CN108681732A - A kind of gunz optimization SAR radar airbound target identifying systems - Google Patents
A kind of gunz optimization SAR radar airbound target identifying systems Download PDFInfo
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Abstract
The invention discloses a kind of gunzs to optimize SAR radar airbound target identifying systems, including SAR radars, database and host computer;SAR radars, database, host computer are sequentially connected, the SAR radars to being monitored in real time in the air, and in the image data storage to the database for obtaining SAR radars, the host computer includes image pre-processing module, characteristic extracting module, feature selection module, classifier training module, gunz optimization module and result display module.A kind of realization online recognition of present invention offer, airflight target identification system with high accuracy.
Description
Technical field
The present invention relates to radar data process fields, particularly, are related to a kind of gunz optimization SAR radar airbound targets
Identifying system.
Background technology
Airflight target identification technology has shown powerful vitality, occurs various theoretical methods over 30 years,
The algorithm of the comparative maturity of research has classical statistics judgement, Subjective Bayes deduction, D-S evidence theory method, neural network and support
Vector machine etc..Because the difference of the algorithm of target identification is possible to that the inconsistent of expression recognition result can be caused, system is
It is convenient for handling and comparing that the expression-form of all kinds of recognition results is unified for subjective probability.In addition to this, some grind
When studying carefully personnel and studying target identification, rough set theory and data mining are introduced.Also someone by data mining with it is coarse
Collection is theoretical to be combined, and is studied target identification system.Currently, inductive learning process is widely used in data mining technology, number
Potential rule can be extracted from mass data according to excavating.Wherein airflight target is monitored using SAR image and
Identification is a current international forward position and hot spot, can be identified by carrying out the monitoring of airflight target to SAR image,
Obtain the important informations parameter such as type, position and the course of airbound target.For obtain airflight target initiative,
It is vital to ensure that the success of airflight goal activity plays the role of.
Invention content
In order to overcome the shortcomings of currently based on not high, the of the invention mesh of the airflight target identification accuracy rate of SAR image
Be provide it is a kind of realize analyze in real time gunz optimization SAR radar airbound target identifying systems.
The technical solution adopted by the present invention to solve the technical problems is:A kind of gunz optimization SAR radar flight mesh
Identifying system, including SAR radars, database and host computer are marked, SAR radars, database and host computer are sequentially connected, described
SAR radars to being monitored in real time in the air, and the image data that SAR radars are obtained is stored into the database, described
Host computer include:
Image pre-processing module is completed to carry out SAR radar image data pretreatments using following process:
1) the SAR image gray level transmitted from database is L, f (x0,y0) it is pixel (x0,y0) at gray value, g
(x0,y0) it is pixel (x0,y0) N × N neighborhoods in pixel average value, wherein x0,y0The abscissa of pixel is indicated respectively
And ordinate;
2) the number of pixels h (m, n) for meeting f=m and g=n by calculating, obtains joint probability density pmn:
pmn=p (m, n)=h (m, n)/M
Wherein, M indicates the total number of image pixel;
3) the mean vector μ of two-dimensional histogram is calculated:
4) probability P that target and background occurs in image is calculated separately0,1With mean vector μ0,1:
Wherein, t, s, subscript 0, subscript 1 indicate f segmentation thresholds, g segmentation thresholds, target area, background area respectively;
5) inter-class variance BCV is calculated:
BCV=P0(μ0-μ)(μ0-μ)′+P1(μ1-μ)(μ1-μ)′;
Wherein, μ indicates mean vector, the transposition of subscript ' representing matrix.
