CN107966683A - A kind of the Intelligent radar sea clutter forecast system and method for the algorithm that leapfroged based on ADAPTIVE MIXED - Google Patents
A kind of the Intelligent radar sea clutter forecast system and method for the algorithm that leapfroged based on ADAPTIVE MIXED Download PDFInfo
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Abstract
The invention discloses a kind of the Intelligent radar sea clutter forecast system and method for the algorithm that leapfroged based on ADAPTIVE MIXED, system is sequentially connected by radar, database and host computer and formed, radar is irradiated detected marine site, and by radar sea clutter data storage to the database, the host computer includes data preprocessing module, robust forecasting model modeling module, intelligent optimizing module, sea clutter forecast module, discrimination model update module and result display module.The present invention is directed to the chaotic characteristic of radar sea clutter, radar sea clutter data are reconstructed, and nonlinear fitting is carried out to the data after reconstruct, introduce shuffled frog leaping algorithm method, so as to establish the intelligent prediction model of radar sea clutter, so as to on-line prediction radar sea clutter.Modeling method used in the present invention only needs less sample；And reduce the influence of human factor, and intelligent height, strong robustness.
Description
Technical Field
The invention relates to the field of radar data processing, in particular to an intelligent radar sea clutter forecasting system and method based on a self-adaptive mixed frog-leaping algorithm.
Background
Sea clutter, i.e. backscattered echoes from a piece of the sea surface illuminated by the radar transmitted signal. The sea clutter has a serious restriction on the detectability of radar echoes of targets such as 'point' targets from the sea surface or targets close to the sea surface, such as navigation buoys and ice blocks floating on the sea, so that the research on the sea clutter has very important influence on the detection performance of ships and other targets in the ocean background, thereby having important theoretical significance and practical value.
The Shanghai clutter is conventionally considered to be a single random process, such as lognormal distribution, K distribution, and the like. However, these models have their specific limitations in practical applications, and one of the important reasons is that the sea clutter appears to be a random waveform and does not actually have a random distribution characteristic.
Disclosure of Invention
In order to overcome the defects that the traditional radar data processing is easily influenced by human factors and is insufficient in intelligence, the invention provides the intelligent radar sea clutter forecasting system and method which are free from the influence of the human factors and are high in intelligence and based on the self-adaptive mixed frog-leaping algorithm.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the utility model provides an intelligence radar sea clutter forecast system based on self-adaptation mixes leapfrog algorithm, includes radar, database and host computer, and radar, database and host computer link to each other in proper order, the radar shines the sea area that detects to with radar sea clutter data storage arrive the database, the host computer include:
the data preprocessing module is used for preprocessing the radar sea clutter data and is completed by adopting the following processes:
(1) The radar irradiates the detected sea area and stores the radar sea clutter data into the database;
(2) Collecting N radar sea clutter echo signal amplitudes x from database i As training samples, i =1, · N;
(3) Carrying out normalization processing on the training sample to obtain a normalized amplitude value x i ：
Wherein minx represents the minimum value in the training samples, and maxx represents the maximum value in the training samples;
(4) Reconstructing the normalized training sample to respectively obtain an input matrix X and a corresponding output matrix Y:
wherein D represents a reconstruction dimension, D is a natural number and is less than N, and the value range of D is 50-70;
the robust forecasting model modeling module is used for establishing a forecasting model and is completed by adopting the following processes:
and substituting the obtained X and Y into the following linear equation:
wherein
Weighting factor v i Calculated from the following formula:
whereinIs the error variable xi i Estimation of the standard deviation, c 1 ,c 2 Is a constant;
solving to obtain a function f (x) to be estimated:
where M is the number of support vectors, 1 v ＝[1,...,1] T ,The superscript T represents the transpose of the matrix,is a Lagrange multiplier, b * Is the offset, K = exp (— | | x) i -x j ||/θ 2 ) Wherein i =1, \8230, M, j =1, \8230, M,and exp (- | | x-x) i ||/θ 2 ) Kernel functions, x, all of which are support vector machines j The amplitude of a jth radar sea clutter echo signal is shown, theta is a nuclear parameter, x represents an input variable, and gamma is a penalty coefficient;
the intelligent optimizing module optimizes the kernel parameter theta and the punishment coefficient gamma of the robust forecasting model by adopting a self-adaptive mixed frog leaping algorithm and finishes the following processes:
step 1: initializing frog group parameters, setting the frog group number as P, the maximum iteration number Maxgen, and the iteration number M of local search max Maximum update length D max Number of groups m and number of frogs n per group, since the model has two parameters to be optimized, the position p i Is 2-dimensional, randomly generating the position p of each frog i ＝(p i1 ,p i2 ) Setting the initial iteration times k =0;
step 2: calculating all frog fitness values, sequencing, and selecting the frog p with the optimal population g ；
And 3, step 3: carrying out mutation operation on all frogs according to the following formula, recalculating the fitness values of the frogs, and carrying out sequencing and grouping;
wherein x is jf Represents the jth frog x j Value of f-th dimension, x gf Is a frog x with the best population g And f, the value of the f dimension, wherein L (j multiplied by f) is a Logistic chaotic sequence value, i is the current global search iteration number, maxgen is the set maximum iteration number, and P is the number of the frog in the population.
