CN107656250A - A kind of Intelligent radar sea target detection system and method based on artificial bee colony algorithm - Google Patents
A kind of Intelligent radar sea target detection system and method based on artificial bee colony algorithm Download PDFInfo
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- CN107656250A CN107656250A CN201711116260.5A CN201711116260A CN107656250A CN 107656250 A CN107656250 A CN 107656250A CN 201711116260 A CN201711116260 A CN 201711116260A CN 107656250 A CN107656250 A CN 107656250A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/36—Means for anti-jamming, e.g. ECCM, i.e. electronic counter-counter measures
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
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Abstract
The invention discloses a kind of Intelligent radar sea target detection system and method based on artificial bee colony algorithm, system is sequentially connected by radar, database and host computer and formed, radar is irradiated to detected marine site, and radar sea clutter data storage to described database, described host computer are included into data preprocessing module, robust forecasting model modeling module, intelligent optimizing module, module of target detection, model modification module and result display module：The present invention is reconstructed, and target data is detected to radar clutter data, introduces artificial bee colony algorithm for the complex characteristics of naval target detection, and a kind of on-line checking, intelligent high radar marine target detection system and method are realized so as to provide.
Description
Technical field
The present invention relates to radar data process field, especially, is related to a kind of Intelligent radar based on artificial bee colony algorithm
Naval target detecting system and method.
Background technology
Sea clutter, that is, come from the radar raster-displaying echo on sea.In recent decades, with the depth recognized sea clutter
Entering, the country such as Germany, Norway attempts to obtain radar wave image using radar observation sea clutter come inverting Wave Information in succession, with
Obtain the real time information on sea state, wave height, direction and the cycle of such as wave, so as to further to marine small objects
Detected, this is of great significance to offshore activities tool.
Naval target detection technique has consequence, there is provided accurate target decision is to the important of extra large radar work
One of task.Radar automatic checkout system makes judgement according to decision rule under given detection threshold value, and strong sea clutter is past
Toward the main interference for turning into weak target signal.Detection of the radar under marine environment will be directly influenced by how handling sea clutter
Ability：1) ice of navigation by recognition buoy, small pieces, the greasy dirt on sea is swum in, these may carry out potential crisis to navigation band；
3) monitoring illegal fishing is an important task of environmental monitoring.
In traditional target detection, sea clutter is considered as that a kind of noise of interference navigation is removed.However, in radar
During to extra large observed object, faint Moving Target Return is usually buried in sea clutter, and signal to noise ratio is relatively low, and radar is not easy to detect
Target, while a large amount of spikes of sea clutter can also cause serious false-alarm, and considerable influence is produced to the detection performance of radar.For each
For kind for sea police's ring and early warning radar, the main target of research is to improve the detectability of target under sea clutter background.Therefore,
Not only there is important theory significance and practical significance, and be also the difficult point and focus of domestic and international naval target detection.
The content of the invention
In order to overcome the shortcomings of that existing radar method for detecting targets at sea can not realize on-line checking, intelligent poor, sheet
Invention offer is a kind of to realize on-line checking, the intelligent strong Intelligent radar sea target detection system based on artificial bee colony algorithm
And method.
The technical solution adopted for the present invention to solve the technical problems is：
A kind of Intelligent radar sea target detection system based on artificial bee colony algorithm, including radar, database and on
Position machine, radar, database and host computer be sequentially connected, and the radar is irradiated to detected marine site, and by radar sea clutter
Data storage includes to described database, described host computer：
Data preprocessing module, to carry out radar sea clutter data prediction, completed using following process：
(1) radar is irradiated to detected marine site, and by radar sea clutter data storage to described database；
(2) N number of radar sea clutter echo-signal amplitude x is gathered from databaseiAs training sample, i=1 ..., N；
(3) training sample is normalized, obtains normalizing amplitude
Wherein, minx represents the minimum value in training sample, and maxx represents the maximum in training sample；
(4) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y：
Wherein, D represents reconstruct dimension, and D is natural number, and D ＜ N, D span are 50-70；
The robust forecasting model modeling module is completed to establish forecasting model using following process：
X, Y that data preprocessing module is obtained substitute into following linear equation：
Wherein
Weight factor viCalculated by following formula：
WhereinIt is error variance ξiThe estimation of standard deviation, c1,c2For constant；
Solve to obtain function f (x) to be estimated：
Wherein, M is the number of supporting vector, 1v=[1 ..., 1]T,Subscript T representing matrixs turn
Put,It is Lagrange multiplier, b*It is amount of bias, K=exp (- | | xi-xj||/θ2), wherein i=1 ..., M, j=1 ..., M,With exp (- | | x-xi||/θ2) be SVMs kernel function, xjFor j-th of radar sea clutter
Echo-signal amplitude, θ are nuclear parameters, and x represents input variable, and γ is penalty coefficient；
Intelligent optimizing module, the nuclear parameter θ and penalty coefficient γ of robust forecasting model are carried out using artificial bee colony algorithm
Optimization, completed using following process：
Step 1：The parameter of artificial bee colony algorithm is initialized, if nectar source number P, greatest iteration number itermax, initial ranging sky
Between minimum value and maximum LdAnd Ud；The feasible solution of the positional representation problem in nectar source, because model has two parameters needs excellent
Change, so position piDimension for 2 dimension, as the following formula at random generation nectar source position pi=(pi1,pi2), put primary iteration number
Iter=0；
pij=Ld+rand()*(Ud-Ld) (i=1,2 ..., P, j=1,2)
Step 2：For nectar source piDistribution one leads honeybee, scans for as the following formula, produces new nectar source Vi；
Step 3：Calculate ViFitness value, according to greediness selection method determine retain nectar source；
Step 4：Calculate lead the nectar source that honeybee is found by more with probability；
Step 5：Follow honeybee to use with leading honeybee identical mode to scan for, determine to retain according to the method for greediness selection
Nectar source；
Step 6：Judge nectar source ViWhether the condition that satisfaction is abandoned, such as meet, it is corresponding to lead honeybee role to be changed into scouting
Honeybee, otherwise pass directly to step 8；
Step 7：Search bee randomly generates new nectar source；
Step 8：Iter=iter+1, judge whether to have been maxed out iterations, optimized parameter exported if meeting,
Otherwise step 2 is gone to.
