CN104090260A - Method for searching for DOA estimation optimum antenna laying - Google Patents

Abstract

The invention relates to a method for searching for DOA estimation optimum antenna laying. The method is characterized in that an antenna population is initialized, a narrow-band signal is received, and fitness of all individuals in the initialized population is calculated; the individuals participating in evolving are selected according to a roulette rule; an optimal parent individual serves as a vaccine female parent, hybridization operation and variation operation are conducted on the selected individuals, new individuals are generated, the population is updated, and fitness of the individuals in the updated population is calculated; the gene number of vaccines is determined according to a chaos mechanism, the vaccines are prepared according to the vaccine female parent and are vaccinated to the individuals, and the vaccines are laid in a mode of partial antenna lay of the optimal parent antenna array; immune annealing selection is conducted on the vaccinated individuals, the selected excellent individuals form a new population, and the optimal parent individuals replace the worst current generation individuals according to an elitism strategy, and the population are updated.

Description

A kind of method of estimating that for searching for DOA optimal antenna lays

Technical field

The present invention relates to a kind of method laying for search signal direction of arrival (Direction of ArrivaL is called for short DOA) estimation optimal antenna.

Background technology

The antenna reception unit of most of DOA methods of estimation is to be all equidistantly linearly aligned, and for fear of occurring phase ambiguity, the interval between equidistant linearly aligned antenna reception unit can not exceed the half of incoming signal wavelength.In the time that antenna number is determined, antenna aperture has just been defined, and the resolution that DOA estimates is also just therefore fixing.Non-isometric line antenna lays mode can break the restriction that antenna spacing is half-wavelength, and under the equal condition of antenna number, non-isometric line antenna lays mode can obtain larger antenna aperture.At present proposed some classical non-isometric line antennas and laid mode, such as minimal redundancy antenna lays mode, the maximum antenna of delay continuously lays mode and minimum clearance antenna lays mode etc.Although laying mode, existing classical non-isometric line antenna can improve to a certain extent DOA estimated performance, but complicated engineering-environment is often difficult to meet the condition that these particular antenna are set lay mode, therefore how under specific constraint condition, to search DOA and estimate that optimal antenna lays mode, improving to greatest extent DOA estimated performance, is technical matters urgently to be resolved hurrily.The optimal antenna that " Alex B.Gershman; A Note on Most Favourable Array Geometries for DOA Estimation and Array Interpolation; IEEE SIGAL PROCESSING LETTERS; VOL.4; NO.8; AUGUST1997 " utilizes traditional genetic algorithm search DOA to estimate lays method, compared with other searching method, the method has strong robustness, the advantage such as easy to use, but it there will be population to degenerate, and Premature Convergence lays the problems such as mode in local optimum antenna.Therefore need to improve traditional genetic algorithm, make its search there is selectivity, purpose, effectively suppress the problems such as population degeneration.

Summary of the invention

The object of the invention is to provide a kind of method of estimating that for searching for DOA optimal antenna lays, effectively suppresses the degradation phenomena that traditional genetic algorithm may occur, can more effectively search DOA and estimate that optimal antenna lays mode.

Realize the object of the invention technical scheme:

A method of estimating that for searching for DOA optimal antenna lays, is characterized in that:

Step 1: antenna initialization of population, said antenna population is the set that several antenna lays mode;

Step 2: receive narrow band signal, the fitness that calculates each individuality in initial population, the individuality that fitness is the highest is optimum individual, the individuality that fitness is minimum is the poorest individuality, retain optimum individual and fitness thereof, the corresponding a kind of antenna of said each individuality lays mode;

Step 3: the individuality of selecting to participate in evolution according to roulette rule;

Step 4: using parent optimum individual as vaccine female parent, through hybridization computing and variation computing, generation is new individual to the individuality of selecting, Population Regeneration calculates population and upgrades rear individual fitness;

Step 5: determine the gene number of vaccine according to mechanism of chaos, according to the maternal vaccine of making of vaccine, in individuality, said gene is antenna by vaccine inoculation, and said vaccine is that parent optimal antenna array part antenna lays mode;

Step 6: the individuality after vaccine inoculation is carried out to immune annealing selection, form new population by the defect individual of selecting, and according to elite's retention strategy, replace the poorest individuality of this generation, Population Regeneration with parent optimum individual;

Step 7: the optimum individual in minute book generation and fitness thereof, if do not reach maximum iteration time, select to participate in the individuality of evolution next time according to roulette rule, then return to step 4, otherwise, output optimum individual.