And 4, step 4: updating the worst frogs in the subgroups according to the formula, reordering the frogs in the subgroups, and updating the worst frogs in the subgroups; repeat the local search process M max Secondly;
D＝rand×(p b -p w )
p′ w ＝p w +D,-D max ≤D≤D max
wherein p is w Is the worst frog of the subgroup, p b Frog being optimal for subgroup D max Is the maximum variation scale, p' w Is a renewed frog. Firstly, updating by using the optimal frog of the sub-group, and replacing the newly obtained frog if the new frog is superior to the worst frog of the original sub-group; otherwise, replacing the frog with the optimal population for updating the frog with the optimal population, and replacing the frog with the optimal population if the newly obtained frog is superior to the original frog with the worst population; otherwise, a frog is randomly generated to replace the worst frog in the original subgroup.
And 5: when the local search of all subgroups is completed, all frogs are mixed, ordered and grouped, and the frog p with the best group is selected g ；
And 6: k = k +1, if k&If yes, go to step 3; otherwise, outputting the frog x with the optimal population g The algorithm is terminated for the optimal parameters of the robust prediction model;
the initial population size is 200, the number of groups is 10, the number of subgroups in each group is 20, the maximum iteration number of the population is 100, the maximum iteration number of the subgroups is 10, and the maximum update length is 5.
The sea clutter prediction module is used for predicting sea clutter and comprises the following steps:
1) Is acquired at a sampling time tD sea clutter echo signal amplitudes are obtained to obtain TX = [ x = [ [ x ] t-D+1 ,…,x t ]，x t-D+1 Representing the amplitude, x, of the sea clutter echo signal at the t-D +1 th sampling instant t Representing the amplitude of the sea clutter echo signal at the tth sampling moment;
2) Carrying out normalization processing;
3) And substituting the function f (x) to be estimated obtained by the robust prediction model modeling module to calculate and obtain the sea clutter prediction value at the sampling moment (t + 1).
And the judgment model updating module is used for acquiring data according to a set sampling time interval, comparing the obtained actual measurement data with a model forecast value, and if the relative error is more than 10%, adding new data into training sample data and updating the forecast model.
And the result display module is used for displaying the forecast value calculated by the sea clutter forecasting module on the upper computer.
