CN107765103B - 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 invention discloses a kind of the complex environment Electromagnetic Situation inversion method based on multisensor, the substantial amounts of awareness apparatus needed for mainly solving the prior art, the lower problem of the accuracy of Electromagnetic Situation inverting.Its technical solution is: 1, determining and configure complex electromagnetic environment parameter；2, sensor position matrix is constructed；3, build path loss matrix；4, Radar recognition is carried out according to sensor position matrix, path loss matrix, obtains position and the radiant power of radiation source；5, according to the radiation source of identification, Electromagnetic Situation inverting obtains the Electromagnetic Situation of each point.The present invention can realize Radar recognition, and then realize Electromagnetic Situation inverting, can be used for the complex environment Electromagnetic Situation inverting based on multisensor under conditions of a small amount of sensor position random distribution, radiation source positions and radiant power random distribution.
Description
Technical field
The invention belongs to fields of communication technology, are related to frequency spectrum perception technology, and Radar recognition further relates to a kind of base
In the complex environment Electromagnetic Situation inversion technique of multisensor, the Electromagnetic Situation inverting that can be used in complex environment.
Background technique
With wireless communication technique fast development, 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, in addition the influence of natural environment and various ambient noises, form the new space that a complexity becomes fastly ---
Complex electromagnetic environment.Complex electromagnetic environment Electromagnetic Situation refers under complex electromagnetic environment, between ourselves and the enemy with frequency equipment, equipment
With electromagnetic activity and its in multiple domains spaces such as " time domain, frequency domain, airspace and energy domains ", variation is formed by state and shape for configuration
Formula.In the increasingly complicated network-centric warfare of electromagnetic environment, traditional mesh based on spatial position fusion, action reference variable
Standard state gesture is no longer satisfied 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 perception
Refer to the electromagnetic field data around perception 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 converted to the image of lifelike image.U.S. Department of Defense proposes
JDL model Situation Assessment is introduced into military field, which regards Situation Assessment as one level of data fusion, and defines
It is final for " entity being observed in battlefield is distributed the process to associate with activity condition and battlefield surroundings, knowledge base "
Purpose is to form the battlefield picture including Study on Trend and estimation being easy to understand.The research of Electromagnetic Situation is mainly concentrated
At several aspects such as Radar recognition, radar coverage show, electromagnetic environment visualizes, electromagnetic environment complexity is assessed.This
A little technological means are mostly based on the research of certain key technologies in Electromagnetic Situation generation, if radar coverage is just for thunder
It is analyzed up to frequency efficiency, electromagnetic environment visualization is also shown in a manner of frequency domain or coverage area mostly, Bu Nengchong
Element and linked character on the existing various domains of Electromagnetic Situation of fission.In short, lacking more mature technological means at present generates system
The battlefield Electromagnetic Situation of system.
Currently, 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
The important content of experiment and the research of battlefield Electromagnetic Situation is the main foundation of current battlefield electromagnetic spectrum control.All unconventionals et al. mention
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 that is intuitive, truly realizing Electromagnetic Situation of article.Tian Wei et al. is proposed a kind of based on ground for specific equipment
Reason information system can be analyzed effectively with the emulation of frequency station Electromagnetic Situation and visual analysis method, this method with frequency station electromagnetism
Situation distribution and the guidance scientific and reasonable addressing of the frequency station, but can not effectively show whole Electromagnetic Situation.The side He Jun et al.
Delaunay triangulation network is studied again, proposes a kind of improved forward position side push model, by the interpolation of equivalent point, tracking, is put down
The construction of sliding processing and equivalent area, improves the network forming speed of Delaunay triangulation network, realizes two-dimensional electromagnetic situation substantially
Quick visualization.Chen M et al., according to radio-circuit measurement data characteristic of spatial distribution, is mentioned in the case where sampled point is determined
A kind of method that Curved surface spline interpolation algorithm is applied to situation map generation is gone out, and algorithm has been improved.But these sides
There are the substantial amounts of required awareness apparatus, the lower problems of the accuracy of Electromagnetic Situation inverting for method, while these methods are in reality
Following problems are easily caused when existing Electromagnetic Situation visualization:
(1) need to obtain the Electromagnetic Situation data on entire Experimental Area space lattice of a certain moment vertex, required data
Amount is more, this just needs a large amount of awareness apparatus.
