The increasing accessibility of space has made the detection, tracking, and categorisation of the large volume of objects in orbit ever more critical. In this article, Mohammad Dabbah looks at the role artificial intelligence can play in enhancing our capabilities in space situational awareness (SSA) and suggests it is becoming an integral part of the new Space Age. American philosopher Eric Hoffer's much-cited quote (below) conveys a very important message: automation, or artificial intelligence (AI), is not as artificial as we think it is. There have been many exciting developments in the field of autonomy and AI in recent years. Some of these advances can be argued to have been achieved due to the closer approximation of automated systems to nature.

The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris domain, taking steps in the creation of an orbital debris ontology (ODO). The purpose of this ontological system is to (I) represent general orbital debris and SSA domain knowledge, (II) structure, and standardize where needed, orbital data and terminology, and (III) foster semantic interoperability and data-sharing. In doing so I hope to (IV) contribute to solving the orbital debris problem, improving peaceful global SSA, and ensuring safe space travel for future generations.

BAE Systems Information and Electronic Systems Integration, Technology Solutions-Advanced Information Technologies, Burlington, Massachusetts, has been awarded a 9,402,650 cost-plus-fixed-fee contract for software and reports. The contractor will provide development and integration of the latest advancements in machine learning that benefit this effort as well as architectural enhancements that increase the flexibility and agility of applying analytics to solve space situational awareness problems and take full advantage of high-performance computing capabilities and multiple forms of visualization of the reasoning performed by the analytics involved in this effort. This includes demonstration of new capabilities over three-year spirals that progressively demonstrate detection of increasingly complex scenarios and improved detection and prediction capabilities. Work will be performed in Burlington, Massachusetts, and Arlington, Virginia, and is expected to be complete by July 15, 2019. This award is the result of a competitive acquisition, with two offers received.