In step 1, specifically comprise the steps,

Step 1.1: regulation antenna population scale is z, exists z kind antenna to lay mode in initial antenna population; Antenna aperture is K, and every kind of antenna lays in mode and has L gene position, and said gene position is the position that allows to put antenna, and antenna number is 1,2 ..., L; Antenna minimum spacing is d _min=K/ (L-1), antenna number is m, incident narrow band signal number is n; The random number producing between one [0,1] is arbitrarily designated as chaos initial value u ₀and $u_0\∉\left\{\mathrm{0,0.25,0.5,0.75,1}\right\};$

Step 1.2: to the individual binary coding that adopts in antenna population, two-value glossary of symbols that coded identification collection be made up of binit 0 and 10,1}, and 1 represents that this gene position arranges antenna, 0 this gene position of expression does not arrange antenna; Specify the 1st, the gene in the gene position of L position is 1.

In step 2, specifically comprise the steps,

Step 2.1: binary-coded individuality is become to non-spaced antenna row, receive narrow band signal, neighbourhood noise is white Gaussian noise, calculates the data covariance matrix of real antenna array;

Step 2.2: utilize interpolation array transformation method, by the virtual non-spaced antenna row spaced antenna row that become, obtain the transformation relation of the array manifold battle array of real antenna array and the array manifold battle array of virtual antenna array, by the data covariance matrix of this transformation relation and real antenna array, obtain the data covariance matrix of virtual antenna array;

Step 2.3: the data covariance matrix of virtual antenna array is carried out to Eigenvalues Decomposition, obtain noise subspace;

Step 2.4: bring noise subspace into spectrum peak search formula, obtain the spatial spectrum of narrow band signal;

Step 2.5: output region is composed corresponding signal angle information, calculates the root-mean-square error that DOA estimates;

Step 2.6: get the inverse of root-mean-square error as fitness function;

Step 2.7: calculate the fitness of each individuality in initial population, retain fitness maximal value and corresponding individuality thereof.

In step 3, specifically comprise the steps,

Step 3.1: find respectively the individuality that fitness is high and minimum, the individuality that fitness is minimum is the poorest individuality, and the individuality that fitness is the highest is optimum individual, preserves optimum individual;

Step 3.2: select to participate in the individuality of crossing operation, variation computing according to roulette rule, select the probability that individual i is selected based on fitness ratio f _ifor the fitness of individual i; For individual i, calculate since the 1 whole accumulated probability that finish to i and form accumulated probability sequence, utilize randomizer to produce [0,1] random number between, if random number is greater than i value in accumulated probability sequence, represent that the corresponding individual i of numbering i is chosen at random, select altogether z defect individual and participate in crossing operation, variation computing.

In step 4, specifically comprise the steps,

Step 4.1: using parent optimum individual as vaccine female parent, to the defect individual computing that intersects, makes a variation, the random number producing between z [0,1] forms random series, i of the corresponding population of i element of random series is individual, by random series element and crossover probability p _c(0.4<p _c<0.9) relatively, find and be less than p _celement, the individuality of its correspondence is to participate in the individuality of crossing operation, calculates the number of individuals y that participates in crossing operation, and y is adjusted into even number;

Step 4.2: get respectively 2i-1, individuality, a point of crossing of random definition in its all gene position, behind guarantee point of crossing, the number of these two contained genes 1 of individual genetic fragment equates, the gene behind two individual point of crossing is exchanged, said genetic fragment is that part antenna is put form; Said gene is antenna;

Step 4.3: repeating step 4.2 until crossing operation finish, and new individual and do not participate in that (m-y) of crossing operation is individual forms new population by participating in the y that obtains of crossing operation;

Step 4.4: the random number formation z × L that utilizes randomizer to produce between [0,1] ties up stochastic matrix, the gene of the corresponding population at individual of element of stochastic matrix, by the element of stochastic matrix and variation Probability p _m(0.0001<p _m<0.1) relatively, if the element of stochastic matrix is less than variation Probability p _mand this element is not positioned at the 1st row and the L row of stochastic matrix, and the genes of individuals that this element is corresponding participates in variation computing; Otherwise the genes of individuals that this element is corresponding does not participate in the computing that makes a variation; By the gene negate in the gene position of participation variation computing;

Step 4.5: calculate the population at individual fitness after crossing operation, variation computing.

