CN107894584A - A kind of Intelligent radar sea target detection system and method based on mixing artificial bee colony algorithm - Google Patents
A kind of Intelligent radar sea target detection system and method based on mixing artificial bee colony algorithm Download PDFInfo
- Publication number
- CN107894584A CN107894584A CN201711115202.0A CN201711115202A CN107894584A CN 107894584 A CN107894584 A CN 107894584A CN 201711115202 A CN201711115202 A CN 201711115202A CN 107894584 A CN107894584 A CN 107894584A
- Authority
- CN
- China
- Prior art keywords
- mrow
- msub
- mtd
- mtr
- embe
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- 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
- G01S7/28—Details of pulse systems
- G01S7/285—Receivers
- G01S7/292—Extracting wanted echo-signals
- G01S7/2923—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods
- G01S7/2927—Extracting wanted echo-signals based on data belonging to a number of consecutive radar periods by deriving and controlling a threshold value
-
- 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
- G01S7/28—Details of pulse systems
- G01S7/285—Receivers
- G01S7/295—Means for transforming co-ordinates or for evaluating data, e.g. using computers
-
- 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
- G01S7/35—Details of non-pulse systems
- G01S7/352—Receivers
- G01S7/354—Extracting wanted echo-signals
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses a kind of Intelligent radar sea target detection system and method based on mixing 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:Complex characteristics of the present invention for naval target detection, radar clutter data are reconstructed, and target data is detected, mixing artificial bee colony algorithm is introduced, 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 intelligence based on mixing artificial bee colony algorithm
Radar marine target detection 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 based on mixing artificial bee colony algorithm
System and method.
The technical solution adopted for the present invention to solve the technical problems is:
It is a kind of based on mixing artificial bee colony algorithm Intelligent radar sea target detection system, including radar, database with
And host computer, radar, database and host computer are sequentially connected, the radar is irradiated to detected marine site, and by Radar Sea
Clutter data is stored into described database, and described host computer includes:
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, to establish forecasting model, completed 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, nuclear parameter θ and penalty coefficient γ using mixing artificial bee colony algorithm to robust forecasting model
Optimize, completed using following process:
Step 1:The parameter of initialization mixing artificial bee colony algorithm, if nectar source number P, greatest iteration number itermax, initially search
The minimum value and maximum L in rope spacedAnd Ud;The feasible solution of the positional representation problem in nectar source, because model has two parameter needs
Optimization, 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:Employ honeybee EMBEiNew explanation EMBE is produced with tabu search algorithmi', and calculate its fitness value
Step 3:Compare EMBEiAnd EMBEi', if EMBEi' it is better than EMBEi, then EMBE is usedi' replace EMBEi, and will employ
Hire the parameter emlimit of honeybeeiReset;Otherwise, EMBE is kepti' constant, by parameter emlimitiAdd 1;
Step 4:Follow honeybee ONBEiHoneybee EMBE is employed by roulette policy selectionpFollowed, and calculate its fitness
Value.
Step 5:By ONBEiWith employing honeybee EMBEpIt is compared, if ONBEiPreferably, then honeybee is employed with following honeybee to exchange
Role, that is, use ONBEiInstead of EMBEp, and by parameter emlimitpReset, parameter onlimitiAdd 1;If EMBEpIt is poor, then protect
Hold it is original employ honeybee constant, and use EMBEpInstead of ONBEi, by parameter emlimitpAdd 1, parameter onlimitiAdd 1;
Step 6:The solution to be abandoned is determined whether, is replaced originally if it is present searching for a new explanation by search bee
Solution;
Step 7:Record the optimal solution of this circulation;
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.
Mesh in Radar Sea used in a kind of Intelligent radar sea target detection system based on mixing artificial bee colony algorithm
Detection method is marked, described method comprises the following steps:
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;
4) 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,K=exp (- | | xi-xj||/
θ2), the transposition of subscript T representing matrixs,It is Lagrange multiplier, wherein, i=1 ..., M, j=1 ..., M, b*It is biasing
Amount,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;
5) the nuclear parameter θ and penalty coefficient γ of step 4) are optimized with mixing artificial bee colony algorithm, using following mistake
Journey is completed:
Step 1:The parameter of initialization mixing artificial bee colony algorithm, if nectar source number P, greatest iteration number itermax, initially search
The minimum value and maximum L in rope spacedAnd Ud;The feasible solution of the positional representation problem in nectar source, because model has two parameter needs
Optimization, 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:Employ honeybee EMBEiNew explanation EMBE is produced with tabu search algorithmi', and calculate its fitness value
Step 3:Compare EMBEiAnd EMBEi', if EMBEi' it is better than EMBEi, then EMBE is usedi' replace EMBEi, and will employ
Hire the parameter emlimit of honeybeeiReset;Otherwise, EMBE is kepti' constant, by parameter emlimitiAdd 1;
Step 4:Follow honeybee ONBEiHoneybee EMBE is employed by roulette policy selectionpFollowed, and calculate its fitness
Value.