(2) it due to the complexity of electromagnetic environment, can not cannot effectively be shown directly to Electromagnetic Situation data visualization
Whole Electromagnetic Situation.
Summary of the invention
The purpose of the present invention is to overcome the above shortcomings, proposes a kind of complex environment Electromagnetic Situation based on multisensor
Inversion method is effectively reduced the quantity of awareness apparatus, to Radar recognition, and then constructs Electromagnetic Situation, promotes electromagnetism state
The visualization of complex electromagnetic environment Electromagnetic Situation inverting is realized in the accuracy of gesture inverting.
A kind of complex environment Electromagnetic Situation inversion method based on multisensor proposed by the present invention, includes the following steps:
(1) determine and configure complex electromagnetic environment parameter: Experimental Area uses N point grid layout, 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
N number of vertex raster choosing is done N number of reference point by the vector for the M dimension that the received signal strength RSS at place is constituted.
(2) sensor position matrix is constructed, the sensor position matrix Φ can be indicated with following formula:
Wherein,sk∈ [1, N],It is generality quantifier, indicates arbitrary or all
,Indicating that all j are belonged to set [1, N], j indicates j-th of reference point,Indicate all
K is belonged to set [1, M], and k indicates k-th of sensor, sk∈ [1, N] indicates position of k-th of sensor in reference point, passes
Sensor location matrix [Φ]kjIt is M*N matrix.
(3) according to the electromagnetic propagation model of electromagnetic environment, build path loss matrix, the path loss matrix Ψ can be used
Following formula indicates:
Wherein, i, j meetIndicate that all i, j belong to set [1, N], i table
Show the i reference point, j indicates j-th of reference point, Gi,jIndicate the shadow fading between i-th of reference point and j-th of reference point,
dijIndicate the distance between i-th of reference point and j-th of reference point, d0Indicate reference antenna far field distance, α indicates exponential damping
Coefficient, path loss matrix [Ψ]ijIt is a N*N matrix.
(4) Radar recognition is carried out according to the sensor position matrix, the path loss matrix, obtains radiation source
Position and radiant power.
(5) according to the radiation source of identification, Electromagnetic Situation inverting acquires the received signal strength RSS in N number of reference point:
Wherein, column vector Pr∈RNIndicate the received signal strength RSS in N number of reference point, column vector Pt∈RNIndicate N number of
The radiant power of radiation source in reference point,For AWGN (additive white Gaussian noise, Additive White Gaussian
Noise) power.
In some embodiments, step (1) determines and configures complex electromagnetic environment parameter, further includes following steps:
The type of K radiation source is unrestricted, and the type of radiation source can be communication equipment, jammer, emit in equipment
It is one or more.
The number K of radiation source, the number M of sensor, reference point number N meet following relationship:
M≥2*K
In some embodiments, spoke is carried out according to the sensor position matrix, the path loss matrix in step (4)
Identifing source is penetrated, position and the radiant power of radiation source is obtained, includes the following steps:
(4a) calculates sensing matrix R according to the sensor position 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 there are following relationships:
Wherein,For additive white Gaussian noise AWGN power, column vector Pt∈RNIndicate radiation source in N number of reference point
Radiant power, column vector ε ∈ RMIndicate the measurement error of sensor.
(4c) constructs preprocessed data Pproc:
(4d) solves position and the radiant power P of radiation source according to minimum L1- normt:
min||Pt||,s.t.||Pproc-RPt||2≤μ
Wherein, | | | | indicate that 1- norm, meaning are the sum of all elements modulus value in vector, | | | |2Indicate 2- norm,
Meaning be in vector all elements modulus value square and evolution again, μ be convergence precision, min indicates to minimize, and s.t. is
Subject to's writes a Chinese character in simplified form expression " being constrained to ", and the meaning of entire equation, which is meeting constraint condition, is | | Pproc-RPt||2≤μ
Under conditions of, so that PtValue it is minimum.