6) optimal threshold is the Two Dimensional Thresholding vector [s so that when BCV is maximum value0,t0]:
Characteristic extracting module is completed to carry out the extraction of airbound target characteristic feature using following process:
1) the SAR image slice I (m, n) only comprising an airbound target transmitted from image pre-processing module, wherein only
Including the binary map of target area is B (m, n), then only include the image T (m, n) of target:
T (m, n)=I (m, n) × B (m, n)
Wherein, × indicate that respective pixel is multiplied;
2) minimum for acquiring airbound target body region according to the major axes orientation of airbound target individual in B (m, n) is external
Rectangle, then the long side length Length of the rectangle is the length of airbound target individual, and the bond length Width of rectangle is to fly
The width of row target individual;
3) geometry feature is calculated, including perimeter, area, length-width ratio, shape complexity, target centroid position
It sets and rotary inertia:
PerimeterAreaLength-width ratio R=Length/
Width;Shape complexity C=Length2/4 π S;The centroid position of target area
Rotary inertiaIn formula, r represents the distance between target pixel points and barycenter,
4) gray-scale statistical characteristics are calculated, including quality, mean value, coefficient of variation, standard deviation, fractal dimension, add
Weigh packing ratio:
QualityMean valueCoefficient of variation
Standard deviationIn formula
Gray scale logarithm and gray scale logarithm quadratic sum are indicated respectively;Fractal dimension H=(log10N1-log10N2)/(log10d1-log10d2),
The computational methods of this feature are:With one K for remaining target area of SAR image slice structure after segmentation (K=is taken here
50) the binary map B of a brightest pixel point2One size is first d by (m, n)1×d2Window it is continuous in this binary map
Sliding writes down the window sum comprising bright spot in window and is denoted as N1, with a size it is again then d2×d2Window this two
It is continuously slipping in value figure, it writes down the window sum comprising bright spot in the window and is denoted as N2;Weight packing ratio
Feature selection module is completed to select optimal feature subset using following process:
1) inter- object distance is calculatedBetween class distanceAnd class spacing J in classi:
Wherein, i indicates that feature label, ω indicate the label of airbound target classification, | | Fi (ω)||2Indicate feature vector Fi (ω)
2 norms,Indicate the population mean of training set sample, NωIndicate the number of ω class airbound targets
Amount, N indicate that airbound target sum in training set, E indicate it is expected, subscript W, subscript B are indicated respectively in class, between class.
2) normalization coefficient of variation ρ is calculatedi (ω):
ρi (ω)=E [| | Fi (ω)||2 2]-E2[||Fi (ω)||2]/E[||Fi (ω)||2 2]
Wherein, i indicates that feature label, ω indicate the label of airbound target classification, | | Fi (ω)||2Indicate feature vector Fi (ω)
2 norms, E [| | Fi (ω)||2 2] and E2[||Fi (ω)||2] mean value of square of feature and square of mean value are indicated respectively.Feature
Coefficient of variation ρi (ω)It is smaller, show that the stability of the target signature is better;
3) correlation coefficient r is calculatedi,j:
Wherein, i, j indicate feature label, | | Fi||2Indicate feature Fi2 norms,WithF is indicated respectivelyiAnd FjIt is equal
Value, σi,iAnd σj,jF is indicated respectivelyiAnd FjStandard deviation.By the property of related coefficient it is found that 0≤ri,j≤1;If two features
Complete uncorrelated, ri,j=0;If two features are perfectly correlated, ri,j=1;If the correlation between two features is very low, i.e.,
Information redundancy between feature is considerably less, then ri,jIt will be closer to 0;, whereas if the correlation between two features is very
Height, i.e., the information redundancy between feature is very more, then ri,jIt will be closer to 1;
4) optimal feature subset is filtered out by class spacing, normalization coefficient of variation, related coefficient in class obtained above,
Construct optimal input feature value;
Classifier training module is completed to carry out classifier training using following process:
5) N number of SAR radar images x is acquired from feature selection moduleiAs training sample, i=1,2 ..., N;
6) training sample is normalized, obtains normalization sample
7) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y:
Wherein, D indicates reconstruct dimension, and D is natural number, and D<The value range of N, D are 50-70;
8) classifier training set is obtainedWherein xiRepresent the input feature vector of n × 1 to
Amount, tiRepresent the object vector of m × 1.Give the node number of an activation primitive g (x) and hidden layerSo ELM points
Class device is:
Wherein, ωiRepresent the weight vectors between i-th of hidden layer node and input layer, biRepresent i-th of hidden layer section
The biasing of point, βiRepresent the weight vectors between i-th of hidden layer node and output layer, ojRepresent the target of j-th of input data
Output.In addition, ωi·xjRepresent ωiAnd xjInner product.