Step 5:By ONBEiWith employing honeybee EMBEpIt is compared, if ONBEiPreferably, then honeybee is employed with following honeybee to exchange
Role, that is, use ONBEiInstead of EMBEp, and by parameter emlimitpReset, parameter onlimitiAdd 1;If EMBEpIt is poor, then protect
Hold it is original employ honeybee constant, and use EMBEpInstead of ONBEi, by parameter emlimitpAdd 1, parameter onlimitiAdd 1;
Step 6:The solution to be abandoned is determined whether, is replaced originally if it is present searching for a new explanation by search bee
Solution;
Step 7:Record the optimal solution of this circulation;
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.
6) 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;
7) it is normalized;
8) the sea clutter predicted value that sampling instant (t+1) is calculated in the function f (x) to be estimated that step 4) obtains is substituted into.
9) 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;
10) detection judgement is carried out:Work as e2Difference is more than control limit QαWhen, there is target in the point, otherwise without target.
11) the sampling time interval gathered data of setting is pressed, by obtained measured data compared with model prediction value, if
Relative error is more than 10%, then new data is added into training sample data, updates forecasting model.
Described method also includes:The testing result of module of target detection is shown in host computer in described step 10)
Show.
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 mixing 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 mixing artificial bee colony algorithm, including thunder
Up to 1, database 2 and host computer 3, radar 1, database 2 and host computer 3 are sequentially connected, and the radar 1 enters to detected marine site
Row irradiation, 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) 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 xi:
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, to establish forecasting model, completed 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 6, to be to the nuclear parameter θ of robust forecasting model and punishment using mixing artificial bee colony algorithm
Number γ is optimized, and is completed using following process:
Step 1:The parameter of initialization mixing artificial bee colony algorithm, if nectar source number P, greatest iteration number itermax, initially search
The minimum value and maximum L in rope spacedAnd Ud;The feasible solution of the positional representation problem in nectar source, because model has two parameter needs
Optimization, 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:Employ honeybee EMBEiNew explanation EMBE is produced with tabu search algorithmi', and calculate its fitness value
Step 3:Compare EMBEiAnd EMBEi', if EMBEi' it is better than EMBEi, then EMBE is usedi' replace EMBEi, and will employ
Hire the parameter emlimit of honeybeeiReset;Otherwise, EMBE is kepti' constant, by parameter emlimitiAdd 1;
Step 4:Follow honeybee ONBEiHoneybee EMBE is employed by roulette policy selectionpFollowed, and calculate its fitness
Value.
Step 5:By ONBEiWith employing honeybee EMBEpIt is compared, if ONBEiPreferably, then honeybee is employed with following honeybee to exchange
Role, that is, use ONBEiInstead of EMBEp, and by parameter emlimitpReset, parameter onlimitiAdd 1;If EMBEpIt is poor, then protect
Hold it is original employ honeybee constant, and use EMBEpInstead of ONBEi, by parameter emlimitpAdd 1, parameter onlimitiAdd 1;
Step 6:The solution to be abandoned is determined whether, is replaced originally if it is present searching for a new explanation by search bee
Solution;
Step 7:Record the optimal solution of this circulation;
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 mixing artificial bee colony algorithm are 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
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) the nuclear parameter θ and penalty coefficient γ of step (5) are optimized with mixing artificial bee colony algorithm, using as follows
Process is completed:
(6.1) parameter of initialization mixing artificial bee colony algorithm, if nectar source number P, greatest iteration number itermax, initial ranging
The minimum value and maximum L in spacedAnd 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)
(6.2) honeybee EMBE is employediNew explanation EMBE is produced with tabu search algorithmi', and calculate its fitness value
(6.3) EMBE is comparediAnd EMBEi', if EMBEi' it is better than EMBEi, then EMBE is usedi' replace EMBEi, and will employ
The parameter emlimit of honeybeeiReset;Otherwise, EMBE is kepti' constant, by parameter emlimitiAdd 1;
(6.4) honeybee ONBE is followediHoneybee EMBE is employed by roulette policy selectionpFollowed, and calculate its fitness
Value.
