CN107765103A - A kind of complex environment Electromagnetic Situation inversion method based on multisensor - Google Patents
A kind of complex environment Electromagnetic Situation inversion method based on multisensor Download PDFInfo
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Abstract
The problem of the invention discloses a kind of complex environment Electromagnetic Situation inversion method based on multisensor, mainly solves the substantial amounts of awareness apparatus needed for prior art, and the degree of accuracy of Electromagnetic Situation inverting is relatively low.Its technical scheme is：1st, determine and configure complex electromagnetic environment parameter；2nd, sensing station matrix is built；3rd, build path loss matrix；4th, Radar recognition is carried out according to sensing station matrix, path loss matrix, obtains position and the radiant power of radiation source；5th, according to the radiation source of identification, Electromagnetic Situation inverting, the Electromagnetic Situation of each point is obtained.The present invention can realize Radar recognition under conditions of a small amount of sensing station random distribution, radiation source positions and radiant power random distribution, and then realize Electromagnetic Situation inverting, available for the complex environment Electromagnetic Situation inverting based on multisensor.
Description
Technical field
The invention belongs to communication technical field, is related to frequency spectrum perception technology, Radar recognition, further relates to a kind of base
In the complex environment Electromagnetic Situation inversion technique of multisensor, available for the Electromagnetic Situation inverting in complex environment.
Background technology
As wireless communication technology is fast-developing, various communication apparatus are continuously increased, and signal kinds are also more and more, and
The power of signal is big, long transmission distance, and coverage is wide.Various electromagnetic equipment weave ins, transmitted signal share nothing
Line communication environment, influence and various ambient noises plus natural environment, form the new space that a complexity becomes soon ---
Complex electromagnetic environment.Complex electromagnetic environment Electromagnetic Situation refers under complex electromagnetic environment, between ourselves and the enemy with frequency equipment, equipment
Configuration and electromagnetic activity and its change in more domain spaces such as " time domain, frequency domain, spatial domain and energy domains " formed state and shape
Formula.In the increasingly complicated network-centric warfare of electromagnetic environment, it is traditional merged with locus, the mesh based on action reference variable
Standard state gesture can not meet the commander such as electronic countermeasure, communications, technical search, radar detection in Information Constructing of Sea Battlefield operation
Urgent needs of the control function to comprehensive Electromagnetic Situation.
Research for situation, foreign countries propose the concept of Situation Awareness and Situation Assessment very early.Electromagnetic Situation perceives
Refer to perceive the electromagnetic field data around target, and obtained data are shown that the electromagnetic environment of surrounding is worked as by processing, analysis
Preceding state and the variation tendency for predicting future, then abstract data is changed into the image of lifelike image.U.S. Department of Defense proposes
JDL models Situation Assessment is introduced into military field, the model regards Situation Assessment as one level of data fusion, and defines
For " process for associating the entity being observed in battlefield distribution with active situation and battlefield surroundings, knowledge base ", its is final
Purpose is to form the battlefield picture for including Study on Trend and estimation readily appreciated.Research for Electromagnetic Situation is mainly concentrated
At Radar recognition, several aspects such as radar coverage shows, electromagnetic environment is visualized, electromagnetic environment complexity is assessed.This
A little technological means are mostly based on the research of some key technologies in Electromagnetic Situation generation, if radar coverage is just for thunder
Analyzed up to frequency efficiency, electromagnetic environment visualization is also shown in a manner of frequency domain or coverage mostly, it is impossible to is filled
Key element and linked character on the existing various domains of Electromagnetic Situation of split.In a word, more ripe technological means generation system is lacked at present
The battlefield Electromagnetic Situation of system.
At present, both at home and abroad in terms of two-dimensional electromagnetic situation technical research achievement compared with horn of plenty.Two-dimensional electromagnetic situation is emulation
Experiment and the important content of battlefield Electromagnetic Situation research, it is the Main Basiss of current battlefield electromagnetic spectrum management and control.All unconventionals et al. carry
Gone out the multidimensional Electromagnetic Situation improved under parallel coordinates theoretical frame and show method, quantitative expression Electromagnetic Situation data subject but
The more difficult visualization directly perceived, truly realizing Electromagnetic Situation of article.Tian Wei et al. is for specific equipment, it is proposed that one kind is based on ground
Reason information system can be analyzed effectively with frequency station electromagnetism with the emulation of frequency station Electromagnetic Situation and visual analysis method, this method
Situation is distributed and instructs to use the scientific and reasonable addressing of the frequency station, but can not effectively show overall Electromagnetic Situation.He Jun et al. sides
Delaunay triangulation network is studied again, it is proposed that a kind of improved forward position side push model, by the interpolation of equivalent point, tracking, is put down
Sliding processing and the construction in equivalent area, improve the network forming speed of Delaunay triangulation network, realize two-dimensional electromagnetic situation substantially
Quick visualization.Chen M et al., according to radio-circuit measurement data characteristic of spatial distribution, carry in the case where sampled point determines
A kind of method for generating Curved surface spline interpolation algorithm applied to situation map is gone out, and algorithm has been improved.But these sides
Method exist needed for awareness apparatus substantial amounts, the problem of degree of accuracy of Electromagnetic Situation inverting is relatively low, while these methods are in reality
Easily cause problems with during existing Electromagnetic Situation visualization：
(1) need to obtain the Electromagnetic Situation data on whole Experimental Area space lattice summit of a certain moment, required data
Amount is more, and this just needs substantial amounts of awareness apparatus.
