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The care and feeding of the Mars Exploration Rover (MER) Ground Data System (GDS)
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Zero to Integration in Eight Months, the Dawn Ground Data System Engineering Challange
The Dawn Project has presented the Ground Data System (GDS) with technical challenges driven by cost and schedule constraints commonly associated with National Aeronautics and Space Administration (NASA) Discovery Projects. The Dawn mission consists of a new and exciting Deep Space partnership among: the Jet Propulsion Laboratory (JPL), responsible for project management and flight operations; Orbital Sciences Corporation (OSC), spacecraft builder and responsible for flight system test and integration; and the University of California, at Los Angeles (UCLA), responsible for science planning and operations. As a cost-capped mission, one of Dawn s implementation strategies is to leverage from both flight and ground heritage. OSC's ground data system is used for flight system test and integration as part of the flight heritage strategy. Mission operations, however, are to be conducted with JPL s ground system. The system engineering challenge of dealing with two heterogeneous ground systems emerged immediately. During the first technical interchange meeting between the JPL s GDS Team and OSC's Flight Software Team, August 2003, the need to integrate the ground system with the flight software was brought to the table. This need was driven by the project s commitment to enable instrument engineering model integration in a spacecraft simulator environment, for both demonstration and risk mitigation purposes, by April 2004. This paper will describe the system engineering approach that was undertaken by JPL's GDS Team in order to meet the technical challenge within a non-negotiable eight-month schedule. Key to the success was adherence to an overall systems engineering process and fundamental systems engineering practices: decomposition of the project request into manageable requirements; definition of a structured yet flexible development process; integration of multiple ground disciplines and experts into a focused team effort; in-process risk management; and aggregation of the intermediate products to an integrated final product. In addition, this paper will highlight the role of lessons learned from the integration experience. The lessons learned from an early GDS deployment have served as the foundation for the design and implementation of the Dawn Ground Data System.
Zero to Integration in Eight Months, the Dawn Ground Data System Engineering Challenge
The Dawn Project has presented the Ground Data System (GDS) with technical challenges driven by cost and schedule constraints commonly associated with National Aeronautics and Space Administration (NASA) Discovery Projects. The Dawn mission consists of a new and exciting Deep Space partnership among: the Jet Propulsion Laboratory (JPL), manages the project and is responsible for flight operation; Orbital Sciences Corporation (OSC), is the spacecraft builder and is responsible for flight system test and integration; and the University of California, at Los Angeles (UCLA), is responsible for science planning and operations. As a cost-capped mission, one of Dawn's implementation strategies is to leverage from both flight and ground heritage. OSC's ground data system is used for flight system test and integration as part of the flight heritage strategy. Mission operations, however, are to be conducted with JPL's ground system. The system engineering challenge of dealing with two heterogeneous ground systems emerged immediately. During the first technical interchange meeting between the JPL's GDS Team and OSC's Flight Software Team, August 2003, the need to integrate the ground system with the flight software was brought to the table. This need was driven by the project's commitment to enable instrument engineering model integration in a spacecraft simulator environment, for both demonstration and risk mitigation purposes, by April 2004. This paper will describe the system engineering approach that was undertaken by JPL's GDS Team in order to meet the technical challenge within a non-negotiable eight-month schedule. Key to the success was adherence to fundamental systems engineering practices: decomposition of the project request into manageable requirements; integration of multiple ground disciplines and experts into a focused team effort; definition of a structured yet flexible development process; definition of an in-process risk reduction plan; and aggregation of the intermediate products to an integrated final product. In addition, this paper will highlight the role of lessons learned from the integration experience. The lessons learned from an early GDS deployment have served as the foundation for the design and implementation of the Dawn Ground Data System.
Telemetry Metrics: Monitoring Data Quality in the Spacecraft Ground Data System
During the launch of Mars Odyssey, ground data system (GDS) engineers experienced a glitch in the ground data system that caused us to re-evaluate how we looked at spacecraft telemetry, particularly during the spacecraft development period and for critical spacecraft events in flight. Spacecraft telemetry told the subsystem and instrument engineers about the health and status of the spacecraft, but there was surprisingly little information about how well the ground data system was doing in getting information from the spacecraft to the engineers.The problem for the Mars Odyssey launch was with a single channel not updating as often as expected. Spacecraft engineers considered calling off the Launch but eventually decided that this particular channel did not provide information that was crucial for launch. It was only after the post launch acquisition of the Odyssey signal that ground data system engineers heard there had been a concern about the channel....
