CN113866740B - Cluster target dynamic electromagnetic scattering characteristic calculation method and device - Google Patents
Cluster target dynamic electromagnetic scattering characteristic calculation method and device Download PDFInfo
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Abstract
The invention provides a method and a device for calculating electromagnetic scattering characteristics of a dynamic group target, wherein the method comprises the following steps: acquiring scene information of a cluster target to be analyzed; generating constant data according to the scene information of the cluster target to be analyzed; acquiring each frame of scene of the cluster target to be analyzed in real time, and executing for each frame of scene: and calculating variable data corresponding to the frame scene, and calculating and generating electromagnetic scattering characteristics corresponding to the frame scene by utilizing the variable data and the constant data corresponding to the frame scene. According to the scheme, when each frame is calculated, calculation of constant data is omitted, and the calculation amount of dynamic electromagnetic scattering characteristics is greatly reduced.
Description
Technical Field
The embodiment of the invention relates to the technical field of electromagnetism, in particular to a method and a device for calculating dynamic electromagnetic scattering characteristics of a cluster target.
Background
The unmanned aerial vehicle cluster is an intelligent cooperative combat cluster formed by a large number of unmanned aerial vehicles, and has the advantages of miniature platform, strong burst prevention capability, low flying condition, low machine body cost and high efficiency-cost ratio, and becomes one of the main combat forms in the future. Unmanned aerial vehicle cluster mainly executes battle field information reconnaissance and monitoring, saturation attack, electromagnetic interference and suppression, electronic baiting and other battle tasks, brings great threat to future air defense battle, and is urgently needed to develop anti-unmanned aerial vehicle cluster battle research. The first link of the anti-unmanned aerial vehicle cluster is to detect the unmanned aerial vehicle cluster. The unmanned aerial vehicle cluster has the characteristics of small volume and large quantity, when unmanned aerial vehicles in the cluster are densely distributed, the unmanned aerial vehicles occupying a plurality of continuous resolution units are regarded as an expansion target by a radar, but the unmanned aerial vehicle clusters are not simply overlapped by a certain number of unmanned aerial vehicles, and different unmanned aerial vehicles in the clusters have different maneuvering and inching characteristics, so that the detection of the unmanned aerial vehicle cluster targets cannot be simply focused on static and average information under normal or average conditions, but the time-varying dynamic information is effectively fused and utilized, the dynamic modeling and feature extraction algorithm of the existing unmanned aerial vehicle cluster targets is adjusted and improved, the dynamic electromagnetic scattering characteristics of the unmanned aerial vehicle cluster targets are extracted, and the detection and early warning, accurate identification and continuous tracking of the unmanned aerial vehicle cluster targets are realized.
Electromagnetic modeling and simulation techniques for unmanned aerial vehicles are currently also resident on static, traditional numerical methods, such as full-wave simulation methods. The full-wave simulation is further divided into a micro-classification method and an integral method. When analyzing a target, the micro-classification method takes a field in a space as an unknown quantity, and certain space (including air) surrounding the target needs to be discretized, so that the calculation amount of the method is large for clustered unmanned aerial vehicle formation; the integral method only needs to discrete the surface or the internal body area of the target, is suitable for analysis of the clustered target, but has higher calculation complexity and is difficult to bear by a common computer. Therefore, the prior art scheme cannot realize dynamic calculation, and has large calculation amount.
Disclosure of Invention
The embodiment of the invention provides a method and a device for calculating dynamic electromagnetic scattering characteristics of a cluster target, which can realize dynamic calculation and reduce the calculated amount.
In a first aspect, an embodiment of the present invention provides a method for calculating dynamic electromagnetic scattering characteristics of a clustered target, including:
acquiring scene information of a cluster target to be analyzed;
generating constant data according to the scene information of the cluster target to be analyzed;
acquiring each frame of scene of the cluster target to be analyzed in real time, and executing for each frame of scene: and calculating variable data corresponding to the frame scene, and calculating and generating electromagnetic scattering characteristics corresponding to the frame scene by utilizing the variable data and the constant data corresponding to the frame scene.
Preferably, before the generating constant data, the method further includes:
Obtaining a reduced impedance matrix equation based on a moment method according to the scene information of the cluster target to be analyzed; the reduced impedance matrix equation is that the product of the first matrix and the second matrix is equal to the third matrix; wherein the first matrix is an MP×MP order matrix, and the second matrix and the third matrix are MP×1 order matrices; wherein P is the number of sub-targets contained in the cluster target to be analyzed, M, P are integers greater than 1;
For each matrix element of M 2P2 matrix elements in the first matrix, converting the matrix element into a sum of vector bit term effect and scalar bit term effect; the vector bit term action term is the product of a first matrix coefficient, a first expression matrix, a second expression matrix, a rotation matrix and a Grignard function; the scalar bit term is the product of a second matrix coefficient, a third expression matrix and the green function; the rotation matrix is related to the relative postures of the two sub-targets, and the green function is related to the distance between the two sub-targets;
Determining the first matrix coefficient, the second matrix coefficient, the first expression matrix, the second expression matrix and the third expression matrix as constants, and determining the rotation matrix and the green function as variables.
Preferably, the generating constant data includes:
Calculating the value of the first matrix coefficient, calculating the first expression matrix to obtain first constant data, and calculating the second expression matrix to obtain second constant data;
Calculating the value of the second matrix coefficient;
Aiming at each type of sub-targets contained in the cluster targets to be analyzed, combining the sub-targets with other sub-targets of each type to obtain Q 22 combinations; q is the type number of sub-targets contained in the cluster target to be analyzed, and Q is a positive integer not greater than P;
And performing gesture sampling and position sampling on two sub-targets in each combination to obtain a plurality of sampling combinations, and calculating the third expression matrix for each sampling combination to obtain corresponding third constant data.