(2) due to the complexity of electromagnetic environment, it is impossible to directly to Electromagnetic Situation data visualization, it is impossible to effectively show
Overall Electromagnetic Situation.
The content of the invention
It is an object of the invention to overcome above-mentioned deficiency, it is proposed that a kind of complex environment Electromagnetic Situation based on multisensor
Inversion method, the quantity of awareness apparatus is effectively reduced, to Radar recognition, and then builds Electromagnetic Situation, lift electromagnetism state
The degree of accuracy of gesture inverting, realize the visualization of complex electromagnetic environment Electromagnetic Situation inverting.
A kind of complex environment Electromagnetic Situation inversion method based on multisensor proposed by the present invention, comprises the following steps：
(1) determine and configure complex electromagnetic environment parameter：Experimental Area uses N point grid layouts, K radiation source, M biography
Sensor is randomly distributed at N number of vertex raster, column vector Ps∈RMFor its each vertex raster in place of M sensor measurement
The vector for the M dimensions that the received signal strength RSS at place is formed, N number of reference point is done into N number of vertex raster choosing.
(2) sensing station matrix is built, the sensing station matrix Φ can be represented with equation below：
Wherein,sk∈ [1, N],It is generality quantifier, represents arbitrary or all
,Representing that all j belong to set [1, N], j represents j-th of reference point,Represent all
K belongs to set [1, M], and k represents k-th of sensor, sk∈ [1, N] represents position of k-th of sensor in reference point, passes
Sensor location matrix [Φ]kjIt is M*N matrixes.
(3) can be used according to the electromagnetic propagation model of electromagnetic environment, build path loss matrix, the path loss matrix Ψ
Equation below represents：
Wherein, i, j meetRepresent that all i, j belong to set [1, N], i tables
Show the i reference points, j represents j-th of reference point, Gi,jThe shadow fading between i-th of reference point and j-th of reference point is represented,
dijRepresent the distance between i-th reference point and j-th reference point, d0Reference antenna far field distance is represented, α represents exponential damping
Coefficient, path loss matrix [Ψ]ijIt is a N*N matrix.
(4) Radar recognition is carried out according to the sensing station matrix, the path loss matrix, obtains radiation source
Position and radiant power.
(5) according to the radiation source of identification, Electromagnetic Situation inverting, the received signal strength RSS in N number of reference point is tried to achieve：
Wherein, column vector Pr∈RNRepresent the received signal strength RSS in N number of reference point, column vector Pt∈RNRepresent N number of
The radiant power of radiation source in reference point,For AWGN (additive white Gaussian noise, Additive White Gaussian
Noise) power.
In certain embodiments, step (1) determines and configured complex electromagnetic environment parameter, also comprises the following steps：
The type of K radiation source is unrestricted, and the type of radiation source can be in communication equipment, jammer, transmitting equipment
One or more.
The number K of radiation source, the number M of sensor, the number N of reference point meet following relation：
M≥2*K
In certain embodiments, spoke is carried out according to the sensing station matrix, the path loss matrix in step (4)
Identifing source is penetrated, position and the radiant power of radiation source is obtained, comprises the following steps：
(4a) calculates sensing matrix R according to the sensing station matrix Φ, the path loss matrix Ψ：
R=Φ Ψ
The received signal strength P of (4b) sensor measurementsWith the radiant power P of radiation sourcetBetween following relation be present：
Wherein,For additive white Gaussian noise AWGN power, column vector Pt∈RNRepresent radiation source in N number of reference point
Radiant power, column vector ε ∈ RMRepresent the measurement error of sensor.
(4c) construction preprocessed data Pproc：
(4d) solves position and the radiant power P of radiation source according to minimum L1- normst：
min||Pt||,s.t.||Pproc-RPt||2≤μ
Wherein, | | | | 1- norms are represented, implication is the sum of all elements modulus value in vector, | | | |22- norms are represented,
Implication in vector all elements modulus value square and evolution again, μ be convergence precision, min represents to minimize, and s.t. is
Subject to's writes a Chinese character in simplified form expression " being constrained to ", and the implication of whole equation is to meet that constraints is | | Pproc-RPt||2≤μ
Under conditions of so that PtValue it is minimum.
