CN106357316B - Beam forming method and device of array antenna - Google Patents

Beam forming method and device of array antenna Download PDF

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CN106357316B
CN106357316B CN201610970010.7A CN201610970010A CN106357316B CN 106357316 B CN106357316 B CN 106357316B CN 201610970010 A CN201610970010 A CN 201610970010A CN 106357316 B CN106357316 B CN 106357316B
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particle swarm
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CN106357316A (en
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李俊
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GCI Science and Technology Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0617—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal for beam forming
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
    • H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
    • H04B7/0842—Weighted combining
    • H04B7/086—Weighted combining using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming

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Abstract

The invention relates to a beam forming method and a device of an array antenna, wherein the method comprises the following steps: generating a particle swarm according to a preset swarm scale, and initializing each parameter of the particle swarm; calculating the fitness value of each particle in the particle swarm according to a preset fitness function determined according to the required wave beam, wherein the optimal solution of the problem of the fitness function is the amplitude and phase value required by the wave beam; taking the particle swarm as a parent particle swarm, and adopting an immune algorithm to the parent particle swarm according to the fitness value of the particles to obtain a next-generation particle swarm of the parent particle swarm; updating the optimal value of the particles and the optimal solution of the problem according to a preset rule; judging whether a preset maximum iteration number is reached; if so, determining the phase and the signal amplitude of each array element according to the optimal solution of the problem, and forming to obtain the beam of the required array antenna. The searching speed can be effectively improved by combining the particle swarm algorithm and the immune algorithm, the efficiency is high, and the shaped beam of the antenna meets the requirement.

Description

Beam forming method and device of array antenna
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a beam forming method and apparatus for an array antenna.
Background
In many fields such as radar and wireless communication, beam forming is a signal preprocessing technology based on an antenna array, and generates a directional beam by adjusting a weighting coefficient of each array element in the antenna array, so that an obvious array gain can be obtained. And controlling the phase and the signal amplitude of each array element through a beam former to obtain the required beam.
This is a multi-dimensional non-linear optimization problem. The evolutionary algorithm based on group intelligence is very suitable for solving the complex nonlinear optimization problem, and the algorithms have the characteristics of clear concept, simple program and the like. A common evolutionary algorithm of swarm intelligence is the particle swarm algorithm. In the particle swarm optimization, the potential solution of each optimization problem is a 'particle' in the search space, and the position of the particle is the solution of the problem. All particles have a fitness value determined by the function being optimized, and each particle also has a velocity that determines the direction and distance they fly. The particle swarm algorithm first initializes a group of random particles (random solution), flies at a certain speed in a search space, and then finds the optimal solution through iteration. In each iteration, the particle updates itself by tracking two extrema, the first being the optimal solution found by the particle itself, and the other extrema being the optimal solution currently found by the entire population.
The particle swarm algorithm has the characteristics of simple algorithm, easy realization, high searching speed and less contained parameters. However, the basic particle swarm algorithm has the problems of too fast convergence, premature convergence, small search range, easy convergence to a local extreme value and the like, so that the time for determining parameters such as the phase and the signal amplitude of the beam of the array antenna by adopting the particle swarm algorithm is long, and the efficiency is low.
Disclosure of Invention
Based on this, there is a need to provide a beamforming method and apparatus for an array antenna, which can quickly determine the phase and signal amplitude of the beam of the array antenna.
