CN113311395B - Subarray division and subarray weight joint optimization method based on genetic algorithm - Google Patents
Subarray division and subarray weight joint optimization method based on genetic algorithm Download PDFInfo
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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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- 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
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Abstract
The invention discloses a subarray division and subarray weight joint optimization method based on a genetic algorithm, which comprises the following steps: s1, establishing a coding scheme of a chromosome of a genetic algorithm, wherein subarray division adopts Grefenstette coding, and subarray weights adopt binary coding; s2, determining a cost function of subarray division and subarray weight joint optimization; and S3, establishing an optimization iterative process of a genetic algorithm, and giving out an optimal subarray dividing structure and subarray weight after convergence so as to form a differential beam. The invention adopts Grefenstette coding to divide the sub-array to avoid individual deletion or repetition of binary coding, adopts binary coding to the weight of the sub-array, and forms a hybrid chromosome for optimization iteration of genetic algorithm to carry out joint optimization solution, thereby forming ideal differential wave beam and being beneficial to improving the performance of radar and differential monopulse angle measurement and the target tracking capability of radar.
Description
Technical Field
The invention relates to the technical field of subarray division of array antennas, in particular to a subarray division and subarray weight joint optimization method based on a genetic algorithm.
Background
In a large array antenna radar, the subarray technology can greatly reduce the implementation difficulty and the manufacturing cost of a system, so that the subarray technology is widely adopted. However, what sub-array technology is used has a great influence on the performance of the radar system, and is a highly concerned problem.
In many radar systems, the sum beam of the array antenna is usually used for target detection, and the sum beam and the difference beam are used together for target angle measurement, so that the sum beam is required to be optimally formed at the array element level, the difference beam is required to be optimally formed at the subarray level through subarray technology, and then subarray division and subarray weight of the difference beam need to be jointly and optimally solved. Literature (Lwave P, rodriguez J A, ares F.Subarrey weighting for the difference patterns of monopulse antennas: joint optimization of subarray configurations and weights [ J ]. IEEE Trans. On AP,2001,49 (11): 1606-1608.) uses genetic algorithms based on binary coding to divide the subarrays and perform digital difference beamforming. Subarray division is actually to search for separation points among array elements, and the separation points are random numbers, and if a genetic algorithm based on binary codes is adopted, individual deletion or repetition is generated, which is an unavoidable problem.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above and/or problems occurring in the prior binary-coded genetic algorithms.
Therefore, the invention aims to provide a subarray division and subarray weight joint optimization method based on a genetic algorithm, which adopts Grefenstette coding to subarray division so as to avoid individual deletion or repetition problems of binary coding, and adopts binary coding to subarray weight, wherein the two codes form a mixed chromosome for optimization iteration of the genetic algorithm.
In order to solve the technical problems, according to one aspect of the present invention, the following technical solutions are provided:
a subarray division and subarray weight joint optimization method based on a genetic algorithm comprises the following steps:
s1, establishing a coding scheme of a chromosome of a genetic algorithm, wherein subarray division adopts Grefenstette coding, and subarray weights adopt binary coding;
s2, determining a cost function of subarray division and subarray weight joint optimization;
and S3, establishing an optimization iterative process of a genetic algorithm, and giving out an optimal subarray dividing structure and subarray weight after convergence so as to form a differential beam.
As a further preferred embodiment of the present invention, the step S1 establishes a coding scheme of a chromosome of the genetic algorithm, wherein subarray division is encoded by greventette, subarray weights are encoded by binary, specifically,
s1-1), dividing N array elements in the N-element array into M mutually adjacent subarrays, wherein the number of the array elements of the M-th subarray is N m M=1, 2,..m, M, satisfies n=n 1 +N 2 +…+N M The method comprises the steps of carrying out a first treatment on the surface of the N is a natural number and is an even number, and M is a predetermined natural number and is an even number;
the N-element array is an N-element uniform linear array, and the received signals of the N-element array are subjected to phase shifter, array element attenuator and two-stage summation operation to directly form sum beams; a subarray attenuator is added to the subarray output end of the N-element array after the receiving signals pass through the phase shifter, the array element attenuator and the primary summation operation, and the subarray output signals pass through the subarray attenuator and are synthesized into a difference beam;
s1-2), in the chromosome population of the genetic algorithm, each chromosome is composed of M/2+1 genes;
the first gene describes a subarray division scheme, and adopts Grefenstette coding, namely N/2 array elements of a linear array are divided into M/2 subarrays based on symmetry of the uniform linear array to obtain M/2-1 separation points, and then the M/2-1 separation points are converted into Grefenstette codes;
the latter M/2 genes respectively represent the weights of M/2 subarrays, and binary codes are adopted, namely, the genes are composed of M/2 8-bit binary codes;
wherein, the conversion formula from binary coding genes to real numbers of the weight of subarrays is as follows,
in the formula g m Weight of the mth subarray, h m+1 (k) Represents the kth position of the m+1th gene in the chromosome, and a is an intermediate variable.
