Particle Swarm Optimization Based Tracking Window Planning for Cislunar Orbiters Performing Autonomous Radiometric Navigation

In recent years, there has been a growing interest in lunar missions, particularly with the growing role of small satellites facilitated by piggyback launch opportunities. Typically, ground-based radiometric tracking is the workhorse to establish the necessary navigation solution in these missions, however, this could be expensive, while small satellites development is expected to be at low cost. To address this challenge, autonomous navigation presents a potential solution: this study explores the satellite-to-satellite tracking-based autonomous on-board orbit determination method for a satellite formation in cislunar space. Several factors affect the performance of orbit determination, and one critical aspect is the timing of tracking windows. Basically, it is crucial to determine when to collect the most useful observations to optimize the outcome of the navigation filter. In some cases, there might be operational constraints such as inter-satellite distance due to the limited onboard power for ranging. This study investigates particle swarm optimization-based satellite-to-satellite tracking window planning. The findings of this work demonstrate that particle swarm optimization offers a near-optimal solution for tracking windows, taking into account constraints arising from the spacecraft itself or from other design choices. In summary, particle swarm optimization provides near-optimal tracking windows by minimizing the overall orbit determination error. The results presented have the potential to enhance the design of satellite formations performing autonomous on-board orbit determination and contribute to cost- effective mission planning solutions.