Preferably, the calculating variable data corresponding to the frame scene includes:
Determining two sub-targets corresponding to each reduced sub-matrix of P 2 reduced sub-matrices in the first matrix; determining the relative gesture and the distance of the two sub-targets according to the frame scene, calculating according to the relative gesture to obtain a rotation matrix of the reduced sub-matrix, and calculating according to the distance to obtain a green function of the reduced sub-matrix;
the calculating and generating the electromagnetic scattering characteristic corresponding to the frame scene by using the variable data and the constant data corresponding to the frame scene comprises the following steps:
For each matrix element in the first matrix:
Multiplying the value of the first matrix coefficient, the first constant data, the rotation matrix of the reduced submatrix to which the matrix element belongs, the second constant data and the green function of the reduced submatrix to which the matrix element belongs to obtain a vector bit term action term of the matrix element;
Selecting corresponding third constant data according to the relative gesture and distance of two sub-targets corresponding to the matrix element, and multiplying the value of the second matrix coefficient, the selected third constant data and the green function of the reduced submatrix to which the matrix element belongs to obtain a scalar bit term action term of the matrix element;
adding the vector bit term of the matrix element and the scalar bit term of the matrix element to obtain the matrix element;
And solving the reduced impedance matrix equation according to the M 2P2 matrix elements, and obtaining the electromagnetic scattering characteristics corresponding to the frame scene according to the solving result.
Preferably, the obtaining the reduced impedance matrix equation based on the moment method according to the scene information of the cluster target to be analyzed includes:
Constructing an initial impedance matrix equation based on a moment method;
Determining at least one type corresponding to a sub-target contained in the cluster target to be analyzed according to the scene information of the cluster target to be analyzed;
For each of the at least one type: generating a characteristic mode matrix of the type according to the self-acting impedance matrix of the type sub-target;
And performing reduced-order processing on the initial impedance matrix equation by utilizing each type of characteristic mode matrix to obtain a processed reduced-order impedance matrix equation.
Preferably, the generating the characteristic mode matrix of the type according to the self-acting impedance matrix of the type sub-object includes:
According to the self-acting impedance matrix of the type of sub-targets, determining the characteristic values and the characteristic vectors of the type of sub-targets in each characteristic mode;
And determining a set of feature vectors corresponding to the minimum M feature values in the feature values as a feature module matrix of the type.
Preferably, the green function is:
wherein G ' is the Green's function, R ' is the centroid distance of the two sub-targets; k is the wave number.
In a second aspect, an embodiment of the present invention further provides a device for calculating dynamic electromagnetic scattering characteristics of a clustered target, including:
The scene information acquisition unit is used for acquiring scene information of the cluster target to be analyzed;
The constant data generation unit is used for generating constant data according to the scene information of the cluster target to be analyzed;
The dynamic calculation unit is used for acquiring each frame of scene of the cluster target to be analyzed in real time and executing the method aiming at each frame of scene: and calculating variable data corresponding to the frame scene, and calculating and generating electromagnetic scattering characteristics corresponding to the frame scene by utilizing the variable data and the constant data corresponding to the frame scene.
In a third aspect, an embodiment of the present invention further provides a computing device, including a memory and a processor, where the memory stores a computer program, and the processor implements a method according to any embodiment of the present specification when executing the computer program.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform a method according to any of the embodiments of the present specification.
The embodiment of the invention provides a method and a device for calculating dynamic electromagnetic scattering characteristics of a cluster target, wherein the same calculation process is adopted when each frame of the cluster target is calculated, so that constant data is obtained by distinguishing variables and constants in the calculation process of each frame and calculating the constants once, and then when each frame is calculated, the calculated constant data is called only by calculating variable data corresponding to the frame, so that a calculation result corresponding to the frame can be obtained. Therefore, when each frame is calculated, the calculation of constant data is omitted, and the calculation amount of dynamic electromagnetic scattering characteristics is greatly reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for calculating dynamic electromagnetic scattering characteristics of a cluster target according to an embodiment of the present invention;
FIG. 2 is a schematic representation of a RWG basis function provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a distance between two sub-targets according to an embodiment of the invention;
FIG. 4 is a schematic diagram of the definition of green's function distance terms for RWG basis functions between two sub-targets according to one embodiment of the invention;
FIG. 5 is a hardware architecture diagram of a computing device according to one embodiment of the invention;
FIG. 6 is a schematic diagram of a device for calculating dynamic electromagnetic scattering characteristics of clustered targets according to an embodiment of the present invention;
FIG. 7 is a block diagram of another apparatus for calculating dynamic electromagnetic scattering characteristics of clustered targets according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments, and all other embodiments obtained by those skilled in the art without making any inventive effort based on the embodiments of the present invention are within the scope of protection of the present invention.
As described above, the electromagnetic modeling and simulation technology for the unmanned aerial vehicle still stays on a static and traditional numerical method, in the static calculation process, the dynamic scene of the unmanned aerial vehicle cluster is divided frame by frame, each frame is respectively calculated, and the calculation processes of the frames are not related to each other. When calculating for each frame, a corresponding grid model is required to be reestablished for the grid state of the unmanned aerial vehicle cluster of the current frame, and then the electromagnetic scattering characteristics are calculated by using the grid model. Considering that the same calculation process is adopted when each frame is calculated, if the variables and constants used in the calculation process can be distinguished, the constant is calculated once to obtain constant data, and then when each frame is calculated, the calculation result corresponding to the frame can be obtained only by calculating the variable data corresponding to the frame and calling the constant data obtained by calculation. Compared with the prior art, the method omits the calculation of constant data when each frame is calculated, and greatly reduces the calculation amount of dynamic electromagnetic scattering characteristics.
Specific implementations of the above concepts are described below.
Referring to fig. 1, an embodiment of the present invention provides a method for calculating dynamic electromagnetic scattering characteristics of a cluster target, including:
step 100, obtaining scene information of a cluster target to be analyzed.
