widely used in pattern knowledge Not, classify, the field such as detection, and achieve unprecedented success.However, deep learning network is applied to real number field more at present, and Electromagnetic scattering problems and Inverse Problems in Electromagnetics belong to complex field problem, and deep learning framework does not have accurate thing at present Meaning support is managed, it is difficult to directly apply to electromagnetic scattering and Inverse Problems in Electromagnetics to cause existing deep learning framework.
How under the conditions of existing hardware system, propose that precision is high, calculate fast solution electromagnetic scattering and inverse Problem Algorithm have become those skilled in the art be badly in need of solve extremely challenging key technology difficulty.
The content of the invention
In order to solve above-mentioned key technology difficulty, the present invention proposes the deep learning model based on physical mechanism respectively The prediction of the Forecasting Methodology of large scale electromagnetic scattering and the large scale THE INVERSE ELECTROMAGNETIC SCATTERING of deep learning model based on physical mechanism Method；The present invention directly applies to the big chi of prediction by training small yardstick electromagnetic scattering and back scattering data, and then by the network Spend electromagnetic scattering and inverse Problem.
It is an object of the present invention to propose a kind of prediction side of the large scale electromagnetic scattering based on deep learning model Method.
The Forecasting Methodology of the large scale electromagnetic scattering based on deep learning model of the present invention, comprises the following steps：
1) training sample is obtained：
Electromagnetism radar system includes T emitter, M receiver, emitter successively to target area transmission signal, and by Whole receivers receive the scattered field of target area, and contrast function is the reflectivity of target area and corresponding target area Scattered field forms one group of sample, and multigroup sample data is two parts by random division：Wherein Part I sample be used to train Convolutional neural networks are as training sample；Part II sample is used in testing for the generalization ability of convolutional neural networks as test Sample；
2) convolutional neural networks are built：
Convolutional neural networks are built based on physical mechanism in series；
3) convolutional neural networks based on physical mechanism are trained：
A) output using the scattered field of the target area of the training sample obtained in step 1) as convolutional neural networks, will Input of the reflectivity of the target area of training sample as convolutional neural networks, training convolutional neural networks；
B) output using the scattered field of the target area of the test sample obtained in step 1) as convolutional neural networks, will Input of the reflectivity of the target area of test sample as convolutional neural networks, convolutional neural networks are examined, if error exists In critical field, then convolutional neural networks training is completed, into step 4), the scope if error is above standard, and return to step A) re -training network, until error, in critical field, training network terminates, into step 4)；
4) the scattering prediction of large scale electromagnetism scene：
Using the reflectivity of the target area of large scale electromagnetism scene as the input of convolutional neural networks in step 3), then roll up The output of product neutral net is the scattered field of the target area of the corresponding large scale electromagnetism scene of prediction.
Wherein, in step 1), electromagnetism radar system includes T emitter, and M receiver, emitter is successively to target Field emission signal, and by the scattered field of whole receivers reception target area；When t-th of emitter transmission signal, m-th The scattered field for the target area that receiver receivesFor：
And
Wherein, T and M respectively >=2 natural number, t=1,2 ..., T；M=1,2 ..., M；DinvRepresent target area；r With r ' ∈ DinvSite and source point, r are represented respectivelytFor the position where t-th of emitter, rmFor the position where m-th of receiver Put；E(t)(r ') represents the resultant field of target area during t-th of emitter transmission signal；Contrast function (the i.e. reflection of target area Rate) beWherein k and k0=ω/c represents the wave number in target and free space respectively, and ω represents center frequency Rate, c represent the light velocity；Two-dimentional Green's function is represented,Represent the first kind zeroth order Chinese Gram function, i represent imaginary unit.
