CN113361053B - Distributed antenna layout optimization design method and system - Google Patents
Distributed antenna layout optimization design method and system Download PDFInfo
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- CN113361053B CN113361053B CN202110643567.0A CN202110643567A CN113361053B CN 113361053 B CN113361053 B CN 113361053B CN 202110643567 A CN202110643567 A CN 202110643567A CN 113361053 B CN113361053 B CN 113361053B
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Abstract
The invention discloses a distributed antenna layout optimization design method and a system, which belong to the technical field of antenna layout design and comprise the following steps: s1: defining a solution space; s2: defining a fitness function; s3: the random position and speed of the initial particle swarm; s4: gradually searching for the optimal solution space; s5: updating the particle speed; s6: the particle position is updated. By applying the particle swarm algorithm, the invention can obviously reduce the gain difference between the grating lobe and the main lobe by taking the array coordinate position as a variable and optimizing the array arrangement and designing a reasonable corresponding fitness function under the condition of meeting the requirement of the minimum unit interval, thereby achieving the effects of inhibiting the grating lobe and improving the anti-interference capability of the system and being worth being popularized and used.
Description
Technical Field
The invention relates to the technical field of antenna layout design, in particular to a distributed antenna layout optimization design method and a distributed antenna layout optimization design system.
Background
The new generation ground station system adopts a plurality of small-caliber antennas for distributed array formation, and if a plurality of reflector antennas adopt conventional regular grid arrangement, such as rectangular grids, triangular grids and the like, because the distance between array elements far exceeds the wavelength of a working frequency band, a plurality of grating lobes are generated near a main lobe, and the gain value is close to the main lobe. Taking 36 reflecting surface array elements as an example for simulation calculation, under the arrangement of rectangular grids, the gain difference between the grating lobes and the main lobe is about 1dB, and if interference of an adjacent spacecraft enters from the grating lobes, the interference is easily caused to the main lobe. Therefore, by reasonably designing the antenna layout and the unit spacing, the height of the grating lobe in the coverage range needs to be suppressed, and the service quality of each system user needs to be improved.
Since the main reason for generating grating lobes is caused by regular arrangement, an irregular arrangement mode, also called as random arrangement, is considered, and an optimal result is achieved mainly through optimization, so that a distributed antenna layout optimization design method and a distributed antenna layout optimization design system are provided.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method adopts an irregular arrangement mode, uses array coordinate positions as variables and optimizes array arrangement by optimizing array arrangement under the condition of meeting the requirement of minimum unit spacing by applying a particle swarm algorithm and designing a reasonable corresponding fitness function, thereby finally inhibiting grating lobes and realizing the optimization of the distributed antenna layout.
The invention solves the technical problems through the following technical scheme, and the invention comprises the following steps:
s1: defining a solution space
Selecting parameters to be optimized, determining the value range of the parameters, and setting the minimum value and the maximum value of each dimension in a multi-dimensional optimization space;
s2: defining a fitness function
The fitness function chosen is designed as follows:
wherein, theta 1 Is the angular extent of the element beam width, Θ 2 LOSS is the main lobe gain LOSS, a, for other side lobe regions 1 、a 2 And a 3 Weight coefficients of the respective components;
s3: initial particle swarm random position and velocity
Given the initial time random position x and velocity v of each particle, the initial position is the first position each particle encounters, i.e. the individual optimal position p of the particle best First population optimal position g best Selecting from individual optima;
s4: finding an optimum in solution space step by step
Calculating the particle fitness of the current time position x and the last time position p best And g best Comparing the particle fitness, and replacing p if the particle fitness of the current position x is better best And g best ；
S5: updating particle velocity
Velocity of each particle according to p best And g best Changing, and accelerating to the direction with the optimal fitness according to a speed updating formula;
s6: updating particle positions
After the velocity is calculated, the particle moves to its next position and the velocity acts over a given time period Δ t, updating the coordinates of the particle in the multidimensional space.
Further, the position coordinates of each particle after the optimization, that is, the position of each antenna.
Further, in the step S5, the velocity update formula is as follows:
v n+1 ＝ω·v n +c 1 ·rand·(p best,n -x n )+c 2 ·rand·(g best,n -x n )
wherein v is n Is the velocity of the particle at the nth time，x n Is the position of the particle at time n; new velocity v n+1 Inheriting the original speed in proportion of omega and moving to p best And g best Increases in the direction of the particular dimension of (a); c. C 1 And c 2 Indicating the proportionality coefficient of "gravity" in both directions, c 1 Increasing the exploration of the solution space by the excited particles, c 2 Is explored towards global optimality.
Further, in the step S6, the coordinate updating expression is:
x n+1 ＝x n +Δt·v n+1 。
further, in said step S6, the time period Δ t takes the unit l.