In some embodiments, step (4d) solves position and the radiant power P of radiation source according to minimum L1- normt,
It is carried out according to following algorithm:
Input/output argument is arranged in (4d1), and initializes 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 >NIt indicates radiation source in N number of reference point
Radiant power vector PtIt is initialized as the full null vector of N*1.
(4d2) finds out the subscript col of residual error r maximum inner product corresponding with column vector in the sensing matrix R, it may be assumed that
Wherein, i indicates that i-th arranges, R (:, i) indicate that the i-th of sensing matrix R arranges, R (:, i) ' indicate the i-th of sensing matrix R
The function of the 2- norm of vector is sought in the transposition of column, norm () expression, and abs () expression takes absolute value, and max expression is maximized,It indicates to return to the i value that maximum value corresponding i-th arranges.
(4d3) updates index vector Pos=[Pos, col] according to the subscript col, and to alternative column matrix A ug_t into
Row expands, it may be assumed that
Aug_t=[Aug_t, R (:, col)]
(4d4) utilizes nonnegative least, acquires projection vector aug_y:
Aug_y=lsqnonneg (Aug_t, Pproc)
Wherein, lsqnonneg () is least square function.
The new corresponding element of alternative column (the last one element) is big with threshold value P in (4d5) compared projections vector aug_y
It is small, if being less than threshold value P, deletes last in alternative column matrix A ug_t and arrange, delete the last one element in index vector Pos,
Residual error r is constant；If more than threshold value P, then alternative column matrix A ug_t is constant, and index vector Pos is constant, and residual error r updates are as follows:
R=Pproc-Aug_t*aug_y
The col column of sensing matrix R are set 0 by (4d6):
R (:, col)=zeros (M, 1)
Wherein, zeros (M, 1) indicates that dimension is the null vector of M*1.
(4d7) judges whether full | | r | |2< u, if satisfied, otherwise then loop termination executes step (4d2).
(4d8) acquires 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 at the position of radiation source, radiant power
Vector PtCorresponding element is nonzero value.
The invention has the following advantages that
1, in Electromagnetic Situation inverting visualization, Experimental Area equally uses N point grid layout, and the prior art is needed every
A sensor is arranged at a vertex raster to be used to measure the received signal strength at the vertex raster, and the present invention only needs to set
M sensor is set, and M is much smaller than N, the present invention can effectively reduce the quantity of awareness apparatus.
2, the present invention realizes the identification to radiation source, in turn due to the received signal strength RSS first with sensor measurement
According to electromagnetic environment propagation model, realizes the inverting of entire environment electromagnetics situation, improve range and the accuracy of situation inverting.
3, 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.
Detailed description of the invention
By reading a detailed description of non-restrictive embodiments in the light of the attached drawings below, the application's is other
Feature, purpose, technical process and advantage will become more apparent upon:
Fig. 1 is implementation flow chart of the invention；
Fig. 2 is the implementation flow chart of Radar recognition；
Fig. 3 is the analogous diagram in actual emanations source and sensor random distribution position；
Fig. 4 is the relative error of Radar recognition and actual emanations source corresponding 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
The simulation comparison figure of border Electromagnetic Situation and Electromagnetic Situation inverting；
Fig. 6 is different moments Radar recognition and Electromagnetic Situation inverting analogous diagram under the present invention.
Specific embodiment
The present invention is used for the inverting of complex environment Electromagnetic Situation, and sensor receives signal on each vertex raster, and docks
It receives signal strength to be pre-processed, realizes the identification of radiation source, the final inverting for realizing Electromagnetic Situation.
Referring to Fig.1, implementation process Figure 100 of the invention is shown, the specific steps are as follows:
Step 101, determine and configure complex electromagnetic environment parameter.
Actual electromagnetic environment is ever-changing, it is impossible to be imitated with a blanket, accurate mathematical model
Very.Carry out following reasonable hypothesis thus and simplify: Experimental Area uses N point grid layout, in the Experimental Area, according to war
Field scale needs that a certain number of radiation sources are arranged in the Experimental Area, and the number that radiation source is arranged 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.?