102, Generating constant data according to scene information of the cluster target to be analyzed;
Step 104, acquiring each frame scene of the cluster target to be analyzed in real time, and executing for each frame scene: and calculating variable data corresponding to the frame scene, and calculating and generating electromagnetic scattering characteristics corresponding to the frame scene by utilizing the variable data and the constant data corresponding to the frame scene.
In the embodiment of the invention, because the same calculation process is adopted when each frame of the cluster target is calculated, constant data is obtained by carrying out one-time calculation on the constant by distinguishing the variable and the constant in the calculation process of each frame, and then when each frame is calculated, the calculation result corresponding to the frame can be obtained by only calculating the variable data corresponding to the frame and calling the constant data which is obtained by calculation. Therefore, when each frame is calculated, the calculation of constant data is omitted, and the calculation amount of dynamic electromagnetic scattering characteristics is greatly reduced.
The manner in which the individual steps shown in fig. 1 are performed is described below.
First, for step 100, scene information of a cluster target to be analyzed is acquired.
In one embodiment of the present invention, the scene information of the cluster target to be analyzed may include: the type of sub-targets included in the cluster target, the number of sub-targets, the grid of each sub-target, and the like.
The type of the sub-object included in the cluster object may be at least one type, for example, the cluster includes 10 sub-objects, where the 10 sub-objects are all of the same type, or the 10 sub-objects correspond to two types. The grid is a grid line used to characterize the sub-target structure.
In one embodiment of the present invention, to be able to distinguish between constant and variable in each frame calculation process, following step 100, before step 102, the following steps 1011-1013 may be further included:
Step 1011: obtaining a reduced impedance matrix equation based on a moment method according to the scene information of the cluster target to be analyzed; the reduced impedance matrix equation is that the product of the first matrix and the second matrix is equal to the third matrix; wherein the first matrix is an MP×MP order matrix, and the second matrix and the third matrix are MP×1 order matrices; wherein 2P is the number of sub-targets contained in the cluster target to be analyzed, and M, P is an integer greater than 1.
In one embodiment of the present invention, this step 1011 may be implemented at least in one of the following ways:
A1: an initial impedance matrix equation is constructed based on a moment method.
Assuming that the cluster target to be analyzed comprises P sub-targets, the number of unknown quantities of each sub-target is N 0, the number of unknown quantities in the cluster target is N 0 P, wherein P is an integer greater than 1, and N 0 is a positive integer. Inducing current under radar wave irradiation aiming at the cluster target, generating a scattered field, and solving the scattered field based on a moment method to obtain the following initial impedance matrix equation:
Where Z ii represents the self-acting impedance matrix of the ith sub-target, Z ij represents the interaction matrix between the ith and jth sub-targets, and I i and V i represent the surface current and excitation, respectively, of the ith sub-target.
A2: and determining at least one type corresponding to the sub-target contained in the cluster target to be analyzed according to the scene information of the cluster target to be analyzed.
A3: for each of the at least one type: the characteristic mode matrix of the type is generated from the self-acting impedance matrix of the type sub-object.
As can be seen from the formula (1), each matrix element Z ii、Zij in the left-side N 0P×N0 P-order matrix is an N 0×N0 -order matrix, and the calculation amount is large, so that the reduction processing needs to be performed on the formula (1), and each matrix in the left-side N 0P×N0 P-order matrix is related to the self-acting impedance matrix and the interaction matrix of each sub-target, so that the corresponding eigenmode matrix needs to be extracted for each sub-target.
Because of the repeatability of the same type of sub-targets in the cluster targets, in one embodiment of the invention, one sub-target can be arbitrarily selected from each type of sub-targets, and the characteristic mode matrix of the type can be generated by using the self-acting impedance matrix of the selected sub-target.
The step A3 may include: according to the self-acting impedance matrix of the type of sub-targets, determining the characteristic values and the characteristic vectors of the type of sub-targets in each characteristic mode; and determining a set of feature vectors corresponding to the minimum M feature values in the feature values as a feature module matrix of the type.
Specifically, for each type of sub-target selected, this step A3 may be constructed to obtain the following formula (2):
XiiJn＝λnRiiJn (2)
Wherein, R ii and X ii are real part and imaginary part matrixes of Z ii respectively, and lambda n and J n are eigenvalues and eigenvectors of an nth mode respectively.
If the feature vectors in all modes calculated by the above formula are constructed to obtain a feature matrix, considering that the obtained feature matrix has more redundant modes, the matrix order cannot be effectively reduced to improve the calculation efficiency, so that M feature vectors with larger mode coefficients can be selected to construct the feature matrix, that is, the feature vector of the mode corresponding to M (M < N 0) feature values calculated by using the implicit restart arnold method is used to form the feature matrix J cmi, where J cmi＝[J1 J2 … JM is a set of M feature vectors, and the dimension is N 0 ×m.
It will be appreciated that for the same type of sub-object, assuming that the 1 st, 2 nd and 3 rd sub-objects are all of the same type and that the characteristic mode matrix is generated using the self-acting impedance matrix of the 1 st sub-object in generating the characteristic mode matrix, then the characteristic mode matrix of the three sub-objects is J cm1＝Jcm2＝Jcm3.
A4: and performing reduced-order processing on the initial impedance matrix equation by utilizing each type of characteristic mode matrix to obtain a processed reduced-order impedance matrix equation.
Performing reduced processing on the initial impedance matrix equation in the formula (1) by using the characteristic mode matrix J cmi＝[J1 J2 … JM obtained in the A3 to obtain the following reduced impedance matrix equation:
the left MP×MP order matrix of the formula (3) is a first matrix, the left MP×1 order matrix is a second matrix, and the right MP×1 order matrix is a third matrix.
Since M < < N 0, it can be seen that the matrix order of equation (3) is much smaller than that of equation (1), so that the calculation amount of the impedance matrix equation can be reduced.