A method of beamforming for an array antenna, comprising:
generating a particle swarm according to a preset swarm scale, and initializing each parameter of the particle swarm; the parameters include: the starting position and initial velocity of the particle;
calculating the fitness value of each particle in the particle swarm according to a preset fitness function determined according to the required wave beam; the optimal solution of the problem of the fitness function is an amplitude value and a phase value required by synthesizing a target beam;
taking the particle swarm as a parent particle swarm, and adopting an immune algorithm to the parent particle swarm according to the fitness value of the particles to obtain a next-generation particle swarm of the parent particle swarm; the immune algorithm comprises cloning, high-frequency variation and selection operation;
updating the optimal value of the particle and the optimal solution of the problem according to a preset rule, wherein the optimal value of the particle and the optimal solution of the problem are determined according to the position of the particle, and the preset rule is as follows:
if it is
Figure BDA0001145312450000021
Then order
Figure BDA0001145312450000022
If it is
Figure BDA0001145312450000023
Then
Figure BDA0001145312450000024
Wherein f isi(t) is the fitness value of the particle;
Figure BDA0001145312450000025
the optimum value of the particle, i.e. the best position to be experienced by the ith particle in the population,
Figure BDA0001145312450000026
the corresponding adaptive value degree is set;
Figure BDA0001145312450000027
the optimal solution to the problem, i.e. the best position that all particles in the population have experienced,
Figure BDA0001145312450000028
is its corresponding fitness value; x is the number ofi(t) is the position of the ith particle at the time of the tth iteration, and t is the iteration number;
judging whether a preset maximum iteration number is reached;
if so, determining the phase and the signal amplitude of each array element according to the optimal solution of the problem, and forming to obtain the beam of the required array antenna.
In one embodiment, the step of obtaining the next-generation particle group of the parent particle group by using the immune algorithm on the parent particle group according to the fitness value of the particles with the particle group as the parent particle group comprises:
cloning the particles according to the fitness value of the particles to obtain progeny particles of the particles; the number of cloned particles is positively correlated with the fitness value of the particles;
performing high-frequency variation operation on the progeny particles to obtain variant progeny particles;
and calculating the fitness value of the variant particles according to the fitness function, selecting n1 particles with larger fitness values from a set consisting of a parent and variant offspring, and forming a next-generation particle swarm by n-n1 new particles generated randomly.
In one embodiment, if the preset maximum iteration number is not reached, the speed and the position of the particle are updated; and calculating the fitness value of each particle in the next generation of particle swarm according to the fitness function and the updated speed and position of the particle.
In one embodiment, the number of progeny particles of a particle obtained by performing a cloning operation on the particle is:
Figure BDA0001145312450000031
wherein m isiThe number of cloned i-th particles; n is the total number of particles in the population; f. ofi(t) is the fitness value of the particle.
In one embodiment, the formula for updating the velocity of the particle is:
Figure BDA0001145312450000032
the formula for updating the position of the particle is: x is the number ofid(t+1)＝xid(t)+vid(t+1)；
Where i is 1, 2, …, m denotes the ith particle, d is 1, 2, …, n denotes the d-th dimension of the particle, w is the inertial weight, c1 and c2 are acceleration constants, r1 and r2 are random functions uniformly distributed in the (0, 1) interval, and t is the step size of the number of iterations.
A beamforming apparatus of an array antenna, comprising: the system comprises an initialization module, a fitness calculation module, an immune calculation module, an updating module, a judgment module and a shaping module;
the initialization module is used for generating a particle swarm according to a preset swarm scale and initializing each parameter of the particle swarm; the parameters include: the starting position and initial velocity of the particle;
the fitness calculation module is used for calculating the fitness value of each particle in the particle swarm according to a preset fitness function determined according to the required wave beam; the optimal solution of the problem of the fitness function is an amplitude value and a phase value required by synthesizing a target beam;
the immune calculation module is used for taking the particle swarm as a parent particle swarm, and adopting an immune algorithm to the parent particle swarm according to the fitness value of the particles to obtain a next-generation particle swarm of the parent particle swarm; the immune algorithm comprises cloning, high-frequency variation and selection operation;
the updating module is used for updating the optimal value of the particles and the optimal value of the population according to a preset rule, wherein the preset rule is as follows:
if it is
Figure BDA0001145312450000041
Then order
Figure BDA0001145312450000042
If it is
Figure BDA0001145312450000043
Then
Figure BDA0001145312450000044
Wherein f isi(t) is the fitness value of the particle;
Figure BDA0001145312450000045
the optimum value of the particle, i.e. the best position to be experienced by the ith particle in the population,
Figure BDA0001145312450000046
the corresponding adaptive value degree is set;
Figure BDA0001145312450000047
the optimal solution to the problem, i.e. the best position that all particles in the population have experienced,
Figure BDA0001145312450000048
is its corresponding fitness value; x is the number ofi(t) is the position of the ith particle at the time of the tth iteration, and t is the iteration number;
the judging module is used for judging whether the preset maximum iteration times are reached;
and the shaping module is used for determining the phase and the signal amplitude of each array element according to the optimal solution of the problem and shaping to obtain the beam of the required array antenna when the judgment result of the judgment module is yes.