Wherein, nectar source number is 100, the minimum value and maximum 0 and 100 in initial ranging space, maximum iteration 100.
Module of target detection, to carry out target detection, completed using following process：
1) gather D sea clutter echo-signal amplitude in sampling instant t and obtain TX=[xt-D+1,…,xt], xt-D+1Represent the
The sea clutter echo-signal amplitude of t-D+1 sampling instants, xtRepresent the sea clutter echo-signal amplitude of t sampling instants；
2) it is normalized；
3) substitute into the function f (x) to be estimated that robust forecasting model modeling module obtains and sampling instant (t+1) is calculated
Sea clutter predicted value.
4) difference e of sea clutter predicted value and radar return measured value is calculated, calculates control limit Qα：
Wherein, α is confidence level, θ1,θ2,θ3,h0It is intermediate variable, λj iRepresent the i of j-th of characteristic value of covariance matrix
Power, k are sample dimensions, CαIt is the statistics that normal distribution confidence level is α；
5) detection judgement is carried out：Work as e2Difference is more than control limit QαWhen, there is target in the point, otherwise without target.
Model modification module, to by the sampling time interval of setting, gathered data, by obtained measured data and model
Predicted value compares, if relative error is more than 10%, new data is added into training sample data, updates forecasting model.
Result display module, the testing result of module of target detection to be shown in host computer.
Radar naval target used in a kind of Intelligent radar sea target detection system based on artificial bee colony algorithm is examined
Survey method, described method comprise the following steps：
(1) radar is irradiated to detected marine site, and by radar sea clutter data storage to described database；
(2) N number of radar sea clutter echo-signal amplitude x is gathered from databaseiAs training sample, i=1 ..., N；
(3) training sample is normalized, obtains normalizing amplitude
Wherein, minx represents the minimum value in training sample, and maxx represents the maximum in training sample；
(4) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y：
Wherein, D represents reconstruct dimension, and D is natural number, and D ＜ N, D span are 50-70；
(5) obtained X, Y are substituted into following linear equation：
Wherein
Weight factor viCalculated by following formula：
WhereinIt is error variance ξiThe estimation of standard deviation, c1,c2For constant；
Solve to obtain function f (x) to be estimated：
Wherein, M is the number of supporting vector, 1v=[1 ..., 1]T,Subscript T representing matrixs turn
Put,It is Lagrange multiplier, b*It is amount of bias, K=exp (- | | xi-xj||/θ2), wherein i=1 ..., M, j=1 ..., M,With exp (- | | x-xi||/θ2) be SVMs kernel function, xjFor j-th of radar sea clutter
Echo-signal amplitude, θ are nuclear parameters, and x represents input variable, and γ is penalty coefficient；
(6) manually ant colony algorithm optimizes to the nuclear parameter θ and penalty coefficient γ of step (5), using following process
Complete：
(6.1) parameter of artificial bee colony algorithm is initialized, if nectar source number P, greatest iteration number itermax, initial ranging space
Minimum value and maximum LdAnd Ud；The feasible solution of the positional representation problem in nectar source, because model has two parameters to need to optimize,
So position piDimension for 2 dimension, as the following formula at random generation nectar source position pi=(pi1,pi2), put primary iteration number iter
=0；
pij=Ld+rand()*(Ud-Ld) (i=1,2 ..., P, j=1,2)
(6.2) it is nectar source piDistribution one leads honeybee, scans for as the following formula, produces new nectar source Vi；
(6.3) V is calculatediFitness value, according to greediness selection method determine retain nectar source；
(6.4) calculate lead the nectar source that honeybee is found by more with probability；
(6.5) follow honeybee to use with leading honeybee identical mode to scan for, determine to retain according to the method for greediness selection
Nectar source；
(6.6) nectar source V is judgediWhether the condition that satisfaction is abandoned, such as meet, it is corresponding to lead honeybee role to be changed into scouting
Honeybee, otherwise pass directly to step (6.8)；
(6.7) search bee randomly generates new nectar source；
(6.8) iter=iter+1, judge whether to have been maxed out iterations, optimized parameter exported if meeting,
Otherwise step (6.2) is gone to.