Step 1012: for each matrix element of M 2P2 matrix elements in the first matrix, converting the matrix element into a sum of vector bit term effect and scalar bit term effect; the vector bit term action term is the product of a first matrix coefficient, a first expression matrix, a second expression matrix, a rotation matrix and a Grignard function; the scalar bit term is the product of a second matrix coefficient, a third expression matrix and the green function; the rotation matrix is related to the relative pose of the two sub-objects and the green function is related to the distance of the two sub-objects.
Step 1013: determining the first matrix coefficient, the second matrix coefficient, the first expression matrix, the second expression matrix and the third expression matrix as constants, and determining the rotation matrix and the green function as variables.
For example, matrix elements of the first matrix species are such as Etc. For the interaction matrix Z ij among the matrix elements, since one sub-object includes a plurality of basis functions, the matrix interior thereof includes a plurality of elements, for the interaction between the mth basis function on one sub-object s and the nth basis function on the other sub-object t, the impedance matrix Z mn of the basis functions is expressed as follows:
wherein the mth basis function is located on the sub-target s, the nth basis function is located on the sub-target t, K is wave number, eta is wave impedance, r and r 'are unit vectors of origin to field point direction and source point direction respectively, f m(r)、fn (r) is RWG basis function, S and S' represent field and source surface element respectively,/>R= |r-R' |. ngo and ngs represent the number of gaussian integral points for the field and source, respectively, W o(Ko) and W s(Ks) represent the corresponding gaussian integral parameters for the field and source, respectively.
The RWG basis function is schematically shown in fig. 2, and is shown as follows:
wherein, And/>Respectively represent the upper triangle and the lower triangle corresponding to the basis function,/>And/>The areas of the upper triangle and the lower triangle are respectively, r is any point in the triangle,/>Is from the vertex of the upper triangle/>Basis function corresponding vector pointing to r,/>Is directed from r to the vertex of the triangle/>I n is the length of the common edge.
With changes in position and attitude, the amounts of change in the impedance matrix Z mn are ρ m·ρn and R, where ρ m·ρn changes only with changes in attitude, and R changes with both attitude and position. Dividing the impedance matrix Z mn into two processes, vector bit term Z A and scalar bit term
As can be seen from equation (6), when the position changes, i.e., the distance between sub-targets changes, only the green function G needs to be changed; and when the gesture is changed, only the vector bit item Z A is required to be changed. Thus, when there is a distance between sub-objects in the clustered objects, the distance between the basis functions of different sub-objects (such as R 11、R12、R13 in FIG. 3) may be approximately replaced by the sub-object centroid distance R'.
The following is directed to vector bit term Z A and scalar bit termThe description of the deployment process is made separately.
1. Vector bit term Z A.
Vector bit term Z A can be expanded to:
Where ω is angular frequency, η 0 is free space wave impedance, μ 0 is free space permeability, the superscript "+" is denoted as the upper triangle and the superscript "-" is denoted as the lower triangle.
When the relative gesture between two sub-targets is changed, a local coordinate system can be established by taking the centroid of the sub-target s as the origin, namely ρ m is unchanged, ρ n is only rotated, ρ' n＝T·ρn after gesture change is a rotation matrix with 3 x 3 dimensions, and then:
Since all the basis functions of the sub-object T are rotated by the same angle, the rotation matrix T is applied to all the basis functions in the sub-object T. For R in the green function G where the sub-object s and the sub-object t interact with each other corresponding to a bin, the unused bins correspond to different R, where the centroid distance R' between the sub-object s and the sub-object t is approximated, then there are:
vector bit item Z A is rewritable as:
then, the vector bit term function term of each matrix element in the first matrix can be obtained
As can be seen from equation (11), the first matrix coefficientsFirst expression matrix/>Second expression matrix/>The rotation matrix T (related to the relative pose of the two sub-objects) and the green function G' (related to the distance of the two sub-objects) are variables, which are constants.
2. Scalar bit term
Scalar bit termThe unfolding method comprises the following steps:
Wherein ε 0 is the dielectric constant of free space, The definition is shown in fig. 4.
If the approximation method of vector bit term is used, the green function is directly proposed, and the scalar bit term of the original matrix is generated byThe difference between the target position and the posture change is small and close to 0, and the influence of the target position and the posture change on the scalar bit item is ignored. Thus, it is desirable to take into account the effects of pose and relative position on scalar bits, and in one embodiment of the invention, errors in this term can be reduced by creating a table look-up.
Before the table is built, the distance between sub-targets and the gesture can be separated, and the separation process is as follows:
wherein G' is an approximate green function of the unit 1 and the unit 2 obtained according to the formula (9).
Then, scalar bit term terms for each matrix element in the first matrix can be derived
From equation (14), the second matrix coefficientsThird expression matrix/>Is constant and the green function G' is variable.
From the above embodiments it can be derived that: the constants are: a first matrix coefficient, a second matrix coefficient, a first expression matrix, a second expression matrix, and a third expression matrix; the variables are: rotation matrix and greens function.
Then, for step 102, constant data is generated according to the scene information of the cluster target to be analyzed.
The step may include:
Step 1021: calculating the value of the first matrix coefficient, calculating the first expression matrix to obtain first constant data, and calculating the second expression matrix to obtain second constant data; and calculating the value of the second matrix coefficient.
The first matrix coefficients can be derived according to equation (11)First expression matrix/>Second expression matrixIn this way, the contents in the scene information are substituted into the calculation formula, the value of the first matrix coefficient can be calculated, the first expression matrix is calculated to obtain first constant data, and the second expression matrix is calculated to obtain second constant data.
The value of the second matrix coefficient passesAnd (5) calculating to obtain the product.
Step 1022: aiming at each type of sub-targets contained in the cluster target to be analyzed, combining the sub-targets with each other type of sub-targets to obtain Q 22 combinations; q is the type number of sub-targets contained in the cluster target to be analyzed, and Q is a positive integer not greater than P.