In one embodiment, the immune calculation module comprises: a clone operation module, a mutation operation module and a selection operation module;
the clone operation module is used for carrying out clone operation on the particle according to the fitness value of the particle to obtain a progeny particle of the particle; the number of cloned particles is positively correlated with the fitness value of the particles;
the variation operation module is used for performing high-frequency variation operation on the progeny particles to obtain variant progeny particles;
the selection operation module is used for calculating the fitness value of the variant particle according to the fitness function, selecting n1 particles with larger fitness values from a set consisting of a parent and variant filial generations, and forming a next-generation particle swarm by n-n1 new particles which are randomly generated.
In an embodiment, the beam forming apparatus of the array antenna further includes a parameter updating module, where the parameter updating module is configured to update the speed and the position of the particle if the determination result of the determining module is negative, and the fitness calculating module calculates the fitness value of each particle in the next generation of particle swarm according to the fitness function and the updated speed and position of the particle.
In one embodiment, the number of progeny particles of a particle obtained by performing a cloning operation on the particle is:
Figure BDA0001145312450000049
wherein m isiThe number of cloned i-th particles; n is the total number of particles in the population; f. ofi(t) is the fitness value of the particle.
In one embodiment, the formula for updating the velocity of the particle is:
Figure BDA0001145312450000051
the formula for updating the position of the particle is:
xid(t+1)＝xid(t)+vid(t+1)；
where i is 1, 2, …, m denotes the ith particle, d is 1, 2, …, n denotes the d-th dimension of the particle, w is the inertial weight, c1 and c2 are acceleration constants, r1 and r2 are random functions uniformly distributed in the (0, 1) interval, and t is the step size of the number of iterations.
According to the beam forming method of the array antenna, the optimal solution of the problem of the fitness function is solved by adopting the particle swarm algorithm and the immune algorithm, the immune algorithm is integrated into the optimal solution on the basis of the particle swarm algorithm, and the diversity of the particle swarm and the convergence speed of the algorithm are improved by utilizing the operations of clone replication, high-frequency variation, clone selection and the like of the immune algorithm, so that the characteristic selection efficiency is effectively improved, and the antenna formed beam meets the requirements.
Drawings
Fig. 1 is a flow chart of a beamforming method of an array antenna according to an embodiment;
FIG. 2 is a schematic diagram of an array antenna of an embodiment;
fig. 3 is a directional diagram of a beam obtained by the array antenna shown in fig. 2 by using the beam forming method of the array antenna of the present invention;
FIG. 4 is a simulation directional diagram of a beam obtained by adding amplitude-phase excitation data of the beam shown in Table 1 to simulation software;
fig. 5 is a functional block diagram of a beamforming apparatus of an array antenna according to an embodiment.
Detailed Description
As shown in fig. 1, a beam forming method of an array antenna includes the following steps:
s102: and generating a particle swarm according to a preset swarm size, and initializing each parameter of the particle swarm.
Particle swarmThe population scale of (1) is preset, a particle group of a corresponding scale is generated, and each parameter of the particle group is initialized. The parameters of the particle population include: starting position x of the particlei(t)＝(xi1，xi2，……，xid) Initial velocity vi(t)＝(vi1，vi2，……，vid). Wherein each xi(t) represents one possible solution, in the present invention, the optimization variables are abstracted as the position vectors of the particles.
S104: and calculating the fitness value of each particle in the particle swarm according to a preset fitness function determined according to the required wave beam.
The fitness function is determined in advance according to the required beam. The fitness function is related to the parameters of the shaped beam. The fitness function required for different beams may vary. And calculating the fitness value of each particle in the particle swarm according to the fitness function. The optimal solution to the problem of the fitness function is the amplitude and phase values required to synthesize the target beam. Mapping to the particle swarm algorithm is the best location for the particle.