The output of the network can be ad infinitum close to N number of sample of input, i.e.,:
It can obtain:
Above formula can be expressed as matrix form:H β=T
Wherein, H indicates that the output matrix of hidden layer, the i-th row of H indicate i-th of node of hidden layer corresponding to N number of respectively
Input x1,x2,…,xNOutput valve.The input weights of Single hidden layer feedforward neural networks (SLFNs) and the deviation of hidden layer are in net
It need not be adjusted during network training, it can be any given.Based on above-mentioned theory, output weight can be by calculating H β=T
Least square solutionIt acquires:
Non trivial solution can be quickly acquired using linear method, as shown in formula:
Wherein,The Moore-Penrose generalized inverse matrix of H are represented,LS solution of the least norm is represented, it is just
It is the solution of Norm minimum in least square solution well.Compared to many existing classifier systems, extreme learning machine passes through this
The solution of Moore-Penrose generalized inverses can reach good training effect at a very rapid rate.
Gunz optimization module, to use the optimization module based on swarm intelligence algorithm to the nuclear parameter θ of grader and punishment
Factor gamma optimizes, and is completed using following process:
1) algorithm initialization constructs initial disaggregation S=(s according to grader form to be optimized1,s2,…,sn), really
Determine the size m of ant colony, the threshold value MaxGen of ant optimization algorithm iteration number is set and initializes the iterations of ant optimization
Serial number gen=0;
2) the corresponding fitness value Fit of disaggregation S are calculatedi(i=1,2 ..., n), fitness value is bigger to represent Xie Yuehao;
The probability P that solution concentrates each solution to be fetched into the initial solution as ant optimizing is determined further according to following formulai(i=1,2 ..., n)
Initialization executes the ant number i=0 of optimizing algorithm;
3) ant i chooses initial solution of the solution as optimizing in S, and selection rule is to do wheel disc choosing according to P;
4) ant i carries out optimizing on the basis of the initial solution of selection, finds preferably solution si′;
If 5) i<M, then i=i+1, return to step 3);Otherwise continue to execute step 6) downwards;
If 6) gen<MaxGen, then gen=gen+1, replaces S using the best solution that all ants in step 4) obtain
In homographic solution, return to step 2);Otherwise step 7) is executed downwards;
7) the corresponding fitness value Fit of disaggregation S are calculatedi(i=1,2 ..., n) chooses the maximum solution conduct of fitness value
The optimal solution of algorithm terminates algorithm and returns.
Result display module shows the type for inputting airbound target in SAR image the display of result is identified
Show in screen.
The present invention technical concept be:The present invention is directed to SAR radars round-the-clock, all weather operations and the spy penetrated by force
Property, image preprocessing is carried out to the aerial image that SAR radars monitor, then carries out the extraction of feature and the selection of feature,
Airflight Model of Target Recognition is established finally by the training process of grader, to realize SAR radar airbound targets
Identification.
Beneficial effects of the present invention are mainly manifested in:1, airflight target can be identified in real time;2, recognition methods used
Only need less training sample;3, intelligent, small by interference from human factor.
Description of the drawings
Fig. 1 is the overall structure figure of system proposed by the invention;
Fig. 2 is the functional block diagram of host computer proposed by the invention.
Specific implementation mode
The present invention is illustrated below according to attached drawing.Above-described embodiment is used for illustrating the present invention, rather than to this hair
It is bright to be limited, in the protection domain of spirit and claims of the present invention, to any modifications and changes for making of the present invention,
Both fall within protection scope of the present invention.
Embodiment
Referring to Fig.1, Fig. 2, a kind of gunz optimization SAR radar airbound target identifying systems, including SAR radars 1, data
Library 2 and host computer 3, SAR radars 1, database 2 and host computer 3 are sequentially connected, and the SAR radars 1 shine monitored marine site
It penetrates, and by SAR radar images storage to the database 2, the host computer 3 includes:
Image pre-processing module 4 is completed to carry out SAR radar image data pretreatments using following process:
1) the SAR image gray level transmitted from database is L, f (x0,y0) it is pixel (x0,y0) at gray value, g
(x0,y0) it is pixel (x0,y0) N × N neighborhoods in pixel average value, wherein x0,y0The abscissa of pixel is indicated respectively
And ordinate;
2) the number of pixels h (m, n) for meeting f=m and g=n by calculating, obtains joint probability density pmn:
pmn=p (m, n)=h (m, n)/M
Wherein, M indicates the total number of image pixel;
3) the mean vector μ of two-dimensional histogram is calculated:
4) probability P that target and background occurs in image is calculated separately0,1With mean vector μ0,1:
Wherein, t, s, subscript 0, subscript 1 indicate f segmentation thresholds, g segmentation thresholds, target area, background area respectively;
5) inter-class variance BCV is calculated:
BCV=P0(μ0-μ)(μ0-μ)′+P1(μ1-μ)(μ1-μ)′;
Wherein, μ indicates mean vector, the transposition of subscript ' representing matrix.