As a further preferred embodiment of the present invention, the step S2 determines a cost function for joint optimization of subarray division and subarray weight, specifically,
the maximum sidelobe level of the difference beam pattern is approximated to a desired sidelobe level, and thus the cost function is used,
min[|α-α d |D(α-α d )] (2)
where α is the current differential beam pattern maximum side lobe level, α d For the desired side lobe level, D (·) is a step function, i.e
As a further preferable scheme of the invention, the step S3 establishes an optimization iterative process of a genetic algorithm, gives out an optimal subarray dividing structure and subarray weight after convergence, thereby forming a difference beam, specifically S3-1), and generates an initial chromosome population;
randomly generating M/2-1 different natural numbers on the interval 1-N/2-1 as subarray separation points, and then converting the M/2-1 separation points into Grefenstette codes; randomly generating M/2 different 8-bit binary random sequences as subarray weights; the Grefenstette code and the M/2 binary sequences constitute one chromosome of the genetic algorithm; repeating the above processes to produce a plurality of chromosomes, the plurality of chromosomes forming an initial chromosome population;
s3-2), evaluating the fitness value;
calculating a difference beam pattern corresponding to each chromosome, obtaining the maximum sidelobe level of each difference beam pattern, substituting the obtained maximum sidelobe level into formula (2), evaluating each chromosome in the population by taking the reciprocal of the formula (2) as a fitness value, finding out the chromosome with the current optimal fitness value which is the largest and the chromosome with the worst fitness value which is the smallest, and then replacing the worst chromosome with the optimal chromosome, thereby generating the next generation;
s3-3), selecting operation;
selecting by roulette according to the fitness value obtained in the step S3-2);
s3-4), grefenstette code;
converting the natural number in the first gene to a greventette code prior to crossover and mutation;
s3-5), cross operation;
adopting discrete two-point intersection;
s3-6), mutation operation;
chromosome with probability P m If the Grefenstette code is mutated, the code at the mutated position is replaced by any of the remaining M/2-2 Grefenstette codes to produce a new chromosome; if a binary code is mutated, the code of the mutated position is replaced by any one of the remaining 4M-1 binary codes to generate a new chromosome;
s3-7), grefenstette code inverse conversion;
after crossover and mutation, reverse converting the gretens code in the first gene to subarray separation points;
s3-8), evaluating the fitness value again;
calculating a difference beam pattern corresponding to each chromosome, obtaining a current fitness value, evaluating each chromosome in the population through the current fitness value again, finding out a current optimal chromosome and a worst chromosome, and replacing the worst chromosome with the optimal chromosome;
s3-9), circulation and termination;
if the fitness value does not reach an acceptable magnitude or the optimization iterative process does not reach a preset maximum evolution algebra, returning to the step S3-3), continuing the loop process from the step S3-3) to the step S3-8), otherwise, terminating the loop to obtain an optimal chromosome, and giving an optimal subarray division structure and subarray weight.
As a further preferable mode of the invention, the value range of a in the formula (1) is 0-31.875 dB, g m The range of the value of (2) is 0.0255-1.