In step 2), a convolutional neural networks of the N layers based on physical mechanism, wherein n-th layer network output table are built It is shown as：
E(n)=Ein+A(n)χ(E(n-1)) (3)
Wherein, EinRepresent corresponding in-field during generation training sample；E(n-1)Represent that the (n-1)th layer network exports, and be the The input of n-layer network, n are natural number and 1≤n≤N-1；A(n)Represent the convolution kernel of n-th layer network；χ represents the mesh of training sample Mark the reflectivity in region；E(n)Represent the output of n-th layer network；The input of initial network is E(0)=Ein；N-th layer network exports table It is shown as：
E(N)=A(N)χ(E(N-1)) (4)
Wherein, A(N)=G (rm,rt) represent n-th layer network convolution kernel, G (rm,rt) represent rtLocate emitter to rmPlace connects The Green's function of receipts machine.
In step 3), critical field≤0.02.
It is another object of the present invention to provide a kind of the pre- of large scale THE INVERSE ELECTROMAGNETIC SCATTERING based on deep learning model Survey method.
The Forecasting Methodology of the large scale THE INVERSE ELECTROMAGNETIC SCATTERING based on deep learning model of the present invention, comprises the following steps：
1) training sample is obtained：
Electromagnetism radar system includes T emitter, M receiver, emitter successively to target area transmission signal, and by Whole receivers receive the scattered field of target area, and contrast function is the reflectivity of target area and corresponding target area Scattered field forms one group of sample, and multigroup sample data is two parts by random division：Wherein Part I sample be used to train Convolutional neural networks are as training sample；Part II sample is used in testing for the generalization ability of convolutional neural networks as test Sample；
2) convolutional neural networks are built：
Convolutional neural networks are built based on physical mechanism in series；
3) convolutional neural networks based on physical mechanism are trained：
A) scattered field of the target area of the training sample obtained in step 1) is subjected to rear orientation projection's imaging, rear orientation projection Input of the imaging results as convolutional neural networks, using the reflectivity of the target area of training sample as convolutional neural networks Output, training convolutional neural networks；
B) output using the reflectivity of the target area of the test sample obtained in step 1) as convolutional neural networks, will Input of the rear orientation projection's imaging results of the target area of test sample as convolutional neural networks, convolutional neural networks are examined, If error, in critical field, convolutional neural networks training is completed, into step 4), the scope if error is above standard, Then return to step a) re -trainings network, until error, in critical field, training network terminates, into step 4)；
4) the back scattering prediction of large scale electromagnetism scene：
Using rear orientation projection's imaging results of large scale electromagnetism scene as the input of convolutional neural networks in step 3), then roll up The output of product neutral net is the imaging results of the target area of the corresponding large scale electromagnetism scene of prediction.
Wherein, in step 2), N layer series connection convolutional neural networks of the structure one based on physical mechanism, wherein single laminate roll Product neutral net is divided into three sublayers：First sublayer is input layer, characterizes observational variable, such as rear orientation projection's imaging results；The Two sublayers are characterized layer, characterize the induced-current of reconstruct；Third layer is output layer, characterizes the reflectivity of the target area of reconstruct.
The observational variable of input layer with K convolution kernel convolution, obtains K eigenmatrix of the second sublayer, that is, characterized respectively Induced-current under different visual angles.K eigenmatrix connect entirely or multilayer convolution by way of obtain the target area of output layer The reflectivity in domain.
Advantages of the present invention：
The present invention proposes two kinds of convolution god based on physical mechanism respectively for electromagnetic scattering and Inverse Problems in Electromagnetics Through network；Existing convolutional neural networks framework is generalized to complex field from real number field, and assigns its corresponding physical meaning；This hair Bright scattering and the Forecasting Methodology of back scattering are applied to conventional various radar systems；The convolutional neural networks of the present invention are applied to Training algorithms all at present；Network is convolution operation, thus has extremely strong applicability, suitable for any large scale electromagnetism Scene.It is proposed by the invention to have using based on the convolutional neural networks of physical mechanism when solving electromagnetic scattering and inverse Problem Have that computational efficiency is high, the characteristics such as time delay is low, to realize that base has been established in the real-time solution of large scale electromagnetic scattering and inverse Problem Plinth.