Wherein, nectar source number is 100, the minimum value and maximum 0 and 100 in initial ranging space, maximum iteration 100.
(7) gather D sea clutter echo-signal amplitude in sampling instant t and obtain TX=[xt-D+1,…,xt], xt-D+1Represent
The sea clutter echo-signal amplitude of t-D+1 sampling instants, xtRepresent the sea clutter echo-signal amplitude of t sampling instants；
(8) it is normalized；
(9) the sea clutter forecast that sampling instant (t+1) is calculated in the function f (x) to be estimated that step (5) obtains is substituted into
Value.
(10) difference e of sea clutter predicted value and radar return measured value is calculated, calculates control limit Qα：
Wherein, α is confidence level, θ1,θ2,θ3,h0It is intermediate variable, λj iRepresent the i of j-th of characteristic value of covariance matrix
Power, k are sample dimensions, CαIt is the statistics that normal distribution confidence level is α；
(11) detection judgement is carried out：Work as e2Difference is more than control limit QαWhen, there is target in the point, otherwise without target.
(12) the sampling time interval gathered data of setting is pressed, by obtained measured data compared with model prediction value, such as
Fruit relative error is more than 10%, then new data is added into training sample data, updates forecasting model.
The present invention technical concept be：The present invention is directed to the chaotic characteristic of radar sea clutter, and radar sea clutter data are entered
Row reconstruct, and nonlinear fitting is carried out to the data after reconstruct, the forecasting model of radar sea clutter is established, calculates radar sea clutter
Predicted value and measured value difference, when having the error in the presence of target to be noticeably greater than not have target, introduce artificial bee colony algorithm,
So as to realize that the strong intelligent Target under sea clutter background detects.
Beneficial effects of the present invention are mainly manifested in：1st, can on-line checking naval target；2nd, detection method used only needs
Less sample；3rd, it is intelligent it is strong, be affected by human factors it is small.
Brief description of the drawings
Fig. 1 is the hardware structure diagram of system proposed by the invention；
Fig. 2 is the functional block diagram of host computer proposed by the invention.
Embodiment
The invention will be further described below in conjunction with the accompanying drawings.The embodiment of the present invention is used for illustrating the present invention, without
It is to limit the invention, in the protection domain of spirit and claims of the present invention, any is repaiied to what the present invention made
Change and change, both fall within protection scope of the present invention.
Embodiment 1
Reference picture 1, Fig. 2, a kind of Intelligent radar sea target detection system based on artificial bee colony algorithm, including radar 1,
Database 2 and host computer 3, radar 1, database 2 and host computer 3 are sequentially connected, and the radar 1 is shone detected marine site
Penetrate, and radar sea clutter data storage to described database 2, described host computer 3 are included：
Data preprocessing module 4, to carry out radar sea clutter data prediction, completed using following process：
1) N number of radar sea clutter echo-signal amplitude x is gathered from databaseiAs training sample, i=1 ..., N；
2) training sample is normalized, obtains normalizing amplitude
Wherein, minx represents the minimum value in training sample, and maxx represents the maximum in training sample；
3) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y：
Wherein, D represents reconstruct dimension, and D is natural number, and D ＜ N, D span are 50-70；
Robust forecasting model modeling module 5, to establish forecasting model, completed using following process：
Obtained X, Y are substituted into following linear equation：
Wherein
Weight factor viCalculated by following formula：
WhereinIt is error variance ξiThe estimation of standard deviation, c1,c2For constant
Solve to obtain function f (x) to be estimated：
Wherein, M is the number of supporting vector, 1v=[1 ..., 1]T,Subscript T representing matrixs turn
Put,It is Lagrange multiplier, b*It is amount of bias, K=exp (- | | xi-xj||/θ2), wherein i=1 ..., M, j=1 ..., M,With exp (- | | x-xi||/θ2) be SVMs kernel function, xjFor j-th of radar sea clutter
Echo-signal amplitude, θ are nuclear parameters, and x represents input variable, and γ is penalty coefficient；
Intelligent optimizing module 6, to the nuclear parameter θ and penalty coefficient γ using artificial bee colony algorithm to robust forecasting model
Optimize, completed using following process：
Step 1：The parameter of artificial bee colony algorithm is initialized, if nectar source number P, greatest iteration number itermax, initial ranging sky
Between minimum value and maximum LdAnd Ud；The feasible solution of the positional representation problem in nectar source, because model has two parameters needs excellent
Change, so position piDimension for 2 dimension, as the following formula at random generation nectar source position pi=(pi1,pi2), put primary iteration number
Iter=0；
pij=Ld+rand()*(Ud-Ld) (i=1,2 ..., P, j=1,2)
Step 2：For nectar source piDistribution one leads honeybee, scans for as the following formula, produces new nectar source Vi；
Step 3：Calculate ViFitness value, according to greediness selection method determine retain nectar source；
Step 4：Calculate lead the nectar source that honeybee is found by more with probability；
Step 5：Follow honeybee to use with leading honeybee identical mode to scan for, determine to retain according to the method for greediness selection
Nectar source；
Step 6：Judge nectar source ViWhether the condition that satisfaction is abandoned, such as meet, it is corresponding to lead honeybee role to be changed into scouting
Honeybee, otherwise pass directly to step 8；
Step 7：Search bee randomly generates new nectar source；
Step 8：Iter=iter+1, judge whether to have been maxed out iterations, optimized parameter exported if meeting,
Otherwise step 2 is gone to.