6) optimal threshold is the Two Dimensional Thresholding vector [s so that when BCV is maximum value0,t0]:
Characteristic extracting module 5 is completed to carry out the extraction of airbound target characteristic feature using following process:
1) the SAR image slice I (m, n) only comprising an airbound target transmitted from image pre-processing module, wherein only
Including the binary map of target area is B (m, n), then only include the image T (m, n) of target:
T (m, n)=I (m, n) × B (m, n)
Wherein, × indicate that respective pixel is multiplied;
2) minimum for acquiring airbound target body region according to the major axes orientation of airbound target individual in B (m, n) is external
Rectangle, then the long side length Length of the rectangle is the length of airbound target individual, and the bond length Width of rectangle is to fly
The width of row target individual;
3) geometry feature is calculated, including perimeter, area, length-width ratio, shape complexity, target centroid position
It sets and rotary inertia:
PerimeterAreaLength-width ratio R=Length/
Width;Shape complexity C=Length2/4πS;The centroid position of target area
Rotary inertiaIn formula, r represents the distance between target pixel points and barycenter,
4) gray-scale statistical characteristics are calculated, including quality, mean value, coefficient of variation, standard deviation, fractal dimension, add
Weigh packing ratio:
QualityMean valueCoefficient of variation
Standard deviationIn formula
Gray scale logarithm and gray scale logarithm quadratic sum are indicated respectively;Fractal dimension H=(log10N1-log10N2)/(log10d1-log10d2),
The computational methods of this feature are:With one K for remaining target area of SAR image slice structure after segmentation (K=is taken here
50) the binary map B of a brightest pixel point2One size is first d by (m, n)1×d2Window it is continuous in this binary map
Sliding writes down the window sum comprising bright spot in window and is denoted as N1, with a size it is again then d2×d2Window this two
It is continuously slipping in value figure, it writes down the window sum comprising bright spot in the window and is denoted as N2;Weight packing ratio
Feature selection module 6 is completed to select optimal feature subset using following process:
1) inter- object distance is calculatedBetween class distanceAnd class spacing J in classi:
Wherein, i indicates that feature label, ω indicate the label of airbound target classification, | | Fi (ω)||2Indicate feature vector Fi (ω)
2 norms,Indicate the population mean of training set sample, NωIndicate the number of ω class airbound targets
Amount, N indicate that airbound target sum in training set, E indicate it is expected, subscript W, subscript B are indicated respectively in class, between class.
2) normalization coefficient of variation ρ is calculatedi (ω):
ρi (ω)=E [| | Fi (ω)||2 2]-E2[||Fi (ω)||2]/E[||Fi (ω)||2 2]
Wherein, i indicates that feature label, ω indicate the label of airbound target classification, | | Fi (ω)||2Indicate feature vector Fi (ω)
2 norms, E [| | Fi (ω)||2 2] and E2[||Fi (ω)||2] mean value of square of feature and square of mean value are indicated respectively.Feature
Coefficient of variation ρi (ω)It is smaller, show that the stability of the target signature is better;
3) correlation coefficient r is calculatedi,j:
Wherein, i, j indicate feature label, | | Fi||2Indicate feature Fi2 norms,WithF is indicated respectivelyiAnd FjIt is equal
Value, σi,iAnd σj,jF is indicated respectivelyiAnd FjStandard deviation.By the property of related coefficient it is found that 0≤ri,j≤1;If two features
Complete uncorrelated, ri,j=0;If two features are perfectly correlated, ri,j=1;If the correlation between two features is very low, i.e.,
Information redundancy between feature is considerably less, then ri,jIt will be closer to 0;, whereas if the correlation between two features is very
Height, i.e., the information redundancy between feature is very more, then ri,jIt will be closer to 1;
4) optimal feature subset is filtered out by class spacing, normalization coefficient of variation, related coefficient in class obtained above,
Construct optimal input feature value;
Classifier training module 7 is completed to carry out classifier training using following process:
1) N number of SAR radar images x is acquired from feature selection moduleiAs training sample, i=1,2 ..., N;
2) training sample is normalized, obtains normalization sample
3) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y:
Wherein, D indicates reconstruct dimension, and D is natural number, and D<The value range of N, D are 50-70;
4) classifier training set is obtainedWherein xiRepresent the input feature vector of n × 1 to
Amount, tiRepresent the object vector of m × 1.Give the node number of an activation primitive g (x) and hidden layerSo ELM points
Class device is:
Wherein, ωiRepresent the weight vectors between i-th of hidden layer node and input layer, biRepresent i-th of hidden layer section
The biasing of point, βiRepresent the weight vectors between i-th of hidden layer node and output layer, ojRepresent the target of j-th of input data
Output.In addition, ωi·xjRepresent ωiAnd xjInner product.