Brief description of the drawings
Fig. 1 is that the Forecasting Methodology of large scale electromagnetic scattering based on deep learning model and the back scattering of the present invention is applicable Exemplary two dimensional electromagnetic scattering and the schematic diagram of back scattering system；
Fig. 2 is the large scale electromagnetic scattering prediction method based on deep learning model of the present invention based on physical mechanism The structured flowchart of convolutional neural networks；
Fig. 3 be the present invention based on the large scale electromagnetic scattering prediction method of deep learning model under small yardstick scene The comparison figure of prediction effect；
Fig. 4 is the Forecasting Methodology of the small yardstick electromagnetic scattering based on deep learning model of the present invention under large scale scene Prediction effect comparison figure；
Fig. 5 is the Forecasting Methodology of the large scale THE INVERSE ELECTROMAGNETIC SCATTERING based on deep learning model of the present invention based on physical machine Make the structured flowchart of the convolutional neural networks of structure；
Fig. 6 is the Forecasting Methodology of the small yardstick THE INVERSE ELECTROMAGNETIC SCATTERING based on deep learning model of the present invention in small yardstick scene Under convolutional neural networks prediction effect figure；
Fig. 7 is the Forecasting Methodology of the large scale THE INVERSE ELECTROMAGNETIC SCATTERING based on deep learning model of the present invention in large scale scene Under convolutional neural networks prediction effect figure.
Embodiment
Below in conjunction with the accompanying drawings, by specific embodiment, the present invention is expanded on further.
Embodiment one
In the present embodiment, the structure of two-dimensional simulation system is as shown in figure 1, radar system uses bistatic.
The Forecasting Methodology of the large scale electromagnetic scattering based on deep learning model of the present embodiment, comprises the following steps：
1) training sample is obtained：
Electromagnetism radar system includes T=1 emitter, M=19 receiver, is R=10 λ away from origin radius, uniformly divides Cloth is on the semicircle between -90 ° to 90 °.The incident plane wave signal of target area is 4GHz simple signals, target area (L= 1.4 λ) uniformly it is split into the λ of 196 0.1 λ × 0.1 square net.Emitter successively to target area transmission signal, and The scattered field of target area is received by whole receivers, contrast function is the reflectivity of target area and corresponding target area Scattered field formed one group of sample.10000 groups of sample datas are two parts by random division：Wherein 5000 groups of samples of Part I It is used for training network as training sample；5000 groups of samples of Part II are used in testing for the generalization ability of network as test Sample.
2) convolutional neural networks are built：
Convolutional neural networks are built based on physical mechanism in series as shown in Fig. 2 being set to 5 layer networks. Preceding four layers of convolution kernel Initialize installation is the random matrix of the Gaussian Profile of 14 × 14 zero-mean.
3) convolutional neural networks based on physical mechanism are trained：
Using the scattered field of the target area of the training sample obtained in step 1) as the output of convolutional neural networks, will instruct Practice input of the reflectivity of the target area of sample as convolutional neural networks, training convolutional neural networks.Using back-propagating Algorithm updates network parameter.During back-propagating, cost function is expressed as：
Wherein EtrueRepresent measurement scattered field.
The gradient of each layer network parameter can be expressed as：
The error between the cost function quantitative analysis measurement scattered field and prediction scattered field in formula (8) is utilized simultaneously：
WhereinWithThe measurement scattered field and prediction scattered field of s group test samples are represented respectively, and M represents to receive The number of machine, the sum of S table test samples.
To illustrate the validity of the convolutional neural networks based on physical mechanism, the present embodiment will use traditional Born approximation side Prediction scattering field data caused by method does further comparison with neural network forecast scattering field data.Traditional Born approximation method is predicted Scattering field data can be obtained by formula (9)：
As shown in figure 3, the measurement scattered field in test sample and the scattering using neural network forecast of the invention in step 1) Field error is minimum, and error E RR is only 0.0016；And the scattered field obtained by Born approximation is then with testing scattered field difference very Greatly, only part trend is identical.