Assuming that the cluster target comprises Q types of sub-targets, aiming at a first type of sub-target, respectively combining the sub-targets with any one type of sub-targets in the Q types of sub-targets to obtain Q combinations; similarly, Q combinations may be obtained for the second type of sub-target, … …, and Q combinations may be obtained for the Q type of sub-target; thus, Q 2 combinations can be obtained.
Step 1023: and performing gesture sampling and position sampling on two sub-targets in each combination to obtain a plurality of sampling combinations, and calculating the third expression matrix for each sampling combination to obtain corresponding third constant data.
In one embodiment of the present invention, it is desirable to pair the third expression matrix according to formulas (13) and (14)The specific process of building the table is as follows:
1) Posture sampling: the pitch angle, the yaw angle and the roll angle respectively take 4 angles of 0, pi/2, pi, 3 pi/2 and the like for carrying out gesture sampling calculation, and the total of 4 multiplied by 4=64 gestures;
2) Position sampling: and (3) taking 6 directions of + -x, + -y, + -z and the like to perform position sampling calculation, wherein the sampling distance is fixed as R 0.
Then the 64 poses and the 6 directions can be combined respectively, 64×6=384 combinations can be obtained, and in each case, the combinations can be substituted into the third expression matrix, and corresponding third constant data can be obtained by calculation. The third constant data is stored in a pre-established table. The table includes 3832Q 2 pieces of third constant data.
It should be noted that, the sampling parameter values of the gesture sampling and the position sampling are sample values, and may also be sampled for other angles and distances. The more sampling combinations, the higher the accuracy, but the more third constant data is required to be calculated, the larger the calculation amount, so that the sampling parameter values of the above-mentioned attitude sampling and position sampling are a preferable sampling mode.
Step 104, acquiring each frame scene of the cluster target to be analyzed in real time, and executing for each frame scene: and calculating variable data corresponding to the frame scene, and calculating and generating electromagnetic scattering characteristics corresponding to the frame scene by utilizing the variable data and the constant data corresponding to the frame scene.
Since the corresponding gesture and position may change in each frame of the dynamic scene of the cluster target, the calculation needs to be performed frame by frame.
In one embodiment of the present invention, calculating variable data corresponding to the frame scene may include: determining two sub-targets corresponding to each reduced sub-matrix of P 2 reduced sub-matrices in the first matrix; and determining the relative gesture and the distance of the two sub-targets according to the frame scene, calculating according to the relative gesture to obtain a rotation matrix of the reduced sub-matrix, and calculating according to the distance to obtain a green function of the reduced sub-matrix.
The rotation matrix T and the green 'function G' can be calculated by equations (8) and (9), respectively.
It should be noted that, for P 2 reduced submatrices in the first matrix, each reduced submatrix includes M 2 matrix elements, and the rotation matrix and the green function of different matrix elements in the same reduced submatrix are the same, so that for different matrix elements in the same reduced submatrix, only one rotation matrix and green function are calculated.
At this time, the calculation of the constant data and the variable data is completed, and in step 104, the calculation of the electromagnetic scattering characteristic corresponding to the frame scene by using the variable data and the constant data corresponding to the frame scene may include:
For each matrix element in the first matrix:
values of the first matrix coefficients The first constant data/>A rotation matrix T of a reduced-order submatrix to which the matrix element belongs, and the second constant data/>Multiplying the reduced submatrix with the green function G' of the matrix element to obtain the vector bit term function term/>, of the matrix element
Selecting corresponding third constant data according to the relative gesture and distance of two sub-targets corresponding to the matrix element, and obtaining the value of the second matrix coefficientMultiplying the selected third constant data with the green function G' of the reduced submatrix to which the matrix element belongs to obtain the scalar bit term acting term/>, of the matrix elementFor example, the relative attitude is pitch angle pi/2, yaw angle pi/2, roll angle pi, and position is z-direction distance R 0, a combination of the relative attitude and position is obtained, and then a table is looked up to obtain third constant data of the combination.
Vector bit term function term of the matrix elementAnd scalar bit term terms/>, of the matrix elementAdding to obtain the matrix element;
And solving the reduced impedance matrix equation according to the M 2P2 matrix elements, and obtaining the electromagnetic scattering characteristics corresponding to the frame scene according to the solving result.
In the reduced impedance matrix equation of the formula (3), M 2P2 matrix elements in the first matrix are calculated and substituted into the first matrix, then each characteristic mode matrix is substituted into the second matrix and the third matrix, the formula (3) is solved through a direct solution or iterative solution method, the current coefficient of the cluster target can be obtained, and then the far field of the cluster target, namely the electromagnetic scattering characteristic corresponding to the frame scene, is calculated according to the current coefficient of the cluster target.
As shown in fig. 5 and 6, an embodiment of the present invention provides a cluster target dynamic electromagnetic scattering characteristic calculating device. The apparatus embodiments may be implemented by software, or may be implemented by hardware or a combination of hardware and software. In terms of hardware, as shown in fig. 5, a hardware architecture diagram of a computing device where a cluster target dynamic electromagnetic scattering property computing device provided by an embodiment of the present invention is located, where the computing device where the embodiment is located may include other hardware, such as a forwarding chip responsible for processing a packet, in addition to the processor, the memory, the network interface, and the nonvolatile memory shown in fig. 5. Taking a software implementation as an example, as shown in fig. 6, as a device in a logic sense, the device is formed by reading a corresponding computer program in a nonvolatile memory into a memory by a CPU of a computing device where the device is located. The device for calculating dynamic electromagnetic scattering characteristics of clustered targets provided in this embodiment includes:
a scene information obtaining unit 601, configured to obtain scene information of a cluster target to be analyzed;
A constant data generating unit 602, configured to generate constant data according to the scene information of the cluster target to be analyzed;
The dynamic computing unit 603 is configured to acquire each frame of scene of the cluster target to be analyzed in real time, and execute, for each frame of scene: and calculating variable data corresponding to the frame scene, and calculating and generating electromagnetic scattering characteristics corresponding to the frame scene by utilizing the variable data and the constant data corresponding to the frame scene.