In step 5, specifically comprise the steps,

Step 5.1: according to mechanism of chaos, determine the gene number of vaccine:, remember the required vaccine gene number c extracting of inoculation the l time from vaccine female parent _lfor

u _l＝4u _l-1(1-u _l-1)，l＝z(g-1)+1，z(g-1)+2,…，z(g-1)+z，

U _lchaos value while representing to extract vaccine female parent for the l time, g represents iteration the g time, the vaccine female parent that is L, extracts c from length _lindividual gene, is made into the vaccine of inoculating for the l time, according to the gene on the gene expression characteristics amendment offspring individual corresponding gene position of vaccine, produces antibody, and said antibody is the new antenna structure producing after vaccine inoculation;

Step 5.2: judge the relation of number x and the antenna number m of gene 1 in antibody, if x<m increases the number of gene 1 in antibody; If x>m,, in the situation that ensureing that the 1st of antibody, L position gene are constant, reduces the number of gene 1 in antibody, until x equates with m.

In step 6, specifically comprise the steps,

Step 6.1: ideal adaptation degree after calculating vaccine inoculation;

Step 6.2: according to Metropolis criterion, individuality is carried out to immune annealing selection, fitness size before and after more each individual vaccine inoculation, if ideal adaptation degree is greater than the front ideal adaptation degree of vaccine inoculation after vaccine inoculation, the individuality after vaccine inoculation is accepted as the individuality of new population with probability 1; If individual fitness is less than individual fitness before vaccine inoculation after vaccine inoculation, after vaccine inoculation individual i according to Probability p _a(i) be accepted;

p _a(i)＝exp(gΔE/G)，g＝1，...，G

Wherein, Δ E is ideal adaptation degree poor before ideal adaptation degree and vaccine inoculation after vaccine inoculation, and G represents maximum iteration time, p _a(i) be the number between [0,1]; Utilize randomizer to produce the random number between [0,1], if p _a(i) be greater than this random number, accept individuality after the vaccine inoculation individuality as new population, otherwise accept individuality before the vaccine inoculation individuality as new population;

Step 6.3: the individuality drawing by immune annealing selection, according to the descending sort of its fitness height, is found to the poorest individuality of this generation, according to elite's retention strategy, replace the poorest individuality of this generation, Population Regeneration with parent optimum individual.

The beneficial effect that the present invention has:

The present invention enumerates limited kind of antenna and puts form, receive narrow band signal, utilize the MUSIC algorithm based on interpolation array transformation to carry out DOA estimation to narrow band signal, DOA is estimated to the inverse of root-mean-square error is as the fitness function of the immune genetic algorithm based on mechanism of chaos; Select the good aerial array of this iteration and retain optimal antenna array by the calculating of fitness; Using parent optimal antenna array as vaccine female parent, good aerial array is hybridized to computing and variation computing, produce new aerial array; Determine the mrna length of vaccine according to mechanism of chaos, extract the Partial Feature information of vaccine female parent and make vaccine, be inoculated in filial generation aerial array; Find good aerial array Population Regeneration according to immune annealing selection, and select to participate in the aerial array of evolution next time according to roulette rule, until reach maximum iteration time, the optimal antenna that output DOA estimates lays mode.

The present invention is applied to search DOA by the immune genetic algorithm based on mechanism of chaos and estimates that optimal antenna lays mode.Utilize mechanism of chaos to determine the length of vaccine gene, maternal and make vaccine using parent optimal antenna array as vaccine, be inoculated in individuality, there is selection, on purpose utilized the gene expression characteristics of parent optimal antenna array, improve ideal adaptation degree, suppress the degradation phenomena that traditional genetic algorithm may occur, can search more efficiently and accurately DOA and estimate that optimal antenna lays mode.

Brief description of the drawings

Fig. 1 the inventive method process flow diagram;

10 Monte Carlo Experiment analogous diagram of Fig. 2 the present invention;

10 Monte Carlo Experiment emulation comparison diagrams of Fig. 3 the inventive method and traditional genetic algorithm.

Embodiment

As shown in Figure 1, the present invention includes following steps:

Step 1: antenna initialization of population, said antenna population is the set that several antenna lays mode.

Step 1.1 regulation antenna population scale is z, in initial antenna population, exists z kind antenna to lay mode; Antenna aperture is K, and every kind of antenna lays in mode and has L gene position, and said gene position is the position that allows to put antenna, and antenna number is 1,2 ..., L; Regulation antenna minimum spacing is d _min=K/ (L-1), antenna number is m, incident narrow band signal number is n; The random number producing between one [0,1] is arbitrarily designated as chaos initial value u ₀and $u_0\&NotElement;\left\{\mathrm{0,0.25,0.5,0.75,1}\right\};$

Step 1.2: to the individual binary coding that adopts in antenna population, two-value glossary of symbols that coded identification collection be made up of binit 0 and 10,1}, and 1 represents that this gene position arranges antenna, 0 this gene position of expression does not arrange antenna; Specify the 1st, the gene in the gene position of L position is 1.