The output of the network can be ad infinitum close to N number of sample of input, i.e.,:
It can obtain:
Above formula can be expressed as matrix form:H β=T
Wherein, H indicates that the output matrix of hidden layer, the i-th row of H indicate i-th of node of hidden layer corresponding to N number of respectively
Input x1,x2,…,xNOutput valve.The input weights of Single hidden layer feedforward neural networks (SLFNs) and the deviation of hidden layer are in net
It need not be adjusted during network training, it can be any given.Based on above-mentioned theory, output weight can be by calculating H β=T
Least square solutionIt acquires:
Non trivial solution can be quickly acquired using linear method, as shown in formula:
Wherein,The Moore-Penrose generalized inverse matrix of H are represented,LS solution of the least norm is represented, it is just
It is the solution of Norm minimum in least square solution well.Compared to many existing classifier systems, extreme learning machine passes through this
The solution of Moore-Penrose generalized inverses can reach good training effect at a very rapid rate.
Gunz optimization module 9, to use the optimization module based on swarm intelligence algorithm to the nuclear parameter θ of grader and punishment
Factor gamma optimizes, and is completed using following process:
1) algorithm initialization constructs initial disaggregation S=(s according to grader form to be optimized1,s2,…,sn), really
Determine the size m of ant colony, the threshold value MaxGen of ant optimization algorithm iteration number is set and initializes the iterations of ant optimization
Serial number gen=0;
2) the corresponding fitness value Fit of disaggregation S are calculatedi(i=1,2 ..., n), fitness value is bigger to represent Xie Yuehao;
The probability P that solution concentrates each solution to be fetched into the initial solution as ant optimizing is determined further according to following formulai(i=1,2 ..., n)
Initialization executes the ant number i=0 of optimizing algorithm;
3) ant i chooses initial solution of the solution as optimizing in S, and selection rule is to do wheel disc choosing according to P;
4) ant i carries out optimizing on the basis of the initial solution of selection, finds preferably solution si′;
If 5) i<M, then i=i+1, return to step 3);Otherwise continue to execute step 6) downwards;
If 6) gen<MaxGen, then gen=gen+1, replaces S using the best solution that all ants in step 4) obtain
In homographic solution, return to step 2);Otherwise step 7) is executed downwards;
7) the corresponding fitness value Fit of disaggregation S are calculatedi(i=1,2 ..., n) chooses the maximum solution conduct of fitness value
The optimal solution of algorithm terminates algorithm and returns.
Result display module 8 will input the type of airbound target in SAR image the display of result is identified
It is shown in host computer.
The hardware components of the host computer 3 include:I/O elements, the transmission of acquisition and information for data;Data store
Device, the required data sample of storage running and operating parameter etc.;The software journey of function module is realized in program storage, storage
Sequence;Arithmetic unit executes program, realizes specified function;Display module shows the parameter and recognition result of setting.
Above-described embodiment is used for illustrating the present invention, rather than limits the invention, the present invention spirit and
In scope of the claims, to any modifications and changes that the present invention makes, protection scope of the present invention is both fallen within.
Claims (5)
1. a kind of gunz optimizes SAR radar airbound target identifying systems, it is characterised in that:Including SAR radars, database with
And host computer, SAR radars, database and host computer are sequentially connected, the SAR radars to being monitored in real time in the air, and by SAR
To in the database, the host computer includes that image pre-processing module, feature carry for the image data storage that radar obtains
Modulus block, classifier training module, classifier training module, gunz optimization module and result display module, described image are located in advance
Reason module, characteristic extracting module, classifier training module, classifier training module and result display module are sequentially connected, and are classified
Device training module is connected with gunz optimization module.