Mars Reconnaissance Orbiter, Ground Data System, Receivables and Deliverables (REC/DELs)
This paper presents one JPL element manager's approach to describe a complex Ground Data System (GDS) with its receivables and deliverables (REC/DEL). The Mars Reconnaissance Orbiter (MRO) Ground Data System is the integrated set of ground software, hardware, facilities and networks that support mission operation. REC/DEL is a powerful tool for specifying hierarchy of commitments among systems and teams. Receivable of a system is a deliverable of another system. Focusing on tangible products enables the manager to objectively measure progress in a schedule. Jet Propulsion Laboratory mandates the use of REC/DEL for flight projects. Tutorial and training is provided for managers to create integrated REC/DEL database using automated systems. Project schedules are based on REC/DELs. This paper is not focusing on the mechanics of REC/DEL database creation, but it provides a guideline how one systematically creates categories of deliverables and receivables for ground data system components.
Mars Reconnaissance Orbiter, Ground Data System, Receivables and Deliverables (REC/DELs)
This paper presents one JPL element manager's approach to describe a complex Ground Data System (GDS) with its receivables and deliverables (REC/DEL). The Mars Reconnaissance Orbiter (MRO) Ground Data System is the integrated set of ground software, hardware, facilities and networks that support mission operation. REC/DEL is a powerful tool for specifying hierarchy of commitments among systems and teams. Receivable of a system is a deliverable of another system. Focusing on tangible products enables the manager to objectively measure progress in a schedule. The Jet Propulsion Laboratory mandates the use of REC/DEL for flight projects. Tutorial and training is provided for managers to create an integrated REC/DEL database using automated systems. Project schedules are based on REC/DELs. This paper is not focusing on the mechanics of REC/DEL database creation, but it provides a guideline how one systematically creates categories of deliverables and receivables for ground data system components...
The Mars 2020 Ground Data System Architecture
The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.
The Interim: Until You Achieve an Operationally Responsive Ground System
Everyone wants to achieve a 'Responsive' Ground Data System (GDS), but that takes time. What do you do in the interim? Our group, called the Integration, Test and Deployment Team (ITD), is a group of responsive engineers whose primary focus is to assist JPL projects to successfully adapt, test, integrate and deploy their ground data system. The team configures and adapts the GDS for a project, so that analysts, engineers and scientist do not need to be experts in the GDS to operate it. The team has developed a human interface to accommodate all types of users. It provides Graphical User Interfaces (GUI's) for those that want GUI's, command line interfaces for those that want control, and selection button interfaces for other users. The cornerstone of a responsive Ground Data System is responsive people. Without individuals who can be aware of a project's changing needs and requirements, how can the GDS become responsive?.
The Interim : until you achieve an operationally responsive ground system
Everyone wants to achieve a 'Responsive' Ground Data System (GDS), but that takes time. What do you do in the interim? Our group, called the Integration, Test and Deployment Team (ITD), is a group of responsive engineers whose primary focus is to assist JPL projects to successfully adapt, test, integrate and deploy their ground data system. The team configures and adapts the GDS for a project, so that analysts, engineers and scientist do not need to be experts in the GDS to operate it. The team has developed a human interface to accommodate all types of users. It provides Graphical User Interfaces (GUI's) for those that want GUI's, command line interfaces for those that want control, and selection button interfaces for other users. The cornerstone of a responsive Ground Data System is responsive people. Without individuals who can be aware of a project's changing needs and requirements, how can the GDS become responsive
Life sciences Spacelab Mission Development test 3 (SMD 3) data management report
Development of a permanent data system for SMD tests was studied that would simulate all elements of the shuttle onboard, telemetry, and ground data systems that are involved with spacelab operations. The onboard data system (ODS) and the ground data system (GDS) were utilized. The air-to-ground link was simulated by a hardwired computer-to-computer interface. A patch board system was used on board to select experiment inputs, and the downlink configuration from the ODS was changed by a crew keyboard entry to support each experiment. The ODS provided a CRT display of experiment parameters to enable the crew to monitor experiment performance. An onboard analog system, with recording capability, was installed to handle high rate data and to provide a backup to the digital system. The GDS accomplished engineering unit conversion and limit sensing, and provided realtime parameter display on CRT's in the science monitoring area and the test control area.