In certain embodiments, step (4d) solves position and the radiant power P of radiation source according to minimum L1- normst,
Carried out according to following algorithm：
(4d1) sets input/output argument, and carries out initialization operation to parameter.
Input：Sensing matrix R, preprocessed data Pproc, convergence precision μ, threshold value P；
Output：The radiant power vector P of radiation source in N number of reference pointt；
Initialization：Index position vector Pos=[], residual error r=Pproc, projection vector aug_y=[], selection column matrix
Aug_t=[], Pt=＜ 0,0 ..., 0 ＞N, wherein, Pt=＜ 0,0 ..., 0 ＞NRepresent radiation source in N number of reference point
Radiant power vector PtIt is initialized as N*1 full null vector.
(4d2) finds out the subscript col of residual error r maximum inner products corresponding with column vector in the sensing matrix R, i.e.,：
Wherein, i represents that i-th arranges, R (:, i) and represent that the i-th of sensing matrix R arranges, R (:, i) ' represent the i-th of sensing matrix R
The transposition of row, norm () represent to ask the function of vectorial 2- norms, and abs () represents to take absolute value, and max represents to take maximum,Represent to return the i values of the i-th row corresponding to maximum.
(4d3) enters according to the subscript col, renewal index vector Pos=[Pos, col] to alternative column matrix A ug_t
Row expands, i.e.,：
Aug_t=[Aug_t, R (:,col)]
(4d4) utilizes nonnegative least, tries to achieve projection vector aug_y：
Aug_y=lsqnonneg (Aug_t, Pproc)
Wherein, lsqnonneg () is least square function.
New element (last element) corresponding to alternative column is big with threshold value P in (4d5) compared projections vector aug_y
It is small, if being less than threshold value P, delete last in alternative column matrix A ug_t and arrange, delete last element in index vector Pos,
Residual error r is constant；If being more than threshold value P, alternative column matrix A ug_t is constant, and index vector Pos is constant, and residual error r is updated to：
R=Pproc-Aug_t*aug_y
(4d6) sets to 0 sensing matrix R col row：
R(:, col) and=zeros (M, 1)
Wherein, zeros (M, 1) represents the null vector that dimension is M*1.
(4d7) judges whether full | | r | |2＜ u, if satisfied, then loop termination, otherwise, performs step (4d2).
(4d8) tries to achieve the position of radiation source and radiant power vector P in N number of reference pointt：
Pt(pos (i))=aug_y (i)
Wherein, index position vector Pos is the position vector of radiation source, only the opening position in radiation source, radiant power
Vectorial PtCorresponding element is nonzero value.
The present invention has advantages below：
1st, in Electromagnetic Situation inverting visualization, Experimental Area equally uses N point grid layouts, and prior art is needed every
A sensor is set to be used to measure the received signal strength at the vertex raster at individual vertex raster, and the present invention only needs to set
M sensor is put, and M is much smaller than N, the present invention can effectively reduce the quantity of awareness apparatus.
2nd, the present invention realizes the identification to radiation source due to the received signal strength RSS measured first with sensor, and then
According to electromagnetic environment propagation model, the inverting of whole environment electromagnetics situation is realized, improves range and the degree of accuracy of situation inverting.
3rd, it is of the invention due to only needing a small amount of sample (received signal strength of M sensor measurement) to carry out algorithm realization, therefore
Computation complexity is low, the time is short, can meet the requirement of real-time of Electromagnetic Situation inverting.
Brief description of the drawings
By reading the detailed description made to non-limiting example made with reference to the following drawings, the application's is other
Feature, purpose, technical process and advantage will become more apparent upon：
Fig. 1 is the implementation process figure of the present invention；
Fig. 2 is the implementation process figure of Radar recognition；
Fig. 3 is the analogous diagram of actual emanations source and sensor random distribution position；
Fig. 4 is the relative error of Radar recognition and actual emanations source correspondence position radiant power under the present invention；
Fig. 5 is the simulation comparison figure of actual emanations source radiation power and identification radiation source radiant power under the present invention；It is real
Border Electromagnetic Situation and the simulation comparison figure of Electromagnetic Situation inverting；
Fig. 6 is Radar recognition and Electromagnetic Situation inverting analogous diagram at different moments under the present invention.
Embodiment
The present invention is used for complex environment Electromagnetic Situation inverting, sensor reception signal on each vertex raster, and docks
Receive signal intensity to be pre-processed, realize the identification of radiation source, finally realize the inverting of Electromagnetic Situation.
Reference picture 1, implementation process Figure 100 of the present invention is shown, is comprised the following steps that：
Step 101, it is determined that and configuration complex electromagnetic environment parameter.