In one embodiment of the present invention, referring to fig. 7, the cluster target dynamic electromagnetic scattering property calculating apparatus may further include:
The constant variable separating unit 604 is specifically configured to perform:
Obtaining a reduced impedance matrix equation based on a moment method according to the scene information of the cluster target to be analyzed; the reduced impedance matrix equation is that the product of the first matrix and the second matrix is equal to the third matrix; wherein the first matrix is an MP×MP order matrix, and the second matrix and the third matrix are MP×1 order matrices; wherein P is the number of sub-targets contained in the cluster target to be analyzed, M, P are integers greater than 1;
For each matrix element of M 2P2 matrix elements in the first matrix, converting the matrix element into a sum of vector bit term effect and scalar bit term effect; the vector bit term action term is the product of a first matrix coefficient, a first expression matrix, a second expression matrix, a rotation matrix and a Grignard function; the scalar bit term is the product of a second matrix coefficient, a third expression matrix and the green function; the rotation matrix is related to the relative postures of the two sub-targets, and the green function is related to the distance between the two sub-targets;
Determining the first matrix coefficient, the second matrix coefficient, the first expression matrix, the second expression matrix and the third expression matrix as constants, and determining the rotation matrix and the green function as variables.
In one embodiment of the present invention, the constant data generating unit 602 is specifically configured to calculate a value of the first matrix coefficient, calculate the first expression matrix to obtain first constant data, and calculate the second expression matrix to obtain second constant data; calculating the value of the second matrix coefficient; aiming at each type of sub-targets contained in the cluster targets to be analyzed, combining the sub-targets with other sub-targets of each type to obtain Q 22 combinations; q is the type number of sub-targets contained in the cluster target to be analyzed, and Q is a positive integer not greater than P; and performing gesture sampling and position sampling on two sub-targets in each combination to obtain a plurality of sampling combinations, and calculating the third expression matrix for each sampling combination to obtain corresponding third constant data.
In one embodiment of the present invention, the dynamic computing unit 603, when executing the computing of the variable data corresponding to the frame scene, specifically includes: determining two sub-targets corresponding to each reduced sub-matrix of P 2 reduced sub-matrices in the first matrix; determining the relative gesture and the distance of the two sub-targets according to the frame scene, calculating according to the relative gesture to obtain a rotation matrix of the reduced sub-matrix, and calculating according to the distance to obtain a green function of the reduced sub-matrix;
The dynamic calculating unit 603, when executing the calculation to generate the electromagnetic scattering characteristic corresponding to the frame scene by using the variable data and the constant data corresponding to the frame scene, specifically includes:
For each matrix element in the first matrix:
Multiplying the value of the first matrix coefficient, the first constant data, the rotation matrix of the reduced submatrix to which the matrix element belongs, the second constant data and the green function of the reduced submatrix to which the matrix element belongs to obtain a vector bit term action term of the matrix element;
Selecting corresponding third constant data according to the relative gesture and distance of two sub-targets corresponding to the matrix element, and multiplying the value of the second matrix coefficient, the selected third constant data and the green function of the reduced submatrix to which the matrix element belongs to obtain a scalar bit term action term of the matrix element;
adding the vector bit term of the matrix element and the scalar bit term of the matrix element to obtain the matrix element;
And solving the reduced impedance matrix equation according to the M 2P2 matrix elements, and obtaining the electromagnetic scattering characteristics corresponding to the frame scene according to the solving result.
In one embodiment of the present invention, the constant variable separation unit 604, when obtaining the reduced impedance matrix equation based on the moment method according to the scene information of the cluster target to be analyzed, specifically includes: constructing an initial impedance matrix equation based on a moment method; determining at least one type corresponding to a sub-target contained in the cluster target to be analyzed according to the scene information of the cluster target to be analyzed; for each of the at least one type: generating a characteristic mode matrix of the type according to the self-acting impedance matrix of the type sub-target; and performing reduced-order processing on the initial impedance matrix equation by utilizing each type of characteristic mode matrix to obtain a processed reduced-order impedance matrix equation.
In one embodiment of the present invention, the constant variable separation unit 604 specifically includes, when generating the characteristic mode matrix of the type according to the self-acting impedance matrix of the sub-target of the type: according to the self-acting impedance matrix of the type of sub-targets, determining the characteristic values and the characteristic vectors of the type of sub-targets in each characteristic mode; and determining a set of feature vectors corresponding to the minimum M feature values in the feature values as a feature module matrix of the type.
In one embodiment of the present invention, the green function is:
wherein G ' is the Green's function, R ' is the centroid distance of the two sub-targets; k is the wave number.
It will be appreciated that the structure illustrated in the embodiments of the present invention does not constitute a specific limitation on a cluster target dynamic electromagnetic scattering property calculation device. In other embodiments of the invention, a clustered target dynamic electromagnetic scattering characteristics calculation device may include more or fewer components than shown, or certain components may be combined, certain components may be split, or different arrangements of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
The content of information interaction and execution process between the modules in the device is based on the same conception as the embodiment of the method of the present invention, and specific content can be referred to the description in the embodiment of the method of the present invention, which is not repeated here.
The embodiment of the invention also provides a computing device, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the method for computing the dynamic electromagnetic scattering characteristics of the cluster targets in any embodiment of the invention when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program causes the processor to execute the method for calculating the dynamic electromagnetic scattering characteristics of the cluster target in any embodiment of the invention.
Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium.
In this case, the program code itself read from the storage medium may realize the functions of any of the above-described embodiments, and thus the program code and the storage medium storing the program code form part of the present invention.
Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (e.g., CD-ROMs, CD-R, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs, DVD+RWs), magnetic tapes, nonvolatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer by a communication network.