A radar sea clutter forecasting method used by an intelligent radar sea clutter forecasting system based on a self-adaptive mixed frog leaping algorithm comprises the following steps:
1) Collecting N radar sea clutter echo signal amplitudes x from database i As training samples i = 1.., N;
2) Carrying out normalization processing on the training sample to obtain a normalized amplitude value
Wherein minx represents the minimum value in the training samples, and maxx represents the maximum value in the training samples;
3) Reconstructing the normalized training sample to respectively obtain an input matrix X and a corresponding output matrix Y:
wherein D represents a reconstruction dimension, D is a natural number and is less than N, and the value range of D is 50-70;
4) And substituting the obtained X and Y into the following linear equation:
wherein
Weighting factor v i Calculated from the following formula:
whereinIs the error variable xi i Estimation of the standard deviation, c 1 ,c 2 Is a constant;
solving to obtain a function f (x) to be estimated:
where M is the number of support vectors, 1 v ＝[1,...,1] T ,K＝exp(-||x i -x j ||/θ 2 ) The superscript T, denotes the transpose of the matrix,is a lagrange multiplier, where i =1 * Is the amount of the offset that is,and exp (- | | x-x) i ||/θ 2 ) Kernel functions, x, all of which are support vector machines j The amplitude of a jth radar sea clutter echo signal is shown, theta is a nuclear parameter, x represents an input variable, and gamma is a penalty coefficient;
5) Optimizing the nuclear parameter theta and the penalty coefficient gamma in the step 4) by using a self-adaptive mixed frog-leaping algorithm, and completing the following steps:
step 1: initializing frog group parameters, setting the frog group number as P, the maximum iteration number Maxgen, and the iteration number M of local search max Maximum update length D max Number of groups m and number of frogs n per group, since the model has two parameters to be optimized, the position p i Is 2-dimensional, randomly generating the position p of each frog i ＝(p i1 ,p i2 ) Setting the initial iteration times k =0;
step 2: calculating all frog fitness values, sequencing, and selecting the frog p with the optimal population g ；
And 3, step 3: carrying out variation operation on all frogs according to the following formula, recalculating the fitness values of the frogs, and carrying out sequencing and grouping;
wherein x is jf Represents the jth frog x j Value of f-th dimension, x gf Is a frog x with the best population g And f, the value of the f dimension, wherein L (j multiplied by f) is a Logistic chaotic sequence value, i is the current global search iteration number, maxgen is the set maximum iteration number, and P is the number of the frog in the population.
And 4, step 4: updating the worst frogs in the subgroups according to the formula, reordering the frogs in the subgroups, and updating the worst frogs in the subgroups; the local search process M is repeated max Secondly;
D＝rand×(p b -p w )
p′ w ＝p w +D,-D max ≤D≤D max
wherein p is w Is the worst frog of the subgroup, p b Frog being optimal for subgroup D max Is the maximum variation scale, p' w Is a renewed frog. Firstly, updating by using the optimal frog of the sub-group, and replacing the newly obtained frog if the new frog is superior to the worst frog of the original sub-group; otherwise, replacing the frog with the optimal population for updating the frog with the optimal population, and replacing the frog with the optimal population if the newly obtained frog is superior to the original frog with the worst population; otherwise, a frog is randomly generated to replace the worst frog in the original subgroup.
And 5: when the local search of all subgroups is finished, all frogs are mixed, ordered and grouped, and a frog p with the best population is selected g ；
Step 6: k = k +1, if k&If yes, turning to the step 3; otherwise, outputting the frog x with the optimal population g The algorithm is terminated for the optimal parameters of the robust forecasting model;
the initial population size is 200, the number of groups is 10, the number of subgroups in each group is 20, the maximum iteration number of the population is 100, the maximum iteration number of the subgroups is 10, and the maximum update length is 5.
(7) Acquiring D sea clutter echo signal amplitudes at a sampling time t to obtain TX = [ x = t-D+1 ,…,x t ]，x t-D+1 Representing the amplitude, x, of the sea clutter echo signal at the t-D +1 th sampling instant t Representing the amplitude of the sea clutter echo signal at the tth sampling moment;
(8) Carrying out normalization processing;
(9) And (5) substituting the function f (x) to be estimated obtained in the step (5) to calculate and obtain the sea clutter prediction value at the sampling moment (t + 1).
(10) And acquiring data according to a set sampling time interval, comparing the obtained actual measurement data with a model forecast value, and if the relative error is more than 10%, adding new data into training sample data and updating the forecast model.
The method further comprises the following steps: in the step 8), the calculated sea clutter forecast value is displayed on an upper computer.
The technical conception of the invention is as follows: the method aims at the chaotic characteristic of the radar sea clutter, reconstructs radar sea clutter data, performs nonlinear fitting on the reconstructed data, introduces a self-adaptive mixed frog-leaping algorithm method, and establishes an intelligent forecasting model of the radar sea clutter.
The invention has the following beneficial effects: 1. a radar sea clutter prediction model is established, and radar sea clutter can be predicted on line; 2. the modeling method only needs fewer samples; 3. the influence of human factors is reduced, and the method is high in intelligence and strong in robustness.
