Average and Maximum Revisit Time Trade Studies for Satellite Constellations Using a Multiobjective Genetic Algorithm

Williams, Edwin A.; Crossley, William A.; Lang, Thomas

doi:10.1007/bf03546229

Cited by 39 publications

(3 citation statements)

References 4 publications

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“…Equation (5) shows the relation between the transmitter frequency, altitude of the satellite, and the value of space loss. The value of antenna gain in the transmitter and receiver, G t and G r , can be obtained from Equation (6). All of parameters in equation ( 4), ( 5), and (6) have units in dB.…”

Section: Optimization Methodologymentioning

confidence: 99%

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Design Optimization for Mass Sizing of LEO Communication Satellites

Fathurrohim¹,

Poetro²,

Fariz³

2019

JMechE

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This paper focuses on the design optimization of communication satellites for the constellation in low earth orbit (LEO). The optimization starts with defining an objective function, which is the total mass of satellites, as a function of their orbit altitude, antenna transmit diameter, and nominal data rates. The optimization routine was done in order to minimize the total mass of satellites in order to reduce the total launch mass and the total cost. The use of three different design parameters has led to a genetic algorithm (GA) as an optimization method to find the best value for each design parameter. The results show that the optimization process using a genetic algorithm is successfully applied in solving complex multidisciplinary design problems such as the design of a communication satellite system with acceptable error.

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Section: Optimization Methodologymentioning

confidence: 99%

“…The value of antenna gain in the transmitter and receiver, G t and G r , can be obtained from Equation (6). All of parameters in equation ( 4), ( 5), and (6) have units in dB. The link equation has the following design constraint [13].…”

Section: Optimization Methodologymentioning

confidence: 99%

Design Optimization for Mass Sizing of LEO Communication Satellites

Fathurrohim¹,

Poetro²,

Fariz³

2019

JMechE

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“…A number of tools have been developed to facilitate constellation design, usually with defined objective functions, such as minimizing revisit time, maximizing coverage, or balancing cost and utility (Marcuccio et al 2019;Williams et al 2001;St. Germain et al 2018;Nag et al 2015).…”

mentioning

confidence: 99%

Sampled Together: Assessing the Value of Simultaneous Collocated Measurements for Optimal Satellite Configurations

Powell,

Ruf,

Gleason

et al. 2024

Bulletin of the American Meteorological Society

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This work applies a quantitative metric in order to capture the relative representativeness of non-simultaneous or non-co-located observations and quantify how these observations decorrelate in both space and time. This methodology allows for the effective determination of thresholding decisions for representative matchup conditions, and is especially useful for informing future network designs and architectures. Future weather and climate satellite missions must consider a range of architectural trades to meet observing requirements. Frequently, fundamental decisions such as the number of observatories, the instruments manifested, and orbit parameters are determined based upon assumptions about the characteristic temporal and spatial scales of variability of the target observation. With the introduced methodology, representativity errors due to separations in space and time can be quantified without prior knowledge of instrument performance, and errors driven by constellation design can be estimated without model ingest or analysis.

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