Actual electromagnetic environment is Protean, it is impossible to is imitated with a blanket, accurate mathematical modeling
Very.Following rational hypothesis is carried out for this and is simplified：Experimental Area uses N point grid layouts, in the Experimental Area, according to war
Scale needs to set a number of radiation source in the Experimental Area, and the number for setting radiation source herein is K, K radiation source
It is randomly distributed at N number of vertex raster, the number and Location-Unknown of radiation source, the radiant power random distribution of radiation source.
The Experimental Area sets M sensor, and M sensor is randomly distributed at N number of vertex raster, and M sensor is in N number of grid
The position of apex is known, and the number M of sensor is much smaller than vertex raster number N.Column vector Ps∈RMSurveyed for M sensor
The M dimensions that received signal strength (RSS, Received Signal Strength) where its of amount at each vertex raster is formed
Vector, N number of reference point is done into above-mentioned N number of vertex raster choosing, wherein, RMRepresent the vector space of M dimensions, Ps∈RMRepresent PsFor M
The vector of dimension.
In certain embodiments, the type of K radiation source is unrestricted, and the type of radiation source can be communication equipment, do
Disturb the one or more in machine, transmitting equipment.The number K of radiation source, the number M of sensor, the number N of reference point meet as follows
Relation：
M≥2*K
Step 102, sensing station matrix is built.
In a step 101, M sensor of experiment is randomly distributed at N number of vertex raster every time, but experiment M every time
The position of sensor is known, and available sensors location matrix represents, sensing station matrix Φ can be represented with equation below：
Wherein,sk∈ [1, N],It is generality quantifier, represents arbitrary or all
,Representing that all j belong to set [1, N], j represents j-th of reference point,Represent all
K belongs to set [1, M], and k represents k-th of sensor, sk∈ [1, N] represents position of k-th of sensor in reference point, passes
Sensor location matrix [Φ]kjIt is M*N matrixes.
Step 103, according to the electromagnetic propagation model of electromagnetic environment, build path loss matrix.
Electromagnetic wave is generally propagated in irregular, non-single environment, and electromagnetic wave all can when any medium is penetrated
It is lossy.When estimated path is lost, it is necessary to consider the landform on propagation path, landforms, it is considered however that building, trees,
The barriers such as electric pole, so different path transmission models should be selected in different environments.Conventional outdoor electromagnetic propagation mould
Type has path loss model of Okumura models, Hata models, free space etc..The present invention is damaged using the path of free space
Model is consumed, but the present disclosure applies equally to Okumura models, Hata models, it is only necessary to path loss matrix corresponding to replacement.
PiRepresent the radiant power at i-th of reference point, PjRepresent the receiving power at j-th of reference point, PiWith PjMeet
Following relation：
Then path loss matrix Ψ can be represented with equation below：
Wherein, i, j meetRepresent that all i, j belong to set [1, N], i tables
Show the i reference points, j represents j-th of reference point, Gi,jThe shadow fading between i-th of reference point and j-th of reference point is represented,
dijRepresent the distance between i-th reference point and j-th reference point, d0Reference antenna far field distance is represented, α represents exponential damping
Coefficient, path loss matrix [Ψ]ijIt is a N*N matrix.
Step 104, Radar recognition is carried out according to sensing station matrix, path loss matrix, obtains the position of radiation source
Put and radiant power.
Fig. 2 shows the implementation process figure of Radar recognition.Spoke is carried out according to sensing station matrix, path loss matrix
Identifing source is penetrated, the position and radiant power for obtaining radiation source comprise the following steps：
(4a) calculates sensing matrix R according to sensing station matrix Φ, path loss matrix Ψ：
R=Φ Ψ
The received signal strength P of (4b) sensor measurementsWith the radiant power P of radiation sourcetBetween following relation be present：
Wherein,For additive white Gaussian noise AWGN power, column vector Pt∈RNRepresent radiation source in N number of reference point
Radiant power, column vector ε ∈ RMRepresent the measurement error of sensor, RNRepresent the vector space of N-dimensional, Pr∈RNRepresent PrFor N-dimensional
Vector, RMRepresent the vector space of M dimensions, ε ∈ RMIt is the vector of M dimensions to represent ε.
(4c) construction preprocessed data Pproc：
(4d) solves position and the radiant power P of radiation source according to minimum L1- normst；
min||Pt||,s.t.||Pproc-RPt||2≤μ
Wherein, | | | | 1- norms are represented, implication is the sum of all elements modulus value in vector, | | | |22- norms are represented,
Implication in vector all elements modulus value square and evolution again, μ be convergence precision, min represents to minimize, and s.t. is
Subject to's writes a Chinese character in simplified form expression " being constrained to ", and the implication of whole equation is to meet that constraints is | | Pproc-RPt||2≤μ
Under conditions of so that PtValue it is minimum.