Further, it should be apparent that the functions of any of the above-described embodiments may be implemented not only by executing the program code read out by the computer, but also by causing an operating system or the like operating on the computer to perform part or all of the actual operations based on the instructions of the program code.
Further, it is understood that the program code read out by the storage medium is written into a memory provided in an expansion board inserted into a computer or into a memory provided in an expansion module connected to the computer, and then a CPU or the like mounted on the expansion board or the expansion module is caused to perform part and all of actual operations based on instructions of the program code, thereby realizing the functions of any of the above embodiments.
It is noted that 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. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: various media in which program code may be stored, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (14)
1. The method for calculating the dynamic electromagnetic scattering characteristics of the clustered targets is characterized by comprising the following steps of:
acquiring scene information of a cluster target to be analyzed;
Obtaining a reduced impedance matrix equation based on a moment method according to the scene information of the cluster target to be analyzed; the reduced impedance matrix equation is that the product of the first matrix and the second matrix is equal to the third matrix; the reduced impedance matrix equation is:
In the reduced impedance matrix equation, the left mp×mp order matrix is the first matrix, the left mp×1 order matrix is the second matrix, and the right mp×1 order matrix is the third matrix; wherein P is the number of sub-targets contained in the cluster target to be analyzed, M, P are integers greater than 1; j cmi is a characteristic mode matrix formed by characteristic vectors of modes corresponding to M characteristic values, J cmi＝[J1 J2 … JM is a set of M characteristic vectors, the dimension is N 0×M,N0 which is a positive integer, M < N 0;Zii represents a self-acting impedance matrix of an ith sub-target, Z ij represents an interaction matrix between the ith and jth sub-targets, and I i and V i respectively represent surface current and excitation of the ith sub-target;
For each matrix element of M 2P2 matrix elements in the first matrix, converting the matrix element into a sum of vector bit term effect and scalar bit term effect; the vector bit term action term is the product of a first matrix coefficient, a first expression matrix, a second expression matrix, a rotation matrix and a Grignard function; the scalar bit term is the product of a second matrix coefficient, a third expression matrix and the green function; the rotation matrix is related to the relative postures of the two sub-targets, and the green function is related to the distance between the two sub-targets;
Determining the first matrix coefficient, the second matrix coefficient, the first expression matrix, the second expression matrix and the third expression matrix as constants, and determining the rotation matrix and the green function as variables;
generating constant data according to the scene information of the cluster target to be analyzed;
acquiring each frame of scene of the cluster target to be analyzed in real time, and executing for each frame of scene: and calculating variable data corresponding to the frame scene, and calculating and generating electromagnetic scattering characteristics corresponding to the frame scene by utilizing the variable data and the constant data corresponding to the frame scene.
2. The method of claim 1, wherein the generating constant data comprises:
Calculating the value of the first matrix coefficient, calculating the first expression matrix to obtain first constant data, and calculating the second expression matrix to obtain second constant data;
Calculating the value of the second matrix coefficient;
Aiming at each type of sub-targets contained in the cluster targets to be analyzed, combining the sub-targets with other sub-targets of each type to obtain Q 2 combinations; q is the type number of sub-targets contained in the cluster target to be analyzed, and Q is a positive integer not greater than P;
And performing gesture sampling and position sampling on two sub-targets in each combination to obtain a plurality of sampling combinations, and calculating the third expression matrix for each sampling combination to obtain corresponding third constant data.
3. The method of claim 2, wherein the step of determining the position of the substrate comprises,
The calculating variable data corresponding to the frame scene comprises the following steps:
Determining two sub-targets corresponding to each reduced sub-matrix of P 2 reduced sub-matrices in the first matrix; determining the relative gesture and the distance of the two sub-targets according to the frame scene, calculating according to the relative gesture to obtain a rotation matrix of the reduced sub-matrix, and calculating according to the distance to obtain a green function of the reduced sub-matrix;
the calculating and generating the electromagnetic scattering characteristic corresponding to the frame scene by using the variable data and the constant data corresponding to the frame scene comprises the following steps:
For each matrix element in the first matrix:
Multiplying the value of the first matrix coefficient, the first constant data, the rotation matrix of the reduced submatrix to which the matrix element belongs, the second constant data and the green function of the reduced submatrix to which the matrix element belongs to obtain a vector bit term action term of the matrix element;
Selecting corresponding third constant data according to the relative gesture and distance of two sub-targets corresponding to the matrix element, and multiplying the value of the second matrix coefficient, the selected third constant data and the green function of the reduced submatrix to which the matrix element belongs to obtain a scalar bit term action term of the matrix element;
adding the vector bit term of the matrix element and the scalar bit term of the matrix element to obtain the matrix element;
And solving the reduced impedance matrix equation according to the M 2P2 matrix elements, and obtaining the electromagnetic scattering characteristics corresponding to the frame scene according to the solving result.
4. The method according to claim 1, wherein the obtaining the reduced impedance matrix equation based on the moment method according to the scene information of the cluster target to be analyzed includes:
Constructing an initial impedance matrix equation based on a moment method;
Determining at least one type corresponding to a sub-target contained in the cluster target to be analyzed according to the scene information of the cluster target to be analyzed;
For each of the at least one type: generating a characteristic mode matrix of the type according to the self-acting impedance matrix of the type sub-target;
And performing reduced-order processing on the initial impedance matrix equation by utilizing each type of characteristic mode matrix to obtain a processed reduced-order impedance matrix equation.
5. The method of claim 4, wherein generating the type of eigenmode matrix from the type of sub-target's self-acting impedance matrix comprises:
According to the self-acting impedance matrix of the type of sub-targets, determining the characteristic values and the characteristic vectors of the type of sub-targets in each characteristic mode;
And determining a set of feature vectors corresponding to the minimum M feature values in the feature values as a feature module matrix of the type.
