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Variable step size constellation orbit optimization method and device based on genetic algorithm

A genetic algorithm and variable step size technology, applied in the field of variable step size constellation orbit optimization algorithms and devices, can solve the problems of increasing calculation costs, increasing optimization calculation time, reducing efficiency, etc., achieving good time resolution coverage and improving optimization Time accuracy, effect of reducing optimization time

Active Publication Date: 2019-04-16
BEIHANG UNIV
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Problems solved by technology

The traditional constellation orbit optimization method only improves the optimization method to increase the convergence speed of the system, but does not notice that during the optimization process, the calculation of the coverage time of the target area takes up more computing resources and reduces efficiency, and the existing If the method wants to improve the coverage time accuracy, it will exponentially increase the optimization calculation time and increase the calculation cost

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  • Variable step size constellation orbit optimization method and device based on genetic algorithm
  • Variable step size constellation orbit optimization method and device based on genetic algorithm
  • Variable step size constellation orbit optimization method and device based on genetic algorithm

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Embodiment 1

[0020] refer to figure 1 , which shows a flow chart of the steps of a genetic algorithm-based variable-step constellation orbit optimization method provided by an embodiment of the present invention, which may specifically include the following steps:

[0021] Step 101: Set the number of orbital planes of the target satellite constellation and the number of satellites in each orbital plane; wherein, the number of satellites in each orbital plane is the same.

[0022] In the embodiment of the present invention, the number of orbital planes of the target satellite constellation and the number of satellites in each orbital plane are artificially set in advance, and the specific number of orbital planes and the number of satellites in each orbital plane are not limited in the embodiment of the present invention .

[0023] And, in the number of satellites in each orbital plane that is set, the number of satellites in each orbital plane is the same. For example, the set orbital pla...

Embodiment 2

[0168] refer to Figure 5 , which shows a schematic structural diagram of a genetic algorithm-based variable-step constellation orbit optimization device provided by an embodiment of the present invention, which may specifically include:

[0169] The number setting module 210 is used to set the number of orbital planes of the target satellite constellation and the number of satellites on each orbital plane; wherein, the number of satellites on each orbital plane is the same; the population initialization module 220 is used to set the number of orbital planes according to the number of orbital planes and the number of satellites, initialize to obtain the genetic algorithm population, and set genetic algorithm preset parameters; wherein, the genetic algorithm preset parameters include population size, crossover rate, mutation rate and maximum genetic algebra; fitness function calculation module 230 , used to calculate the time resolution of the target satellite constellation by ...

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Abstract

The invention discloses a variable step size constellation orbit optimization method and device based on a genetic algorithm. The method comprises: the orbital plane number of a target satellite constellation and the satellite number of each orbital plane are set; initializing to obtain a genetic algorithm population according to the number of orbital planes and the number of satellites, and setting preset parameters of a genetic algorithm; wherein the preset parameters of the genetic algorithm comprise population size, crossover rate, variation rate and maximum genetic algebra; calculating toobtain the time resolution of the satellite constellation by adopting a variable step size strategy, and taking the time resolution as a fitness function; according to the fitness function, the crossover rate and the variation rate, performing crossover and variation processing on a part of individuals randomly selected from the genetic algorithm population; and under the condition that selection, crossover and mutation operations are determined to be completed according to the maximum genetic algebra, obtaining the individual with the highest fitness value in the new generation of population, and decoding to obtain the constellation configuration. According to the invention, the time precision of constellation orbit optimization can be improved, the optimization time is reduced, and thecoverage of better time resolution is realized.

Description

technical field [0001] The invention belongs to the technical field of spacecraft constellation system design, and in particular relates to a variable-step constellation orbit optimization algorithm and device based on a genetic algorithm. Background technique [0002] Satellite constellation refers to a satellite network composed of multiple satellites according to certain rules and shapes that can provide certain coverage performance. It is the basic form of multiple satellites working together. In order to meet the wide range of needs in the fields of communication, navigation and earth observation, satellite constellation technology has received extensive attention. Among them, satellite constellation design is the premise and key to the establishment of satellite constellation system. In order to achieve the coverage of the multi-target area with the minimum number of satellites, it is necessary to optimize the design of the orbital parameters of the satellite constell...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/12
CPCG06F30/15G06F2111/06G06N3/126
Inventor 徐明李庆龙郭东辉刘轶和星吉白雪
Owner BEIHANG UNIV
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