S106: taking the particle swarm as a parent particle swarm, and adopting an immune algorithm to the parent particle swarm according to the fitness value of the particles to obtain a next-generation particle swarm of the parent particle swarm; the immune algorithm includes cloning, high frequency variation and selection operations.
The beam forming method of the array antenna of the embodiment fuses the immune algorithm and the particle swarm algorithm, and introduces the cloning, high-frequency variation and selection operation of the immune algorithm into the particle swarm algorithm. The cloning operation enables excellent particles in the population to be stored, the high-frequency variation operation generates new individuals, the diversity of the population is effectively increased, the selection operation selects the optimal individuals, and the algorithm degradation is avoided.
S108: updating the optimal value of the particle and the optimal solution of the problem according to a preset rule, wherein the optimal value of the particle and the optimal solution of the problem are determined according to the position of the particle, and the preset rule is as follows:
if it is
Figure BDA0001145312450000061
Then order
Figure BDA0001145312450000062
If it is
Figure BDA0001145312450000063
Then
Figure BDA0001145312450000064
Wherein f isi(t) is the fitness value of the particle;
Figure BDA0001145312450000065
the optimum value of the particle, i.e. the best position to be experienced by the ith particle in the population,
Figure BDA0001145312450000066
the corresponding adaptive value degree is set;
Figure BDA0001145312450000067
the optimal solution to the problem, i.e. the best position that all particles in the population have experienced,
Figure BDA0001145312450000068
is its corresponding fitness value; x is the number ofi(t) is the position of the ith particle at the time of the tth iteration, and t is the number of iterations. Wherein, the optimal solution of the problem and the optimal value of the particles are obtained by calculation according to the position.
S110: and judging whether the preset maximum iteration times are reached. If so, step S112 is executed, and if not, step S113 is executed.
S112: and determining the phase and the signal amplitude of each array element according to the optimal solution of the problem, and forming to obtain the beam of the required array antenna.
According to the beam forming method of the array antenna, the optimal solution of the problem of the fitness function is solved by adopting the particle swarm algorithm and the immune algorithm, the immune algorithm is integrated into the optimal solution on the basis of the particle swarm algorithm, and the diversity of the particle swarm and the convergence speed of the algorithm are improved by utilizing the operations of clone replication, high-frequency variation, clone selection and the like of the immune algorithm, so that the characteristic selection efficiency is effectively improved, and the antenna formed beam meets the requirements.
In another embodiment, step S106 includes the following steps 1 to 3:
step 1: cloning the particles according to the fitness value of the particles to obtain progeny particles of the particles; the number of cloned particles is positively correlated with the fitness value of the particles.
Specifically, the number of progeny particles obtained by cloning the particles is as follows:
Figure BDA0001145312450000071
wherein m isiThe number of cloned i-th particles; n is the total number of particles in the population; f. ofi(t) is the fitness value of the particle.
Excellent particles in the parent population are preserved by cloning the particles.
Step 2: and carrying out high-frequency variation operation on the progeny particles to obtain the variant progeny particles.
Wherein the mutation probability satisfies a gaussian distribution N (0, 1). And new individuals are generated through mutation operation, so that the population diversity is effectively increased.
And step 3: and calculating the fitness value of the variant particles according to the fitness function, selecting n1 particles with larger fitness values from a set consisting of a parent and variant offspring, and forming a next-generation particle swarm by n-n1 new particles generated randomly.
Through the selection operation, the optimal individual is selected, and algorithm degradation is avoided.
In another embodiment, if the determination result in step S110 is no, that is, the preset maximum iteration number is not reached, the following steps are executed:
s113: the velocity and position of the particles are updated. After the step of step S113, the process returns to step S114, and the fitness value of each particle in the next-generation particle group is calculated from the fitness function and the updated speed and position of the particle.
The formula for updating the velocity of the particle is:
Figure BDA0001145312450000072
the formula for updating the position of the particle is:
xid(t+1)＝xid(t)+vid(t+1)；
where i is 1, 2, …, m denotes the ith particle, d is 1, 2, …, n denotes the d-th dimension of the particle, w is the inertial weight, c1 and c2 are acceleration constants, r1 and r2 are random functions uniformly distributed in the (0, 1) interval, and t is the step size of the number of iterations.
