GPS radio occultation constellation design with the optimal performance in Asia Pacific region

Asgarimehr, Milad; Hossainali, Masoud Mashhadi

doi:10.1007/s00190-015-0795-3

Cited by 7 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both Juang et al [24] and Asgarimehr et al [26] used a GA to search for the optimal LEO constellation configuration for GNSS RO performance over the target region. Considering that the optimized constellation configuration sought depends on the optimization algorithm used, it stands to reason to use more than one evolutionary algorithm in the optimization process only if the comparison between the algorithms is fair [27].…”

Section: Discussionmentioning

confidence: 99%

“…As to the comparison between the two constellation patterns, Asgarimehr et al [26] found that for a LEO constellation composed of six satellites, the daily number of GPS ROEs, which occur over the Asia and Pacific region observed by the optimized 2D-LFC, is larger than those observed by the optimized 3D-LFC. Our study further confirms that for a LEO constellation composed of n(n = 6, 7, 8, .…”

Section: Discussionmentioning

confidence: 99%

“…Here the LEO constellation patterns of 2D-LFC and 3D-LFC are taken into consideration. Considering that the uniformity and the symmetry of the constellation can be affected by elliptical orbits in 2D-LFC [26], only circular orbits are considered for optimizations based on this pattern, while 3D-LFC is applied for constellation pattern of elliptical orbits.…”

Section: Leo Constellation Patternsmentioning

confidence: 99%

“…Particularly, the one-point crossover approach and the mutation processes are applied to generate next generations of population. In order to avoid a premature convergence of GA, which leads to the convergence of the solution to a local maximum, the value of the one-point crossover probability and mutation probability is set as 0.85 and 0.2, respectively [26,34]. The GA process is terminated when the process is iterated for 100 generations.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

See 3 more Smart Citations

Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms

Han

Luo

et al. 2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Given the great achievements of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission in providing huge amount of GPS radio occultation (RO) data for weather forecasting, climate research, and ionosphere monitoring, further Global Navigation Satellite System (GNSS) RO missions are being followingly planned. Higher spatial and also temporal sampling rates of RO observations, achievable with higher number of GNSS/receiver satellites or optimization of the Low Earth Orbit (LEO) constellation, are being studied by high number of researches. The objective of this study is to design GNSS RO missions which provide multi-GNSS RO events (ROEs) with the optimal performance over the globe. The navigation signals from GPS, GLONASS, BDS, Galileo, and QZSS are exploited and two constellation patterns, the 2D-lattice flower constellation (2D-LFC) and the 3D-lattice flower constellation (3D-LFC), are used to develop the LEO constellations. To be more specific, two evolutionary algorithms, including the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used for searching the optimal constellation parameters. The fitness function of the evolutionary algorithms takes into account the spatio-temporal sampling rate. The optimal RO constellations are obtained for which consisting of 6–12 LEO satellites. The optimality of the LEO constellations is evaluated in terms of the number of global ROEs observed during 24 h and the coefficient value of variation (COV) representing the uniformity of the point-to-point distributions of ROEs. It is found that for a certain number of LEO satellites, the PSO algorithm generally performs better than the GA, and the optimal 2D-LFC generally outperforms the optimal 3D-LFC with respect to the uniformity of the spatial and temporal distributions of ROEs.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Leo Constellation Patternsmentioning

confidence: 99%

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

See 2 more Smart Citations

Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms

Han

Luo

et al. 2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some evolutionary algorithms such as the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA) have been developed in the fields of computational intelligence and evolutionary computation in recent years. Although it has been testified that these evolutionary algorithms are practical and effective in the optimal designs for the constellations of different scientific missions such as the constellations of the optimal global coverage of geometric dilution of precision (GDOP) [17], and the GNSS radio occultation (RO) constellations [18,19], they have not been applied in the optimization design of GNSS-R constellations. On the other hand, although the present representative operational GNSS-R mission, CYGNSS, is mainly aimed to study the ocean winds and waves in tropical cyclones, and so the LEO constellation was designed with low inclination orbits, it is expected that in the future, GNSS-R will be used to sense ocean, land, and ice features globally using larger constellations [2].…”

mentioning

confidence: 99%

On the Constellation Design of Multi-GNSS Reflectometry Mission Using the Particle Swarm Optimization Algorithm

Han

Luo

2019

Atmosphere

View full text Add to dashboard Cite

Due to the great success of the CYclone Global Navigation Satellite System (CYGNSS) mission, the follow-on GNSS Reflectometry (GNSS-R) missions are being planned. In the perceivable future, signal sources for GNSS-R missions can originate from multiple global navigation satellite systems (GNSSs) including Global Positioning System (GPS), Galileo, GLONASS, and BeiDou. On the other hand, to facilitate the operational capability for sensing ocean, land, and ice features globally, multi-satellite low Earth orbit (LEO) constellations with global coverage and high spatio-temporal resolutions should be considered in the design of the follow-on GNSS-R constellation. In the present study, the particle swarm optimization (PSO) algorithm was applied to seek the optimal configuration parameters of 2D-lattice flower constellations (2D-LFCs) composed of 8, 24, 60, and 120 satellites, respectively, for global GNSS-R observations, and the fitness function was defined as the length of the time for the percentage coverage of the reflection observations reaches 90% of the globe. The configuration parameters for the optimal constellations are presented, and the performances of the optimal constellations for GNSS-R observations including the visited and the revisited coverages, and the spatial and temporal distributions of the reflections were further compared. Although the results showed that all four optimized constellations could observe GNSS reflections with proper temporal and spatial distributions, we recommend the optimal 24-and 60-satellite 2D-LFCs for future GNSS-R missions, taking into account both the performance and efficiency for the deployment of the GNSS-R missions.

show abstract

Automated VLBI scheduling using AI-based parameter optimization

Schartner

Plötz

Soja

2021

J Geod

View full text Add to dashboard Cite

Within this work, a new geodetic very long baseline interferometry (VLBI) scheduling approach inspired by evolutionary processes based on selection, crossover and mutation is presented. It mimics the biological concept “surviving of the fittest” to iteratively explore the scheduling parameter space looking for the best solution. Besides providing high-quality results, one main benefit of the proposed approach is that it enables the generation of fully automated and individually optimized schedules. Moreover, it generates schedules based on transparent rules, well-defined scientific goals and by making decisions based on Monte Carlo simulations. The improvements in terms of precision of geodetic parameters are discussed for various observing programs organized by the International VLBI Service for Geodesy and Astrometry (IVS), such as the OHG, R1, and T2 programs. In the case of schedules with a difficult telescope network, an improvement in the precision of the geodetic parameters up to 15% could be identified, as well as an increase in the number of observations of up to 10% compared to classical scheduling approaches. Due to the high quality of the produced schedules and the reduced workload for the schedulers, various IVS observing programs are already making use of the evolutionary parameter selection, such as the AUA, INT2, INT3, INT9, OHG, T2 and VGOS-B program.

show abstract

GPS radio occultation constellation design with the optimal performance in Asia Pacific region

Cited by 7 publications

References 28 publications

Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms

Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms

On the Constellation Design of Multi-GNSS Reflectometry Mission Using the Particle Swarm Optimization Algorithm

Automated VLBI scheduling using AI-based parameter optimization

Contact Info

Product

Resources

About