Wherein, nectar source number is 100, the minimum value and maximum 0 and 100 in initial ranging space, maximum iteration 100.
Module of target detection 7, to carry out target detection, completed using following process：
1) gather D sea clutter echo-signal amplitude in sampling instant t and obtain TX=[xt-D+1,…,xt], xt-D+1Represent the
The sea clutter echo-signal amplitude of t-D+1 sampling instants, xtRepresent the sea clutter echo-signal amplitude of t sampling instants；
2) it is normalized；
3) substitute into the function f (x) that robust forecasting model modeling module obtains and obtain the sea clutter forecast of sampling instant (t+1)
Value；
4) difference e of sea clutter predicted value and radar return measured value is calculated, calculates control limit Qα：
Wherein, α is confidence level, θ1,θ2,θ3,h0It is intermediate variable, λj iRepresent the i of j-th of characteristic value of covariance matrix
Power, k are sample dimensions, CαIt is the statistics that normal distribution confidence level is α；
5) detection judgement is carried out：Work as e2Difference is more than control limit QαWhen, there is target in the point, otherwise without target.
Model modification module 8, to by the sampling time interval gathered data of setting, by obtained measured data and model
Predicted value compares, if relative error is more than 10%, new data is added into training sample data, updates forecasting model.
Result display module 9, the testing result of module of target detection to be shown in host computer.
The hardware components of the host computer 3 include：I/O elements, for the collection of data and the transmission of information；Data storage
Device, data sample and operational factor needed for storage running etc.；The software program of functional module is realized in program storage, storage；
Arithmetic unit, configuration processor, realize the function of specifying；Display module, show the parameter and testing result of setting.
Embodiment 2
Reference picture 1, Fig. 2, a kind of Intelligent radar sea target detection method based on artificial bee colony algorithm, described method
Comprise the following steps：
(1) radar is irradiated to detected marine site, and by radar sea clutter data storage to described database；
(2) N number of radar sea clutter echo-signal amplitude x is gathered from databaseiAs training sample, i=1 ..., N；
(3) training sample is normalized, obtains normalizing amplitude
Wherein, minx represents the minimum value in training sample, and maxx represents the maximum in training sample；
(4) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y：
Wherein, D represents reconstruct dimension, and D is natural number, and D ＜ N, D span are 50-70；
(5) obtained X, Y are substituted into following linear equation：
Wherein
Weight factor viCalculated by following formula：
WhereinIt is error variance ξiThe estimation of standard deviation, c1,c2For constant；
Solve to obtain function f (x) to be estimated：
Wherein, M is the number of supporting vector, 1v=[1 ..., 1]T,Subscript T representing matrixs turn
Put,It is Lagrange multiplier, b*It is amount of bias, K=exp (- | | xi-xj||/θ2), wherein i=1 ..., M, j=1 ..., M,With exp (- | | x-xi||/θ2) be SVMs kernel function, xjFor j-th of radar sea clutter
Echo-signal amplitude, θ are nuclear parameters, and x represents input variable, and γ is penalty coefficient；
(6) manually ant colony algorithm optimizes to the nuclear parameter θ and penalty coefficient γ of step (5), using following process
Complete：
(6.1) parameter of artificial bee colony algorithm is initialized, if nectar source number P, greatest iteration number itermax, initial ranging space
Minimum value and maximum LdAnd Ud；The feasible solution of the positional representation problem in nectar source, because model has two parameters to need to optimize,
So position piDimension for 2 dimension, as the following formula at random generation nectar source position pi=(pi1,pi2), put primary iteration number iter
=0；
pij=Ld+rand()*(Ud-Ld) (i=1,2 ..., P, j=1,2)
(6.2) it is nectar source piDistribution one leads honeybee, scans for as the following formula, produces new nectar source Vi；
(6.3) V is calculatediFitness value, according to greediness selection method determine retain nectar source；
(6.4) calculate lead the nectar source that honeybee is found by more with probability；
(6.5) follow honeybee to use with leading honeybee identical mode to scan for, determine to retain according to the method for greediness selection
Nectar source；
(6.6) nectar source V is judgediWhether the condition that satisfaction is abandoned, such as meet, it is corresponding to lead honeybee role to be changed into scouting
Honeybee, otherwise pass directly to step (6.8)；
(6.7) search bee randomly generates new nectar source；
(6.8) iter=iter+1, judge whether to have been maxed out iterations, optimized parameter exported if meeting,
Otherwise step (6.2) is gone to.
