CN107703492A - A kind of Intelligent radar sea clutter forecast system and method based on improvement shuffled frog leaping algorithm - Google Patents
A kind of Intelligent radar sea clutter forecast system and method based on improvement shuffled frog leaping algorithm Download PDFInfo
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- 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
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- G—PHYSICS
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- 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
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Abstract
The invention discloses a kind of based on the Intelligent radar sea clutter forecast system and method that improve shuffled frog leaping 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, 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, introduces and improves 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 prediction system and method based on an improved 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 of the influence of the human factors and high in intelligence and are based on the improved 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 improve mixed frog leaping 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 reach radar sea clutter data storage 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) 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;
the robust forecasting model modeling module 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;
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 optimizes the kernel parameter theta and the penalty coefficient gamma of the robust forecasting model by adopting an improved mixed frog leap 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, grouping and selecting the frog p with the optimal population g ；
And 3, step 3: updating the worst frogs in the subgroups according to the formula, reordering the worst frogs in the subgroups, and updating the worst frogs in the subgroups; repeat the local search process M max Secondly;
D(k+1)＝W×(r 1 ×D(k)+r 2 ×D(k-1)+r 3 ×His(p w ))+r 4 ×(p b -p w )
p' w (k+1)＝p w (k)+D,-D max ≤D≤D max
W＝W e +(W s -W e )×a
a＝exp(-30×(k/Maxgen) s )
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 For the renewed frog, r 1 、r 2 、r 3 、r 4 Is a random number between 0 and 1, k is the current number of iterations, W is a weight factor, W s 、W e Is the initial and final values of the weighting factor, s being an integer greater than 1. 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 4, step 4: 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 5: 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;
wherein, 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, the maximum updating length is 5, the initial value and the final value of the weight factor are 0.9 and 0.4 respectively, and s is 3.
The sea clutter prediction module is used for predicting sea clutter and is completed by adopting the following processes:
1) D sea clutter echo signal amplitudes are collected at the 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;
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 an improved mixed frog leaping algorithm comprises the following steps:
1) Collecting N radar sea clutter echo signal amplitudes x from a 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 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 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 ,K＝exp(-||x i -x j ||/θ 2 ) The superscript T, which represents 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 an improved 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;
and 2, step: calculating the fitness values of all frogs, sequencing, grouping and selecting the frog p with the optimal population g ；
And step 3: 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(k+1)＝W×(r 1 ×D(k)+r 2 ×D(k-1)+r 3 ×His(p w ))+r 4 ×(p b -p w )
p' w (k+1)＝p w (k)+D,-D max ≤D≤D max
W＝W e +(W s -W e )×a
a＝exp(-30×(k/Maxgen) s )
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 For the renewed frog, r 1 、r 2 、r 3 、r 4 Is a random number between 0 and 1, k is the current number of iterations, W is a weight factor, W s 、W e Is the initial and final values of the weighting factor, s being an integer greater than 1. 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 4, step 4: 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 5: 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;
wherein, 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, the maximum updating length is 5, the initial value and the final value of the weight factor are 0.9 and 0.4 respectively, and s is 3.
6) D sea clutter echo signal amplitudes are collected at the 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;
7) Carrying out normalization processing;
8) 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) 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: and in the step 8), displaying the calculated sea clutter prediction value 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, and introduces an improved mixed frog leaping algorithm method, so as to establish 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, the intelligence is high, and the robustness is strong.
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 examples are intended to illustrate the invention, but not to limit the invention, and any modifications and variations of the invention within the spirit and scope of the claims are intended to fall within the scope of the invention.
Example 1
Referring to fig. 1 and 2, an intelligence radar sea clutter forecast system based on improve mixed frog-leaping algorithm, 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 arrive 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) Collecting N radar sea clutter echo signal amplitudes x from a 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 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 an 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, all of which are 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 an improved 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 times k =0;
step 2: calculating all frog fitness values, sequencing, grouping and selecting the frog p with the optimal population g ；
And 3, step 3: 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(k+1)＝W×(r 1 ×D(k)+r 2 ×D(k-1)+r 3 ×His(p w ))+r 4 ×(p b -p w )
p' w (k+1)＝p w (k)+D,-D max ≤D≤D max
W＝W e +(W s -W e )×a
a＝exp(-30×(k/Maxgen) s )
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 For the renewed frog, r 1 、r 2 、r 3 、r 4 Is a random number between 0 and 1, k is the current iteration number, W is a weight factor, W s 、W e Is the initial and final values of the weighting factor, s being an integer greater than 1. 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 4, step 4: 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 ；
And 5: 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;
wherein, 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, the maximum updating length is 5, the initial value and the final value of the weight factor are 0.9 and 0.4 respectively, and s is 3.