4) the scattering prediction of large scale electromagnetism scene：
Imaging region is increased into the square area that the length of side is L=2.8 λ, then imaging region is divided into 784 0.1 λ × 0.1 λ square net.The target area of large scale increases four times greater compared to the target area of step 1) Small and Medium Sized.It is other Parameter constant, produce 5000 groups of test samples.Rolled up using the reflectivity of the target area of large scale electromagnetism scene as in step 3) The input of product neutral net, then the output of convolutional neural networks is the target area of the corresponding large scale electromagnetism scene of prediction Scattered field.As shown in figure 4, network measure scattered field and prediction scattered field are basically identical, error E RR is smaller, and ERR is only 0.017.
Embodiment two
In the present embodiment, the structure of two-dimensional simulation system is as shown in figure 1, radar system uses bistatic.
The Forecasting Methodology of the large scale THE INVERSE ELECTROMAGNETIC SCATTERING based on deep learning model of the present embodiment, comprises the following steps：
1) training sample is obtained：
System architecture such as embodiment one, centered on origin, transmitter and receiver is rotated to 45 ° of acquisitions, one group of number simultaneously According to the total that rotates a circle obtains eight groups of data as a sample data.Caused 10000 groups of sample stochastic averaginas are divided into two Part, wherein 5000 groups of samples of Part I are used for training network as training sample, and 5000 groups of samples of Part II are used to survey Network efficiency is tried as test sample.
2) convolutional neural networks are built：
The convolutional neural networks built in series based on physical mechanism are as shown in figure 5, be individual layer by network settings Network, and be eight convolution kernels.Convolution kernel Initialize installation is the random matrix of the Gaussian Profile of 14 × 14 zero-mean.
3) convolutional neural networks based on physical mechanism are trained：
The scattered field of the target area of the training sample obtained in step 1) is subjected to rear orientation projection's imaging, rear orientation projection into As input of the result as convolutional neural networks, using the reflectivity of the target area of training sample as the defeated of convolutional neural networks Go out, training convolutional neural networks.
And weigh neural network forecast error E RR with the cost function in formula (10)：
WhereinWithThe reflectivity and prediction target area in the real goal region of s group test samples are represented respectively The reflectivity in domain.P represent imaging region grid number, S be test sample sum, s=1 ..., S.
As shown in fig. 6, rear orientation projection's imaging results noise is larger, objective fuzzy, can not be imaged by human eye Direct Recognition Target, mean error are up to ERR=0.023.And passing through training network using rear orientation projection's result as input, its training network is defeated The prediction imaging results gone out are then clear in structure, and noise is smaller, and mean error is only ERR=0.009.
4) the back scattering prediction of large scale electromagnetism scene：
Imaging region is increased into the square area that the length of side is L=2.8 λ, then imaging region is divided into 784 0.1 λ × 0.1 λ square net.The target area of large scale increases four times greater compared to the target area of step 1) Small and Medium Sized.It is other Parameter constant, produce 5000 groups of test samples.Using the target area rear orientation projection imaging results of large scale test sample as step It is rapid 3) in convolutional neural networks input, then network output be prediction large scale target area imaging results.Can by Fig. 7 Know, under large scale scene, neural network forecast imaging results are much better than rear orientation projection's imaging results.Rear orientation projection and neural network forecast knot The mean error of fruit is respectively 0.044 and 0.020.
It is finally noted that the purpose for publicizing and implementing example is that help further understands the present invention, but this area Technical staff be appreciated that：Without departing from the spirit and scope of the invention and the appended claims, it is various to replace and repair It is all possible for changing.Therefore, the present invention should not be limited to embodiment disclosure of that, and the scope of protection of present invention is to weigh The scope that sharp claim defines is defined.