M sensor is arranged in the Experimental Area, 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 be it is known, the number M of sensor is much smaller than vertex raster number N.Column vector Ps∈RMIt is surveyed for M sensor
The M dimension that received signal strength (RSS, Received Signal Strength) where its of amount at each vertex raster is constituted
Vector, N number of reference point is done into above-mentioned N number of vertex raster choosing, wherein RMIndicate the vector space of M dimension, Ps∈RMIndicate PsFor M
The vector of dimension.
In some embodiments, the type of K radiation source is unrestricted, and the type of radiation source can be communication equipment, do
Disturb one of machine, transmitting equipment or a variety of.The number K of radiation source, the number M of sensor, the number N satisfaction of reference point are as follows
Relationship:
M≥2*K
Step 102, sensor position matrix is constructed.
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 be it is known, available sensors location matrix indicates that sensor position matrix Φ can indicate with following formula:
Wherein,sk∈ [1, N],It is generality quantifier, indicates arbitrary or all
,Indicating that all j are belonged to set [1, N], j indicates j-th of reference point,Indicate all
K is belonged to set [1, M], and k indicates k-th of sensor, sk∈ [1, N] indicates position of k-th of sensor in reference point, passes
Sensor location matrix [Φ]kjIt is M*N matrix.
Step 103, according to the electromagnetic propagation model of electromagnetic environment, build path loss matrix.
Electromagnetic wave is usually propagated in irregular, non-single environment, and electromagnetic wave all can when penetrating any medium
It is lossy.In estimated path loss, need to consider 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.Common outdoor electromagnetic propagation mould
Type has Okumura model, Hata model, path loss model of 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 model, it is only necessary to replace corresponding path loss matrix.
PiIndicate the radiant power at i-th of reference point, PjIndicate the reception power at j-th of reference point, PiWith PjMeet
Following relationship:
Then path loss matrix Ψ can be indicated with following formula:
Wherein, i, j meetIndicate that all i, j belong to set [1, N], i table
Show the i reference point, j indicates j-th of reference point, Gi,jIndicate the shadow fading between i-th of reference point and j-th of reference point,
dijIndicate the distance between i-th of reference point and j-th of reference point, d0Indicate reference antenna far field distance, α indicates exponential damping
Coefficient, path loss matrix [Ψ]ijIt is a N*N matrix.
Step 104, Radar recognition is carried out according to sensor position matrix, path loss matrix, obtains the position of radiation source
It sets and radiant power.
Fig. 2 shows the implementation flow charts of Radar recognition.Spoke is carried out according to sensor position matrix, path loss matrix
Identifing source is penetrated, the position and radiant power for obtaining radiation source include the following steps:
(4a) calculates sensing matrix R according to sensor position 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 there are following relationships:
Wherein,For additive white Gaussian noise AWGN power, column vector Pt∈RNIndicate radiation source in N number of reference point
Radiant power, column vector ε ∈ RMIndicate the measurement error of sensor, RNIndicate the vector space of N-dimensional, Pr∈RNIndicate PrFor N-dimensional
Vector, RMIndicate the vector space of M dimension, ε ∈ RMIndicate that ε is the vector of M dimension.
(4c) constructs preprocessed data Pproc:
(4d) solves position and the radiant power P of radiation source according to minimum L1- normt；
min||Pt||,s.t.||Pproc-RPt||2≤μ
Wherein, | | | | indicate that 1- norm, meaning are the sum of all elements modulus value in vector, | | | |2Indicate 2- norm,
Meaning be in vector all elements modulus value square and evolution again, μ be convergence precision, min indicates to minimize, and s.t. is
Subject to's writes a Chinese character in simplified form expression " being constrained to ", and the meaning of entire equation, which is meeting constraint condition, is | | Pproc-RPt||2≤μ
Under conditions of, so that PtValue it is minimum.
Step (4d) is implemented as follows:
Input/output argument is arranged in (4d1), and initializes 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 >NIt indicates radiation source in N number of reference point
Radiant power vector PtIt is initialized as the full null vector of N*1.