According to the beam forming method of the array antenna, the optimal solution of the problem of the fitness function is solved by adopting the particle swarm algorithm and the immune algorithm, the immune algorithm comprises cloning, high-frequency variation and selection operation, excellent particles in a population are stored through the cloning operation, new individuals are generated through the high-frequency variation operation, the population diversity is effectively increased, the optimal individuals are selected through the selection operation, and algorithm degradation is avoided. The optimal solution of the problem of the fitness function is the amplitude and phase value required by the wave beam, the problems that the fitness is low and the global optimal solution is not easy to find in the integration of the wave beam of the array antenna in the prior art can be solved by combining the particle swarm algorithm and the immune algorithm, the searching speed can be effectively improved, the efficiency is high, and the shaped wave beam of the antenna meets the requirement.
Next, a beam forming method of an array antenna according to the present invention will be described with reference to specific examples.
In this embodiment, an antenna schematic diagram of a ten-unit mobile communication base station is shown in fig. 2, ten half-wave oscillators form an equidistant linear array structure, the oscillator spacing is 0.8 λ, and the height of the oscillator from the reflection plate is 0.25 λ, where λ is the wavelength of the working frequency. By changing the amplitude and the phase of the feed current of each oscillator, the sidelobe suppression on the vertical plane directional diagram is smaller than-20 dB, and the lower first zero point filling is larger than-15 dB, so that the adjacent area interference is suppressed and the condition that the tower is dark is avoided.
And synthesizing the directional diagram of the 2.2GHz central frequency point by adopting a beam forming method of the array antenna, and calculating the amplitude and phase value required by the array antenna to form a target beam.
In this example, the particle group size is set to 40, the number of iterations is 30, the inertial weight w is 0.5, and the acceleration constants c1 and c2 are taken to be 2. The fitness function is defined as:
Fitness＝α∣USL-G1SL∣+β∣DNF-GNF∣+γ∣DSL-G2SL∣
where USL is the upper side lobe maximum level, DNF is the lower first null depth, DSL is the lower side lobe maximum level, G1SL and G2SL are the desired side lobe levels, GNF is the desired null depth, α, β, and γ are weighting factors.
The directional pattern of the beam of the array antenna obtained by shaping is shown in fig. 3, and it can be seen from the diagram that the level of the upper side lobe of the antenna array is suppressed to be below minus 20dB while the main lobe is formed, a null point filling area of minus 15dB is formed in the coverage area, the integrated directional pattern is consistent with the directional pattern of the target beam, and the amplitude-phase excitation data of the shaped beam is shown in table 1.
TABLE 1 amplitude-phase excitation data of shaped beams
Unit cell 1 2 3 4 5 6 7 8 9 10
Amplitude (w) 1 1.1 1.4 1.4 1.8 2.7 3 3 1.5 1.6
Phase (°) 25 4.6 5.3 14.8 24 25 23.8 18.6 0 0
The amplitude phase excitation is substituted into a high-frequency structure simulation software three-dimensional high-frequency electromagnetic field simulation tool for confirmation, and the radiation pattern of the antenna array is obtained and is shown in figure 4. As can be seen from comparing fig. 3 and fig. 4, the coincidence degree of the simulation result and the result obtained by the algorithm is very high, thereby verifying the effectiveness of the beam forming method of the array antenna of the present invention.
A beamforming apparatus for an array antenna, as shown in fig. 5, includes: an initialization module 501, a fitness calculation module 502, an immune calculation module 503, an update module 504, a judgment module 505 and a shaping module 506.
An initialization module 501, configured to generate a particle swarm according to a preset swarm scale, and initialize each parameter of the particle swarm; the parameters include: the starting position and the initial velocity of the particle.
A fitness calculation module 502, configured to calculate a fitness value of each particle in the particle swarm according to a preset fitness function determined according to the required beam; the optimal solution of the problem of the fitness function is the amplitude and phase value required by the synthesized target beam;
the immune calculation module 503 is configured to use the particle swarm as a parent particle swarm, and obtain a next-generation particle swarm of the parent particle swarm by using an immune algorithm for the parent particle swarm according to the fitness value of the particles; the immune algorithm includes cloning, high frequency variation and selection operations.