Compared with the prior art, the invention has the beneficial effects that:
the method provided by the invention can be used for carrying out joint optimization solving on the subarray division and subarray weight calculation problems of the large array antenna, and can form the differential beam with ideal main lobe shape and main-side lobe ratio for the radar adopting the sum and the differential beam to carry out monopulse angle measurement, thereby being beneficial to improving the performances of the radar and the differential monopulse angle measurement and the target tracking capability of the radar.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following detailed description of the embodiments of the present invention will be given with reference to the accompanying drawings, which are to be understood as merely some embodiments of the present invention, and from which other drawings can be obtained by those skilled in the art without inventive faculty. Wherein:
FIG. 1 is a flow chart of a method for subarray division and subarray weight joint optimization based on genetic algorithm of the present invention;
FIG. 2 is a matrix element level sum beam and subarray level difference beam of the subarray dividing and subarray weight joint optimization method based on genetic algorithm;
FIG. 3 is a structure of a hybrid encoding chromosome;
FIG. 4 is an evolutionary process of a genetic algorithm;
fig. 5 is a subarray level difference beam pattern of a genetic algorithm based on the genetic algorithm subarray division and subarray weight joint optimization method of the invention.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Next, the present invention will be described in detail with reference to the drawings, wherein the sectional view of the device structure is not partially enlarged to general scale for the convenience of description, and the drawings are only examples, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.
The invention provides a subarray division and subarray weight joint optimization method based on a genetic algorithm, which adopts Grefenstette coding for subarray division so as to avoid individual deletion or repetition problems of binary coding, and adopts binary coding for subarray weight, wherein the two codes form a hybrid chromosome for optimization iteration of the genetic algorithm.
Referring to fig. 1, the method for jointly optimizing subarray division and subarray weight based on genetic algorithm provided by the invention comprises the following steps:
s1, establishing a coding scheme of a chromosome of a genetic algorithm, wherein subarray division adopts Grefenstette coding, and subarray weights adopt binary coding;
s2, determining a cost function of subarray division and subarray weight joint optimization;
and S3, establishing an optimization iterative process of a genetic algorithm, and giving out an optimal subarray dividing structure and subarray weight after convergence so as to form a differential beam.
Wherein S1 establishes a coding scheme of a chromosome of a genetic algorithm, subarray division adopts Grefenstette coding, subarray weight adopts binary coding, in particular,
s1-1), dividing N array elements in the N-element array into M mutually adjacent subarrays, wherein the number of the array elements of the M-th subarray is N m M=1, 2, …, M, satisfying n=n 1 +N 2 +...+N M The method comprises the steps of carrying out a first treatment on the surface of the N is a natural number and is an even number, and M is a predetermined natural number and is an even number;
the N-element array is an N-element uniform linear array, as shown in fig. 2, and the received signals of the N-element array directly form a sum beam after passing through a phase shifter, an array element attenuator and two-stage summation operation; and adding a subarray attenuator to the subarray output end of the N-element array after the receiving signals pass through the phase shifter, the array element attenuator and the primary summation operation, and synthesizing the subarray output signals into a difference beam after passing through the subarray attenuator.
As shown in fig. 2, for an N-element uniform linear array, a phase shifter and an element attenuator are connected after each element for controlling and directing the side lobe level and direction of the beam. In sum and difference beam forming of fig. 2, the weights { w } of the array element attenuators n N=1, 2,..n } is optimal for sum beamforming, however the subarray division and the weights of the subarray attenuators { g } m M=1, 2,..m } is unknown. On the basis that the number M of subarrays is predetermined, the subarray dividing and subarray attenuator weight values are simultaneously and optimally solved through a genetic algorithm, so that more ideal difference beams can be given.
S1-2), in the chromosome population of the genetic algorithm, each chromosome is composed of M/2+1 genes;
the first gene describes a subarray division scheme, and adopts Grefenstette coding, namely N/2 array element arrays of a linear array are divided into M/2 subarrays based on symmetry of the uniform linear array to obtain M/2-1 separation points, and then the M/2-1 separation points are converted into Grefenstette codes;
the latter M/2 genes respectively represent the weights of M/2 subarrays, and binary codes are adopted, namely, the genes are composed of M/2 8-bit binary codes;
wherein, the conversion formula from binary coding genes to real numbers of the weight of subarrays is as follows,
in the formula g m Weight of the mth subarray, h m+1 (k) Represents the kth position of the m+1th gene in the chromosome, and a is an intermediate variable.
The value range of a in the formula (1) is 0-31.875 dB, g m The value range of (2) is 0.0255-1; if the number of bits of the gene is too small, the accuracy is insufficient, and if it is too large, it is unnecessary.