The invention also provides a distributed antenna layout optimization design system, which adopts the optimization design method to carry out optimization design on the antenna layout, and comprises the following steps:
the solution space definition module is used for selecting parameters to be optimized and determining the value range of the parameters, and the minimum value and the maximum value of each dimension are set in the multi-dimensional optimization space;
the function definition module is used for designing and selecting a fitness function;
the initialization module is used for giving an initial time random position x and a speed v of each particle;
the optimal searching module is used for calculating the particle fitness of the current time position x and the previous time position p best And g best Comparing the particle fitness, and replacing p if the particle fitness of the current position x is better best And g best ；
The speed updating module is used for accelerating to the direction with the optimal fitness according to a speed updating formula;
the position updating module is used for updating the coordinates of the particles in the multi-dimensional space when the particles move to the next position after the velocity is calculated and the velocity acts within a given time period delta t;
the central processing module is used for sending instructions to other modules to complete related actions;
the solution space definition module, the function definition module, the initialization module, the optimal searching module, the speed updating module and the position updating module are all electrically connected with the central processing module.
Compared with the prior art, the invention has the following advantages: according to the distributed antenna layout optimization design method, the particle swarm algorithm is applied, the array coordinate position is used as a variable under the condition that the requirement of the minimum unit interval is met, the fitness function which is reasonably corresponding to the array layout is designed through optimization of array arrangement, the gain difference between the grating lobe and the main lobe can be remarkably reduced, the grating lobe is restrained, the anti-interference capacity of a system is improved, and the distributed antenna layout optimization design method is worthy of being popularized and used.
Drawings
Fig. 1 is a schematic view of an antenna arrangement scenario of a distributed small-aperture ground station in an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating an implementation of the optimal design method according to an embodiment of the present invention;
FIG. 3a is a schematic diagram of a rectangular grid arrangement of 36 small-aperture ground station antennas in an embodiment of the present invention;
fig. 3b is a schematic diagram of a grating lobe suppression situation under rectangular grid arrangement of 36 small-aperture ground station antennas in the embodiment of the present invention;
fig. 4a is a schematic diagram of irregular arrangement of 36 small-aperture ground station antennas after optimization according to an embodiment of the present invention;
fig. 4b is a schematic diagram of a grating lobe suppression situation under an irregular arrangement of 36 small-aperture ground station antennas in the embodiment of the present invention.
Detailed Description
The following examples are given for the detailed implementation and specific operation of the present invention, but the scope of the present invention is not limited to the following examples.
As shown in fig. 1, which is a schematic view of an antenna arrangement scenario of a distributed small-aperture ground station in this embodiment, position coordinates after the optimization of each particle, that is, a position of each antenna, are described below.
As shown in fig. 2, the present embodiment provides a technical solution: a distributed antenna layout optimization design method comprises the following steps:
(1) Defining a solution space: in the first step of executing the particle swarm algorithm, parameters x to be optimized are selected and given appropriate value ranges, in the embodiment, the value range of x is limited to the range of an actual distributed ground station antenna installation site, a planar rectangular coordinate system can be correspondingly established for the two-dimensional space optimization problem, and if the actual site range is 100m multiplied by 50m, the value range of x abscissa is [0,100], the value range of x ordinate is [0,50] and the unit is m.
(2) Defining a fitness function: this step is important, linking the physical space and the optimization algorithm. For different specific problems, a reasonably corresponding fitness function needs to be designed to ensure that the convergence direction approaches to the expected direction, which is beneficial to solving the problem. A fitness function should be associated with each optimized parameter. The fitness function designed and selected in the optimization design algorithm is as follows:
wherein, theta 1 Angular extent of element beamwidth, Θ 2 LOSS is the main lobe gain LOSS, a, for other side lobe regions 1 、a 2 And a 3 Respectively, the weight coefficients of the respective components.
(3) Initial particle random position x and velocity v: an initial time random position x and velocity v for each particle are given. Since the initial position is the first position each particle encounters, this position is also considered to be the individual optimal position p of the particle best . First population optimal position g best Selected from individual optima.
(4) Finding the optimum in the solution space step by step: calculating the particle fitness of the current time position x and the last time position p best And g best Comparing the particle fitness, and replacing p if the particle fitness of the current position x is better best And g best 。
(5) Update particle velocity v: the processing of the particle velocity is the core of the algorithm optimization, each particleVelocity according to p best And g best Changing, accelerating to the direction with the optimal fitness according to:
v n+1 ＝ω·v n +c 1 ·rand·(p best,n -x n )+c 2 ·rand·(g best,n -x n )
wherein v is n Is the velocity, x, of the particle at time n n Is the position of the particle at time n; the formula calculates for each dimension of the particle in the multi-dimensional optimization space. As can be seen from the above formula, the new speed inherits the original speed in the proportion of omega and moves to p best And g best Increases in direction of the particular dimension of (a). c. C 1 And c 2 A proportionality coefficient representing "gravity" in both directions, also known as self-cognition and social influence rate, c 1 Can stimulate the exploration of the solution space by the particles, c 2 The increase in (c) may be explored towards global optimality.
