Autonomous navigation for deep space small satellites: Scientific and technological advances

In recent years, there is a growing interest in small satellites for deep space exploration. The current approach for planetary navigation is based on ground-based radiometric tracking. A new era of low-cost small satellites for space exploration will require autonomous deep space navigation. This will decrease the reliance on ground-based tracking and provide a substantial reduction in operational costs because of crowded communication networks. In addition, it will be an enabler for future missions currently impossible. This review investigates available deep space navigation methods from an autonomy perspective, considering trends in proposed deep space small satellite missions. Autonomous crosslink radiometric navigation, which is one of the best methods for small satellites due to its simplicity and the use of existing technologies, is studied, including available measurement methods, enabling technologies, and applicability to the currently proposed missions. The main objective of this study is to fill the gap in the scientific literature on the autonomous deep space navigation methods, deeply for crosslink radiometric navigation and to aim at showing the potential advantages that this technique could offer to the missions being analyzed. In this study, a total of 64 proposed deep space small satellite missions have been analyzed found from a variety of sources including journal papers, conference proceedings, and mission websites. In those missions, the most popular destinations are found to be cislunar space and small bodies with the purpose of surface mapping and characterization. Even though various autonomous navigation methods have been proposed for those missions, most of them have planned to use the traditional ground-based radiometric tracking for navigation purposes. This study also shows that more than half of the missions can benefit from the crosslink radiometric navigation through the inter-satellite link.