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GSAW 2019 Working Groups

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Session 11A: The Making of “Smarter Ground Systems” – Brainstorming

Lead: Donald Sather, The Aerospace Corporation

It is said the only constant in the world is change. To meet the challenges of an ever-changing threat and technology environment missions and enterprises themselves need to become flexible, responsive and cost effective to meet current and future needs. While new algorithms, analytic “apps”, software paradigms and cool human interfaces are essential and keep users from drowning in data they are just the most upper layers of a mission system or an even larger responsive enterprise. In the past, IT infrastructure was treated much as the foundation of a house – built once and largely forgotten until it fails. Typically, in the past, a replacement infrastructure, much like a house foundation, required the “tear down and rebuilding” of the entire system to replace it and was quite time consuming (not to mention expensive). Can future ground systems (“smart” or not) still operate in this fashion and be successful in the long term? In the past, the introduction of new capabilities into a system took months or even years to become operational, will this speed be enough to match future needs? Probably not. So, what constitutes a “smarter ground system” and what does it take to build a successful one? What constitutes a “successful” system of the future?

To address this, the Working Group will hear from a group of government and commercial entities that will share what their organizations are doing to develop “smarter ground systems” and address the foundational elements that serve as the underpinning of the algorithms and “apps”. All the workshop participants will then address the questions of:

  • What are the elements of a successful “smarter ground system”?
  • What does it take to build and sustain those elements especially over the long term in a world of constant change? Is it possible, to any extent, to “future proof” a system or at least make it “future resistant”?

Participants in this working group will gain insight into various approaches that different organizations are taking as well as perhaps gaining new insight from addressing the questions that they can take back to enhance the efforts they are supporting.


Session 11B: Intelligent Systems / Machine Learning for Space Ground Systems

Leads: Thomas Kashangaki and Daniel Balderston, The Aerospace Corporation

Adaptive, reliable automation and intelligent decision making are essential for the success of our space ground systems. The challenge is finding the proper balance between human control and autonomy. Applied intelligent systems and machine learning technologies have begun to address these challenges through self-evolving, efficient, and value-focused capabilities. These approaches, however are often misunderstood, misapplied, too complex or costly to sustain, or insufficient for mission needs. In addition, un-realized opportunities exist for applying established, or rapidly emerging technologies, solutions and architectures to the area of ground system space control and mission processing.

This second year of the GSAW “Intelligent Systems / Machine Learning for Space Ground Systems” working group will explore deeper the questions of:

  • Where do intelligent systems and machine learning currently exist in space ground systems?
  • What underlying parts of the space ground systems, enterprise and operations are suited to intelligent systems and machine learning
  • What emerging capabilities and technologies are being developed in the community
  • What are real-world impediments for adoption
  • What capability and technology gaps exist and might seed further research and investment

Underlying space ground system areas of focus will include:

Space Operations: Resilient space systems, particularly their command and control, require timely and effective detection and response when anomalies occur. System design strives to define and manage when anticipated anomalies occur, but unforeseen events and abnormalities often result in mission interruption or failure. Detection and response must be both timely and accurate for mission success, but must also evolve with the systems, environments and actors involved. All segments are involved (i.e., space vehicle, ground control and mission data or service capabilities).

Mission Tasking and Resource Management: Space constellation resource tasking has proven to be a highly challenging problem, particularly as constellations grow, mission needs expand and demand accelerates. Providing adaptive, efficient and time-responsive tasking for stakeholders in an era of greater cost constraints has been an area ripe for intelligent systems and machine learning.

Mission Data Processing: Space system operations involve extremely large volumes of dynamic data. Some missions involve time critical (time dominant) processing of that data. Intelligent systems and machine learning support more accurate, timely and often automated decision making. Other missions are content-focused (content dominant). The growth in variety and depth of multiple phenomenology sensor data, and improved access to archives has enabled intelligent mining and multi-INT fusion to extract new data products.

Space Enterprise Management: As the lines between space operations, mission processing, product demand and product dissemination blur, as multiple space systems feed a broader enterprise of producers and consumers, and as governance and stakeholders themselves undergo change, the evolution of the space enterprise becomes a gargantuan task. Using intelligent systems and machine learning promise to help bas


Session 11C: Creating Smarter Ground Systems through Cybersecurity Prototyping, Testing and
on-orbit Experiments

Leads: Scott Niebuhr and Michelle Yohannes, The Aerospace Corporation

Description to be posted soon.


Session 11D: Achieving a Smarter Ground Enterprise Through Model-Based Engineering

Leads: Ryan Noguchi and Robert Pettit IV, The Aerospace Corporation

In this Working Group Session, we hope to foster a mutually beneficial discussion of the community’s lessons learned and best practices in Model-Based Engineering (MBE). As in previous years, we plan to facilitate an open discussion of the issues and concerns of MBE to encourage broad participation from the assembled participants. We plan to open the session with a very brief presentation that sets the stage, but we have found that the discussion evolves on its own accord, leads the group in directions we can’t predict in advance, and results in the beneficial emergence of insightful conclusions and the identification of key challenges and opportunities that face the community.

In keeping with this year’s GSAW theme, we would like to focus the working group’s discussions on how MBE can help enterprises to re-architect and re-engineer themselves to become smarter, more efficient, and more effective. We would like to share lessons learned from model-based engineering efforts, to understand how these modern methods and tools are able to improve organizations’ ability to achieve agility, enable greater proactivity, and capitalize on advances being made in machine learning, intelligent systems, automation, and innovative accelerated development processes.


Session 11E: Cloud Computing and Big Data Technologies for Ground Systems IX

Leads: Ramesh Rangachar and Craig Lee, The Aerospace Corporation

This is the ninth year of this working group. The main objective of the working group is tocontinue discussion on the adoption of cloud computing and Big Data in satellite groundsystems. The Cloud Reference Model and Roadmap produced by Aerospace will be usedto frame the discussion. The working group will focus on:

  • State of the art in cloud computing and Big Data;
  • Cloud and Big Data reference models;
  • Cloud-based ground systems;
  • Cloud and Big Data technologies;
  • Cloud security, standards, and compliance;
  • Acquisition strategies for cloud-based systems;
  • Cloud computing economics; and
  • Cloud performance management.

This working group will consist of two parts. Part 1 will include presentations, case studies, and demonstrations related to cloud computing and Big Data for ground systems. Part 2 will be a town hall meeting on cloud computing and Big Data for ground systems. This will include a moderated discussion on the focus issues mentioned above, with expert opinions from panelists.

Presenters, panelists, and participants will include ground systems providers, integrators, and operators, cloud solutions providers, and others interested in ground systems and cloud computing.


Session 11F: Smarter Acquisition with Agile Approaches

Leads: Supannika Mobasser and Jodene Sasine, The Aerospace Corporation

Agile software and system development is no longer a new topic for the Government sector. Several programs have gradually started to embrace agile methods. Ground software systems usually have large scale and high complexity, hence, there is a big challenge to use agile as it is used in commercial software-intensive industry. Additional challenge is how to smartly apply agile, not only to the software system development, but to the whole ground system acquisition life-cycle. What should we do differently so that we can have smarter acquisition strategies?

This working group will provide an opportunity for agile practitioners to share their experiences and learn from others on several topics regarding challenges in agile acquisition on the following topics:

  • Smarter checkpoints and feasibility evidence
  • Smarter roles and responsibilities
  • Smarter tools and techniques
  • Smarter quality assurance
  • Smarter practices and antipatterns

The format of the working group will be a combination of presentations, case studies, and interactive discussion focusing on different aspects of agile adoption on ground software system development.