Drawings
FIG. 1 is a hardware block diagram of the system proposed by the present invention;
fig. 2 is a functional block diagram of the upper computer according to the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The present embodiments are to be considered as illustrative and not restrictive, and all changes and modifications that come within the spirit of the invention and the scope of the appended claims are intended to be embraced therein.
Example 1
Referring to fig. 1 and 2, an intelligence radar sea clutter forecast system based on algorithm of mixed frog leaping of self-adaptation, includes database 2 and host computer 3 that radar 1 connects, and radar 1, database 2 and host computer 3 link to each other in proper order, radar 1 shines the sea area that detects to reach radar sea clutter data storage database 2, host computer 3 include:
the data preprocessing module 4 is used for preprocessing the radar sea clutter data and is completed by adopting the following processes:
(1) The radar irradiates the detected sea area and stores the radar sea clutter data into the database;
(2) Collecting N radar sea clutter echo signal amplitudes x from database i As training samples, i = 1.., N;
(3) Carrying out normalization processing on the training sample to obtain a normalized amplitude value
Wherein minx represents the minimum value in the training samples, and maxx represents the maximum value in the training samples;
(4) Reconstructing the normalized training sample to respectively obtain an input matrix X and a corresponding output matrix Y:
wherein D represents a reconstruction dimension, D is a natural number and is less than N, and the value range of D is 50-70;
the robust forecasting model modeling module 5 is used for building a forecasting model and is completed by adopting the following processes:
and substituting the obtained X and Y into the following linear equation:
wherein
Weighting factor v i Calculated from the following formula:
whereinIs the error variable xi i Estimation of the standard deviation, c 1 ,c 2 Is a constant quantity
Solving to obtain a function f (x) to be estimated:
where M is the number of support vectors, 1 v ＝[1,...,1] T ,The superscript T represents the transpose of the matrix,is a Lagrange multiplier, b * Is the offset, K = exp (— | | x) i -x j ||/θ 2 ) Wherein i =1, \8230;, M, j =1, \8230;, M,and exp (- | x-x) i ||/θ 2 ) Kernel functions, x, both support vector machines j For the jth radar sea clutter echo signal amplitude, theta is a nuclear parameter, x represents an input variable, and gamma is a penalty coefficient;
the intelligent optimizing module 6 is used for optimizing a kernel parameter theta and a penalty coefficient gamma of the robust forecasting model by adopting a self-adaptive mixed frog leaping algorithm, and is completed by adopting the following processes:
step 1: initializing frog group parameters, setting the frog group number as P, the maximum iteration number Maxgen, and the iteration number M of local search max Maximum update Length D max Number of groups m and number of frogs n per group, since the model has two parameters to be optimized, the position p i Is 2-dimensional, randomly generating the position p of each frog i ＝(p i1 ,p i2 ) Setting the initial iteration number k =0;
step 2: calculating all frog fitness values, sequencing, and selecting the frog p with the optimal population g ；
And 3, step 3: carrying out mutation operation on all frogs according to the following formula, recalculating the fitness values of the frogs, and carrying out sequencing and grouping;
wherein x is jf Represents the jth frog x j Value of the f-th dimension, x gf Is a frog x with the best population g And (4) the f-dimension value, L (j multiplied by f) is a Logistic chaotic sequence value, i is the current global search iteration number, maxgen is the set maximum iteration number, and P is the number of the frog in the population.
And 4, step 4: updating the worst frogs in the subgroups according to the formula, reordering the frogs in the subgroups, and updating the worst frogs in the subgroups; the local search process M is repeated max Secondly;
D＝rand×(p b -p w )
p′ w ＝p w +D,-D max ≤D≤D max
wherein p is w Is the worst frog in subgroup p b Frog being optimal for subgroup D max Is the maximum variation scale, p' w Is an updated frog. Firstly, updating by using the optimal frog of the sub-group, and replacing the new frog with the worst frog of the original sub-group if the new frog is superior to the worst frog of the original sub-group; otherwise, replacing the frog with the optimal population for updating the frog with the optimal population, and replacing the frog with the optimal population if the newly obtained frog is superior to the original frog with the worst population; otherwise, a frog is randomly generated to replace the worst frog in the original subgroup.