Wherein, nectar source number is 100, the minimum value and maximum 0 and 100 in initial ranging space, maximum iteration 100.
(7) gather D sea clutter echo-signal amplitude in sampling instant t and obtain TX=[xt-D+1,…,xt], xt-D+1Represent
The sea clutter echo-signal amplitude of t-D+1 sampling instants, xtRepresent the sea clutter echo-signal amplitude of t sampling instants；
(8) it is normalized；
(9) the sea clutter forecast that sampling instant (t+1) is calculated in the function f (x) to be estimated that step (5) obtains is substituted into
Value.
(10) difference e of sea clutter predicted value and radar return measured value is calculated, calculates control limit Qα：
Wherein, α is confidence level, θ1,θ2,θ3,h0It is intermediate variable, λj iRepresent the i of j-th of characteristic value of covariance matrix
Power, k are sample dimensions, CαIt is the statistics that normal distribution confidence level is α；
(11) detection judgement is carried out：Work as e2Difference is more than control limit QαWhen, there is target in the point, otherwise without target.
(12) the sampling time interval gathered data of setting is pressed, by obtained measured data compared with model prediction value, such as
Fruit relative error is more than 10%, then new data is added into training sample data, updates forecasting model.
From above example, the present invention establishes Intelligent radar sea target detection system and method, can be online
Detect radar target；And detection method used only needs less sample；In addition, reduce the influence of human factor, intelligence
Property high, strong robustness.
Claims (2)
1. a kind of Intelligent radar sea target detection system based on artificial bee colony algorithm, including radar, database and upper
Machine, radar, database and host computer are sequentially connected, it is characterised in that：The radar is irradiated to detected marine site, and by thunder
Up to sea clutter data storage to described database, described host computer includes data preprocessing module, robust forecasting model is built
Mould module, intelligent optimizing module, module of target detection, model modification module and result display module：
The data preprocessing module, to carry out radar sea clutter data prediction, completed using following process：
(1) radar is irradiated to detected marine site, and by radar sea clutter data storage to described database；
(2) N number of radar sea clutter echo-signal amplitude x is gathered from databaseiAs training sample, i=1 ..., N；
(3) training sample is normalized, obtains normalizing amplitude
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(4) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y：
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Wherein, D represents reconstruct dimension, and D is natural number, and D ＜ N, D span are 50-70；
The robust forecasting model modeling module is completed to establish forecasting model using following process：
X, Y that data preprocessing module is obtained substitute into following linear equation：
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<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mi>Y</mi>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein
Weight factor viCalculated by following formula：
WhereinIt is error variance ξiThe estimation of standard deviation, c1,c2For constant；
Solve to obtain function f (x) to be estimated：
<mrow>
<mi>f</mi>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>M</mi>
</munderover>
<msup>
<msub>
<mi>&alpha;</mi>
<mi>i</mi>
</msub>
<mo>*</mo>
</msup>
<mi>exp</mi>
<mrow>
<mo>(</mo>
<mo>-</mo>
<mo>|</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>|</mo>
<mo>|</mo>
<mo>/</mo>
<msup>
<mi>&theta;</mi>
<mn>2</mn>
</msup>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msup>
<mi>b</mi>
<mo>*</mo>
</msup>
</mrow>
Wherein, M is the number of supporting vector, 1v=[1 ..., 1]T,The transposition of subscript T representing matrixs,It is Lagrange multiplier, b*It is amount of bias, K=exp (- | | xi-xj||/θ2), wherein i=1 ..., M, j=1 ..., M,With exp (- | | x-xi||/θ2) be SVMs kernel function, xjFor j-th of radar sea clutter
Echo-signal amplitude, θ are nuclear parameters, and x represents input variable, and γ is penalty coefficient；
The intelligent optimizing module, to the nuclear parameter θ and penalty coefficient γ using artificial bee colony algorithm to robust forecasting model
Optimize, completed using following process：
(A) parameter of artificial bee colony algorithm is initialized, if nectar source number P, greatest iteration number itermax, the minimum in initial ranging space
Value and maximum LdAnd Ud；The feasible solution of the positional representation problem in nectar source, because model has two parameters to need to optimize, so position
Put piDimension for 2 dimension, as the following formula at random generation nectar source position pi=(pi1,pi2), put primary iteration number iter=0；
pij=Ld+rand()*(Ud-Ld) (i=1,2 ..., P, j=1,2)
(B) it is nectar source piDistribution one leads honeybee, scans for as the following formula, produces new nectar source Vi；
(C) V is calculatediFitness value, according to greediness selection method determine retain nectar source；
(D) calculate lead the nectar source that honeybee is found by more with probability；
(E) honeybee is followed to use with leading honeybee identical mode to scan for, the nectar source for determining to retain according to the method for greediness selection；
(F) nectar source V is judgediWhether the condition that satisfaction is abandoned, such as meet, it is corresponding to lead honeybee role to be changed into search bee, otherwise directly
It is switched to (H)
(G) search bee randomly generates new nectar source；
(H) iter=iter+1, judge whether to have been maxed out iterations, export optimized parameter if meeting, otherwise turn
To step (B).