Step 2: receive narrow band signal, the fitness that calculates each individuality in initial population, the individuality that fitness is the highest is optimum individual, the individuality that fitness is minimum is the poorest individuality, retain optimum individual and fitness thereof, the corresponding a kind of antenna of described each individuality lays mode;

Step 2.1: binary-coded individuality is become to non-spaced antenna row, receive narrow band signal, neighbourhood noise is white Gaussian noise, calculates the data covariance matrix of real antenna array;

Step 2.2: utilize interpolation array transformation method, by the virtual non-spaced antenna row spaced antenna row that become, obtain the transformation relation of the array manifold battle array of real antenna array and the array manifold battle array of virtual antenna array, by the data covariance matrix of this transformation relation and real antenna array, obtain the data covariance matrix of virtual antenna array;

Step 2.3: the data covariance matrix of virtual antenna array is carried out to Eigenvalues Decomposition, obtain noise subspace;

Step 2.4: bring noise subspace into spectrum peak search formula, obtain the spatial spectrum of narrow band signal;

Step 2.5: output region is composed corresponding signal angle information, calculates the root-mean-square error that DOA estimates;

Step 2.6: get the inverse of root-mean-square error as fitness function;

Step 2.7: calculate the fitness of each individuality in initial population, retain fitness maximal value and corresponding individuality thereof.

Step 3: the individuality of selecting to participate in evolution according to roulette rule;

Step 3.1: find respectively the individuality that fitness is high and minimum, the individuality that fitness is minimum is the poorest individuality, and the individuality that fitness is the highest is optimum individual, preserves optimum individual;

Step 3.2: select to participate in the individuality of crossing operation, variation computing according to roulette rule, select the probability that individual i is selected based on fitness ratio f _ifor the fitness of individual i; For individual i, calculate since the 1 whole accumulated probability that finish to i and form accumulated probability sequence, utilize randomizer to produce [0,1] random number between, if random number is greater than i value in accumulated probability sequence, represent that the corresponding individual i of numbering i is chosen at random, select altogether z defect individual and participate in crossing operation, variation computing.

Step 4: using parent optimum individual as vaccine female parent, through hybridization computing and variation computing, generation is new individual to the individuality of selecting, Population Regeneration calculates population and upgrades rear individual fitness;

Step 4.1: using parent optimum individual as vaccine female parent:, to the defect individual computing that intersects, makes a variation, the random number producing between z [0,1] forms random series, i of the corresponding population of i element of random series is individual, by random series element and crossover probability p _c(0.4<p _c<0.9) relatively, find and be less than p _celement, the individuality of its correspondence is to participate in the individuality of crossing operation, calculates the number of individuals y that participates in crossing operation, and y is adjusted into even number;

Step 4.2: get respectively 2i-1, individuality, in its all gene position, a point of crossing of random definition, ensures these two individual genetic fragments behind point of crossing, the number of contained gene 1 equates, gene behind two individual point of crossing is exchanged, and said genetic fragment is that part antenna is put form, and said gene is antenna;

Step 4.3: repeating step 4.2 until crossing operation finish, and new individual and do not participate in that (m-y) of crossing operation is individual forms new population by participating in the y that obtains of crossing operation;

Step 4.4: the random number formation z × L that utilizes randomizer to produce between [0,1] ties up stochastic matrix, the gene of the corresponding population at individual of element of stochastic matrix, by the element of stochastic matrix and variation Probability p _m(0.0001<p _m<0.1) relatively, if the element of stochastic matrix is less than variation Probability p _mand this element is not positioned at the 1st row and the L row of stochastic matrix, and the genes of individuals that this element is corresponding participates in variation computing; Otherwise the genes of individuals that this element is corresponding does not participate in the computing that makes a variation; By the gene negate in the gene position of participation variation computing;

Step 4.5: calculate the population at individual fitness after crossing operation, variation computing.