And 5: when the local search of all subgroups is completed, all frogs are mixed, ordered and grouped, and the frog p with the best group is selected g ；
And 6: k = k +1, if k&If yes, turning to the step 3; otherwise, outputting the frog x with the optimal population g The algorithm is terminated for the optimal parameters of the robust forecasting model;
the initial population size is 200, the number of groups is 10, the number of subgroups in each group is 20, the maximum iteration number of the population is 100, the maximum iteration number of the subgroups is 10, and the maximum update length is 5.
The sea clutter prediction module 7 is used for predicting sea clutter and comprises the following steps:
1) Acquiring D sea clutter echo signal amplitudes at a sampling time t to obtain TX = [ x = t-D+1 ,…,x t ]，x t-D+1 Represents the amplitude, x, of the sea clutter echo signal at the t-D +1 th sampling time t Representing the amplitude of the sea clutter echo signal at the tth sampling moment;
2) Carrying out normalization processing;
3) And substituting the function f (x) obtained by the robust forecasting model modeling module to obtain the sea clutter forecasting value at the sampling moment (t + 1).
And the discrimination model updating module 8 is used for acquiring data according to a set sampling time interval, comparing the obtained actual measurement data with a model prediction value, and if the relative error is more than 10%, adding new data into training sample data and updating the prediction model.
And the result display module 9 is used for displaying the forecast value calculated by the sea clutter forecast module on the upper computer.
The hardware part of the upper computer 3 comprises: the I/O element is used for collecting data and transmitting information; the data memory is used for storing data samples, operation parameters and the like required by operation; a program memory storing a software program for realizing the functional module; an arithmetic unit that executes a program to realize a designated function; and the display module displays the set parameters and the operation result.
Example 2
Referring to fig. 1 and 2, an intelligent radar sea clutter prediction method based on an adaptive mixed frog leaping algorithm includes the following steps:
(1) The radar irradiates the detected sea area and stores the radar sea clutter data into the database;
(2) Collecting N radar sea clutter echo signal amplitudes x from database i As training samples, i = 1.., N;
(3) Carrying out normalization processing on the training sample to obtain a normalized amplitude value
Wherein minx represents the minimum value in the training samples, and maxx represents the maximum value in the training samples;
3) Reconstructing the normalized training sample to respectively obtain an input matrix X and a corresponding output matrix Y:
wherein D represents a reconstruction dimension, D is a natural number and is less than N, and the value range of D is 50-70;
(4) And substituting the obtained X and Y into the following linear equation:
wherein
Weighting factor v i Calculated from the following formula:
whereinIs the error variable xi i Estimation of the standard deviation, c 1 ,c 2 Is a constant quantity
Solving to obtain a function f (x) to be estimated:
where M is the number of support vectors, 1 v ＝[1,...,1] T ,The superscript T represents the transpose of the matrix,is a Lagrange multiplier, b * Is the offset, K = exp (- | | x) i -x j ||/θ 2 ) Wherein i =1, \8230, M, j =1, \8230, M,and exp (- | x-x) i ||/θ 2 ) Kernel functions, x, both support vector machines j For the jth radar sea clutter echo signal amplitude, theta is a nuclear parameter, x represents an input variable, and gamma is a penalty coefficient;
(5) Optimizing the nuclear parameter theta and the penalty coefficient gamma in the step 4) by using a self-adaptive mixed frog-leaping algorithm, and completing the following steps:
step 1: initializing frog group parameters, setting the frog group number as P, the maximum iteration number Maxgen, and the iteration number M of local search max Maximum update Length D max The number of groups m and the number of frogs n per group n, the position p being the model for which two parameters need to be optimized i Is 2-dimensional, randomly generating the position p of each frog i ＝(p i1 ,p i2 ) Setting the initial iteration times k =0;
and 2, step: calculating the fitness values of all frogs, sequencing, and selecting the frog p with the optimal population g ；
And 3, step 3: carrying out mutation operation on all frogs according to the following formula, recalculating the fitness values of the frogs, and carrying out sequencing and grouping;
wherein x is jf Represents the jth frog x j Value of the f-th dimension, x gf Is a frog x with the best population g And f, the value of the f dimension, wherein L (j multiplied by f) is a Logistic chaotic sequence value, i is the current global search iteration number, maxgen is the set maximum iteration number, and P is the number of the frog in the population.