Wherein, nectar source number is 100, the minimum value and maximum 0 and 100 in initial ranging space, maximum iteration 100.
The module of target detection, to carry out target detection, completed using following process：
(a) gather D sea clutter echo-signal amplitude in sampling instant t and obtain TX=[xt-D+1,...,xt], xt-D+1Represent t-
The sea clutter echo-signal amplitude of D+1 sampling instants, xtThe sea clutter echo-signal amplitude of t sampling instants is represented, TX is represented
Signal amplitude matrix of the sea clutter from t-D+1 sampling instants to t sampling instants；
(b) it is normalized；
<mrow>
<mover>
<mrow>
<mi>T</mi>
<mi>X</mi>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>=</mo>
<mfrac>
<mrow>
<mi>T</mi>
<mi>X</mi>
<mo>-</mo>
<mi>min</mi>
<mi> </mi>
<mi>x</mi>
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<mi>x</mi>
<mo>-</mo>
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<mi>x</mi>
</mrow>
</mfrac>
</mrow>
(c) sea that sampling instant (t+1) is calculated in the function f (x) to be estimated that robust forecasting model modeling module obtains is substituted into
Clutter predicted value.
(d) difference e of sea clutter predicted value and radar return measured value is calculated, calculates control limit Qα：
<mrow>
<msub>
<mi>Q</mi>
<mi>&alpha;</mi>
</msub>
<mo>=</mo>
<msub>
<mi>&theta;</mi>
<mn>1</mn>
</msub>
<msup>
<mrow>
<mo>&lsqb;</mo>
<mfrac>
<mrow>
<msub>
<mi>C</mi>
<mi>&alpha;</mi>
</msub>
<msub>
<mi>h</mi>
<mn>0</mn>
</msub>
<msqrt>
<mrow>
<mn>2</mn>
<msub>
<mi>&theta;</mi>
<mn>2</mn>
</msub>
</mrow>
</msqrt>
</mrow>
<msub>
<mi>&theta;</mi>
<mn>1</mn>
</msub>
</mfrac>
<mo>+</mo>
<mn>1</mn>
<mo>+</mo>
<mfrac>
<mrow>
<msub>
<mi>&theta;</mi>
<mn>2</mn>
</msub>
<msub>
<mi>h</mi>
<mn>0</mn>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>h</mi>
<mn>0</mn>
</msub>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<msub>
<mi>&theta;</mi>
<mn>2</mn>
</msub>
</mfrac>
<mo>&rsqb;</mo>
</mrow>
<mfrac>
<mn>1</mn>
<msub>
<mi>h</mi>
<mn>0</mn>
</msub>
</mfrac>
</msup>
</mrow>
<mrow>
<msub>
<mi>&theta;</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msubsup>
<mi>&lambda;</mi>
<mi>j</mi>
<mi>i</mi>
</msubsup>
<mo>,</mo>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>3</mn>
</mrow>
<mrow>
<msub>
<mi>h</mi>
<mn>0</mn>
</msub>
<mo>=</mo>
<mn>1</mn>
<mo>-</mo>
<mfrac>
<mrow>
<mn>2</mn>
<msub>
<mi>&theta;</mi>
<mn>1</mn>
</msub>
<msub>
<mi>&theta;</mi>
<mn>3</mn>
</msub>
</mrow>
<mrow>
<mn>3</mn>
<msubsup>
<mi>&theta;</mi>
<mn>2</mn>
<mn>2</mn>
</msubsup>
</mrow>
</mfrac>
</mrow>
Wherein, α is confidence level, θ1,θ2,θ3,h0It is intermediate variable, λj iThe i powers of j-th of characteristic value of covariance matrix are represented,
K is sample dimension, CαIt is the statistics that normal distribution confidence level is α；
(e) detection judgement is carried out：Work as e2Difference is more than control limit QαWhen, there is target in the point, otherwise without target.
The model modification module, to by the sampling time interval gathered data of setting, by obtained measured data and model
Predicted value compares, if relative error is more than 10%, new data is added into training sample data, updates forecasting model.