Step (4d) is implemented as follows：
(4d1) sets input/output argument, and carries out initialization operation to parameter：
Input：Sensing matrix R, preprocessed data Pproc, convergence precision μ, threshold value P.
Output：The radiant power vector P of radiation source in N number of reference pointt。
Initialization：Index position vector Pos=[], residual error r=Pproc, projection vector aug_y=[], selection column matrix
Aug_t=[], Pt=＜ 0,0 ..., 0 ＞N, wherein, Pt=＜ 0,0 ..., 0 ＞NRepresent radiation source in N number of reference point
Radiant power vector PtIt is initialized as N*1 full null vector.
(4d2) finds out the subscript col of above-mentioned residual error r maximum inner products corresponding with column vector in above-mentioned sensing matrix R, i.e.,：
Wherein, i represents that i-th arranges, R (:, i) and represent that the i-th of sensing matrix R arranges, R (:, i) ' represent the i-th of sensing matrix R
The transposition of row, norm () represent to ask the function of vectorial 2- norms, and abs () represents to take absolute value, and max represents to take maximum,Represent to return the value i of the i-th row corresponding to maximum.
(4d3) enters according to above-mentioned subscript col, renewal index vector Pos=[Pos, col] to alternative column matrix A ug_t
Row expands, i.e.,：
Aug_t=[Aug_t, R (:,col)]
Wherein, Aug_t=[Aug_t, R (:, col)] represent sensing matrix R col row adding selection matrix Aug_
t。
(4d4) utilizes nonnegative least, tries to achieve projection vector aug_y：
Aug_y=lsqnonneg (Aug_t, Pproc)
Wherein, lsqnonneg () is least square function.
Element (last element) and threshold corresponding to new alternative column (col row) in (4d5) compared projections vector aug_y
Value P size, if being less than threshold value P, deletion is selected last in column matrix Aug_t and arranged, i.e.,：Aug_t(:, end)=[], delete
Last element in index vector Pos, i.e.,：Pos (end)=[], residual error r are constant；If being more than threshold value P, column matrix is selected
Aug_t is constant, and index vector Pos is constant, and residual error r is updated to：
R=Pproc-Aug_t*aug_y
(4d6) sets to 0 sensing matrix R col row：
R(:, col) and=zeros (M, 1)
Wherein, zeros (M, 1) represents the null vector that dimension is M*1.
(4d7) judges whether full | | r | |2＜ u, if satisfied, then loop termination, otherwise, performs step (4d2).
The condition of loop termination is | | r | |2＜ u, i.e., | | Pproc-RPt||2≤μ；Residual error r and sensing are all found in circulation every time
Maximally related row in matrix R, make projection value minimum, final to obtain the min for meeting constraints | | Pt||。
(4d8) tries to achieve the position of radiation source and radiant power vector P in N number of reference pointt：
Pt(pos (i))=aug_y (i)
Wherein, index position vector Pos is the position vector of radiation source, only the opening position in radiation source, radiant power
Vectorial PtCorresponding element is nonzero value.
Step 105, according to the radiation source of identification, Electromagnetic Situation inverting, the received signal strength in N number of reference point is tried to achieve
RSS。
The vectorial P of the received signal strength RSS in N number of reference point is sought according to equation belowr：
Wherein, column vector Pr∈RNRepresent the received signal strength RSS in N number of reference point, i.e., the reception in N number of reference point
Power, column vector Pt∈RNThe radiant power of radiation source in N number of reference point is represented,Represent additive white Gaussian noise AWGN work(
Rate, RNRepresent the vector space of N-dimensional, Pr∈RN、Pt∈RNRepresent Pr、PtFor the vector of N-dimensional.
The effect of the present invention can be further illustrated by following emulation：
A, simulated conditions
Experimental Area is arranged on 200m*200m square, is divided into 20*20 grid, the face of each grid
Product is 100m2, total vertex raster number N=400.By M=100 sensor, K=8 radiation source is randomly distributed over 400 grid
On lattice summit.Assuming that radiation frequency is 3MHz, the probable value of radiant power is P0 integral multiple, i.e. the random distribution of transmission power
In power set { P0,2P0 ..., Pm, wherein, P0 is reference power, PmRepresent power maximum.The property of radiant power reconstruct
It can be represented with relative error.When radiant power reconstructs, radiation source number and Location-Unknown.
B, emulation content and result
Emulation 1：A certain moment, 100 sensors, 8 radiation sources are randomly distributed on 400 vertex rasters, emulation
As a result it is as shown in Figure 3；
As seen from Figure 3, radiation source and sensor are all random distributions, and the real-time dynamic for embodying electromagnetic environment becomes
Change, the robustness of algorithm can be realized very well.
