CN113361053A - Distributed antenna layout optimization design method and system - Google Patents

Distributed antenna layout optimization design method and system Download PDF

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CN113361053A
CN113361053A CN202110643567.0A CN202110643567A CN113361053A CN 113361053 A CN113361053 A CN 113361053A CN 202110643567 A CN202110643567 A CN 202110643567A CN 113361053 A CN113361053 A CN 113361053A
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王烁
吴瑞荣
任伟龙
赵忠超
姚艳军
章明明
赵宇峰
王昕�
裴恒
魏驷
张志成
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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: initializing a random position and speed of a 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

Distributed antenna layout optimization design method and system
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 main lobe is easily interfered. Therefore, the grating lobe height in the coverage area needs to be suppressed by reasonably designing the antenna layout and the unit spacing, and the service quality of each system user is improved.
Because the main reason for generating the grating lobes is caused by regular array arrangement, an irregular array arrangement mode, also called random array 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 array arrangement mode, adopts an array coordinate position as a variable by applying a particle swarm algorithm under the condition of meeting the requirement of minimum unit spacing, designs a reasonable corresponding fitness function by optimizing array arrangement, finally restrains grating lobes and realizes 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 selected by design is as follows:
Figure BDA0003108911460000011
wherein, theta1Is the angular extent of the element beam width, Θ2LOSS is the main lobe gain LOSS, a, for other side lobe regions1、a2And a3Weight 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 particlebestFirst population optimal position gbestSelecting from individual optima;
s4: finding optima in solution space step by step
Calculating the particle fitness of the current time position x and the last time position pbestAnd gbestComparing the particle fitness, and replacing p if the particle fitness of the current position x is betterbestAnd gbest；
S5: updating particle velocity
Velocity of each particle according to pbestAnd gbestChanging, 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:
vn+1＝ω·vn+c1·rand·(pbest,n-xn)+c2·rand·(gbest,n-xn)
wherein v isnIs the velocity, x, of the particle at time nnIs the position of the particle at time n; new velocity vn+1Inheriting the original speed in proportion of omega and moving to pbestAnd gbestIncreases in the direction of the particular dimension of (a); c. C1And c2Indicating the proportionality coefficient of "gravity" in both directions, c1Increasing the exploration of the solution space by the excited particles, c2The increase in (c) is explored towards global optimality.
Further, in the step S6, the coordinate updating expression is:
xn+1＝xn+Δt·vn+1。
further, in the 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 pbestAnd gbestComparing the particle fitness, and replacing p if the particle fitness of the current position x is betterbestAnd gbest；
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.
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.
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Fig. 1 is a schematic diagram 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 of an implementation of the optimal design method in the 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 implementation example, the value range of x is limited to the range of an actual distributed ground station antenna installation site, and a planar rectangular coordinate system can be correspondingly established for the two-dimensional space optimization problem, wherein if the actual site range is 100m × 50m, the x abscissa value range is [0,100], the x ordinate value range 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 optimized design algorithm is as follows:
Figure BDA0003108911460000041
wherein, theta1Is a unit beamAngular extent of width, Θ2LOSS is the main lobe gain LOSS, a, for other side lobe regions1、a2And a3Respectively, the weight coefficients of the respective components.
(3) Initial particle random position x and velocity v: an initial time-instant random position x and velocity v for each particle is 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 particlebest. First population optimal position gbestSelected 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 pbestAnd gbestComparing the particle fitness, and replacing p if the particle fitness of the current position x is betterbestAnd gbest。
(5) Update particle velocity v: the processing of the particle velocities is the core of the algorithm optimization, the velocity of each particle being in accordance with pbestAnd gbestChanging, accelerating to the direction with the optimal fitness according to:
vn+1＝ω·vn+c1·rand·(pbest,n-xn)+c2·rand·(gbest,n-xn)
wherein v isnIs the velocity, x, of the particle at time nnIs 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 equation, the new velocity inherits the original velocity in the proportion of omega and goes to pbestAnd gbestIncreases in direction of the particular dimension of (a). c. C1And c2A proportionality coefficient representing the "gravity" in both directions, also called self-cognition and social influence rate, c1Can stimulate the exploration of the solution space by the particles, c2The 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:
xn+1＝xn+Δt·vn+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 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 (6)

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 selected by design is as follows:
Figure FDA0003108911450000011
wherein, theta1Is the angular extent of the element beam width, Θ2LOSS is the main lobe gain LOSS, a, for other side lobe regions1、a2And a3Weight 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 particlebestFirst population optimal position gbestSelecting from individual optima;
s4: finding optima in solution space step by step
Calculating the particle fitness of the current time position x and the last time position pbestAnd gbestComparing the particle fitness, and replacing p if the particle fitness of the current time position x is betterbestAnd gbest；
S5: updating particle velocity
Velocity of each particle according to pbestAnd gbestChanging, 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.
2. The method according to claim 1, wherein the method comprises: the position coordinates of each particle after the completion of the optimization, that is, the position of each antenna.
3. The method according to claim 1, wherein the method comprises: in step S5, the velocity update formula is as follows:
vn+1＝ω·vn+c1·rand·(pbest,n-xn)+c2·rand·(gbest,n-xn)
wherein v isnIs the velocity, x, of the particle at time nnIs the position of the particle at time n; new velocity vn+1Inheriting the original speed in proportion of omega and moving to pbestAnd gbestIncreases in the direction of the particular dimension of (a); c. C1And c2Representing the proportionality coefficient of gravity in both directions, c1Increasing the exploration of the solution space by the exciting particles, c2The increase is explored towards global optimality.
4. The method according to claim 1, wherein the method comprises: in step S6, the coordinate update expression is:
xn+1＝xn+Δt·vn+1。
5. the method according to claim 1, wherein the method comprises: in step S6, the time period Δ t takes the unit l.
6. A distributed antenna layout optimization design system, which adopts the optimization design method of any claim 1-5 to optimize the antenna layout, 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 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 pbestAnd gbestComparing the particle fitness, and replacing p if the particle fitness of the current position x is betterbestAnd gbest；
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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