(6) Movement of the particles: once the velocity is calculated, the particle can move to its next position. The speed acts over a given time period Δ t, which is typically in units of l for ease of solution to the optimization time formula. The particles update coordinates in the multidimensional space as:
x n+1 ＝x n +Δt·v n+1
after the coordinates of each particle are updated, the position of each antenna is correspondingly determined.
As shown in fig. 3, which is a schematic diagram of the rectangular grid arrangement and grating lobe suppression situation of the 36 small-aperture ground station antennas in this embodiment, it can be seen that the gain difference between the grating lobe and the main lobe is about 1dB before suppression.
As shown in fig. 4, a schematic diagram of the irregular array of 36 small-aperture ground station antennas and the grating lobe suppression situation after the algorithm optimization is performed by using the present embodiment, it can be seen that the gain difference between the grating lobe and the main lobe is greater than 10dB after the particle swarm algorithm is applied to suppress, and the grating lobe is significantly suppressed.
In summary, the distributed antenna layout optimization design method of the embodiment is characterized in that the particle swarm algorithm is applied, the array coordinate position is used as a variable under the condition that the minimum unit interval requirement is met, the array arrangement is optimized, and a reasonable corresponding fitness function is designed, so that the gain difference between the grating lobe and the main lobe can be remarkably reduced, the grating lobe is suppressed, the anti-interference capability of the system is improved, and the distributed antenna layout optimization design method is worthy of being popularized and used.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (5)
1. A distributed antenna layout optimization design method is characterized by comprising the following steps:
s1: defining a solution space
Selecting parameters to be optimized, determining the value range of the parameters, and setting the minimum value and the maximum value of each dimension in a multi-dimensional optimization space;
s2: defining a fitness function
The fitness function chosen is designed as follows:
wherein, theta 1 Is the angular extent of the element beam width, Θ 2 LOSS is the main lobe gain LOSS, a, for other side lobe regions 1 、a 2 And a 3 Weight coefficients of the respective components;
s3: initial particle swarm random position and velocity
Given the initial time random position x and velocity v of each particle, the initial position is the first position each particle encounters, i.e. the individual optimal position p of the particle best First population optimal position g best Selecting from individual optima;
s4: finding an optimum in solution space step by step
Calculating the particle fitness of the current time position x and the last time position p best And g best Particle adaptationDegree comparison, if the particle fitness of the current time position x is better, replacing p best And g best ；
S5: velocity of renewed particles
Velocity of each particle according to p best And g best Changing, and accelerating to the direction with the optimal fitness according to a speed updating formula;
s6: updating particle positions
After the speed is calculated, the particle moves to the next position, the speed acts in a given time period delta t, and the coordinate of the particle in the multi-dimensional space is updated;
the position coordinates of each particle after the completion of the optimization, that is, the position of each antenna.
2. The method according to claim 1, wherein the method comprises: in step S5, the velocity update formula is as follows:
v n+1 ＝ω·v n +c 1 ·rand·(p best,n -x n )+c 2 ·rand·(g best,n -x n )
wherein v is n Is the velocity, x, of the particle at time n n Is the position of the particle at time n; new velocity v n+1 Inheriting the original speed in proportion of omega and moving to p best And g best Increases in the direction of the particular dimension of (a); c. C 1 And c 2 Representing the proportionality coefficient of gravity in both directions, c 1 Increasing the exploration of the solution space by the exciting particles, c 2 The increase is explored towards global optimality.
3. The method according to claim 1, wherein the method comprises: in step S6, the coordinate updating expression is:
x n+1 ＝x n +Δt·v n+1 。
4. the method according to claim 1, wherein the method comprises: in said step S6, the time period Δ t takes the unit l.
5. A distributed antenna layout optimization design system for performing optimization design on an antenna layout by using the optimization design method according to any one of claims 1 to 4, comprising:
the solution space definition module is used for selecting parameters to be optimized and determining the value range of the parameters, and the minimum value and the maximum value of each dimension are set in the multi-dimensional optimization space;
the function definition module is used for designing and selecting the fitness function, and the fitness function is designed and selected as follows:
wherein, theta 1 Is the angular extent of the element beam width, Θ 2 LOSS is the main lobe gain LOSS, a, for other side lobe regions 1 、a 2 And a 3 Weight coefficients of the respective components;
the initialization module is used for giving an initial time random position x and a speed v of each particle;
the optimal searching module is used for calculating the particle fitness of the current time position x and the previous time position p best And g best Particle fitness is compared, and if the particle fitness of the current position x is better, p is replaced best And g best ；
The speed updating module is used for accelerating to the direction with the optimal fitness according to a speed updating formula;
the position updating module is used for moving the particle to the next position after the velocity is calculated, the velocity acts within a given time period delta t, and the coordinate of the particle in the multidimensional space is updated;
the central processing module is used for sending instructions to other modules to complete related actions;
the solution space definition module, the function definition module, the initialization module, the optimal searching module, the speed updating module and the position updating module are all electrically connected with the central processing module.
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