(4d2) finds out the subscript col of above-mentioned residual error r maximum inner product corresponding with column vector in above-mentioned sensing matrix R, it may be assumed that
Wherein, i indicates that i-th arranges, R (:, i) indicate that the i-th of sensing matrix R arranges, R (:, i) ' indicate the i-th of sensing matrix R
The function of the 2- norm of vector is sought in the transposition of column, norm () expression, and abs () expression takes absolute value, and max expression is maximized,It indicates to return to the value i that maximum value corresponding i-th arranges.
(4d3) updates index vector Pos=[Pos, col] according to above-mentioned subscript col, and to alternative column matrix A ug_t into
Row expands, it may be assumed that
Aug_t=[Aug_t, R (:, col)]
Wherein, Aug_t=[Aug_t, R (:, col)] indicates the col column of sensing matrix R selection matrix Aug_ is added
t。
(4d4) utilizes nonnegative least, acquires projection vector aug_y:
Aug_y=lsqnonneg (Aug_t, Pproc)
Wherein, lsqnonneg () is least square function.
The corresponding element (the last one element) of new alternative column (col column) and threshold in (4d5) compared projections vector aug_y
The size of value P, if being less than threshold value P, deletion is selected last in column matrix Aug_t and is arranged, it may be assumed that Aug_t (:, end)=[], it deletes
The last one element in index vector Pos, it may be assumed that Pos (end)=[], residual error r are constant；If more than threshold value P, then column matrix is selected
Aug_t is constant, and index vector Pos is constant, and residual error r updates are as follows:
R=Pproc-Aug_t*aug_y
The col column of sensing matrix R are set 0 by (4d6):
R (:, col)=zeros (M, 1)
Wherein, zeros (M, 1) indicates that dimension is the null vector of M*1.
(4d7) judges whether full | | r | |2< u, if satisfied, otherwise then loop termination executes step (4d2).
The condition of loop termination is | | r | |2< u, i.e., | | Pproc-RPt||2≤μ；Circulation all finds residual error r and sensing every time
Maximally related column in matrix R keep projection value minimum, final to obtain the min for meeting constraint condition | | Pt||。
(4d8) acquires 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 at the position of radiation source, radiant power
Vector PtCorresponding element is nonzero value.
Step 105, according to the radiation source of identification, Electromagnetic Situation inverting acquires the received signal strength in N number of reference point
RSS。
The vector P of the received signal strength RSS in N number of reference point is sought according to following formular:
Wherein, column vector Pr∈RNIndicate 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∈RNIndicate the radiant power of radiation source in N number of reference point,Indicate additive white Gaussian noise AWGN function
Rate, RNIndicate the vector space of N-dimensional, Pr∈RN、Pt∈RNIndicate Pr、PtFor the vector of N-dimensional.
Effect of the invention can be further illustrated by following emulation:
A, simulated conditions
Experimental Area is arranged on the square of 200m*200m, the grid of 20*20, the face of each grid are divided into
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 vertex.Assuming that radiation frequency is 3MHz, the probable value of radiant power is the integral multiple of P0, the i.e. random distribution of transmission power
In power set { P0,2P0 ..., Pm, wherein P0 is reference power, PmIndicate power maximum value.The property of radiant power reconstruct
It can be indicated 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 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, can realize the robustness of algorithm very well.
Emulation 2: under conditions of the radiant power of 8 radiation sources is randomly distributed over power set, to spoke of the invention
The recognition performance for penetrating source is emulated.The performance of Radar recognition indicates that calculation method is to take radiation with relative error PowE
The sum and reference radiation power P 0 of the absolute value of the true radiant power vector sum identification radiant power vector corresponding element difference in source
Ratio:
Wherein, PtFor N*1 dimension radiation source true radiant power vector,For the identification radiation of the radiation source of N*1 dimension
Vector power, P0 are reference power.Every time in experiment, radiation source and sensor are randomly distributed on 400 vertex rasters, spoke
It penetrates power to be randomly distributed in power set, repeats experiment 100 times, the recognition performance simulation result of radiation source is as shown in Figure 4.