And 4, step 4: updating the worst frogs in the subgroups according to the formula, reordering the frogs in the subgroups, and updating the worst frogs in the subgroups; repeat the local search process M max Secondly;
D＝rand×(p b -p w )
p′ w ＝p w +D,-D max ≤D≤D max
wherein p is w Is the worst frog of the subgroup, p b Frog being optimal for subgroup D max Is the maximum variation scale, p' w Is a renewed frog. Firstly, updating by using the optimal frog of the sub-group, and replacing the new frog with the worst frog of the original sub-group if the new frog is superior to the worst frog of the original sub-group; otherwise, replacing the optimal frog of the sub-group with the optimal frog of the population for updating, and replacing the newly obtained frog with the worst frog of the original sub-group if the newly obtained frog is better than the worst frog of the original sub-group; otherwise, a frog is randomly generated to replace the worst frog in the original subgroup.
And 5: when the local search of all subgroups is completed, all frogs are mixed, ordered and grouped, and the frog p with the best group is selected g ；
Step 6: k = k +1, if k&If yes, turning to the step 3; otherwise, outputting the frog x with the optimal population g The algorithm is terminated for the optimal parameters of the robust prediction model;
the initial population size is 200, the number of groups is 10, the number of subgroups in each group is 20, the maximum iteration number of the population is 100, the maximum iteration number of the subgroups is 10, and the maximum update length is 5.
(7) Acquiring D sea clutter echo signal amplitudes at a sampling time t to obtain TX = [ x = t-D+1 ,…,x t ]，x t-D+1 Represents the amplitude, x, of the sea clutter echo signal at the t-D +1 th sampling time t Representing the amplitude of the sea clutter echo signal at the tth sampling moment;
(8) Carrying out normalization processing;
(9) Substituting the function f (x) to be estimated obtained in the step (5) to calculate and obtain a sea clutter prediction value at the sampling moment (t + 1);
(10) And acquiring data according to a set sampling time interval, comparing the obtained actual measurement data with a model forecast value, and if the relative error is more than 10%, adding new data into training sample data and updating the forecast model.
According to the embodiment, the radar sea clutter prediction model is established, and the radar sea clutter can be predicted on line; the modeling method only needs fewer samples; in addition, the influence of human factors is reduced, and the method is high in intelligence and strong in robustness.
Claims (2)
1. The utility model provides an intelligence radar sea clutter forecast system based on self-adaptation mixes leapfrog algorithm, includes radar, database and host computer, and radar, database and host computer link to each other its characterized in that in proper order: the radar irradiates the detected sea area and stores the radar sea clutter data into the database, and the upper computer comprises a data preprocessing module, a robust forecasting model modeling module, an intelligent optimizing module, a sea clutter forecasting module, a discrimination model updating module and a result display module;
the data preprocessing module is used for preprocessing the radar sea clutter data and is completed by adopting the following processes:
(1) The radar irradiates the detected sea area and stores the radar sea clutter data into the database;
(2) Collecting N radar sea clutter echo signal amplitudes x from database i As training samples, i = 1.., N;
(3) Carrying out normalization processing on the training sample to obtain a normalized amplitude value
Wherein minx represents the minimum value in the training samples, and maxx represents the maximum value in the training samples;
(4) Reconstructing the normalized training sample to respectively obtain an input matrix X and a corresponding output matrix Y:
wherein D represents reconstruction dimension, D is a natural number and is less than N, and the value range of D is 50-70;
the robust forecasting model modeling module is used for building a forecasting model and is completed by adopting the following processes:
substituting X and Y obtained by the data preprocessing module into the following linear equation:
wherein
Weighting factor v i Calculated from the following formula:
whereinIs the error variable xi i Estimation of the standard deviation, c 1 ,c 2 Is a constant;
solving to obtain a function f (x) to be estimated:
where M is the number of support vectors, 1 v ＝[1,...,1] T ,The superscript T represents the transpose of the matrix,is a Lagrange multiplier, b * Is the offset, K = exp (— | | x) i -x j ||/θ 2 ) Wherein i =1, \8230;, M, j =1, \8230;, M,and exp (- | x-x) i ||/θ 2 ) Kernel functions, x, both support vector machines j The amplitude of a jth radar sea clutter echo signal is shown, theta is a nuclear parameter, x represents an input variable, and gamma is a penalty coefficient;
the intelligent optimizing module is used for optimizing a kernel parameter theta and a penalty coefficient gamma of the robust forecasting model by adopting a self-adaptive mixed leapfrog algorithm, and the intelligent optimizing module is completed by adopting the following processes:
(A) The method comprises the following steps Initializing frog group parameters, setting the frog group number as P, the maximum iteration number Maxgen, and the iteration number M of local search max Maximum update length D max Number of groups m and number of frogs n per group, since the model has two parameters to be optimized, the position p i Is 2-dimensional, randomly generating the position p of each frog i ＝(p i1 ,p i2 ) Setting the initial iteration number k =0;
(B) The method comprises the following steps Calculating all frog fitness values, sequencing, and selecting the frog p with the optimal population g ；
(C) The method comprises the following steps Carrying out variation operation on all frogs according to the following formula, recalculating the fitness values of the frogs, and carrying out sequencing and grouping;
wherein x is jf Represents the jth frog x j Value of f-th dimension, x gf Is a frog x with the best population g And f, the value of the f dimension, wherein L (j multiplied by f) is a Logistic chaotic sequence value, i is the current global search iteration number, maxgen is the set maximum iteration number, and P is the number of the frog in the population.