Emulation 2：Under conditions of the radiant power of 8 radiation sources is randomly distributed over power set, to the spoke of the present invention
The recognition performance for penetrating source is emulated.The performance of Radar recognition represents that computational methods are to take radiation with relative error PowE
Source true radiant power vector sum identification radiant power vector corresponding element difference absolute value and with reference radiation power P 0
Ratio：
Wherein, PtTrue radiant power for the radiation source of N*1 dimensions is vectorial,For the identification radiation of the radiation source of N*1 dimensions
Vector power, P0 are reference power.Every time in experiment, radiation source and sensor are randomly distributed on 400 vertex rasters, spoke
Penetrate power to be randomly distributed in power set, repeat experiment 100 times, the recognition performance simulation result of radiation source is as shown in Figure 4.
As seen from Figure 4, in 100 experiments, relative error is maintained at 10-12On, relative error is especially small and more steady
It is fixed, illustrate that the recognition performance of the radiation source of the present invention is superior, and to radiation source positions, sensing station, radiation source radiant power
There is robustness.
Emulation 3：Under simulated conditions, to identifying that the radiant power of radiation source emulates, and by itself and true radiation source
Radiant power is contrasted；Inverting to Electromagnetic Situation is emulated, and itself and true Electromagnetic Situation figure are contrasted.Emulation
As a result it is as shown in Figure 5.
A kind of improved orthogonal matching pursuit algorithm OMPSE is known algorithm.
Fig. 5 (a) is actual emanations source power；Fig. 5 (b) is that OMPSE reconstructs power of radiation source；Fig. 5 (c) identifies for the present invention
Power of radiation source；Fig. 5 (d) is actual electromagnetic situation；Fig. 5 (e) is OMPSE Electromagnetic Situation invertings；Fig. 5 (f) is electromagnetism of the present invention
Situation inverting.
As seen from Figure 5, the depth of color can represent the size of power, and contour illustrates the covering model of radiation source
Enclose, can intuitively find out position and the radiant power size of radiation source, and the Electromagnetic Situation of each point.Fig. 5 (a), Fig. 5 (b),
Fig. 5 (c) power of radiation source figure contrasts, during identification of the present invention to radiation source, its reconstruct the size and location of power substantially with
The power and bit of actual emanations source radiation is put unanimously, and the degree of accuracy of reconstruct power of radiation source of the invention reconstructs than OMPSE algorithm
The degree of accuracy of power of radiation source is high；Fig. 5 (d), Fig. 5 (e), the contrast of Fig. 5 (f) Electromagnetic Situations figure, Electromagnetic Situation figure refer to test block
The visualization figure of received signal strength RSS (receiving power) in the reference point of 400, domain, the inverting of the invention to Electromagnetic Situation
Figure is substantially consistent with actual electromagnetic situation map, i.e., the received signal strength RSS in the reference point of inverting of the present invention (receives work(
Rate) size and location it is substantially consistent with actual received signal strength RSS, the degree of accuracy ratio of Electromagnetic Situation inverting of the invention
The degree of accuracy of OMPSE algorithm Electromagnetic Situation invertings is high.Illustrate that the present invention more can accurately realize the identification of radiation source, and then realize
The inverting of Electromagnetic Situation.
Emulation 4：Under simulated conditions, each moment, radiation source and sensor are randomly distributed over 400 vertex rasters
On, radiant power is randomly distributed in power set, and the inverting to Radar recognition at different moments and Electromagnetic Situation is imitated
Very.Simulation result is as shown in Figure 6.
The Radar recognitions of Fig. 6 (a) at different moments；The Electromagnetic Situation invertings of Fig. 6 (b) at different moments.
As seen from Figure 6, three figure layers correspond to the radiation source of three moment identification, three moment spokes respectively in Fig. 6 (a)
Penetrate source position and watt level all there occurs random to change, three figure layers correspond to the Electromagnetic Situation at three moment respectively in Fig. 6 (b)
Inverting, embodying the Electromagnetic Situation at three moment, also there occurs respective change.Thus the present invention can realize radiates at different moments
The identification in source, and then realize the inverting of complex environment Electromagnetic Situation.
Summary simulation analysis, the present invention can be in a small amount of sensing station random distribution, radiation source positions and radiation work(
Under conditions of rate random distribution, Radar recognition is realized, and then realize the complex environment Electromagnetic Situation inverting based on multisensor.