Examples of gretens coding: let n=20, subarray separation point s: [2,13,16,18], which can be encoded as greventette code s': [2,12,14,15], the procedure is as follows:
dividing the points s by array elements 0 :[1,2,3,…,19]For reference, the first number, i.e. "2", is taken from s, and taken at s 0 The position in (a) is taken as Grefenstette code, namely '2', and then is taken as s 0 Delete this number to get updated s 0 :[1,3,…,19]。
The above process is repeated and each number in s is processed continuously to obtain the greventette code of s.
The detailed encoding process is shown in Table 1, wherein the first column is a subarray separation point, the second column is a sequential array element separation point, and the third column is the resulting Grefenstette code.
TABLE 1 Grefaenstette encoding procedure
For the Grefantette code obtained through the conversion, the original subarray separation point is easily converted through the inverse process.
When the genetic algorithm is applied, the chromosome is required to be encoded, the subarray division and the subarray weight are optimized at the same time, the subarray division is encoded by Grefenstette, the subarray weight is encoded by binary, and the subarray division and the subarray weight form the hybrid encoding chromosome, so that the problem of individual deletion or repetition of the binary encoding is avoided. For the uniform linear array, considering the symmetry of the array, only half array surfaces are needed to be optimized, and half arrays are divided into M/2 subarrays, so that M/2-1 separation points are provided and converted into M/2-1 Grefenstette codes. The latter M/2 genes represent weights of M/2 subarrays, respectively, each gene consisting of 8-bit binary codes. The hybrid encoding chromosome structure is shown in FIG. 3.
Wherein, step S2 determines a cost function of subarray division and subarray weight joint optimization, specifically,
the maximum sidelobe level of the difference beam pattern is approximated to a desired sidelobe level, and thus the cost function is used,
min[|α-α d |D(α-α d )] (2)
where α is the current differential beam pattern maximum side lobe level, α d For the desired side lobe level, D (·) is a step function, i.e
Wherein, step S3 establishes an optimization iterative process of the genetic algorithm, and gives out an optimal subarray dividing structure and subarray weight after convergence, thereby forming a differential beam, specifically,
s3-1), generating an initial chromosome population;
randomly generating M/2-1 different natural numbers on the interval 1-N/2-1 as subarray separation points, and then converting the M/2-1 separation points into Grefenstette codes; randomly generating M/2 different 8-bit binary random sequences as subarray weights; the Grefenstette code and the M/2 binary sequences constitute one chromosome of the genetic algorithm; repeating the above process to generate a plurality of chromosomes, wherein the plurality of chromosomes form an initial chromosome population, and the structure of the chromosome population is shown in figure 3;
s3-2), evaluating the fitness value;
calculating a difference beam pattern corresponding to each chromosome, obtaining the maximum sidelobe level of each difference beam pattern, substituting the obtained maximum sidelobe level into formula (2), evaluating each chromosome in the population by taking the reciprocal of the formula (2) as a fitness value, finding out the chromosome with the current optimal fitness value which is the largest and the chromosome with the worst fitness value which is the smallest, and then replacing the worst chromosome with the optimal chromosome, thereby generating the next generation;
s3-3), selecting operation;
selecting by roulette according to the fitness value obtained in the step S3-2);
s3-4), grefenstette code;
converting the natural number in the first gene to a greventette code prior to crossover and mutation;
s3-5), cross operation;
adopting discrete two-point intersection;
the parent generation is the generation of the parent,
f p ＝{G p,1 ,...,|G p,i ,...,G p,j ,|...,G p,M/2-1 ；b p，1 ,...,|b p,k ,...,b p,l ,|...,b p,4M }
f q ＝{G q,1 ,...,|G q,i ,...,G q,j ,|...,G q,M/2-1 ；b q,1 ,...,|b q,k ,...,b q,l ,|...,b q,4M }
then after the two points cross, the offspring is,
s p ＝{G p,1 ,...,|G q,i ,...,G q,j ,|...,G p,M/2-1 ；b p,1 ,...,|b q,k ,...,b q,l ,|...,b p,4M }
s q ＝{G q,1 ,...,|G p,i ,...,G p,j ,|...,G q,M/2-1 ；b q,1 ,...,|b p,k ,...,b p,l ,|...,b q,4M }；
s3-6), mutation operation;
chromosome with probability P m If the Grefenstette code is mutated, the code at the mutated position is replaced by any of the remaining M/2-2 Grefenstette codes to produce a new chromosome; if a binary code is mutated, the code of the mutated position is replaced by any one of the remaining 4M-1 binary codes to generate a new chromosome;