Step 5: determine the gene number of vaccine according to mechanism of chaos, according to the maternal vaccine of making of vaccine, in individuality, said vaccine is that parent optimal antenna array part antenna lays mode by vaccine inoculation;

Step 5.1: according to mechanism of chaos, determine the gene number of vaccine, remember the required vaccine gene number c extracting of inoculation the l time from vaccine female parent _lfor

u _l＝4u _l-1(1-u _l-1)，l＝z(g-1)+1，z(g-1)+2,…，z(g-1)+z，

U _lchaos value while representing to extract vaccine female parent for the l time, g represents iteration the g time, the vaccine female parent that is L, extracts c from length _lindividual gene, is made into the vaccine of inoculating for the l time, according to the gene on the gene expression characteristics amendment offspring individual corresponding gene position of vaccine, produces antibody, and said antibody is the new antenna structure producing after vaccine inoculation;

Step 5.2: judge the relation of number x and the antenna number m of gene 1 in antibody, if x<m increases the number of gene 1 in antibody; If x>m,, in the situation that ensureing that the 1st of antibody, L position gene are constant, reduces the number of gene 1 in antibody, until x equates with m.

Step 6: the individuality after vaccine inoculation is carried out to immune annealing selection, form new population by the defect individual of selecting, and according to elite's retention strategy, replace the poorest individuality of this generation, Population Regeneration with parent optimum individual;

Step 6.1: ideal adaptation degree after calculating vaccine inoculation;

Step 6.2: according to Metropolis criterion, individuality is carried out to immune annealing selection, fitness size before and after more each individual vaccine inoculation, if ideal adaptation degree is greater than the front ideal adaptation degree of vaccine inoculation after vaccine inoculation, the individuality after vaccine inoculation is accepted as the individuality of new population with probability 1; If individual fitness is less than individual fitness before vaccine inoculation after vaccine inoculation, after vaccine inoculation individual i according to Probability p _a(i) be accepted;

p _a(i)＝exp(gΔE/G)，g＝1，...，G

Wherein, Δ E is ideal adaptation degree poor before ideal adaptation degree and vaccine inoculation after vaccine inoculation, and G represents maximum iteration time, p _a(i) be the number between [0,1]; Utilize randomizer to produce the random number between [0,1], if p _a(i) be greater than this random number, accept individuality after the vaccine inoculation individuality as new population, otherwise accept individuality before the vaccine inoculation individuality as new population;

Step 6.3: the individuality drawing by immune annealing selection, according to the descending sort of its fitness height, is found to the poorest individuality of this generation, according to elite's retention strategy, replace the poorest individuality of this generation, Population Regeneration with parent optimum individual.

Step 7: the optimum individual in minute book generation and fitness thereof, if do not reach maximum iteration time, select to participate in the individuality of evolution next time according to roulette rule, then return to step 4, otherwise, output optimum individual.

Below in conjunction with instantiation, further illustrate beneficial effect of the present invention.

First, fixed antenna aperture, puts arbitrarily m root antenna in several different mode, when n × 1 dimension narrow band signal

S(t)＝[s ₁(t),s ₂(t),...,s _n(t)] ^T

When incident, the array manifold battle array of real antenna array

A＝[a(θ ₁),a(θ ₂),...,a(θ _n)]

Wherein, steering vector a (θ _q)=[1, exp (j2 π d ₂sin (θ _q)) ..., exp (j2 π d _msin (θ _q))] ^t, q=1,2 ..., n, d _k(k=2,3 ..., m) be the distance of k root antenna to reference antenna (first antenna).

The data covariance matrix of real antenna array

$\hat{R}=A{\hat{R}}_S{A}^H+{\mathrm{\&sigma;}}^{2}I$

Neighbourhood noise is white Gaussian noise, and power is σ ²i, for the auto-covariance matrix of signal.Utilize interpolation array transformation thought, by the virtual non-spaced antenna row spaced antenna row that become, the array manifold battle array of virtual spaced antenna row is

$\stackrel{\&OverBar;}{A}=[\stackrel{\&OverBar;}{a}\left({\mathrm{\&theta;}}_1\right),\stackrel{\&OverBar;}{a}\left({\mathrm{\&theta;}}_2\right),...,\stackrel{\&OverBar;}{a}\left({\mathrm{\&theta;}}_n\right)]$

Wherein, steering vector $\stackrel{\&OverBar;}{a}\left({\mathrm{\&theta;}}_q\right)=\exp (-\mathrm{j\&pi;}\sin \left({\mathrm{\&theta;}}_q\right)),$ q＝1,...,n。

Taking Δ θ as sweep spacing, at certain viewing area interior by the array manifold battle array A of real antenna array, the array manifold battle array of virtual antenna array carry out angular divisions