Automated Data Accountability for Missions in Mars Rover Data
As the Mars Curiosity Rover transmits data to the JPL Ground Data System (GDS), it frequently observes data loss and corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the downlink process. As new missions are launched, the GDSA team redistributes analysts to these new missions, causing shortages in previous missions. The GDSA team can significantly benefit from the automation and optimization of the downlink process of telemetry data. In fact, there is a need for a better understanding of why the data is corrupted, so that the GDSA team can best determine the root cause of the issues in the GDS. This paper presents machine learning and deep learning based approaches to automate and optimize the detection of data loss. We first created a pipeline to automatically accumulate data from the telemetry databases (MAROS, Telemetry Data Storage, and GDS Elastic Search Database) in the downlink process. With our newly created datasets, we perform feature selection to supplement the GDSA understanding of the downlink process and provide supplemental analysis on the importance of different features. We implement various machine learning and deep learning based models, including support vector machines, ensemble methods, and deep neural networks and evaluate their accuracies in identifying whether a downlink process is complete or incomplete. We utilize fast hyperparameter optimization methods that allow our models to quickly be re-trained, allowing them to quickly be tuned and optimized on daily incoming data in real time. This hyperparameter optimization also allows our methods to be quickly integrated into other JPL missions. Our results show that our best-performing machine learning and deep learning based models outperform the existing GDSA detection software by 6 accuracy points and can aid analysts by providing insights into the data accountability problem. Since these various machine learning and deep learning approaches vary significantly in interpretability, we provide a discussion on the tradeoffs between their performance and trustworthiness in helping detect issues in data transmission.
Using Quality Attributes to Bridge Systems Engineering Gaps : A Juno Ground Data Systems Case Study
The Juno Mission to Jupiter is the second mission selected by the NASA New Frontiers Program. Juno launched August 2011 and will reach Jupiter July 2016. Juno's payload system is composed of nine instruments plus a gravity science experiment. One of the primary functions of the Juno Ground Data System (GDS) is the assembly and distribution of the CFDP (CCSDS File Delivery Protocol) product telemetry, also referred to as raw science data, for eight out of the nine instruments. The GDS accomplishes this with the Instrument Data Pipeline (IDP). During payload integration, the first attempt to exercise the IDP in a flight like manner revealed that although the functional requirements were well understood, the system was unable to meet latency requirements with the as-is heritage design. A systems engineering gap emerged between Juno instrument data delivery requirements and the assumptions behind the heritage flight-ground interactions. This paper describes the use of quality attributes to measure and overcome this gap by introducing a new systems engineering activity, and a new monitoring service architecture that successfully delivered the performance metrics needed to validate Juno IDP.
Adapting a Large-Scale Multi-Mission Ground System for Low-Cost CubeSats
The majority of today's CubeSat fleet consists of Earth-orbiting missions that mostly use existing ground systems developed by universities because of availability, simplicity, and low-cost. The Interplanetary NanoSpacecraft Pathfinder In Relevant Environment (INSPIRE) mission is a revolutionary CubeSat mission that will launch a pair of CubeSats into deep space to study the feasibility of CubeSats beyond low-Earth orbit. This uncovers a new set of systems and software engineering challenges to the development of a robust and reliable ground system in a low-cost environment. In this paper, we discuss the approach to these challenges by using the Jet Propulsion Laboratory's (JPL) Advanced Multimission Operation System (AMMOS) Ground Data System (GDS) as well as the methodologies used to engineer the flight system to work with an existing ground system developed for large-scale missions. Specifically we will focus on the command and telemetry subsystem of AMMOS, the Multimission Data Processing and Control System. We conclude with a retrospective on the challenges encountered and a brief discussion on our efforts to provide AMMOS to support future deep space CubeSat missions.