The result display module, the testing result of module of target detection to be shown in host computer.
2. thunder used in the Intelligent radar sea target detection system based on artificial bee colony algorithm described in a kind of claim 1
Up to method for detecting targets at sea, it is characterised in that：Described method comprises the following steps：
(1) radar is irradiated to detected marine site, and by radar sea clutter data storage to described database；
(2) N number of radar sea clutter echo-signal amplitude x is gathered from databaseiAs training sample, i=1 ..., N；
(3) training sample is normalized, obtains normalizing amplitude
<mrow>
<msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<mi>min</mi>
<mi> </mi>
<mi>x</mi>
</mrow>
<mrow>
<mi>max</mi>
<mi> </mi>
<mi>x</mi>
<mo>-</mo>
<mi>min</mi>
<mi> </mi>
<mi>x</mi>
</mrow>
</mfrac>
</mrow>
Wherein, minx represents the minimum value in training sample, and maxx represents the maximum in training sample；
(4) training sample after normalization is reconstructed, respectively obtains input matrix X and corresponding output matrix Y：
<mrow>
<mi>Y</mi>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>D</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>D</mi>
<mo>+</mo>
<mn>2</mn>
</mrow>
</msub>
</mtd>
</mtr>
<mtr>
<mtd>
<mtable>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
</mtable>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mi>N</mi>
</msub>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein, D represents reconstruct dimension, and D is natural number, and D ＜ N, D span are 50-70；
(5) obtained X, Y are substituted into following linear equation：
<mrow>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<msubsup>
<mn>1</mn>
<mi>v</mi>
<mi>T</mi>
</msubsup>
</mtd>
</mtr>
<mtr>
<mtd>
<msub>
<mn>1</mn>
<mi>v</mi>
</msub>
</mtd>
<mtd>
<mrow>
<mi>K</mi>
<mo>+</mo>
<msub>
<mi>V</mi>
<mi>&gamma;</mi>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<msup>
<mi>b</mi>
<mo>*</mo>
</msup>
</mtd>
</mtr>
<mtr>
<mtd>
<msup>
<mi>&alpha;</mi>
<mo>*</mo>
</msup>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
</mtr>
<mtr>
<mtd>
<mi>Y</mi>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein
Weight factor viCalculated by following formula：
WhereinIt is error variance ξiThe estimation of standard deviation, c1,c2For constant；
Solve to obtain function f (x) to be estimated：
<mrow>
<mi>f</mi>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>M</mi>
</munderover>
<msup>
<msub>
<mi>&alpha;</mi>
<mi>i</mi>
</msub>
<mo>*</mo>
</msup>
<mi>exp</mi>
<mrow>
<mo>(</mo>
<mo>-</mo>
<mo>|</mo>
<mo>|</mo>
<mi>x</mi>
<mo>-</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>|</mo>
<mo>|</mo>
<mo>/</mo>
<msup>
<mi>&theta;</mi>
<mn>2</mn>
</msup>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msup>
<mi>b</mi>
<mo>*</mo>
</msup>
</mrow>
Wherein, M is the number of supporting vector, 1v=[1 ..., 1]T,The transposition of subscript T representing matrixs,It is Lagrange multiplier, b*It is amount of bias, K=exp (- | | xi-xj||/θ2), wherein i=1 ..., M, j=1 ..., M,With exp (- | | x-xi||/θ2) be SVMs kernel function, xjFor j-th of radar sea clutter
Echo-signal amplitude, θ are nuclear parameters, and x represents input variable, and γ is penalty coefficient；
(6) manually ant colony algorithm optimizes to the nuclear parameter θ and penalty coefficient γ of step (5), is completed using following process：
(6.1) parameter of artificial bee colony algorithm is initialized, if nectar source number P, greatest iteration number itermax, initial ranging space is most
Small value and maximum LdAnd Ud；The feasible solution of the positional representation problem in nectar source, because model has two parameters to need to optimize, so
Position piDimension for 2 dimension, as the following formula at random generation nectar source position pi=(pi1,pi2), put primary iteration number iter=0；
pij=Ld+rand()*(Ud-Ld) (i=1,2 ..., P, j=1,2)
(6.2) it is nectar source piDistribution one leads honeybee, scans for as the following formula, produces new nectar source Vi；
(6.3) V is calculatediFitness value, according to greediness selection method determine retain nectar source；
(6.4) calculate lead the nectar source that honeybee is found by more with probability；
(6.5) honeybee is followed to use with leading honeybee identical mode to scan for, the honey for determining to retain according to the method for greediness selection
Source；
(6.6) nectar source V is judgediWhether the condition that satisfaction is abandoned, such as meet, it is corresponding to lead honeybee role to be changed into search bee, otherwise
Pass directly to step (6.8)；
(6.7) search bee randomly generates new nectar source；
(6.8) iter=iter+1, judge whether to have been maxed out iterations, export optimized parameter if meeting, otherwise
Go to step (6.2).