A＝[a(θ _β),a(θ _β+Δθ),...,a(θ _r)]

$\stackrel{\&OverBar;}{A}=[\stackrel{\&OverBar;}{a}\left({\mathrm{\&theta;}}_{\mathrm{\&beta;}}\right),\stackrel{\&OverBar;}{a}({\mathrm{\&theta;}}_{\mathrm{\&beta;}}+\mathrm{\&Delta;\&theta;}),...,\stackrel{\&OverBar;}{a}\left({\mathrm{\&theta;}}_r\right)]$

Can obtain the transformation relation between real antenna array and virtual antenna array

$B={\left({\mathrm{AA}}^H\right)}^{-1}A{\stackrel{\&OverBar;}{A}}^H$

According to the data covariance matrix of this transformation relation and real antenna array, can obtain the data covariance matrix of virtual spaced antenna row

$\stackrel{\&OverBar;}{R}=B^H\hat{R}B$

Data covariance matrix to virtual spaced antenna row is carried out a gust Eigenvalues Decomposition, obtains noise subspace, utilizes MUSIC algorithm, brings in the formula of Estimation of Spatial Spectrum, through spectrum peak search, obtains incoming signal angle information.Calculate the root-mean-square error that DOA estimates, using its inverse as fitness function.Calculate every kind of fitness that aerial array is corresponding, select the higher aerial array of fitness to participate in evolution according to roulette rule, and record the aerial array that fitness is the highest, set it as vaccine female parent.

According to crossover probability p _c, selection participates in the aerial array of crossing operation.The present invention adopts single-point to intersect, from the aerial array of all participation crossing operations, appoint and get two aerial arrays, the a certain truncation points of random selection, after guarantee truncation points, the antenna number of two aerial arrays equates, the antenna exchanging after two aerial array truncation points is put form.Repeat aforesaid operations until crossing operation is participated in and only participated in one time to the aerial array of all participation crossing operations.

According to variation Probability p _m, selection participates in the antenna of variation computing, by its corresponding binary coding negate, is about to 0 and puts 1, sets to 01.Antenna is put in gene 1 this position of expression, and antenna is not put in gene 0 this position of expression.Crossing operation and variation computing have improved the diversity of antenna set-up mode, have upgraded antenna population.

Calculate the fitness of aerial array in new population.Determine gene number c in vaccine according to mechanism of chaos _l.From vaccine female parent, extract c _lindividual gene is made vaccine inoculation in filial generation aerial array, design the antenna of this generation aerial array correspondence position according to the set-up mode of some antenna of optimal antenna array, owing to having comprised the Partial Feature of optimal antenna array in vaccine, the inoculation of vaccine can improve fitness to a certain extent.The antenna number of adjusting vaccine inoculation aft antenna array is m, and antenna aperture is K.

According to Metropolis criterion, the aerial array after vaccine inoculation is carried out to immune annealing selection.The relatively fitness of aerial array before and after vaccine inoculation, if fitness increases after vaccine inoculation, accept aerial array that vaccine inoculation the produces individuality as new population, otherwise, illustrate after vaccine inoculation, having there is degradation phenomena in antenna population, accepts the aerial array after vaccinate according to certain probability.Immunity annealing selection has upgraded antenna population.Calculate the fitness of new population aerial array, and according to elite's retention strategy, replace the poorest aerial array in this generation with the optimal antenna array of parent, preserve optimal antenna array that this evolution obtains and the fitness of optimal antenna array.

Judge whether to reach maximum iteration time, if not, select good aerial array to participate in of future generation evolution until reach maximum iteration time according to roulette rule; If so, export optimum individual and be the optimal antenna array that under constraint antenna aperture condition, DOA estimates.Draw the relation of iterations and fitness as shown in Figure 2.Fig. 2 is that antenna number is 10, incident information source number is 2, and arrival bearing is respectively 10 °, 20 °, and signal to noise ratio (S/N ratio) is 10dB, fast umber of beats is 1000, iterations is 100, and genes of individuals length is 37, and Population Size is 50, antenna aperture is 4.5 λ (λ is incoming signal wavelength), antenna minimum interval is 0.125 λ, and sweep spacing is 0.01, p _cbe 0.6, p _mbe 0.005, utilize the immune genetic algorithm search DOA based on mechanism of chaos to estimate optimal antenna array, 10 Monte Carlo Experiment analogous diagram.