The sea clutter prediction module 7 is used for predicting sea clutter and is completed by adopting the following processes:
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 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) 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 forecasting 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 is used for displaying the set parameters and the operation result.
Example 2
Referring to fig. 1 and 2, an intelligent radar sea clutter forecasting method based on an improved mixed frog leaping algorithm includes 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
Weight 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, all of which are 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 an improved 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 number k =0;
step 2: calculating all frog fitness values, sequencing, grouping and selecting the frog p with the optimal population g ；
And step 3: 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(k+1)＝W×(r 1 ×D(k)+r 2 ×D(k-1)+r 3 ×His(p w ))+r 4 ×(p b -p w )
p' w (k+1)＝p w (k)+D,-D max ≤D≤D max
W＝W e +(W s -W e )×a
a＝exp(-30×(k/Maxgen) s )
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 For the renewed frog, r 1 、r 2 、r 3 、r 4 Is a random number between 0 and 1, k is the current iteration number, W is a weight factor, W s 、W e Is the initial and final values of the weighting factor, s being an integer greater than 1. 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 4, step 4: 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 5: 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;
wherein, the initial population size is 200, the group number is 10, the group number of each group is 20, the maximum iteration number of the population is 100, the maximum iteration number of the group is 10, the maximum update length is 5, the initial value and the final value of the weight factor are respectively 0.9 and 0.4, and s is 3.
6) D sea clutter echo signal amplitudes are collected at the 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;
7) Carrying out normalization processing;
8) Substituting the function f (x) to be estimated obtained in the step 4) to calculate and obtain a sea clutter prediction value at the sampling moment (t + 1);
9) 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: and in the step 8), displaying the calculated sea clutter prediction value on an upper computer.
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 improved intelligent optimization method for the mixed frog leap can avoid premature convergence of the algorithm to local optimum, reduce the influence of human factors, and has high intelligence and strong robustness.
Claims (2)
1. The utility model provides an intelligence radar sea clutter forecast system based on improve mixed 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 in 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 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 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 an improved mixed frog leaping algorithm, and 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 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;
(B) The method comprises the following steps Calculating all frog fitness values, sequencing, grouping and selecting the frog p with the optimal population g ；
(C) 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; the local search process M is repeated max Secondly;
D(k+1)＝W×(r 1 ×D(k)+r 2 ×D(k-1)+r 3 ×His(p w ))+r 4 ×(p b -p w )
p' w (k+1)＝p w (k)+D,-D max ≤D≤D max
W＝W e +(W s -W e )×a
a＝exp(-30×(k/Maxgen) s )
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 For the renewed frog, r 1 、r 2 、r 3 、r 4 Is a random number between 0 and 1, k is the current number of iterations, W is a weight factor, W s 、W e Is the initial and final values of the weighting factor, s being an integer greater than 1. 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.
(D) 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 ；
(E) The method comprises the following steps 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;
wherein, the initial population size is 200, the group number is 10, the group number of each group is 20, the maximum iteration number of the population is 100, the maximum iteration number of the group is 10, the maximum update length is 5, the initial value and the final value of the weight factor are respectively 0.9 and 0.4, and s is 3.
The sea clutter prediction module is used for predicting sea clutter and comprises the following steps:
(a) D sea clutter echo signal amplitudes are collected at the 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 The method comprises the steps that the signal amplitude of a sea clutter echo at the t-th sampling moment is represented, and TX represents a signal amplitude matrix from the t-D + 1-th sampling moment to the t-th sampling moment of the sea clutter;
(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 of the intelligent radar sea clutter prediction system based on the improved mixed frog leap algorithm, according to 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 a 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, 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;
(6) And (3) optimizing the nuclear parameter theta and the penalty coefficient gamma in the step (5) by using an improved 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 times k =0;
(6.2) calculating the fitness values of all frogs, sequencing, grouping and selecting the frogs p with the optimal population g ；
(6.3) 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; repeat the local search process M max Secondly;
D(k+1)＝W×(r 1 ×D(k)+r 2 ×D(k-1)+r 3 ×His(p w ))+r 4 ×(p b -p w )
p' w (k+1)＝p w (k)+D,-D max ≤D≤D max
W＝W e +(W s -W e )×a
a＝exp(-30×(k/Maxgen) s )
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 For the renewed frog, r 1 、r 2 、r 3 、r 4 Is a random number between 0 and 1, k is the current number of iterations, W is a weight factor, W s 、W e Are the initial and final values of the weighting factors,s is an integer greater than 1. 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.
(6.4) when the local search of all subgroups is finished, mixing, sequencing and grouping all frogs, and selecting a frog p with the best group g ；
(6.5) 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 prediction model;
wherein, 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, the maximum updating length is 5, the initial value and the final value of the weight factor are 0.9 and 0.4 respectively, and s is 3.
(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.
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