Claims (6)

  1. A kind of 1. Forecasting Methodology of the large scale electromagnetic scattering based on deep learning model, it is characterised in that the Forecasting Methodology Comprise the following steps：
    1) training sample is obtained：
    Electromagnetism radar system includes T emitter, and M receiver, emitter is successively to target area transmission signal, and by whole Receiver receives the scattered field of target area, and contrast function is reflectivity and the scattering of corresponding target area of target area Field forms one group of sample, and multigroup sample data is two parts by random division：Wherein Part I sample is used for training convolutional Neutral net is as training sample；Part II sample is used in testing for the generalization ability of convolutional neural networks as test specimens This；
    2) convolutional neural networks are built：
    Convolutional neural networks are built based on physical mechanism in series；
    3) convolutional neural networks based on physical mechanism are trained：
    A) using the scattered field of the target area of the training sample obtained in step 1) as the output of convolutional neural networks, will train Input of the reflectivity of the target area of sample as convolutional neural networks, training convolutional neural networks；
    B) using the scattered field of the target area of the test sample obtained in step 1) as the output of convolutional neural networks, will test Input of the reflectivity of the target area of sample as convolutional neural networks, convolutional neural networks are examined, if error is in standard In the range of, then convolutional neural networks training is completed, into step 4), the scope if error is above standard, and return to step a) weights New training network, until error, in critical field, training network terminates, into step 4)；
    4) the scattering prediction of large scale electromagnetism scene：
    Input using the reflectivity of the target area of large scale electromagnetism scene as convolutional neural networks in step 3), then convolution is refreshing Output through network is the scattered field of the target area of the corresponding large scale electromagnetism scene of prediction.
  2. 2. Forecasting Methodology as claimed in claim 1, it is characterised in that in step 1), electromagnetism radar system includes T transmitting Machine, M receiver, emitter successively to target area transmission signal, and by whole receivers receive target area scattered field； When t-th of emitter transmission signal, the scattered field for the target area that m-th of receiver receivesFor：
    <mrow> <msubsup> <mi>E</mi> <mrow> <mi>s</mi> <mi>c</mi> <mi>a</mi> </mrow> <mrow> <mo>(</mo> <mi>t</mi> <mi>m</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <msub> <mi>r</mi> <mi>m</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>k</mi> <mn>0</mn> <mn>2</mn> </msubsup> <munder> <mo>&amp;Integral;</mo> <msub> <mi>D</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>v</mi> </mrow> </msub> </munder> <mi>G</mi> <mrow> <mo>(</mo> <msub> <mi>r</mi> <mi>m</mi> </msub> <mo>,</mo> <msub> <mi>r</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> <mi>&amp;chi;</mi> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> <msup> <mi>E</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> <mi>d</mi> <mi>r</mi> </mrow>
    And
    <mrow> <msup> <mi>E</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>E</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> <mo>+</mo> <msubsup> <mi>k</mi> <mn>0</mn> <mn>2</mn> </msubsup> <munder> <mo>&amp;Integral;</mo> <msub> <mi>D</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>v</mi> </mrow> </msub> </munder> <mi>G</mi> <mrow> <mo>(</mo> <mi>r</mi> <mo>,</mo> <msup> <mi>r</mi> <mo>&amp;prime;</mo> </msup> <mo>)</mo> </mrow> <mi>&amp;chi;</mi> <mrow> <mo>(</mo> <msup> <mi>r</mi> <mo>&amp;prime;</mo> </msup> <mo>)</mo> </mrow> <msup> <mi>E</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msup> <mi>r</mi> <mo>&amp;prime;</mo> </msup> <mo>)</mo> </mrow> <msup> <mi>dr</mi> <mo>&amp;prime;</mo> </msup> </mrow>
    Wherein, T and M respectively >=2 natural number, t=1,2 ..., T；M=1,2 ..., M；DinvRepresent target area；R and r ' ∈DinvSite and source point, r are represented respectivelytFor the position where t-th of emitter, rmFor the position where m-th of receiver；E(t)(r ') represents the resultant field of target area during t-th of emitter transmission signal；The reflectivity of target area isWherein k and k0=ω/ c represents the wave number in target and free space respectively, and ω represents centre frequency, c tables Show the light velocity；Two-dimentional Green's function is represented,First kind zeroth order Hunk function is represented, I represents imaginary unit.