Wherein, nectar source number is 100, the minimum value and maximum 0 and 100 in initial ranging space, maximum iteration 100.
(7) gather D sea clutter echo-signal amplitude in sampling instant t and obtain TX=[xt-D+1,...,xt], xt-D+1Represent t-
The sea clutter echo-signal amplitude of D+1 sampling instants, xtRepresent the sea clutter echo-signal amplitude of t sampling instants；
(8) it is normalized；
<mrow>
<mover>
<mrow>
<mi>T</mi>
<mi>X</mi>
</mrow>
<mo>&OverBar;</mo>
</mover>
<mo>=</mo>
<mfrac>
<mrow>
<mi>T</mi>
<mi>X</mi>
<mo>-</mo>
<mi>min</mi>
<mi> </mi>
<mi>x</mi>
</mrow>
<mrow>
<mi>max</mi>
<mi> </mi>
<mi>x</mi>
<mo>-</mo>
<mi>min</mi>
<mi> </mi>
<mi>x</mi>
</mrow>
</mfrac>
</mrow>
(9) the sea clutter predicted value that sampling instant (t+1) is calculated in the function f (x) to be estimated that step (5) obtains is substituted into.
(10) difference e of sea clutter predicted value and radar return measured value is calculated, calculates control limit Qα：
<mrow>
<msub>
<mi>Q</mi>
<mi>&alpha;</mi>
</msub>
<mo>=</mo>
<msub>
<mi>&theta;</mi>
<mn>1</mn>
</msub>
<msup>
<mrow>
<mo>&lsqb;</mo>
<mfrac>
<mrow>
<msub>
<mi>C</mi>
<mi>&alpha;</mi>
</msub>
<msub>
<mi>h</mi>
<mn>0</mn>
</msub>
<msqrt>
<mrow>
<mn>2</mn>
<msub>
<mi>&theta;</mi>
<mn>2</mn>
</msub>
</mrow>
</msqrt>
</mrow>
<msub>
<mi>&theta;</mi>
<mn>1</mn>
</msub>
</mfrac>
<mo>+</mo>
<mn>1</mn>
<mo>+</mo>
<mfrac>
<mrow>
<msub>
<mi>&theta;</mi>
<mn>2</mn>
</msub>
<msub>
<mi>h</mi>
<mn>0</mn>
</msub>
<mrow>
<mo>(</mo>
<msub>
<mi>h</mi>
<mn>0</mn>
</msub>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<msub>
<mi>&theta;</mi>
<mn>2</mn>
</msub>
</mfrac>
<mo>&rsqb;</mo>
</mrow>
<mfrac>
<mn>1</mn>
<msub>
<mi>h</mi>
<mn>0</mn>
</msub>
</mfrac>
</msup>
</mrow>
<mrow>
<msub>
<mi>&theta;</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msubsup>
<mi>&lambda;</mi>
<mi>j</mi>
<mi>i</mi>
</msubsup>
<mo>,</mo>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>3</mn>
</mrow>
<mrow>
<msub>
<mi>h</mi>
<mn>0</mn>
</msub>
<mo>=</mo>
<mn>1</mn>
<mo>-</mo>
<mfrac>
<mrow>
<mn>2</mn>
<msub>
<mi>&theta;</mi>
<mn>1</mn>
</msub>
<msub>
<mi>&theta;</mi>
<mn>3</mn>
</msub>
</mrow>
<mrow>
<mn>3</mn>
<msubsup>
<mi>&theta;</mi>
<mn>2</mn>
<mn>2</mn>
</msubsup>
</mrow>
</mfrac>
</mrow>
Wherein, α is confidence level, θ1,θ2,θ3,h0It is intermediate variable, λj iThe i powers of j-th of characteristic value of covariance matrix are represented,
K is sample dimension, CαIt is the statistics that normal distribution confidence level is α；
(11) detection judgement is carried out：Work as e2Difference is more than control limit QαWhen, there is target in the point, otherwise without target.
(12) by the sampling time interval gathered data of setting, by obtained measured data compared with model prediction value, if phase
10% is more than to error, then new data is added into training sample data, updates forecasting model.
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| CN109669169A (en) * | 2018-12-05 | 2019-04-23 | 南京信息工程大学 | A kind of weak target signal detection method under sea clutter background |
| CN109669169B (en) * | 2018-12-05 | 2022-11-08 | 南京信息工程大学 | Method for detecting weak target signal under sea clutter background |
| CN110940970A (en) * | 2019-11-06 | 2020-03-31 | 河海大学 | MIMO radar target detection method and system for floating oil sea surface |
| CN112162286A (en) * | 2020-09-29 | 2021-01-01 | 中国船舶重工集团公司第七二四研究所 | Radar detection environment estimation method based on artificial intelligence |
| CN112162286B (en) * | 2020-09-29 | 2023-08-01 | 中国船舶集团有限公司第七二四研究所 | Radar detection environment estimation method based on artificial intelligence |
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