William Oldroyd, Katalyst Space Technologies; Jeffrey Uyekawa, Katalyst Space Technologies; Jeremy Correa, Katalyst Space Technologies; Fernando Aguirre, Katalyst Space Technologies
Keywords: Satellite Characterization, Resident Space Object (RSO) Characterization, Space Domain Awareness (SDA), Space Situational Awareness (SSA), Tactically Responsive Space (TacRS)
Abstract:
The security of space assets and operations increasingly depends on our ability to effectively task limited Space Domain Awareness (SDA) sensor resources across a rapidly expanding population of resident space objects (RSOs). However, current methods remain predominantly reactive rather than proactive in detecting emerging threats. Dynamic sensor tasking for SDA faces unique challenges due to vast observational volumes, complex orbital mechanics, and limited sensor resources as the number of RSOs on orbit grows exponentially. Current dynamic sensor tasking methodologies predominantly rely on SDA operators identifying high-priority objects for tracking and then using kinematic-based state estimation approaches for sensor tasking to maintain consistent observations of these objects. These approaches present significant limitations. First, since it is not feasible to maintain consistent coverage of every RSO, operators are forced to pre-define high-priority objects, creating potential blind spots that nefarious actors can exploit. Second, kinematic-only approaches only leverage a subset of available data that can be used to characterize objects to inform tasking priorities. Third, existing frameworks struggle with resource allocation optimization when faced with numerous competing priorities. As a result, traditional sensor tasking routines lack the capability to automatically detect and respond to planned maneuvers, anomalous behavior, or previously unknown threats without significant operator intervention, creating bottlenecks in the decision-making process.
In this work, we explore the advantages of leveraging characteristic and behavior-based insights together with standard kinematic methods to improve automated dynamic sensor tasking workflows while also identifying potential threats that would otherwise go unnoticed. Our approach consists of three main steps. First, we use an ensemble of statistical and functional models to ingest simulated sensor observations in real time to produce a set of indicators. These indicate whether a given object is acting uncharacteristically and should be prioritized for follow up observations even if this was not part of the original mission concept of operations (CONOPS). Next, we assign each object a priority score based on its indicator combinations, as well as missing information, at the time the indicators were computed. Then, we employ a dynamic tasking algorithm, which takes this prioritized list as an input together with kinematic data to generate tasking requests to sensors. This algorithm is able to balance sensor availability and resource management concerns with the data coverage requirements necessary for our models to refine their characterization in subsequent steps. This process results in an automated feedback loop that can identify anomalous behavior in RSOs and maintain custody of objects of interest when standard methods would likely lose custody.
To validate our proposed workflow, we utilize a robust simulation environment in which we model a network of optical sensors with defined capabilities and availability constraints together with synthesized observations of realistic scenarios of RSO activity. Our tasking algorithm is capable of locally optimal solutions to tasking problems, resulting in an effective balance between computational efficiency and resource utilization. We anticipate that this framework will provide similar results when real-world data is incorporated. Our research shows great promise for a multi-source approach to dynamic tasking. This study offers a realistic path toward more efficient utilization of limited SDA resources while simultaneously improving capabilities to identify and characterize previously unrecognized threats. This marks an important contribution to the SDA community as space becomes an increasingly congested and contested domain. Our proposed workflow offers an automated pipeline that addresses current bottlenecks in the decision-making process while also mitigating potential blind spots in dynamic tasking routines.
Date of Conference: September 16-19, 2025
Track: Space Domain Awareness