the parent generation is the generation of the parent,
f p ＝{G p,1 ,...,G p,i ,G p,i+1 ,...,G p,M/2-1 ；b p,1 ,...,b p,k ,b p,k+1 ,...,b p,4M }
if the greventette code is mutated, the resulting offspring is,
s p ＝{G p,1 ,...,G p,j ,G p,i+1 ,...,G p,M/2-1 ；b p,1 ,...,b p,k ,b p,k+1 ,...,b p,4M }
wherein G is p,j To divide G in father p,i Any one of the outer M/2-2 Grefenstette codes;
if the binary code is mutated, the obtained offspring is,
s p ＝{G p,1 ,...,G p,i ,G p,i+1 ,...,G p,M/2-1 ；b p,1 ,...,b p,l ,b p,k+1 ,...,b p,4M }
wherein b p,l To divide b in father p,k Any one of the outer 4M-1 binary codes;
s3-7), grefenstette code inverse conversion;
after crossover and mutation, reverse converting the gretens code in the first gene to subarray separation points;
s3-8), evaluating the fitness value again;
calculating a difference beam pattern corresponding to each chromosome, obtaining a current fitness value, evaluating each chromosome in the population through the current fitness value again, finding out a current optimal chromosome and a worst chromosome, and replacing the worst chromosome with the optimal chromosome;
s3-9), circulation and termination;
if the fitness value does not reach an acceptable magnitude or the optimization iterative process does not reach a preset maximum evolution algebra, returning to the step S3-3), continuing the loop process from the step S3-3) to the step S3-8), otherwise, terminating the loop to obtain an optimal chromosome, and giving an optimal subarray division structure and subarray weight.
Referring to fig. 4 and 5, the method for optimizing design of the present invention is verified by simulation examples by adopting a method for jointly optimizing subarray division and subarray weight based on genetic algorithm of the present invention.
Considering a 40-element uniform linear array, wherein the array element distance is half wavelength, the array element level and the wave beam adopt a Taylor window of-35 dB, dividing the linear array into 10 subarrays, considering the symmetry of the array, only optimizing the half array surface by using the genetic algorithm, searching 4 separation points among 5 subarrays, and weighting values output by 5 subarrays, symmetrically dividing the subarrays of the other half array surface, and taking the opposite weight.
After 50 iterative cycles, the algorithm tends to converge, the convergence process is shown in fig. 4, at this time, the subarray spacing point is [2,13,16,18] (see table 1), and the subarray level weight is [0.3073,1.0000,0.6131,0.3278,0.1014]. The difference beam pattern calculated after convergence, as shown in fig. 5, shows that the main lobe shape is good, the side lobe level is uniformly distributed (about-28 dB), and no grating lobe is obvious.
Although the invention has been described hereinabove with reference to embodiments, various modifications thereof may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the features of the disclosed embodiments may be combined with each other in any manner as long as there is no structural conflict, and the exhaustive description of these combinations is not given in this specification merely for the sake of omitting the descriptions and saving resources. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (2)
1. The subarray division and subarray weight joint optimization method based on the genetic algorithm is characterized by comprising the following steps of:
s1, establishing a coding scheme of a chromosome of a genetic algorithm, wherein subarray division adopts Grefenstette coding, and subarray weights adopt binary coding;
s2, determining a cost function of subarray division and subarray weight joint optimization;
s3, establishing an optimization iterative process of a genetic algorithm, and giving out an optimal subarray dividing structure and subarray weight after convergence so as to form a differential beam;
the step S1 establishes a coding scheme of a chromosome of a genetic algorithm, wherein subarray division adopts Grefenstette coding, subarray weights adopt binary coding, in particular,
s1-1), dividing N array elements in the N-element array into M mutually adjacent subarrays, wherein the number of the array elements of the M-th subarray is N m M=1, 2,..m, M, satisfies n=n 1 +N 2 +…+N M The method comprises the steps of carrying out a first treatment on the surface of the N is a natural number and is an even number, and M is a predetermined natural number and is an even number;