The optimal antenna array structure that utilizes method of the present invention to search is as shown in the table:

Antenna number

1

4

7

12

16

19

24

33

34

37

Fig. 3 compares the immune genetic algorithm and the traditional genetic algorithm that the present invention is based on mechanism of chaos, utilizes as seen the inventive method search DOA to estimate that the optimal antenna mode of structuring the formation can obtain higher fitness.

Claims (7)

1. a method of estimating that for searching for DOA optimal antenna lays, is characterized in that:

Step 1: antenna initialization of population, said antenna population is the set that several antenna lays mode;

Step 2: receive narrow band signal, the fitness that calculates each individuality in initial population, the individuality that fitness is the highest is optimum individual, the individuality that fitness is minimum is the poorest individuality, retain optimum individual and fitness thereof, the corresponding a kind of antenna of said each individuality lays mode;

Step 3: the individuality of selecting to participate in evolution according to roulette rule;

Step 4: using parent optimum individual as vaccine female parent, through hybridization computing and variation computing, generation is new individual to the individuality of selecting, Population Regeneration calculates population and upgrades rear individual fitness;

Step 5: determine the gene number of vaccine according to mechanism of chaos, according to the maternal vaccine of making of vaccine, in individuality, said gene is antenna by vaccine inoculation, and said vaccine is that parent optimal antenna array part antenna lays mode;

Step 6: the individuality after vaccine inoculation is carried out to immune annealing selection, form new population by the defect individual of selecting, and according to elite's retention strategy, replace the poorest individuality of this generation, Population Regeneration with parent optimum individual;

Step 7: the optimum individual in minute book generation and fitness thereof, if do not reach maximum iteration time, select to participate in the individuality of evolution next time according to roulette rule, then return to step 4, otherwise, output optimum individual.

2. the method for estimating that for searching for DOA optimal antenna lays according to claim 1, is characterized in that: in step 1, specifically comprises the steps,

Step 1.1: regulation antenna population scale is z, exists z kind antenna to lay mode in initial antenna population; Antenna aperture is K, and every kind of antenna lays in mode and has L gene position, and said gene position is the position that allows to put antenna, and antenna number is 1,2 ..., L; Antenna minimum spacing is d _min=K/ (L-1), antenna number is m, incident narrow band signal number is n; The random number producing between one [0,1] is arbitrarily designated as chaos initial value u ₀and $u_0\&NotElement;\left\{\mathrm{0,0.25,0.5,0.75,1}\right\};$

Step 1.2: to the individual binary coding that adopts in antenna population, two-value glossary of symbols that coded identification collection be made up of binit 0 and 10,1}, and 1 represents that this gene position arranges antenna, 0 this gene position of expression does not arrange antenna; Specify the 1st, the gene in the gene position of L position is 1.

3. the method for estimating that for searching for DOA optimal antenna lays according to claim 2, is characterized in that: in step 2, specifically comprises the steps,

Step 2.1: binary-coded individuality is become to non-spaced antenna row, receive narrow band signal, neighbourhood noise is white Gaussian noise, calculates the data covariance matrix of real antenna array;

Step 2.2: utilize interpolation array transformation method, by the virtual non-spaced antenna row spaced antenna row that become, obtain the transformation relation of the array manifold battle array of real antenna array and the array manifold battle array of virtual antenna array, by the data covariance matrix of this transformation relation and real antenna array, obtain the data covariance matrix of virtual antenna array;

Step 2.3: the data covariance matrix of virtual antenna array is carried out to Eigenvalues Decomposition, obtain noise subspace;

Step 2.4: bring noise subspace into spectrum peak search formula, obtain the spatial spectrum of narrow band signal;

Step 2.5: output region is composed corresponding signal angle information, calculates the root-mean-square error that DOA estimates;

Step 2.6: get the inverse of root-mean-square error as fitness function;

Step 2.7: calculate the fitness of each individuality in initial population, retain fitness maximal value and corresponding individuality thereof.

4. the method for estimating that for searching for DOA optimal antenna lays according to claim 3, is characterized in that: in step 3, specifically comprises the steps,

Step 3.1: find respectively the individuality that fitness is high and minimum, the individuality that fitness is minimum is the poorest individuality, and the individuality that fitness is the highest is optimum individual, preserves optimum individual;

Step 3.2: select to participate in the individuality of crossing operation, variation computing according to roulette rule, select the probability that individual i is selected based on fitness ratio f _ifor the fitness of individual i; For individual i, calculate since the 1 whole accumulated probability that finish to i and form accumulated probability sequence, utilize randomizer to produce [0,1] random number between, if random number is greater than i value in accumulated probability sequence, represent that the corresponding individual i of numbering i is chosen at random, select altogether z defect individual and participate in crossing operation, variation computing.

