Optimisation of Satellite Navigation Constellations using Genetic Algorithms

Abstract

Global Navigation Satellite Systems (GNSS) are used on a daily basis, providing Positioning, Navigation and Timing (PNT) services for various applications ranging from smartphones over the financial sector up to areas such as aviation and space. Classical GNSS constellations positioned in Medium Earth Orbit (MEO) often experience reduced performance in areas of low visibility like forests and cities. To rectify this, augmentation constellations are deployed, improving the provided positioning accuracy. Recent proposals for augmentation systems have often been based in Low Earth Orbit (LEO), which, for global coverage, require a large number of satellites and are complex to design due to dependencies, coverage requirements and the large search space. This makes the constellation design problem well-suited for applying Genetic Algorithms (GA) to find an optimal solution. However, previous research has only addressed highly constrained versions of the problem. This paper presents an approach for applying GAs to constellation designs with a large search space. In particular, the focus is on the description of the multi-objective fitness function and the simulation necessary for its evaluation, options for the solution encoding, and a discussion of algorithmic features applicable in this scenario.