Claims (4)
1. a kind of complex environment Electromagnetic Situation inversion method based on multisensor, it is characterised in that methods described includes as follows
Step：
(1) determine and configure complex electromagnetic environment parameter：Experimental Area uses N point grid layouts, K radiation source, M sensor
It is randomly distributed at N number of vertex raster, column vector Ps∈RMAt its each vertex raster in place for M sensor measurement
The vector for the M dimensions that received signal strength RSS is formed, N number of reference point is done into N number of vertex raster choosing；
(2) sensing station matrix is built, the sensing station matrix Φ can be represented with equation below：
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<mi>k</mi>
</msub>
<mo>=</mo>
<mi>j</mi>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
<mo>,</mo>
<msub>
<mi>s</mi>
<mi>k</mi>
</msub>
<mo>&NotEqual;</mo>
<mi>j</mi>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein,sk∈ [1, N],It is generality quantifier, represents arbitrary or all,Representing that all j belong to set [1, N], j represents j-th of reference point,Represent all k all
Belong to set [1, M], k represents k-th of sensor, sk∈ [1, N] represents position of k-th of sensor in reference point, sensing
Device location matrix [Φ]kjIt is M*N matrixes；
It (3) can use as follows according to the electromagnetic propagation model of electromagnetic environment, build path loss matrix, the path loss matrix Ψ
Formula represents：
<mrow>
<msub>
<mrow>
<mo>&lsqb;</mo>
<mi>&Psi;</mi>
<mo>&rsqb;</mo>
</mrow>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>G</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<msup>
<mrow>
<mo>(</mo>
<mfrac>
<msub>
<mi>d</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<msub>
<mi>d</mi>
<mn>0</mn>
</msub>
</mfrac>
<mo>)</mo>
</mrow>
<mrow>
<mo>-</mo>
<mi>&alpha;</mi>
</mrow>
</msup>
</mrow>
Wherein, i, j meetRepresent that all i, j belong to set [1, N], i represents the
Individual i reference points, j represent j-th of reference point, Gi,jRepresent the shadow fading between i-th of reference point and j-th of reference point, dijTable
Show the distance between i-th reference point and j-th reference point, d0Reference antenna far field distance is represented, α represents exponential damping system
Number, path loss matrix [Ψ]ijIt is a N*N matrix；
(4) Radar recognition is carried out according to the sensing station matrix, the path loss matrix, obtains the position of radiation source
And radiant power；
(5) according to the radiation source of identification, Electromagnetic Situation inverting, the received signal strength RSS in N number of reference point is tried to achieve：
<mrow>
<msub>
<mi>P</mi>
<mi>r</mi>
</msub>
<mo>=</mo>
<msub>
<mi>&Psi;P</mi>
<mi>t</mi>
</msub>
<mo>+</mo>
<msubsup>
<mi>&sigma;</mi>
<mrow>
<mi>a</mi>
<mi>v</mi>
<mi>g</mi>
</mrow>
<mn>2</mn>
</msubsup>
</mrow>
Wherein, column vector Pr∈RNRepresent the received signal strength RSS in N number of reference point, column vector Pt∈RNRepresent N number of reference point
The radiant power of upper radiation source,Represent additive white Gaussian noise AWGN power.
2. a kind of complex environment Electromagnetic Situation inversion method based on multisensor according to claim 1, its feature exist
In the step (1) determines and configuration complex electromagnetic environment parameter, also comprises the following steps：
The type of K radiation source is unrestricted, and the type of radiation source can be one in communication equipment, jammer, transmitting equipment
Kind is a variety of；
The number K of radiation source, the number M of sensor, the number N of reference point meet following relation：
M≥2*K
<mrow>
<mi>M</mi>
<mo>&GreaterEqual;</mo>
<mi>K</mi>
<mo>*</mo>
<mi>l</mi>
<mi>o</mi>
<mi>g</mi>
<mrow>
<mo>(</mo>
<mfrac>
<mi>N</mi>
<mi>K</mi>
</mfrac>
<mo>)</mo>
</mrow>
</mrow>
3. a kind of complex environment Electromagnetic Situation inversion method based on multisensor according to claim 1, its feature exist
In the step (4) carries out Radar recognition according to the sensing station matrix, the path loss matrix, is radiated
The position in source and radiant power, comprise the following steps：
(4a) calculates sensing matrix R according to the sensing station matrix Φ, the path loss matrix Ψ：
R=Φ Ψ
The received signal strength P of (4b) sensor measurementsWith the radiant power P of radiation sourcetBetween following relation be present：
<mrow>
<msub>
<mi>P</mi>
<mi>s</mi>
</msub>
<mo>-</mo>
<msubsup>
<mi>&sigma;</mi>
<mrow>
<mi>a</mi>
<mi>v</mi>
<mi>g</mi>
</mrow>
<mn>2</mn>
</msubsup>
<mo>=</mo>
<msub>
<mi>&Phi;&Psi;P</mi>
<mi>t</mi>
</msub>
<mo>+</mo>
<mi>&epsiv;</mi>
</mrow>
Wherein,For additive white Gaussian noise AWGN power, column vector Pt∈RNRepresent the radiation of radiation source in N number of reference point
Power, column vector ε ∈ RMRepresent the measurement error of sensor；
(4c) construction preprocessed data Pproc：
<mrow>
<msub>
<mi>P</mi>
<mrow>
<mi>p</mi>
<mi>r</mi>
<mi>o</mi>
<mi>c</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>P</mi>
<mi>s</mi>
</msub>
<mo>-</mo>
<msubsup>
<mi>&sigma;</mi>
<mrow>
<mi>a</mi>
<mi>v</mi>
<mi>g</mi>
</mrow>
<mn>2</mn>
</msubsup>
</mrow>
(4d) solves position and the radiant power P of radiation source according to minimum L1- normst：
min||Pt||,s.t.||Pproc-RPt||2≤μ
Wherein, | | | | 1- norms are represented, implication is the sum of all elements modulus value in vector, | | | |2Represent 2- norms, implication
For in vector all elements modulus value square and evolution again, μ be convergence precision, min represents to minimize, and s.t. is subject to
Write a Chinese character in simplified form expression " being constrained to ", the implication of whole equation is to meet that constraints is | | Pproc-RPt||2Under conditions of≤μ, make
Obtain PtValue it is minimum.
