Award

The environment in which space traffic persists is becoming ever increasingly operationally dynamic. It is no longer sufficient to provide answers to the question of where resident space objects (RSOs) are located and where natural motion will take them, the realm of space situational awareness. Nor is it significantly more tactically foolproof to systematically answer the question of where such objects will be headed given an understanding of expected mission profiles, arguably space domain awareness (SDA). Rather, we must seek answers to a more wholistic set of queries. Why are certain RSOs positioned in a region? What will a given object be doing next and with what indicators? What capabilities and behaviors are expected over the course of a day, a week, or a month? What are the potential risks in terms of hardware, programs, or geopolitics? These deeper inquiries rely on a multidomain understanding of mission profiles and warrant a closer look at the traditional algorithms of statistical inference employed in questions of orbit determination. To further the challenge at hand are the complexities of the modern operational picture. With frequent commercial and international launches, new hardware technologies being deployed, autonomous maneuvering, and fast strides in the domain of software and cybersecurity, the pace of information flow threatens to render obsolete traditional methods of RSO encounter analysis. While target tracking has seen the proliferation of a variety of vary capable algorithms, a particular challenge remains in the practical application of ever-sophisticated approaches to statistical reasoning: the imperfect characterizations of dynamical system uncertainty. Where there is purely random phenomena involved, aleatory uncertainty provides a perfect description of the behavior and the probabilistic assumptions are satisfied. In the presence of systematic errors, epistemic uncertainty, it is, however, not uncommon for certain methods to show a more delicate side to their construction. More succinctly, it is becoming necessary for the random, aleatory, component of Bayesian reasoning to be fused with the systematic errors, epistemic uncertainty, surrounding common spacecraft “behavioral” profiles. Drawing from our commercial SDA offerings, and with an aim to both enable faster decision making for the modern operator and in so doing, add a further degree of resiliency to spacecraft mission execution, ExoAnalytic Solutions proposes the Spacecraft Proximal Intent Notifications for Navigation Assurance and Knowledge-Enabled Resiliency (SPINNAKER) effort. By providing data-driven intent alerts and analytics to support informed decision making in the space operational environment, a comprehensive domain understanding can be more greatly leveraged in joint command and control efforts to ensure mission responsiveness on tactical timelines and pave the way for robust, resilient autonomous spacecraft capabilities.