the N-element array is an N-element uniform linear array, and the received signals of the N-element array are subjected to phase shifter, array element attenuator and two-stage summation operation to directly form sum beams; a subarray attenuator is added to the subarray output end of the N-element array after the receiving signals pass through the phase shifter, the array element attenuator and the primary summation operation, and the subarray output signals pass through the subarray attenuator and are synthesized into a difference beam;
s1-2), in the chromosome population of the genetic algorithm, each chromosome is composed of M/2+1 genes;
the first gene describes a subarray division scheme, and adopts Grefenstette coding, namely N/2 array elements of a linear array are divided into M/2 subarrays based on symmetry of the uniform linear array to obtain M/2-1 separation points, and then the M/2-1 separation points are converted into Grefenstette codes;
the latter M/2 genes respectively represent the weights of M/2 subarrays, and binary codes are adopted, namely, the genes are composed of M/2 8-bit binary codes;
wherein, the conversion formula from binary coding genes to real numbers of the weight of subarrays is as follows,
in the formula g m Weight of the mth subarray, h m+1 (k) Represents the kth position of the (m+1) th gene in the chromosome, a is an intermediate variable;
the step S2 is to determine a cost function for joint optimization of subarray division and subarray weights, specifically,
the maximum sidelobe level of the difference beam pattern is approximated to a desired sidelobe level, and thus the cost function is used,
min[|α-α d |D(α-α d )] (2)
where α is the current differential beam pattern maximum side lobe level, α d For the desired side lobe level, D (·) is a step function, i.e
Step S3 establishes an optimization iterative process of the genetic algorithm, and gives an optimal subarray dividing structure and subarray weight after convergence, thereby forming a differential beam, specifically,
s3-1), generating an initial chromosome population;
randomly generating M/2-1 different natural numbers on the interval 1-N/2-1 as subarray separation points, and then converting the M/2-1 separation points into Grefenstette codes; randomly generating M/2 different 8-bit binary random sequences as subarray weights; the Grefenstette code and the M/2 binary sequences constitute one chromosome of the genetic algorithm; repeating the above processes to produce a plurality of chromosomes, the plurality of chromosomes forming an initial chromosome population;
s3-2), evaluating the fitness value;
calculating a difference beam pattern corresponding to each chromosome, obtaining the maximum sidelobe level of each difference beam pattern, substituting the obtained maximum sidelobe level into formula (2), evaluating each chromosome in the population by taking the reciprocal of the formula (2) as a fitness value, finding out the chromosome with the current optimal fitness value which is the largest and the chromosome with the worst fitness value which is the smallest, and then replacing the worst chromosome with the optimal chromosome, thereby generating the next generation;
s3-3), selecting operation;
selecting by roulette according to the fitness value obtained in the step S3-2);
s3-4), grefenstette code;
converting the natural number in the first gene to a greventette code prior to crossover and mutation;
s3-5), cross operation;
adopting discrete two-point intersection;
s3-6), mutation operation;
chromosome with probability P m If the Grefenstette code is mutated, the code at the mutated position is replaced by any of the remaining M/2-2 Grefenstette codes to produce a new chromosome; if a binary code is mutated, the code of the mutated position is replaced by any one of the remaining 4M-1 binary codes to generate a new chromosome;
s3-7), grefenstette code inverse conversion;
after crossover and mutation, reverse converting the gretens code in the first gene to subarray separation points;
s3-8), evaluating the fitness value again;
calculating a difference beam pattern corresponding to each chromosome, obtaining a current fitness value, evaluating each chromosome in the population through the current fitness value again, finding out a current optimal chromosome and a worst chromosome, and replacing the worst chromosome with the optimal chromosome;
s3-9), circulation and termination;
if the fitness value does not reach an acceptable magnitude or the optimization iterative process does not reach a preset maximum evolution algebra, returning to the step S3-3), continuing the loop process from the step S3-3) to the step S3-8), otherwise, terminating the loop to obtain an optimal chromosome, and giving an optimal subarray division structure and subarray weight.