An updating module 504, configured to update the optimal value of the particle and the optimal value of the population according to a preset rule, where the preset rule is:
if it is
Figure BDA0001145312450000091
Then order
Figure BDA0001145312450000092
If it is
Figure BDA0001145312450000093
Then
Figure BDA0001145312450000094
Wherein f isi(t) is the fitness value of the particle;
Figure BDA0001145312450000101
the optimum value of the particle, i.e. the best position to be experienced by the ith particle in the population,
Figure BDA0001145312450000102
the corresponding adaptive value degree is set;
Figure BDA0001145312450000103
the optimal solution to the problem, i.e. the best position that all particles in the population have experienced,
Figure BDA0001145312450000104
is its corresponding fitness value; x is the number ofi(t) is the position of the ith particle at the time of the tth iteration, and t is the number of iterations.
And a judging module 505, configured to judge whether a preset maximum iteration number is reached.
And a shaping module 506, configured to determine the phase and the signal amplitude of each array element according to the optimal solution of the problem and shape to obtain a beam of the required array antenna when the determination result of the determining module is yes.
The beam forming device of the array antenna of the embodiment fuses the immune algorithm and the particle swarm algorithm, and introduces the cloning, high-frequency variation and selection operation of the immune algorithm into the particle swarm algorithm. The cloning operation enables excellent particles in the population to be stored, the high-frequency variation operation generates new individuals, the diversity of the population is effectively increased, the selection operation selects the optimal individuals, and the algorithm degradation is avoided.
In another embodiment, the immune calculation module 503 includes: a clone operation module, a mutation operation module and a selection operation module;
the clone operation module is used for carrying out clone operation on the particles according to the fitness value of the particles to obtain offspring particles of the particles; the number of cloned particles is positively correlated with the fitness value of the particles.
Specifically, the number of progeny particles obtained by cloning the particles is as follows:
Figure BDA0001145312450000105
wherein m isiThe number of cloned i-th particles; n is the total number of particles in the populationCounting; f. ofi(t) is the fitness value of the particle.
The variation operation module is used for carrying out high-frequency variation operation on the progeny particles to obtain variant progeny particles;
and the selection operation module is used for calculating the fitness value of the variation particle according to the fitness function, selecting n1 particles with larger fitness values from a set consisting of a parent and a variation child, and forming a next-generation particle swarm by the n-n1 new particles generated randomly.
According to the beam forming device of the array antenna, the optimal solution of the problem of the fitness function is solved by adopting the particle swarm algorithm and the immune algorithm, the immune algorithm is integrated into the optimal solution on the basis of the particle swarm algorithm, the diversity of the particle swarm and the convergence speed of the algorithm are improved by utilizing the operations of clone replication, high-frequency variation, clone selection and the like of the immune algorithm, the characteristic selection efficiency is effectively improved, and the shaped beam of the antenna meets the requirement.
In yet another embodiment, the beam forming apparatus for an array antenna further includes a parameter updating module, where the parameter updating module is configured to update the speed and the position of the particle when the determination result of the determining module is negative, and the fitness calculating module calculates the fitness value of each particle in the next generation of particle swarm according to the fitness function and the updated speed and position of the particle.