(D) The method comprises the following steps Updating the worst frogs in the subgroups according to the formula, reordering the frogs in the subgroups, and updating the worst frogs in the subgroups; repeat the local search process M max Secondly;
D＝rand×(p b -p w )
p′ w ＝p w +D,-D max ≤D≤D max
wherein p is w Is the worst frog of the subgroup, p b Frog being optimal for subgroup D max Is the maximum variation scale, p' w Is a renewed frog. Firstly, updating by using the optimal frog of the sub-group, and replacing the newly obtained frog if the new frog is superior to the worst frog of the original sub-group; otherwise, replacing the optimal frog of the sub-group with the optimal frog of the population for updating, and replacing the newly obtained frog with the worst frog of the original sub-group if the newly obtained frog is better than the worst frog of the original sub-group; otherwise, a frog is randomly generated to replace the worst frog in the original subgroup.
(E) The method comprises the following steps When the local search of all subgroups is completed, all frogs are mixed, ordered and grouped, and the frog p with the best group is selected g ；
(F) The method comprises the following steps k = k +1, if k&If it is, maxgen, turning to the step (C); otherwise, outputting the frog x with the optimal population g The algorithm is terminated for the optimal parameters of the robust forecasting model;
the initial population size is 200, the number of groups is 10, the number of subgroups in each group is 20, the maximum iteration number of the population is 100, the maximum iteration number of the subgroups is 10, and the maximum update length is 5.
The sea clutter prediction module is used for predicting sea clutter and is completed by adopting the following processes:
(a) Acquiring D sea clutter echo signal amplitudes at a sampling time t to obtain TX = [ x = t-D+1 ,…,x t ]，x t-D+1 Representing the amplitude, x, of the sea clutter echo signal at the t-D +1 th sampling instant t Representing sea clutter at the tth sampling instantThe echo signal amplitude TX represents a signal amplitude matrix of the sea clutter from the t-D +1 sampling moment to the t sampling moment;
(b) Carrying out normalization processing;
(c) And substituting the function f (x) to be estimated obtained by the robust prediction model modeling module to calculate and obtain the sea clutter prediction value at the sampling moment (t + 1).
And the judgment model updating module is used for acquiring data according to a set sampling time interval, comparing the obtained actual measurement data with a model forecast value, and if the relative error is more than 10%, adding new data into training sample data and updating the forecast model.
And the result display module is used for displaying the forecast value calculated by the sea clutter forecasting module on the upper computer.
