Advances in Satellite Selection Algorithms: Reducing Receiver Processing

In a variety of satellite navigation and positioning applications, choosing the right satellites for positioning is crucialto ease the computational load on satellite selection systems. To further alleviate the processing demands on receivers, a satelliteselection method using the Gibbs sampler has been developed. This process begins by randomly selecting visible satellites andgrouping them as an initial selection strategy. The effectiveness of this strategy is assessed using the geometric dilution ofprecision as a key metric. The strategy is refined through the Gibbs sampler algorithm's conditional probability distributionmodel, progressively moving towards the most efficient satellite combination that offers an improved geometric distribution inspace. Our research demonstrates that by employing a neural network-based model, we can optimize satellite constellations forminimal latency. We have found that deploying 12 satellites results in latency levels of less than 5 milliseconds and continuousuptime, significantly enhancing communication efficiency. This research highlights the potential for neural network models torevolutionize satellite constellation design for improved performance.