The formula for updating the velocity of the particle is:
Figure BDA0001145312450000111
the formula for updating the position of the particle is:
xid(t+1)＝xid(t)+vid(t+1)；
where i is 1, 2, …, m denotes the ith particle, d is 1, 2, …, n denotes the d-th dimension of the particle, w is the inertial weight, c1 and c2 are acceleration constants, r1 and r2 are random functions uniformly distributed in the (0, 1) interval, and t is the step size of the number of iterations. According to the beam forming device of the array antenna, the optimal solution of the problem of the fitness function is solved by adopting the particle swarm algorithm and the immune algorithm, the immune algorithm comprises cloning, high-frequency variation and selection operation, excellent particles in a population are stored by the cloning operation, new individuals are generated by the high-frequency variation operation, the population diversity is effectively increased, the optimal individuals are selected by the selection operation, and algorithm degradation is avoided. The optimal solution of the problem of the fitness function is the amplitude and phase value required by the wave beam, the problems that the fitness is low and the global optimal solution is not easy to find in the integration of the wave beam of the array antenna in the prior art can be solved by combining the particle swarm algorithm and the immune algorithm, the searching speed can be effectively improved, the efficiency is high, and the shaped wave beam of the antenna meets the requirement.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (10)

1. A method for beamforming for an array antenna, comprising:
generating a particle swarm according to a preset swarm scale, and initializing each parameter of the particle swarm; the parameters include: the starting position and initial velocity of the particle;
calculating the fitness value of each particle in the particle swarm according to a preset fitness function determined according to the required wave beam; the optimal solution of the problem of the fitness function is an amplitude value and a phase value required by synthesizing a target beam;
taking the particle swarm as a parent particle swarm, and adopting an immune algorithm to the parent particle swarm according to the fitness value of the particles to obtain a next-generation particle swarm of the parent particle swarm; the immune algorithm comprises cloning, high-frequency variation and selection operation;
updating the optimal value of the particle and the optimal solution of the problem according to a preset rule, wherein the optimal value of the particle and the optimal solution of the problem are determined according to the position of the particle, and the preset rule is as follows:
if it is
Figure FDA0002239273180000011
Then order
Figure FDA0002239273180000012
If it is
Figure FDA0002239273180000013
Then
Figure FDA0002239273180000014
Wherein f isi(t) is the fitness value of the particle;
Figure FDA0002239273180000015
the optimum value of the particle, i.e. the best position to be experienced by the ith particle in the population,
Figure FDA0002239273180000016
the corresponding adaptive value degree is set;
Figure FDA0002239273180000017
the optimal solution to the problem, i.e. the best position that all particles in the population have experienced,
Figure FDA0002239273180000018
is its corresponding fitness value; x is the number ofi(t) is the position of the ith particle at the time of the tth iteration, and t is the iteration number;
judging whether a preset maximum iteration number is reached;
if so, determining the phase and the signal amplitude of each array element according to the optimal solution of the problem, and forming to obtain the beam of the required array antenna;
the step of obtaining the next generation of the parent particle swarm by taking the particle swarm as the parent particle swarm and adopting an immune algorithm to the parent particle swarm according to the fitness value of the particles comprises the following steps:
cloning the particles according to the fitness value of the particles to obtain progeny particles of the particles; the number of cloned particles is positively correlated with the fitness value of the particles;
performing high-frequency variation operation on the progeny particles to obtain variant progeny particles, wherein the variation probability of the high-frequency variation operation meets Gaussian distribution N (0, 1);
calculating the fitness value of the variant particles according to the fitness function, selecting n1 particles with larger fitness values from a set consisting of a parent and variant offspring, and forming a next-generation particle swarm with n-n1 new particles which are randomly generated;
the number of progeny particles obtained by cloning the particles is as follows:
Figure FDA0002239273180000021
wherein m isiThe number of cloned i-th particles; n is the total number of particles in the population; f. ofi(t) is the fitness value of the particle.
2. The method of claim 1, wherein the velocity and position of the particle are updated if it is determined that the preset maximum number of iterations has not been reached; and calculating the fitness value of each particle in the next generation of particle swarm according to the fitness function and the updated speed and position of the particle.
3. The method of claim 1, wherein the fitness function is defined as:
Fitness＝α∣USL-G1SL∣+β∣DNF-GNF∣+γ∣DSL-G2SL∣；
fitness is a Fitness function, USL is the maximum level of an upper side lobe, DNF is the first zero notch depth, DSL is the maximum level of a lower side lobe, G1SL and G2SL are expected side lobe levels, GNF is the expected zero notch depth, and α, β and gamma are weight factors.
4. The method of claim 2, wherein the formula for updating the velocity of the particle is:
Figure FDA0002239273180000022
the formula for updating the position of the particle is: x is the number ofid(t+1)＝xid(t)+vid(t+1)；
Where i is 1, 2, …, m denotes the ith particle, d is 1, 2, …, n denotes the d-th dimension of the particle, w is the inertial weight, c1 and c2 are acceleration constants, r1 and r2 are random functions uniformly distributed in the (0, 1) interval, and t is the step size of the number of iterations.