  3. 3. Forecasting Methodology as claimed in claim 1, it is characterised in that in step 2), one N layer of structure is based on physical mechanism Convolutional neural networks, wherein n-th layer network output be expressed as：
    E(n)=Ein+A(n)χ(E(n-1))
    Wherein, EinRepresent corresponding in-field during generation training sample；E(n-1)Represent that the (n-1)th layer network exports, and be n-th layer net The input of network, n are natural number and 1≤n≤N-1；A(n)Represent the convolution kernel of n-th layer network；χ represents the target area of training sample The reflectivity in domain；E(n)Represent the output of n-th layer network；The input of initial network is E(0)=Ein；The output of n-th layer network represents For：
    E(N)=A(N)χ(E(N-1))
    Wherein, A(N)=G (rm,rt) represent n-th layer network convolution kernel, G (rm,rt) represent rtLocate emitter to rmLocate receiver Green's function.
  4. A kind of 4. Forecasting Methodology of the large scale THE INVERSE ELECTROMAGNETIC SCATTERING based on deep learning model, it is characterised in that the prediction side Method comprises the following steps：
    1) training sample is obtained：
    Electromagnetism radar system includes T emitter, and M receiver, emitter is successively to target area transmission signal, and by whole Receiver receives the scattered field of target area, and contrast function is reflectivity and the scattering of corresponding target area of target area Field forms one group of sample, and multigroup sample data is two parts by random division：Wherein Part I sample is used for training convolutional Neutral net is as training sample；Part II sample is used in testing for the generalization ability of convolutional neural networks as test specimens This；
    2) convolutional neural networks are built：
    Convolutional neural networks are built based on physical mechanism in series；
    3) convolutional neural networks based on physical mechanism are trained：
    A) scattered field of the target area of the training sample obtained in step 1) is subjected to rear orientation projection's imaging, rear orientation projection's imaging As a result the input as convolutional neural networks, using the reflectivity of the target area of training sample as the defeated of convolutional neural networks Go out, training convolutional neural networks；
    B) using the reflectivity of the target area of the test sample obtained in step 1) as the output of convolutional neural networks, will test Input of the rear orientation projection's imaging results of the target area of sample as convolutional neural networks, convolutional neural networks are examined, if Error in critical field, then complete, and into step 4), the scope if error is above standard, returns by convolutional neural networks training Step a) re -training networks are returned, until error, in critical field, training network terminates, into step 4)；
    4) the back scattering prediction of large scale electromagnetism scene：
    Input using rear orientation projection's imaging results of large scale electromagnetism scene as convolutional neural networks in step 3), then convolution is refreshing Output through network is the imaging results of the target area of the corresponding large scale electromagnetism scene of prediction.
