Dataset Information

Optimization design of two-layer Walker constellation for LEO navigation augmentation using a dynamic multi-objective differential evolutionary algorithm based on elite guidance.

ABSTRACT: In recent years, low earth orbit navigation augmentation (LEO-NA) has attracted increasing attention and is expected to become a new addition to global navigation satellite systems (GNSSs). When solving complex constellation design problems, traditional optimization algorithms often fail to achieve satisfactory results and are sensitive to parameter settings. We propose a dynamic multi-objective differential evolutionary algorithm based on elite guidance (DMODE-EG). It can select the evolutionary strategy based on the evolutionary state reflected by elite individuals and dynamically modify evolution parameters. Moreover, to achieve more uniform global coverage, we construct a two-layer Walker constellation model for LEO-NA. Then, we use the DMODE-EG algorithm to solve the corresponding multi-objective optimization problem and obtain the optimal constellation parameters. With the augmentation of this LEO-NA constellation to the BeiDou-3 system, the average position dilution of precision (PDOP) values drop to 1.2-2.0 from 1.5-5.5, and the number of visible satellites increases from 8-10 to 13-18. By contrast, some realistic LEO constellations and constellations designed by other algorithms bring weaker improvements and cannot address the problem of high PDOP values in some regions. In addition, simulation results on standard test sets verify the excellent convergence and stability of the DMODE-EG algorithm.

Supplementary information

The online version contains supplementary material available at 10.1007/s10291-022-01366-5.

SUBMITTER: Deng Z

PROVIDER: S-EPMC9702746 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Optimization design of two-layer Walker constellation for LEO navigation augmentation using a dynamic multi-objective differential evolutionary algorithm based on elite guidance.

Deng Zhongliang Z Ge Wenxiao W Yin Lu L Dai Shisheng S

GPS solutions 20221127 1

In recent years, low earth orbit navigation augmentation (LEO-NA) has attracted increasing attention and is expected to become a new addition to global navigation satellite systems (GNSSs). When solving complex constellation design problems, traditional optimization algorithms often fail to achieve satisfactory results and are sensitive to parameter settings. We propose a dynamic multi-objective differential evolutionary algorithm based on elite guidance (DMODE-EG). It can select the evolutionar ...[more]

PMID: 36467334

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Project description:Several optimization problems can be abstracted into many-objective optimization problems (MaOPs). The key to solving MaOPs is designing an effective algorithm to balance the exploration and exploitation issues. This paper proposes a novel many-objective African vulture optimization algorithm (MaAVOA) that simulating the African vultures' foraging and navigation behaviours to solve the MaOPs. MaAVOA is an updated version of the African Vulture Optimization Algorithm (AVOA), which was recently proposed to solve the MaOPs. A new social leader vulture for the selection process is introduced and integrated into the proposed model. In addition, an environmental selection mechanism based on the alternative pool is adapted to improve the selection process to maintain diversity for approximating different parts of the whole Pareto Front (PF). The best-nondominated solutions are saved in an external Archive based on the Fitness Assignment Method (FAM) during the population evolution. FAM is based on a convergence measure that promotes convergence and a density measure that promotes variety. Also, a Reproduction of Archive Solutions (RAS) procedure is developed to improve the quality of archiving solutions. RAS has been designed to help reach out to the missing areas of the PF that the vultures easily miss. Two experiments are conducted to verify and validate the suggested MaAVOA's performance efficacy. First, MaAVOA was applied to the DTLZ functions, and its performance was compared to that of several popular many-objective algorithms and according to the results, MaAVOA outperforms the competitor algorithms in terms of inverted generational distance and hypervolume performance measures and has a beneficial adaptation ability in terms of both convergence and diversity performance measures. Also, statistical tests are implemented to demonstrate the suggested algorithm's statistical relevance. Second, MaAVOA has been applied to solve two real-life constrained engineering MaOPs applications, namely, the series-parallel system and overspeed protection for gas turbine problems. The experiments show that the suggested algorithm can tackle many-objective real-world applications and provide promising choices for decision-makers.

Project description:BackgroundThe uncertain environments of future space missions means that astronauts will need to acquire new skills rapidly; thus, a non-invasive method to enhance learning of complex tasks is desirable. Stochastic resonance (SR) is a phenomenon where adding noise improves the throughput of a weak signal. SR has been shown to improve perception and cognitive performance in certain individuals. However, the learning of operational tasks and behavioral health effects of repeated noise exposure aimed to elicit SR are unknown.ObjectiveWe evaluated the long-term impacts and acceptability of repeated auditory white noise (AWN) and/or noisy galvanic vestibular stimulation (nGVS) on operational learning and behavioral health.MethodsSubjects (n = 24) participated in a time longitudinal experiment to access learning and behavioral health. Subjects were assigned to one of our four treatments: sham, AWN (55 dB SPL), nGVS (0.5 mA), and their combination to create a multi-modal SR (MMSR) condition. To assess the effects of additive noise on learning, these treatments were administered continuously during a lunar rover simulation in virtual reality. To assess behavioral health, subjects completed daily, subjective questionnaires related to their mood, sleep, stress, and their perceived acceptance of noise stimulation.ResultsWe found that subjects learned the lunar rover task over time, as shown by significantly lower power required for the rover to complete traverses (p < 0.005) and increased object identification accuracy in the environment (p = 0.05), but this was not influenced by additive SR noise (p = 0.58). We found no influence of noise on mood or stress following stimulation (p > 0.09). We found marginally significant longitudinal effects of noise on behavioral health (p = 0.06) as measured by strain and sleep. We found slight differences in stimulation acceptability between treatment groups, and notably nGVS was found to be more distracting than sham (p = 0.006).ConclusionOur results suggest that repeatedly administering sensory noise does not improve long-term operational learning performance or affect behavioral health. We also find that repetitive noise administration is acceptable in this context. While additive noise does not improve performance in this paradigm, if it were used for other contexts, it appears acceptable without negative longitudinal effects.

Dataset Information

Optimization design of two-layer Walker constellation for LEO navigation augmentation using a dynamic multi-objective differential evolutionary algorithm based on elite guidance.

Supplementary information

Publications

Optimization design of two-layer Walker constellation for LEO navigation augmentation using a dynamic multi-objective differential evolutionary algorithm based on elite guidance.

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