Advances in Satellite Selection Algorithms: Reducing Receiver Processing

Clicks: 1
ID: 310557
2024
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #250 of 409 articles by views in International Journal of Science and Social Science Research

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 409 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
In a variety of satellite navigation and positioning applications, choosing the right satellites for positioning is crucial to ease the computational load on satellite selection systems. To further alleviate the processing demands on receivers, a satellite selection method using the Gibbs sampler has been developed. This process begins by randomly selecting visible satellites and grouping them as an initial selection strategy. The effectiveness of this strategy is assessed using the geometric dilution of precision as a key metric. The strategy is refined through the Gibbs sampler algorithm's conditional probability distribution model, progressively moving towards the most efficient satellite combination that offers an improved geometric distribution in space. Our research demonstrates that by employing a neural network-based model, we can optimize satellite constellations for minimal latency. We have found that deploying 12 satellites results in latency levels of less than 5 milliseconds and continuous uptime, significantly enhancing communication efficiency. This research highlights the potential for neural network models to revolutionize satellite constellation design for improved performance.
Reference Key
imported_1768936710_696fd506ed1fc Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Muhammad Afnan Aziz Khan
Journal International Journal of Science and Social Science Research
Year 2024
DOI
10.5281/zenodo.13311887
URL
https://www.ijsssr.com/j/article/view/208
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.