  5. 5. a kind of Forecasting Methodology as claimed in claim 4, it is characterised in that in step 1), electromagnetism radar system includes T Emitter, M receiver, emitter receive dissipating for target area successively to target area transmission signal by whole receivers Penetrate field；When t-th of emitter transmission signal, the scattered field for the target area that m-th of receiver receivesFor：
    <mrow> <msubsup> <mi>E</mi> <mrow> <mi>s</mi> <mi>c</mi> <mi>a</mi> </mrow> <mrow> <mo>(</mo> <mi>t</mi> <mo>,</mo> <mi>m</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <msub> <mi>r</mi> <mi>m</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>k</mi> <mn>0</mn> <mn>2</mn> </msubsup> <munder> <mo>&amp;Integral;</mo> <msub> <mi>D</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>v</mi> </mrow> </msub> </munder> <mi>G</mi> <mrow> <mo>(</mo> <msub> <mi>r</mi> <mi>m</mi> </msub> <mo>,</mo> <msub> <mi>r</mi> <mi>t</mi> </msub> <mo>)</mo> </mrow> <mi>&amp;chi;</mi> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> <msup> <mi>E</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> <mi>d</mi> <mi>r</mi> </mrow>
    And
    <mrow> <msup> <mi>E</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mi>E</mi> <mrow> <mi>i</mi> <mi>n</mi> </mrow> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msubsup> <mrow> <mo>(</mo> <mi>r</mi> <mo>)</mo> </mrow> <mo>+</mo> <msubsup> <mi>k</mi> <mn>0</mn> <mn>2</mn> </msubsup> <munder> <mo>&amp;Integral;</mo> <msub> <mi>D</mi> <mrow> <mi>i</mi> <mi>n</mi> <mi>v</mi> </mrow> </msub> </munder> <mi>G</mi> <mrow> <mo>(</mo> <mi>r</mi> <mo>,</mo> <msup> <mi>r</mi> <mo>&amp;prime;</mo> </msup> <mo>)</mo> </mrow> <mi>&amp;chi;</mi> <mrow> <mo>(</mo> <msup> <mi>r</mi> <mo>&amp;prime;</mo> </msup> <mo>)</mo> </mrow> <msup> <mi>E</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </msup> <mrow> <mo>(</mo> <msup> <mi>r</mi> <mo>&amp;prime;</mo> </msup> <mo>)</mo> </mrow> <msup> <mi>dr</mi> <mo>&amp;prime;</mo> </msup> </mrow>
    Wherein, T and M respectively >=2 natural number, t=1,2 ..., T；M=1,2 ..., M；DinvRepresent target area；R and r ' ∈DinvSite and source point, r are represented respectivelytFor the position where t-th of emitter, rmFor the position where m-th of receiver；E(t)(r ') represents the resultant field of target area during t-th of emitter transmission signal；The reflectivity of target area isWherein k and k0=ω/c represents the wave number in target and free space respectively, and ω represents centre frequency, c tables Show the light velocity；Two-dimentional Green's function is represented,First kind zeroth order Hunk function is represented, I represents imaginary unit.
  6. 6. a kind of Forecasting Methodology as claimed in claim 4, it is characterised in that in step 2), structure one is based on physical machine The N layers series connection convolutional neural networks of system, wherein individual layer convolutional neural networks are divided into three sublayers：First sublayer is input layer, table Levy observational variable；Second sublayer is characterized layer, characterizes the induced-current of reconstruct；Third layer is output layer, characterizes the target of reconstruct The reflectivity in region.