(6.5) by ONBEiWith employing honeybee EMBEpIt is compared, if ONBEiPreferably, then employ honeybee and follow honeybee to exchange angle
Color, that is, use ONBEiInstead of EMBEp, and by parameter emlimitpReset, parameter onlimitiAdd 1;If EMBEpIt is poor, then keep
It is original to employ honeybee constant, and use EMBEpInstead of ONBEi, by parameter emlimitpAdd 1, parameter onlimitiAdd 1;
(6.6) solution to be abandoned is determined whether, is replaced originally if it is present searching for a new explanation by search bee
Solution;
(6.7) optimal solution of this circulation is recorded;
(6.9) 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. it is a kind of based on mixing artificial bee colony algorithm Intelligent radar sea target detection system, including radar, database and
Host computer, radar, database and host computer are sequentially connected, it is characterised in that:The radar is irradiated to detected marine site, and
By radar sea clutter data storage to described database, described host computer includes data preprocessing module, robust forecast mould
Type modeling 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
<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>x</mi>
</mrow>
<mrow>
<mi>max</mi>
<mi>x</mi>
<mo>-</mo>
<mi>min</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>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</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;
The robust forecasting model modeling module, to establish forecasting model, completed using following process:
X, Y that data preprocessing module is obtained substitute 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>
<mo>&Sigma;</mo>
<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 be to the nuclear parameter θ of robust forecasting model and punishment using mixing artificial bee colony algorithm
Number γ is optimized, and is completed using following process:
(A) parameter of initialization mixing artificial bee colony algorithm, 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, institute
With 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)
(B) honeybee EMBE is employediNew explanation EMBE is produced with tabu search algorithmi', and calculate its fitness value
(C) EMBE is comparediAnd EMBEi', if EMBEi' it is better than EMBEi, then EMBE is usedi' replace EMBEi, and the ginseng that honeybee will be employed
Number emlimitiReset;Otherwise, EMBE is kepti' constant, by parameter emlimitiAdd 1;
(D) honeybee ONBE is followediHoneybee EMBE is employed by roulette policy selectionpFollowed, and calculate its fitness value.
(E) by ONBEiWith employing honeybee EMBEpIt is compared, if ONBEiPreferably, then employ honeybee and follow honeybee to exchange role, i.e.,
Use ONBEiInstead of EMBEp, and by parameter emlimitpReset, parameter onlimitiAdd 1;If EMBEpIt is poor, then keep original
Employ honeybee constant, and use EMBEpInstead of ONBEi, by parameter emlimitpAdd 1, parameter onlimitiAdd 1;
(F) solution to be abandoned is determined whether, if it is present searching for a new explanation by search bee replaces original solution;
(G) optimal solution of this circulation is recorded;
(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], TX represents sea clutter
From t-D+1 sampling instants to the signal amplitude matrix of t sampling instants;xt-D+1Represent the sea clutter of t-D+1 sampling instants
Echo-signal amplitude, xtRepresent the sea clutter echo-signal amplitude of 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>x</mi>
</mrow>
<mrow>
<mi>max</mi>
<mi>x</mi>
<mo>-</mo>
<mi>min</mi>
<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.
A kind of 2. radar of the Intelligent radar sea target detection system based on mixing artificial bee colony algorithm described in claim 1
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>x</mi>
</mrow>
<mrow>
<mi>max</mi>
<mi>x</mi>
<mo>-</mo>
<mi>min</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>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</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>
<mo>&Sigma;</mo>
<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) the nuclear parameter θ and penalty coefficient γ of step (5) are optimized with mixing artificial bee colony algorithm, using following process
Complete:
(6.1) parameter of initialization mixing artificial bee colony algorithm, 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) honeybee EMBE is employediNew explanation EMBE is produced with tabu search algorithmi', and calculate its fitness value
(6.3) EMBE is comparediAnd EMBEi', if EMBEi' it is better than EMBEi, then EMBE is usedi' replace EMBEi, and honeybee will be employed
Parameter emlimitiReset;Otherwise, EMBE is kepti' constant, by parameter emlimitiAdd 1;
(6.4) honeybee ONBE is followediHoneybee EMBE is employed by roulette policy selectionpFollowed, and calculate its fitness value.