As seen from Figure 4, in 100 tests, relative error is maintained at 10-12On, relative error is especially small and relatively steady
It is fixed, illustrate that the recognition performance of radiation source of the invention is superior, and to radiation source positions, sensor position, radiation source radiant power
There is robustness.
Emulation 3: under simulated conditions, the radiant power of identification radiation source is emulated, and by itself and true radiation source
Radiant power compares；The inverting of Electromagnetic Situation is emulated, and it is compared with true Electromagnetic Situation figure.Emulation
As a result 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) is present invention identification
Power of radiation source；Fig. 5 (d) is actual electromagnetic situation；Fig. 5 (e) is the inverting of OMPSE Electromagnetic Situation；Fig. 5 (f) is electromagnetism of the present invention
Situation inverting.
As seen from Figure 5, the depth of color can indicate the size of power, and contour illustrates the covering model of radiation source
It encloses, can intuitively find out the position of radiation source and the Electromagnetic Situation of radiant power size and each point.Fig. 5 (a), Fig. 5 (b),
The comparison of Fig. 5 (c) power of radiation source figure, when the present invention is to the identification of radiation source, reconstruct the size and location of power substantially with
The power and bit of actual emanations source radiation is set unanimously, the accuracy ratio OMPSE algorithm reconstruct of reconstruct power of radiation source of the invention
The accuracy of power of radiation source is high；Fig. 5 (d), Fig. 5 (e), the comparison of Fig. 5 (f) Electromagnetic Situation 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
Scheme substantially consistent with actual electromagnetic situation map, i.e., received signal strength RSS (the reception function in the reference point of inverting of the present invention
Rate) size and location it is substantially consistent with practical received signal strength RSS, the accuracy ratio of Electromagnetic Situation inverting of the invention
The accuracy of OMPSE algorithm Electromagnetic Situation inverting is high.Illustrate that the present invention more can accurately realize the identification of radiation source, and then realizes
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, is imitated the inverting of different moments Radar recognition and Electromagnetic Situation
Very.Simulation result is as shown in Figure 6.
The Radar recognition of Fig. 6 (a) different moments；The Electromagnetic Situation inverting of Fig. 6 (b) different moments.
As seen from Figure 6, in Fig. 6 (a) three figure layers respectively correspond three moment identification radiation source, three moment spokes
It penetrates source position and watt level and random variation all has occurred, three figure layers respectively correspond the Electromagnetic Situation at three moment in Fig. 6 (b)
Corresponding change also has occurred in inverting, the Electromagnetic Situation for embodying three moment.Thus different moments radiation may be implemented in the present invention
The identification in source, and then realize the inverting of complex environment Electromagnetic Situation.
In summary simulation analysis, the present invention can be in a small amount of sensor position random distribution, radiation source positions and radiation function
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, which is characterized in that the method includes as follows
Step:
(1) determine and configure complex electromagnetic environment parameter: Experimental Area uses N point grid layout, 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
N number of vertex raster choosing is done N number of reference point by the vector for the M dimension that received signal strength RSS is constituted；
(2) sensor position matrix is constructed, the sensor position matrix Φ can be indicated with following formula:
Wherein,sk∈ [1, N],It is generality quantifier, indicates arbitrary or all,Indicating that all j are belonged to set [1, N], j indicates j-th of reference point,Indicate all k
It belongs to set [1, M], k indicates k-th of sensor, sk∈ [1, N] indicates position of k-th of sensor in reference point, passes
Sensor location matrix [Φ]kjIt is M*N matrix；
(3) according to the electromagnetic propagation model of electromagnetic environment, build path loss matrix, the path loss matrix Ψ can use as follows
Formula indicates:
Wherein, i, j meetIndicate that all i, j are belonged to set [1, N], i is indicated
The i reference point, j indicate j-th of reference point, Gi,jIndicate the shadow fading between i-th of reference point and j-th of reference point, dij
Indicate the distance between i-th of reference point and j-th of reference point, d0Indicate reference antenna far field distance, α indicates exponential damping system
Number, path loss matrix [Ψ]ijIt is a N*N matrix；
(4) Radar recognition is carried out according to the sensor position 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 acquires the received signal strength RSS in N number of reference point:
Wherein, column vector Pr∈RNIndicate the received signal strength RSS in N number of reference point, column vector Pt∈RNIndicate N number of reference point
The radiant power of upper radiation source,Indicate additive white Gaussian noise AWGN power.