2. The method for joint optimization of subarray division and subarray weight based on genetic algorithm according to claim 1, wherein the value range of a in the formula (1) is 0-31.875 dB, g m The range of the value of (2) is 0.0255-1.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1482324A2 (en) * | 2003-05-30 | 2004-12-01 | The Boeing Company | Inverse synthetic aperture radar-based covert system for human identification |
| EP2613169A1 (en) * | 2012-01-09 | 2013-07-10 | Raytheon Company | Grating lobe mitigation in presence of simultaneous receive beams |
| CN106342323B (en) * | 2011-12-27 | 2014-06-18 | 中国航空工业集团公司雷华电子技术研究所 | The submatrix weighted value of phased-array radar difference beam Sidelobe Suppression is determined method |
| WO2018094565A1 (en) * | 2016-11-22 | 2018-05-31 | 深圳大学 | Method and device for beamforming under pulse noise |
| CN108987941A (en) * | 2018-05-22 | 2018-12-11 | 中国科学院国家空间科学中心 | A kind of compressed sensing based one-dimensional Antenna Subarray Division |
| CN111896930A (en) * | 2020-08-28 | 2020-11-06 | 西安电子科技大学 | Subarray division method based on motion platform space-time self-adaptive clutter suppression |
| EP3739356A1 (en) * | 2019-05-12 | 2020-11-18 | Origin Wireless, Inc. | Method, apparatus, and system for wireless tracking, scanning and monitoring |
| CN112698324A (en) * | 2020-12-07 | 2021-04-23 | 南京工业职业技术大学 | Sum-difference monopulse angle measurement method of frequency modulation stepping radar |
| CN112926271A (en) * | 2021-03-19 | 2021-06-08 | 电子科技大学 | Linear array subarray division method based on hybrid genetic algorithm |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7668788B2 (en) * | 2005-06-06 | 2010-02-23 | Wren William E | Resource assignment optimization using direct encoding and genetic algorithms |
| US10439690B2 (en) * | 2015-10-23 | 2019-10-08 | Samsung Electronics Co., Ltd. | Precoder codebook for advanced wireless communication systems |
-
2021
- 2021-06-21 CN CN202110685185.4A patent/CN113311395B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1482324A2 (en) * | 2003-05-30 | 2004-12-01 | The Boeing Company | Inverse synthetic aperture radar-based covert system for human identification |
| CN106342323B (en) * | 2011-12-27 | 2014-06-18 | 中国航空工业集团公司雷华电子技术研究所 | The submatrix weighted value of phased-array radar difference beam Sidelobe Suppression is determined method |
| EP2613169A1 (en) * | 2012-01-09 | 2013-07-10 | Raytheon Company | Grating lobe mitigation in presence of simultaneous receive beams |
| WO2018094565A1 (en) * | 2016-11-22 | 2018-05-31 | 深圳大学 | Method and device for beamforming under pulse noise |
| CN108987941A (en) * | 2018-05-22 | 2018-12-11 | 中国科学院国家空间科学中心 | A kind of compressed sensing based one-dimensional Antenna Subarray Division |
| EP3739356A1 (en) * | 2019-05-12 | 2020-11-18 | Origin Wireless, Inc. | Method, apparatus, and system for wireless tracking, scanning and monitoring |
| CN111896930A (en) * | 2020-08-28 | 2020-11-06 | 西安电子科技大学 | Subarray division method based on motion platform space-time self-adaptive clutter suppression |
| CN112698324A (en) * | 2020-12-07 | 2021-04-23 | 南京工业职业技术大学 | Sum-difference monopulse angle measurement method of frequency modulation stepping radar |
| CN112926271A (en) * | 2021-03-19 | 2021-06-08 | 电子科技大学 | Linear array subarray division method based on hybrid genetic algorithm |
Non-Patent Citations (6)
| Title |
|---|
| Multidimensional and multideme genetic algorithms for the construction of fuzzy systems;I. Rojas 等;《International Journal of Approximate Reasoning》;第179-210页 * |
| 一种子阵划分方法及子阵级STAP性能分析;谢文冲 等;《数据采集与处理》;第273-277页 * |
| 基于空域稀疏性的雷达低仰角目标测高;陈希信 等;《现代雷达》;第39-41页 * |
| 基于遗传算法的均匀子阵数字多波束形成研究;江禹生 等;《系统仿真技术》;第102-106页 * |
| 天基雷达空时自适应杂波抑制技术;张增辉;《中国博士学位论文全文数据库 信息科技辑》;第1-149页 * |
| 遗传二进制多粒子群优化算法及其在子阵STAP中的应用;张增辉 等;《信号处理》;第52-57页 * |
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