(6.5) by ONBEiWith employing honeybee EMBEpIt is compared, if ONBEiPreferably, then employ honeybee and follow honeybee to exchange role,
Use ONBEiInstead of EMBEp, and by parameter emlimitpReset, parameter onlimitiAdd 1;If EMBEpIt is poor, then keep former
Employ honeybee constant, and use EMBEpInstead of ONBEi, by parameter emlimitpAdd 1, parameter onlimitiAdd 1;
(6.6) solution to be abandoned is determined whether, if it is present searching for a new explanation by search bee replaces original solution;
(6.7) optimal solution of this circulation is recorded;
(6.9) 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-D
The sea clutter echo-signal amplitude of+1 sampling instant, 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>x</mi>
</mrow>
<mrow>
<mi>max</mi>
<mi>x</mi>
<mo>-</mo>
<mi>min</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.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201711115202.0A CN107894584A (en) | 2017-11-13 | 2017-11-13 | A kind of Intelligent radar sea target detection system and method based on mixing artificial bee colony algorithm |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201711115202.0A CN107894584A (en) | 2017-11-13 | 2017-11-13 | A kind of Intelligent radar sea target detection system and method based on mixing artificial bee colony algorithm |
Publications (1)
Publication Number | Publication Date |
---|---|
CN107894584A true CN107894584A (en) | 2018-04-10 |
Family
ID=61805227
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201711115202.0A Pending CN107894584A (en) | 2017-11-13 | 2017-11-13 | A kind of Intelligent radar sea target detection system and method based on mixing artificial bee colony algorithm |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN107894584A (en) |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080007450A1 (en) * | 2006-07-07 | 2008-01-10 | Jonathan Yedidia | Method and system for determining unwrapped phases from noisy two-dimensional wrapped-phase images |
CN102147465A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | System and method for detecting sea target by chaos optimizing radar |
CN102147463A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | System and method for forecasting Qunzhi radar sea clutters |
CN102147464A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | Intelligent system and method for forecasting robust radar sea clutter |
CN102183749A (en) * | 2011-03-03 | 2011-09-14 | 浙江大学 | Sea target detecting system of adaptive radar and method thereof |
-
2017
- 2017-11-13 CN CN201711115202.0A patent/CN107894584A/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080007450A1 (en) * | 2006-07-07 | 2008-01-10 | Jonathan Yedidia | Method and system for determining unwrapped phases from noisy two-dimensional wrapped-phase images |
CN102147465A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | System and method for detecting sea target by chaos optimizing radar |
CN102147463A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | System and method for forecasting Qunzhi radar sea clutters |
CN102147464A (en) * | 2011-03-03 | 2011-08-10 | 浙江大学 | Intelligent system and method for forecasting robust radar sea clutter |
CN102183749A (en) * | 2011-03-03 | 2011-09-14 | 浙江大学 | Sea target detecting system of adaptive radar and method thereof |
Non-Patent Citations (2)
Title |
---|
张鹏: "多蜂群协同进化算法及其应用研究", 《中国硕士学位论文全文数据库 信息科技辑》 * |
鲁建厦 等: "混合人工蜂群算法在混流装配线排序中的应用", 《计算机集成制造***》 * |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN102147465B (en) | System and method for detecting sea target by chaos optimizing radar | |
CN102183749A (en) | Sea target detecting system of adaptive radar and method thereof | |
CN102147463B (en) | System and method for forecasting Qunzhi radar sea clutters | |
CN102147464B (en) | Intelligent system and method for forecasting robust radar sea clutter | |
CN107942312A (en) | A kind of Intelligent radar sea target detection system and method based on differential evolution invasive weed optimization algorithm | |
CN107656250A (en) | A kind of Intelligent radar sea target detection system and method based on artificial bee colony algorithm | |
CN108983179A (en) | A kind of radar marine target detection system of colony intelligence agility | |
Zhang et al. | Ship motion attitude prediction model based on IWOA-TCN-Attention | |
CN102183745B (en) | Sea clutter forecasting system and method for intelligent radar | |
CN102183746B (en) | Radar marine target detection system and method | |
CN102183751B (en) | Intelligent radar sea target detection system and method | |
CN108983181A (en) | A kind of radar marine target detection system of gunz optimizing | |
CN107942300A (en) | A kind of Intelligent radar sea target detection system and method based on improvement artificial bee colony algorithm | |
CN108983178A (en) | A kind of Intelligent radar sea target detection system that agility is adaptive | |
CN102183754B (en) | System and method for detecting sea target by using robust intelligent radar | |
CN107656251A (en) | A kind of Intelligent radar sea clutter forecast system and method based on improvement invasive weed optimized algorithm | |
CN102147466B (en) | Agile radar data processing system and method | |
CN107942303A (en) | A kind of Intelligent radar sea clutter forecast system and method based on improvement artificial bee colony algorithm | |
CN107894584A (en) | A kind of Intelligent radar sea target detection system and method based on mixing artificial bee colony algorithm | |
CN107976662A (en) | A kind of Intelligent radar sea target detection system and method based on invasive weed optimization algorithm | |
CN102183744A (en) | Swarm-intelligence radar sea target detecting system and method | |
Wang et al. | Intelligent recognition method of infrared imaging target of unmanned autonomous ship based on fuzzy mathematical model | |
CN102183747B (en) | Agile radar target detecting system and method | |
CN107976661A (en) | A kind of Intelligent radar sea clutter forecast system and method based on differential evolution invasive weed optimization algorithm | |
CN102183752A (en) | Self-adaptive radar marine clutter prediction system and method |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180410 |
|
RJ01 | Rejection of invention patent application after publication |