2. gunz optimizes SAR radar airbound target identifying systems according to claim 1, it is characterised in that:The figure
As preprocessing module is to carry out SAR radar image data pretreatments, completed using following process:
1) the SAR image gray level transmitted from database is L, f (x0,y0) it is pixel (x0,y0) at gray value, g (x0,
y0) it is pixel (x0,y0) N × N neighborhoods in pixel average value, wherein x0,y0The abscissa of pixel is indicated respectively and is indulged
Coordinate;
2) the number of pixels h (m, n) for meeting f=m and g=n by calculating, obtains joint probability density pmn:
pmn=p (m, n)=h (m, n)/M
Wherein, M indicates the total number of image pixel;
3) the mean vector μ of two-dimensional histogram is calculated:
4) probability P that target and background occurs in image is calculated separately0,1With mean vector μ0,1:
Wherein, t, s, subscript 0, subscript 1 indicate f segmentation thresholds, g segmentation thresholds, target area, background area respectively;
5) inter-class variance BCV is calculated:
BCV=P0(μ0-μ)(μ0-μ)′+P1(μ1-μ)(μ1-μ)′;
Wherein, μ indicates mean vector, the transposition of subscript ' representing matrix.
6) optimal threshold is the Two Dimensional Thresholding vector [s so that when BCV is maximum value0,t0]:
3. gunz optimizes SAR radar airbound target identifying systems according to claim 1, it is characterised in that:The spy
Extraction module is levied to carry out the extraction of airflight target characteristic feature, is completed using following process:
1) the SAR image slice I (m, n) only comprising an airbound target transmitted from image pre-processing module, wherein including only
The binary map of target area is B (m, n), then only includes the image T (m, n) of target:
T (m, n)=I (m, n) × B (m, n)
Wherein, × indicate that respective pixel is multiplied;
2) minimum enclosed rectangle of airbound target body region is acquired according to the major axes orientation of airbound target individual in B (m, n),
Then the long side length Length of the rectangle is the length of airbound target individual, and the bond length Width of rectangle is flight mesh
Mark the width of individual;
3) geometry feature is calculated, including perimeter, area, length-width ratio, shape complexity, target centroid position with
And rotary inertia:
PerimeterAreaLength-width ratio R=Length/
Width;Shape complexity C=Length2/4πS;The centroid position of target area
Rotary inertiaIn formula, r represents the distance between target pixel points and barycenter,
4) gray-scale statistical characteristics are calculated, are filled out including quality, mean value, coefficient of variation, standard deviation, fractal dimension, weighting
Fill ratio:
QualityMean valueCoefficient of variation
Standard deviationIn formula
Gray scale logarithm and gray scale logarithm quadratic sum are indicated respectively;Fractal dimension H=(log10N1-log10N2)/(log10d1-log10d2),
The computational methods of this feature are:With one K for remaining target area of SAR image slice structure after segmentation (K=is taken here
50) the binary map B of a brightest pixel point2One size is first d by (m, n)1×d2Window it is continuous in this binary map
Sliding writes down the window sum comprising bright spot in window and is denoted as N1, with a size it is again then d2×d2Window this two
It is continuously slipping in value figure, it writes down the window sum comprising bright spot in the window and is denoted as N2;Weight packing ratio
4. gunz optimizes SAR radar airbound target identifying systems according to claim 1, it is characterised in that:The spy
Selecting module is levied to select optimal feature subset, is completed using following process:
1) inter- object distance is calculatedBetween class distanceAnd class spacing J in classi:
Wherein, i indicates that feature label, ω indicate the label of airbound target classification, | | Fi (ω)||2Indicate feature vector Fi (ω)2 models
Number,Indicate the population mean of training set sample, NωIndicate the quantity of ω class airbound targets, N tables
Show that airbound target sum in training set, E indicate it is expected, subscript W, subscript B are indicated respectively in class, between class.