5. the method for estimating that for searching for DOA optimal antenna lays according to claim 4, is characterized in that: in step 4, specifically comprises the steps,

Step 4.1: using parent optimum individual as vaccine female parent, to the defect individual computing that intersects, makes a variation, the random number producing between z [0,1] forms random series, i of the corresponding population of i element of random series is individual, by random series element and crossover probability p _c(0.4<p _c<0.9) relatively, find and be less than p _celement, the individuality of its correspondence is to participate in the individuality of crossing operation, calculates the number of individuals y that participates in crossing operation, and y is adjusted into even number;

Step 4.2: get respectively 2i-1, individuality, a point of crossing of random definition in its all gene position, behind guarantee point of crossing, the number of these two contained genes 1 of individual genetic fragment equates, the gene behind two individual point of crossing is exchanged, said genetic fragment is that part antenna is put form; Said gene is antenna;

Step 4.3: repeating step 4.2 until crossing operation finish, and new individual and do not participate in that (m-y) of crossing operation is individual forms new population by participating in the y that obtains of crossing operation;

Step 4.4: the random number formation z × L that utilizes randomizer to produce between [0,1] ties up stochastic matrix, the gene of the corresponding population at individual of element of stochastic matrix, by the element of stochastic matrix and variation Probability p _m(0.0001<p _m<0.1) relatively, if the element of stochastic matrix is less than variation Probability p _mand this element is not positioned at the 1st row and the L row of stochastic matrix, and the genes of individuals that this element is corresponding participates in variation computing; Otherwise the genes of individuals that this element is corresponding does not participate in the computing that makes a variation; By the gene negate in the gene position of participation variation computing;

Step 4.5: calculate the population at individual fitness after crossing operation, variation computing.

6. the method for estimating that for searching for DOA optimal antenna lays according to claim 5, is characterized in that: in step 5, specifically comprises the steps,

Step 5.1: according to mechanism of chaos, determine the gene number of vaccine:, remember the required vaccine gene number c extracting of inoculation the l time from vaccine female parent _lfor

u _l＝4u _l-1(1-u _l-1)，l＝z(g-1)+1，z(g-1)+2,…，z(g-1)+z，

U _lchaos value while representing to extract vaccine female parent for the l time, g represents iteration the g time, the vaccine female parent that is L, extracts c from length _lindividual gene, is made into the vaccine of inoculating for the l time, according to the gene on the gene expression characteristics amendment offspring individual corresponding gene position of vaccine, produces antibody, and said antibody is the new antenna structure producing after vaccine inoculation;

Step 5.2: judge the relation of number x and the antenna number m of gene 1 in antibody, if x<m increases the number of gene 1 in antibody; If x>m,, in the situation that ensureing that the 1st of antibody, L position gene are constant, reduces the number of gene 1 in antibody, until x equates with m.

7. the method for estimating that for searching for DOA optimal antenna lays according to claim 6, is characterized in that: in step 6, specifically comprises the steps,

Step 6.1: ideal adaptation degree after calculating vaccine inoculation;

Step 6.2: according to Metropolis criterion, individuality is carried out to immune annealing selection, fitness size before and after more each individual vaccine inoculation, if ideal adaptation degree is greater than the front ideal adaptation degree of vaccine inoculation after vaccine inoculation, the individuality after vaccine inoculation is accepted as the individuality of new population with probability 1; If individual fitness is less than individual fitness before vaccine inoculation after vaccine inoculation, after vaccine inoculation individual i according to Probability p _a(i) be accepted;

p _a(i)＝exp(gΔE/G)，g＝1，...，G

Wherein, Δ E is ideal adaptation degree poor before ideal adaptation degree and vaccine inoculation after vaccine inoculation, and G represents maximum iteration time, p _a(i) be the number between [0,1]; Utilize randomizer to produce the random number between [0,1], if p _a(i) be greater than this random number, accept individuality after the vaccine inoculation individuality as new population, otherwise accept individuality before the vaccine inoculation individuality as new population;

Step 6.3: the individuality drawing by immune annealing selection, according to the descending sort of its fitness height, is found to the poorest individuality of this generation, according to elite's retention strategy, replace the poorest individuality of this generation, Population Regeneration with parent optimum individual.