5. A beam forming apparatus for an array antenna, comprising: the system comprises an initialization module, a fitness calculation module, an immune calculation module, an updating module, a judgment module and a shaping module;
the initialization module is used for generating a particle swarm according to a preset swarm scale and initializing each parameter of the particle swarm; the parameters include: the starting position and initial velocity of the particle;
the fitness calculation module is used for calculating the fitness value of each particle in the particle swarm according to a preset fitness function determined according to the required wave beam; the optimal solution of the problem of the fitness function is an amplitude value and a phase value required by synthesizing a target beam;
the immune calculation module is used for taking the particle swarm as a parent particle swarm, and adopting an immune algorithm to the parent particle swarm according to the fitness value of the particles to obtain a next-generation particle swarm of the parent particle swarm; the immune algorithm comprises cloning, high-frequency variation and selection operation;
the updating module is used for updating the optimal value of the particles and the optimal value of the population according to a preset rule, wherein the preset rule is as follows:
if it is
Figure FDA0002239273180000031
Then order
Figure FDA0002239273180000032
If it is
Figure FDA0002239273180000033
Then
Figure FDA0002239273180000034
Wherein f isi(t) is the fitness value of the particle;
Figure FDA0002239273180000035
the optimum value of the particle, i.e. the best position to be experienced by the ith particle in the population,
Figure FDA0002239273180000036
the corresponding adaptive value degree is set;
Figure FDA0002239273180000037
the optimal solution to the problem, i.e. the best position that all particles in the population have experienced,
Figure FDA0002239273180000038
is its corresponding fitness value; x is the number ofi(t) is the position of the ith particle at the time of the tth iteration, and t is the iteration number;
the judging module is used for judging whether the preset maximum iteration times are reached;
the shaping module is used for determining the phase and the signal amplitude of each array element according to the optimal solution of the problem and shaping to obtain the beam of the required array antenna when the judgment result of the judgment module is yes;
the immune calculation module comprises: a clone operation module, a mutation operation module and a selection operation module;
the clone operation module is used for carrying out clone operation on the particle according to the fitness value of the particle to obtain a progeny particle of the particle; the number of cloned particles is positively correlated with the fitness value of the particles;
the variation operation module is used for performing high-frequency variation operation on the progeny particles to obtain variant progeny particles, and the variation probability of the high-frequency variation operation meets Gaussian distribution N (0, 1);
the selection operation module is used for calculating the fitness value of the variant particle according to the fitness function, selecting n1 particles with larger fitness values from a set consisting of a parent and variant filial generations, and forming a next-generation particle swarm by the n-n1 new particles which are randomly generated;
the number of progeny particles obtained by cloning the particles is as follows:
Figure FDA0002239273180000039
wherein m isiThe number of cloned i-th particles; n is the total number of particles in the population; f. ofi(t) is the fitness value of the particle.
6. The apparatus of claim 5, further comprising a parameter updating module, wherein the parameter updating module is configured to update the speed and the position of the particle if the determination result of the determining module is negative, and the fitness calculating module calculates the fitness value of each particle in the next generation of particle swarm according to the fitness function and the updated speed and position of the particle.
7. The apparatus of claim 5, wherein the fitness function is defined as:
Fitness＝α∣USL-G1SL∣+β∣DNF-GNF∣+γ∣DSL-G2SL∣；
fitness is a Fitness function, USL is the maximum level of an upper side lobe, DNF is the first zero notch depth, DSL is the maximum level of a lower side lobe, G1SL and G2SL are expected side lobe levels, GNF is the expected zero notch depth, and α, β and gamma are weight factors.
8. The apparatus of claim 6, wherein the formula for updating the velocity of the particle is:
Figure FDA0002239273180000041
the formula for updating the position of the particle is:
xid(t+1)＝xid(t)+vid(t+1)；
where i is 1, 2, …, m denotes the ith particle, d is 1, 2, …, n denotes the d-th dimension of the particle, w is the inertial weight, c1 and c2 are acceleration constants, r1 and r2 are random functions uniformly distributed in the (0, 1) interval, and t is the step size of the number of iterations.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 4.
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