Naomi Owens Fahrner, Ball Aerospace; Jeremy Correa, Ball Aerospace; Joshua Wysack, Ball Aerospace
Keywords: Sensor scheduling, dynamic tasking, cislunar SDA, capacity analysis
Abstract:
Cislunar space is generally defined as the volume of space between the earth and the moon. This encompasses a radius of nearly 385,000 km. As traffic in cislunar space increases, the need for Space Domain Awareness (SDA) in this area greatly increases. This search space is vast, and the resources in which to observe and track objects in this space are limited. Thus, the allocation of these resources must be optimized.
Architecture assessments for cislunar SDA often stop at observability analysis. These analyses do not include the sensors agility (FOV, sweep rate, etc.). These studies aim to find the best sensor performance required to access a cislunar volume for SDA or Space Traffic Management (STM). The sensor models used account for various higher-order effects such as jitter, straylight, celestial background, and other optical losses. The inclusion of such effects is necessary to provide realistic performance assessments of architectures.
In this paper we will present capacity analysis and optimization algorithms to produce space-based sensor architectures that provide meaningful SDA in cislunar space. Capacity analysis includes the collaboration of multiple sensors to search a volume, subject to constraints such as revisit time. This improvement upon observability analysis considers sensors sensitivities and agilities. Optimal architectures will be found by analyzing a variety of observer orbits, spanning the three-body Earth-Moon system.
To accomplish the capacity analysis, we will utilize a sensor scheduling algorithm to optimize the interoperability of sensors in various orbits. Due to the high dimensionality of this problem, we present an approach that uses multiple optimization techniques to determine the best sensor tasking schedule. The scheduler will optimize the pointing directions of all sensors in an architecture to maximize the volume observed. We will present genetic and greedy algorithm approaches and compare their usage for different aspects of the problem. Our scheduling algorithm will be flexible to allow multiple performance metrics such as revisit time, coverage percentage, pointing preference, etc.
An advantage to the way we use our scheduling algorithms is the coordination of multiple, heterogenous sensors. This requires balancing the tasking of sensors based on solar phase angle, obscuration due to Earth or Moon, and target priority. This sensor coordination provides an efficient and cost-effective tasking scenario.
The scheduling algorithm will be used to perform capacity analysis of cislunar SDA architectures. We provide a comparison of different architectures for cislunar SDA to include most favorable orbits and sensor characteristics. Candidate architectures will utilize trajectories from cislunar orbit families near Lagrange point L1, XGEO orbits, Earth-Moon cycler orbits, NRHOs, etc. Ultimately, we conduct an assessment that evaluates not only the optimal allocation of sensors across orbit families, but also the best period and phasing of the chosen orbits. Our techniques allow for the creation of optimized capacity-based cislunar SDA architectures.
Date of Conference: September 27-20, 2022
Track: Cislunar SSA