2. a kind of complex environment Electromagnetic Situation inversion method based on multisensor according to claim 1, feature exist
In it further includes following steps that the step (1), which determines, and configuration complex electromagnetic environment parameter:
The type of K radiation source is unrestricted, the type of radiation source can be communication equipment, jammer, transmitting equipment in one
Kind is a variety of；
The number K of radiation source, the number M of sensor, reference point number N meet following relationship:
M≥2*K
3. a kind of complex environment Electromagnetic Situation inversion method based on multisensor according to claim 1, feature exist
In the step (4) carries out Radar recognition according to the sensor position matrix, the path loss matrix, is radiated
The position in source and radiant power, include the following steps:
(4a) calculates sensing matrix R according to the sensor position 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 there are following relationships:
Wherein,For additive white Gaussian noise AWGN power, column vector Pt∈RNIndicate the radiation of radiation source in N number of reference point
Power, column vector ε ∈ RMIndicate the measurement error of sensor；
(4c) constructs preprocessed data Pproc:
(4d) solves position and the radiant power P of radiation source according to minimum L1- normt:
min||Pt||,s.t.||Pproc-RPt||2≤μ
Wherein, | | | | indicate that 1- norm, meaning are the sum of all elements modulus value in vector, | | | |2Indicate 2- norm, meaning
For in vector all elements modulus value square and evolution again, μ be convergence precision, min indicates to minimize, and s.t. is subject to
Write a Chinese character in simplified form expression " being constrained to ", the meaning of entire equation, which is meeting constraint condition, is | | Pproc-RPt||2Under conditions of≤μ, make
Obtain PtValue it is minimum.
4. a kind of complex environment Electromagnetic Situation inversion method based on multisensor according to claim 3, feature exist
In the step (4d) solves position and the radiant power P of radiation source according to minimum L1- normt, it is carried out according to following algorithm:
Input/output argument is arranged in (4d1), and initializes 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 >NIt indicates the radiation function of radiation source in N number of reference point
Rate vector PtIt is initialized as the full null vector of N*1；
(4d2) finds out the subscript col of residual error r maximum inner product corresponding with column vector in the sensing matrix R, it may be assumed that
Wherein, i indicates the i-th column, and R (:, i) indicates the i-th column of sensing matrix R, and R (:, i) ' indicates the i-th column of sensing matrix R
The function of the 2- norm of vector is sought in transposition, norm () expression, and abs () expression takes absolute value, and max expression is maximized,It indicates to return to the i value that maximum value corresponding i-th arranges；
(4d3) updates index vector Pos=[Pos, col] according to the subscript col, and expands alternative column matrix A ug_t
It fills, it may be assumed that
Aug_t=[Aug_t, R (:, col)]
(4d4) utilizes nonnegative least, acquires projection vector aug_y:
Aug_y=lsqnonneg (Aug_t, Pproc)
Wherein, lsqnonneg () is least square function；
The corresponding element of the new alternative column i.e. size of the last one element and threshold value P in (4d5) compared projections vector aug_y, if
Less than threshold value P, then deletes last in alternative column matrix A ug_t and arrange, delete the last one element in index vector Pos, residual error r
It is constant；If it is greater than or equal to threshold value P, then alternative column matrix A ug_t is constant, and index vector Pos is constant, and residual error r updates are as follows:
R=Pproc-Aug_t*aug_y
The col column of sensing matrix R are set 0 by (4d6):
R (:, col)=zeros (M, 1)
Wherein, zeros (M, 1) indicates that dimension is the null vector of M*1；
(4d7) judges whether full | | r | |2< u, if satisfied, otherwise then loop termination executes step (4d2)；
(4d8) acquires 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 at the position of radiation source, radiant power vector Pt
Corresponding element is nonzero value.
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