4. a kind of complex environment Electromagnetic Situation inversion technique based on multisensor according to claim 3, its feature exist
In, the step (4d) according to minimum L1- norms, position and the radiant power P of radiation source are solvedt, carried out according to following algorithm：
(4d1) sets input/output argument, and carries out initialization operation to parameter：
Input：Sensing matrix R, preprocessed data Pproc, convergence precision μ, threshold value P；
Output：The radiant power vector P of radiation source in N number of reference pointt；
Initialization：Index position vector Pos=[], residual error r=Pproc, projection vector aug_y=[], alternative column matrix A ug_t
=[], Pt=＜ 0,0 ..., 0 ＞N, wherein, Pt=＜ 0,0 ..., 0 ＞NRepresent the radiation work(of radiation source in N number of reference point
Rate vector PtIt is initialized as N*1 full null vector；
(4d2) finds out the subscript col of residual error r maximum inner products corresponding with column vector in the sensing matrix R, i.e.,：
<mrow>
<mi>c</mi>
<mi>o</mi>
<mi>l</mi>
<mo>=</mo>
<munder>
<mi>arg</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<mi>N</mi>
</mrow>
</munder>
<mi>m</mi>
<mi>a</mi>
<mi>x</mi>
<mi> </mi>
<mi>a</mi>
<mi>b</mi>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>R</mi>
<msup>
<mrow>
<mo>(</mo>
<mrow>
<mo>:</mo>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mo>)</mo>
</mrow>
<mo>,</mo>
</msup>
<mo>*</mo>
<mi>r</mi>
<mo>/</mo>
<mi>n</mi>
<mi>o</mi>
<mi>r</mi>
<mi>m</mi>
<mo>(</mo>
<mrow>
<mi>R</mi>
<mrow>
<mo>(</mo>
<mrow>
<mo>:</mo>
<mo>,</mo>
<mi>i</mi>
</mrow>
<mo>)</mo>
</mrow>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mrow>
Wherein, i represents that i-th arranges, R (:, i) and represent that the i-th of sensing matrix R arranges, R (:, i) ' represent sensing matrix R i-th row
Transposition, norm () represent to ask the function of vectorial 2- norms, and abs () represents to take absolute value, and max represents to take maximum,Represent to return the i values of the i-th row corresponding to maximum；
(4d3) expands alternative column matrix A ug_t according to the subscript col, renewal index vector Pos=[Pos, col]
Fill, i.e.,：
Aug_t=[Aug_t, R (:,col)]
(4d4) utilizes nonnegative least, tries to achieve projection vector aug_y：
Aug_y=lsqnonneg (Aug_t, Pproc)
Wherein, lsqnonneg () is least square function；
The size of new element (last element) corresponding to alternative column and threshold value P in (4d5) compared projections vector aug_y, if
Less than threshold value P, then delete last in alternative column matrix A ug_t and arrange, delete last element in index vector Pos, residual error r
It is constant；If being more than or equal to threshold value P, alternative column matrix A ug_t is constant, and index vector Pos is constant, and residual error r is updated to：
R=Pproc-Aug_t*aug_y
(4d6) sets to 0 sensing matrix R col row：
R(:, col) and=zeros (M, 1)
Wherein, zeros (M, 1) represents the null vector that dimension is M*1；
(4d7) judges whether full | | r | |2＜ u, if satisfied, then loop termination, otherwise, performs step (4d2)；
(4d8) tries to achieve the position of radiation source and radiant power vector P in N number of reference pointt：
Pt(pos (i))=aug_y (i)
Wherein, index position vector Pos is the position vector of radiation source, only the opening position in radiation source, radiant power vector Pt
Corresponding element is nonzero value.
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