Ground Data System Risk Mitigation Techniques for Faster, Better, Cheaper Missions
With the advent of faster, cheaper, and better missions, NASA Projects acknowledged that a higher level of risk was inherent and accepted with this approach. It was incumbent however upon each component of the Project whether spacecraft, payload, launch vehicle, or ground data system to ensure that the mission would nevertheless be an unqualified success. The Small Explorer (SMEX) program's ground data system (GDS) team developed risk mitigation techniques to achieve these goals starting in 1989. These techniques have evolved through the SMEX series of missions and are practiced today under the Triana program. These techniques are: (1) Mission Team Organization--empowerment of a closeknit ground data system team comprising system engineering, software engineering, testing, and flight operations personnel; (2) Common Spacecraft Test and Operational Control System--utilization of the pre-launch spacecraft integration system as the post-launch ground data system on-orbit command and control system; (3) Utilization of operations personnel in pre-launch testing--making the flight operations team an integrated member of the spacecraft testing activities at the beginning of the spacecraft fabrication phase; (4) Consolidated Test Team--combined system, mission readiness and operations testing to optimize test opportunities with the ground system and spacecraft; and (5). Reuse of Spacecraft, Systems and People--reuse of people, software and on-orbit spacecraft throughout the SMEX mission series. The SMEX ground system development approach for faster, cheaper, better missions has been very successful. This paper will discuss these risk management techniques in the areas of ground data system design, implementation, test, and operational readiness.
Cost-Effective Telemetry and Command Ground Systems Automation Strategy for the Soil Moisture Active Passive (SMAP) Mission
Soil Moisture Active Passive (SMAP) is an Earth-orbiting, remote-sensing NASA mission slated for launch in 2014.[double dagger] The ground data system (GDS) being developed for SMAP is composed of many heterogeneous subsystems, ranging from those that support planning and sequencing to those used for real-time operations, and even further to those that enable science data exchange. A full end-to-end automation of the GDS may result in cost savings during mission operations, but it would require a significant upfront investment to develop such comprehensive automation. As demonstrated by the Jason-1 and Wide-field Infrared Survey Explorer (WISE) missions, a measure of "lights-out" automation for routine, orbital pass ground operations can still reduce mission cost through smaller staffing of operators and limited work hours. The challenge, then, for the SMAP GDS engineering team is to formulate an automated operations strategy--and corresponding system architecture--to minimize operator intervention during operations, while balancing the development cost associated with the scope and complexity of automation. This paper discusses the automated operations approach being developed for the SMAP GDS. The focus is on automating the activities involved in routine passes, which limits the scope to real-time operations. A key subsystem of the SMAP GDS--NASA's AMMOS Mission Data Processing and Control System (AMPCS)--provides a set of capabilities that enable such automation. Also discussed are the lights-out pass automations of the Jason-1 and WISE missions and how they informed the automation strategy for SMAP. The paper aims to provide insights into what is necessary in automating the GDS operations for Earth satellite missions.
Cost-Effective Telemetry and Command Ground Systems Automation Strategy for the Soil Moisture Active Passive (SMAP) Mission
Soil Moisture Active Passive (SMAP) is an Earth-orbiting, remote-sensing NASA mission slated for launch in 2014. The ground data system (GDS) being developed for SMAP is composed of many heterogeneous subsystems, ranging from those that support planning and sequencing to those used for real-time operations, and even further to those that enable science data exchange. A full end-to-end automation of the GDS may result in cost savings during mission operations, but it would require a significant upfront investment to develop such a comprehensive automation. As demonstrated by the Jason-1 and Wide-field Infrared Survey Explorer (WISE) missions, a measure of "lights-out" automation for routine, orbital pass, ground operations can still reduce mission costs through smaller staffing of operators and limiting their working hours. The challenge, then, for the SMAP GDS engineering team, is to formulate an automated operations strategy--and corresponding system architecture -- to minimize operator intervention during routine operations, while balancing the development costs associated with the scope and complexity of automation. This paper discusses the automated operations approach being developed for the SMAP GDS. The focus is on automating the activities involved in routine passes, which limits the scope to real-time operations. A key subsystem of the SMAP GDS -- NASA's AMMOS Mission Data Processing and Control System (AMPCS) -- provides a set of capabilities that enable such automation. Also discussed are the lights-out pass automations of the Jason-1 and WISE missions and how they informed the automation strategy for SMAP. The paper aims to provide insights into what is necessary in automating the GDS operations for Earth satellite missions.