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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111428407A (en) * 2020-03-23 2020-07-17 杭州电子科技大学 Electromagnetic scattering calculation method based on deep learning
CN111444601A (en) * 2020-03-23 2020-07-24 杭州电子科技大学 AI learning type electromagnetic scattering calculation method suitable for any incident field
CN111609787A (en) * 2020-05-28 2020-09-01 杭州电子科技大学 Two-step phase-free imaging method for solving electromagnetic backscattering problem based on neural network
CN111610374A (en) * 2020-05-28 2020-09-01 杭州电子科技大学 Scattered field phase recovery method based on convolutional neural network
CN112528869A (en) * 2020-12-14 2021-03-19 北京航空航天大学杭州创新研究院 Phase-free data imaging method based on complex neural network
CN113177356A (en) * 2021-04-28 2021-07-27 北京航空航天大学 Target electromagnetic scattering characteristic rapid prediction method based on deep learning
CN113705031A (en) * 2021-06-15 2021-11-26 西安电子科技大学 Nano antenna array electromagnetic performance prediction method based on deep learning
CN114741951A (en) * 2022-03-11 2022-07-12 上海师范大学 Medium target electromagnetic detection method based on convolutional neural network

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103712955A (en) * 2014-01-02 2014-04-09 李云梅 Class-II water atmospheric correction method based on neural network quadratic optimization
CN106124449A (en) * 2016-06-07 2016-11-16 中国科学院合肥物质科学研究院 A kind of soil near-infrared spectrum analysis Forecasting Methodology based on degree of depth learning art
WO2017004626A1 (en) * 2015-07-01 2017-01-05 The Board Of Trustees Of The Leland Stanford Junior University Systems and methods for providing reinforcement learning in a deep learning system
US20170116520A1 (en) * 2015-10-23 2017-04-27 Nec Laboratories America, Inc. Memory Efficient Scalable Deep Learning with Model Parallelization
KR20170096282A (en) * 2016-02-15 2017-08-24 한국과학기술원 Deep learning type classification method with feature-based weighting

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103712955A (en) * 2014-01-02 2014-04-09 李云梅 Class-II water atmospheric correction method based on neural network quadratic optimization
WO2017004626A1 (en) * 2015-07-01 2017-01-05 The Board Of Trustees Of The Leland Stanford Junior University Systems and methods for providing reinforcement learning in a deep learning system
US20170116520A1 (en) * 2015-10-23 2017-04-27 Nec Laboratories America, Inc. Memory Efficient Scalable Deep Learning with Model Parallelization
KR20170096282A (en) * 2016-02-15 2017-08-24 한국과학기술원 Deep learning type classification method with feature-based weighting
CN106124449A (en) * 2016-06-07 2016-11-16 中国科学院合肥物质科学研究院 A kind of soil near-infrared spectrum analysis Forecasting Methodology based on degree of depth learning art

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111428407A (en) * 2020-03-23 2020-07-17 杭州电子科技大学 Electromagnetic scattering calculation method based on deep learning
CN111444601A (en) * 2020-03-23 2020-07-24 杭州电子科技大学 AI learning type electromagnetic scattering calculation method suitable for any incident field
CN111444601B (en) * 2020-03-23 2023-06-20 杭州电子科技大学 AI learning type electromagnetic scattering calculation method suitable for arbitrary incident field
CN111609787B (en) * 2020-05-28 2021-10-01 杭州电子科技大学 Two-step phase-free imaging method for solving electromagnetic backscattering problem based on neural network
CN111610374A (en) * 2020-05-28 2020-09-01 杭州电子科技大学 Scattered field phase recovery method based on convolutional neural network
CN111610374B (en) * 2020-05-28 2022-08-05 杭州电子科技大学 Scattered field phase recovery method based on convolutional neural network
CN111609787A (en) * 2020-05-28 2020-09-01 杭州电子科技大学 Two-step phase-free imaging method for solving electromagnetic backscattering problem based on neural network
CN112528869A (en) * 2020-12-14 2021-03-19 北京航空航天大学杭州创新研究院 Phase-free data imaging method based on complex neural network
CN112528869B (en) * 2020-12-14 2023-04-25 北京航空航天大学杭州创新研究院 Phase-free data imaging method based on complex neural network
CN113177356A (en) * 2021-04-28 2021-07-27 北京航空航天大学 Target electromagnetic scattering characteristic rapid prediction method based on deep learning
CN113177356B (en) * 2021-04-28 2021-10-15 北京航空航天大学 Target electromagnetic scattering characteristic rapid prediction method based on deep learning
CN113705031A (en) * 2021-06-15 2021-11-26 西安电子科技大学 Nano antenna array electromagnetic performance prediction method based on deep learning
CN114741951A (en) * 2022-03-11 2022-07-12 上海师范大学 Medium target electromagnetic detection method based on convolutional neural network

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