2) normalization coefficient of variation ρ is calculatedi (ω):
Wherein, i indicates that feature label, ω indicate the label of airbound target classification, | | Fi (ω)||2Indicate feature vector Fi (ω)2 models
Number, E [| | Fi (ω)||2 2] and E2[||Fi (ω)||2] mean value of square of feature and square of mean value are indicated respectively.The variance of feature
Coefficient ρi (ω)It is smaller, show that the stability of the target signature is better;
3) correlation coefficient r is calculatedi,j:
Wherein, i, j indicate feature label, | | Fi||2Indicate feature Fi2 norms,WithF is indicated respectivelyiAnd FjMean value,
σi,iAnd σj,jF is indicated respectivelyiAnd FjStandard deviation.By the property of related coefficient it is found that 0≤ri,j≤1;If two features are complete
It is uncorrelated, ri,j=0;If two features are perfectly correlated, ri,j=1;If the correlation between two features is very low, i.e. feature
Between information redundancy it is considerably less, then ri,jIt will be closer to 0;, whereas if the correlation between two features is very high, i.e.,
Information redundancy between feature is very more, then ri,jIt will be closer to 1;
4) optimal feature subset is filtered out by class spacing, normalization coefficient of variation, related coefficient in class obtained above, constructed
Optimal input feature value.
5. gunz optimizes SAR radar airbound target identifying systems according to claim 1, it is characterised in that:Described point
Class device training module is completed to carry out classifier training using following process:
1) N number of SAR radar images x is acquired from feature selection moduleiAs training sample, i=1,2 ..., N;
2) training sample is normalized, obtains normalization sample
3) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y:
Wherein, D indicates reconstruct dimension, and D is natural number, and D<The value range of N, D are 50-70;
4) classifier training set is obtainedWherein xiRepresent the input feature value of n × 1, ti
Represent the object vector of m × 1.Give the node number of an activation primitive g (x) and hidden layerSo ELM graders
For:
Wherein, ωiRepresent the weight vectors between i-th of hidden layer node and input layer, biRepresent i-th hidden layer node
Biasing, βiRepresent the weight vectors between i-th of hidden layer node and output layer, ojThe target for representing j-th of input data is defeated
Go out.In addition, ωi·xjRepresent ωiAnd xjInner product.
The output of the network can be ad infinitum close to N number of sample of input, i.e.,:
It can obtain:
Above formula can be expressed as matrix form:H β=T
Wherein, H indicates that the output matrix of hidden layer, the i-th row of H indicate that i-th of node of hidden layer corresponds to N number of input respectively
x1,x2,…,xNOutput valve.The input weights of Single hidden layer feedforward neural networks (SLFNs) and the deviation of hidden layer are instructed in network
It need not be adjusted in experienced process, it can be any given.Based on above-mentioned theory, output weight can be by calculating H β=T most
Small two multiply solutionIt acquires:
Non trivial solution can be quickly acquired using linear method, as shown in formula:
Wherein,The Moore-Penrose generalized inverse matrix of H are represented,LS solution of the least norm is represented, it is exactly most
Small two multiply the solution of Norm minimum in solution.Compared to many existing classifier systems, extreme learning machine passes through this Moore-
The solution of Penrose generalized inverses can reach good training effect at a very rapid rate.
The gunz optimization module to using the optimization module based on swarm intelligence algorithm to the nuclear parameter θ of grader and punishment because
Sub- γ is optimized, and is completed using following process:
1) algorithm initialization constructs initial disaggregation S=(s according to grader form to be optimized1,s2,…,sn), determine ant
The size m of group is arranged the threshold value MaxGen of ant optimization algorithm iteration number and initializes the iterations serial number of ant optimization
Gen=0;
2) the corresponding fitness value Fit of disaggregation S are calculatedi(i=1,2 ..., n), fitness value is bigger to represent Xie Yuehao;Further according to
Following formula determines the probability P that solution concentrates each solution to be fetched into the initial solution as ant optimizingi(i=1,2 ..., n)
Initialization executes the ant number i=0 of optimizing algorithm;
3) ant i chooses initial solution of the solution as optimizing in S, and selection rule is to do wheel disc choosing according to P;
4) ant i carries out optimizing on the basis of the initial solution of selection, finds preferably solution si′;
If 5) i<M, then i=i+1, return to step 3);Otherwise continue to execute step 6) downwards;
If 6) gen<MaxGen, then gen=gen+1, is replaced using the best solution that all ants in step 4) obtain in S
Homographic solution, return to step 2);Otherwise step 7) is executed downwards;
7) the corresponding fitness value Fit of disaggregation S are calculatedi(i=1,2 ..., n) chooses the maximum solution of fitness value and is used as algorithm
Optimal solution, terminate algorithm simultaneously return.
The result display module shows the type for inputting airbound target in SAR image the display of result is identified
Show in screen.
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