6. The method according to any one of claims 1-5, wherein the green function is:
wherein G ' is the Green's function, R ' is the centroid distance of the two sub-targets; k is the wave number.
7. A clustered object dynamic electromagnetic scattering characteristics computing device, comprising:
The scene information acquisition unit is used for acquiring scene information of the cluster target to be analyzed;
the constant variable separation unit is used for obtaining a reduced impedance matrix equation based on a moment method according to the scene information of the cluster target to be analyzed; the reduced impedance matrix equation is that the product of the first matrix and the second matrix is equal to the third matrix; the reduced impedance matrix equation is:
In the reduced impedance matrix equation, the left mp×mp order matrix is the first matrix, the left mp×1 order matrix is the second matrix, and the right mp×1 order matrix is the third matrix; wherein P is the number of sub-targets contained in the cluster target to be analyzed, M, P are integers greater than 1; j cmi is a characteristic mode matrix formed by characteristic vectors of modes corresponding to M characteristic values, J cmi＝[J1 J2 … JM is a set of M characteristic vectors, the dimension is N 0×M,N0 which is a positive integer, M < N 0;Zii represents a self-acting impedance matrix of an ith sub-target, Z ij represents an interaction matrix between the ith and jth sub-targets, and I i and V i respectively represent surface current and excitation of the ith sub-target;
for each matrix element of M 2P2 matrix elements in the first matrix, converting the matrix element into a sum of vector bit term effect and scalar bit term effect; the vector bit term action term is the product of a first matrix coefficient, a first expression matrix, a second expression matrix, a rotation matrix and a Grignard function; the scalar bit term is the product of a second matrix coefficient, a third expression matrix and the green function; the rotation matrix is related to the relative postures of the two sub-targets, and the green function is related to the distance between the two sub-targets; determining the first matrix coefficient, the second matrix coefficient, the first expression matrix, the second expression matrix and the third expression matrix as constants, and determining the rotation matrix and the green function as variables;
The constant data generation unit is used for generating constant data according to the scene information of the cluster target to be analyzed;
The dynamic calculation unit is used for acquiring each frame of scene of the cluster target to be analyzed in real time and executing the method aiming at each frame of scene: and calculating variable data corresponding to the frame scene, and calculating and generating electromagnetic scattering characteristics corresponding to the frame scene by utilizing the variable data and the constant data corresponding to the frame scene.
8. The apparatus of claim 7, wherein the constant data generating unit is specifically configured to calculate a value of the first matrix coefficient, calculate the first expression matrix to obtain first constant data, and calculate the second expression matrix to obtain second constant data; calculating the value of the second matrix coefficient; aiming at each type of sub-targets contained in the cluster targets to be analyzed, combining the sub-targets with other sub-targets of each type to obtain Q 2 combinations; q is the type number of sub-targets contained in the cluster target to be analyzed, and Q is a positive integer not greater than P; and performing gesture sampling and position sampling on two sub-targets in each combination to obtain a plurality of sampling combinations, and calculating the third expression matrix for each sampling combination to obtain corresponding third constant data.
9. The apparatus of claim 8, wherein the device comprises a plurality of sensors,
The dynamic calculation unit, when executing the calculation of the variable data corresponding to the frame scene, specifically includes: determining two sub-targets corresponding to each reduced sub-matrix of P 2 reduced sub-matrices in the first matrix; determining the relative gesture and the distance of the two sub-targets according to the frame scene, calculating according to the relative gesture to obtain a rotation matrix of the reduced sub-matrix, and calculating according to the distance to obtain a green function of the reduced sub-matrix;
The dynamic calculation unit, when executing the calculation to generate the electromagnetic scattering characteristic corresponding to the frame scene by using the variable data and the constant data corresponding to the frame scene, specifically includes: for each matrix element in the first matrix: multiplying the value of the first matrix coefficient, the first constant data, the rotation matrix of the reduced submatrix to which the matrix element belongs, the second constant data and the green function of the reduced submatrix to which the matrix element belongs to obtain a vector bit term action term of the matrix element; selecting corresponding third constant data according to the relative gesture and distance of two sub-targets corresponding to the matrix element, and multiplying the value of the second matrix coefficient, the selected third constant data and the green function of the reduced submatrix to which the matrix element belongs to obtain a scalar bit term action term of the matrix element; adding the vector bit term of the matrix element and the scalar bit term of the matrix element to obtain the matrix element; and solving the reduced impedance matrix equation according to the M 2P2 matrix elements, and obtaining the electromagnetic scattering characteristics corresponding to the frame scene according to the solving result.
10. The apparatus of claim 7, wherein the constant variable separation unit, when obtaining the reduced impedance matrix equation based on a moment method according to the scene information of the cluster target to be analyzed, specifically comprises: constructing an initial impedance matrix equation based on a moment method; determining at least one type corresponding to a sub-target contained in the cluster target to be analyzed according to the scene information of the cluster target to be analyzed; for each of the at least one type: generating a characteristic mode matrix of the type according to the self-acting impedance matrix of the type sub-target; and performing reduced-order processing on the initial impedance matrix equation by utilizing each type of characteristic mode matrix to obtain a processed reduced-order impedance matrix equation.
11. The apparatus according to claim 10, wherein the constant variable separation unit, when generating the characteristic modulus matrix of the type from the self-acting impedance matrix of the type sub-object, specifically comprises: according to the self-acting impedance matrix of the type of sub-targets, determining the characteristic values and the characteristic vectors of the type of sub-targets in each characteristic mode; and determining a set of feature vectors corresponding to the minimum M feature values in the feature values as a feature module matrix of the type.
12. The apparatus according to any one of claims 7-11, wherein the green function is:
wherein G ' is the Green's function, R ' is the centroid distance of the two sub-targets; k is the wave number.
13. A computing device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the method of any of claims 1-6 when the computer program is executed.
14. A computer readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform the method of any of claims 1-6.
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