2. The radar sea clutter prediction method used by the intelligent radar sea clutter prediction system based on the adaptive mixed frog-leaping algorithm in claim 1, wherein: the method comprises the following steps:
(1) The radar irradiates the detected sea area and stores the radar sea clutter data into the database;
(2) Collecting N radar sea clutter echo signal amplitudes x from database i As training samples, i = 1.., N;
(3) Carrying out normalization processing on the training sample to obtain a normalized amplitude value
Wherein minx represents the minimum value in the training samples, and maxx represents the maximum value in the training samples;
(4) Reconstructing the normalized training sample to respectively obtain an input matrix X and a corresponding output matrix Y:
wherein D represents a reconstruction dimension, D is a natural number and is less than N, and the value range of D is 50-70;
(5) And substituting the obtained X and Y into the following linear equation:
wherein
Weighting factor v i Calculated from the following formula:
whereinIs an error variable xi i Estimation of the standard deviation, c 1 ,c 2 Is a constant;
solving to obtain a function f (x) to be estimated:
where M is the number of support vectors, 1 v ＝[1,...,1] T ,The superscript T represents the transpose of the matrix,is a Lagrange multiplier, b * Is the offset, K = exp (- | | x) i -x j ||/θ 2 ) Wherein i =1, \8230;, M, j =1, \8230;, M,and exp (- | | x-x) i ||/θ 2 ) Kernel functions, x, both support vector machines j The amplitude of a jth radar sea clutter echo signal is shown, theta is a nuclear parameter, x represents an input variable, and gamma is a penalty coefficient;
(6) And (3) optimizing the nuclear parameter theta and the penalty coefficient gamma in the step (5) by using a self-adaptive mixed frog leaping algorithm, and completing the following steps:
(6.1) initializing frog population parameters, setting the number of the frog population as P, the maximum iteration number Maxgen and the iteration number M of local search max Maximum update Length D max The number of groups m and the number of frogs n per group n, the position p being the model for which two parameters need to be optimized i Is 2-dimensional, randomly generating the position p of each frog i ＝(p i1 ,p i2 ) Setting the initial iteration number k =0; (6.2) calculating the fitness values of all frogs, sequencing, and selecting the frogs p with the optimal population g ；
(6.3) carrying out mutation operation on all frogs according to the following formula, recalculating the fitness values of the frogs, and carrying out sequencing and grouping;
wherein x is jf Represents the jth frog x j Value of the f-th dimension, x gf Is a frog x with the best population g The value of the f-th dimension, L (j x f) is the Logistic chaotic sequence value, and i is the current global search iterationAnd the generation times are the set maximum iteration times of the Maxgen, and the P is the number of the frog in the population.
(6.4) updating the worst frogs in the subgroups from the worst frogs in the subgroups according to the following formula, reordering in the subgroups, and updating the worst frogs in the subgroups; the local search process M is repeated max Secondly;
D＝rand×(p b -p w )
p′ w ＝p w +D,-D max ≤D≤D max
wherein p is w Is the worst frog of the subgroup, p b Frog being optimal for subgroup D max Is the maximum variation scale, p' w Is a renewed frog. Firstly, updating by using the optimal frog of the sub-group, and replacing the new frog with the worst frog of the original sub-group if the new frog is superior to the worst frog of the original sub-group; otherwise, replacing the optimal frog of the sub-group with the optimal frog of the population for updating, and replacing the newly obtained frog with the worst frog of the original sub-group if the newly obtained frog is better than the worst frog of the original sub-group; otherwise, a frog is randomly generated to replace the worst frog in the original subgroup.
(6.5) when the local search of all subgroups is finished, mixing, sequencing and grouping all frogs, and selecting the frog p with the optimal population g ；
(6.6) k = k +1, if k&If it is, maxgen, go to step (6.3); otherwise, outputting the frog x with the optimal population g The algorithm is terminated for the optimal parameters of the robust forecasting model;
the initial population size is 200, the number of groups is 10, the number of subgroups in each group is 20, the maximum iteration number of the population is 100, the maximum iteration number of the subgroups is 10, and the maximum update length is 5.
(7) Acquiring D sea clutter echo signal amplitudes at a sampling time t to obtain TX = [ x = t-D+1 ,…,x t ]，x t-D+1 Represents the amplitude, x, of the sea clutter echo signal at the t-D +1 th sampling time t Representing the amplitude of the sea clutter echo signal at the tth sampling moment;
(8) Carrying out normalization processing;
substituting the function f (x) to be estimated obtained in the step 4) to calculate and obtain the sea clutter prediction value of the sampling moment (t + 1).
(9) Sampling time intervals to acquire data, comparing the obtained actual measurement data with a model forecast value, and if the relative error is more than 10%, adding new data into training sample data and updating the forecast model.
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