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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Proposed Array-based Deep Space Network for NASA

The current assets of the Deep Space Network (DSN) of the National Aeronautics and Space Administration (NASA), especially the 70-m antennas, are aging and becoming less reliable. Furthermore, they are expensive to operate and difficult to upgrade for operation at Ka-band (321 GHz). Replacing them with comparable monolithic large antennas would be expensive. On the other hand, implementation of similar high-sensitivity assets can be achieved economically using an array-based architecture, where sensitivity is measured by G/T, the ratio of antenna gain to system temperature. An array-based architecture would also provide flexibility in operations and allow for easy addition of more G/T whenever required. Therefore, an array-based plan of the next-generation DSN for NASA has been proposed. The DSN array would provide more flexible downlink capability compared to the current DSN for robust telemetry, tracking and command services to the space missions of NASA and its international partners in a cost effective way. Instead of using the array as an element of the DSN and relying on the existing concept of operation, we explore a broader departure in establishing a more modern concept of operations to reduce the operations costs. This paper presents the array-based architecture for the next generation DSN. It includes system block diagram, operations philosophy, user's view of operations, operations management, and logistics like maintenance philosophy and anomaly analysis and reporting. To develop the various required technologies and understand the logistics of building the array-based lowcost system, a breadboard array of three antennas has been built. This paper briefly describes the breadboard array system and its performance.

telemetry↗
NASA NTRS1983

The Network Information Management System (NIMS) in the Deep Space Network

In an effort to better manage enormous amounts of administrative, engineering, and management data that is distributed worldwide, a study was conducted which identified the need for a network support system. The Network Information Management System (NIMS) will provide the Deep Space Network with the tools to provide an easily accessible source of valid information to support management activities and provide a more cost-effective method of acquiring, maintaining, and retrieval data.

Wales, K. J.↗
NASA NTRS1972

Deep space network

Background, current status, and sites of Deep Space Network stations are briefly discussed.

Flanagan, F. M.↗
NASA NTRS1972

The deep space network, volume 7

The objectives, functions, and organization of the Deep Space Network are summarized. The Deep Space Instrumentation Facility, the Ground Communications Facility, and the Space Flight Operations Facility are described.

Source record↗
NASA NTRS1977

The Deep Space Network

The functions and facilities of the Deep Space Network, its supporting research and technology and network operations are discussed.

Source record↗
NASA NTRS1979

The deep space network

A report is given of the Deep Space Networks progress in (1) flight project support, (2) tracking and data acquisition research and technology, (3) network engineering, (4) hardware and software implementation, and (5) operations.

Source record↗
NASA NTRS1980

The deep space network

The functions and facilities of the Deep Space Network are considered. Progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is reported.

Source record↗
NASA NTRS1987

Ka-band (32-GHz) performance of 70-meter antennas in the Deep Space Network

Two models are provided of the Deep Space Network (DSN) 70 m antenna performance at Ka-band (32 GHz) and, for comparison purposes, one at X-band (8.4 GHz). The baseline 70 m model represents expected X-band and Ka-band performance at the end of the currently ongoing 64 m to 70 m mechanical upgrade. The improved 70 m model represents two sets of Ka-band performance estimates (the X-band performance will not change) based on two separately developed improvement schemes: the first scheme, a mechanical approach, reduces tolerances of the panels and their settings, the reflector structure and subreflector, and the pointing and tracking system. The second, an electronic/mechanical approach, uses an array feed scheme to compensate fo lack of antenna stiffness, and improves panel settings using microwave holographic measuring techniques. Results are preliminary, due to remaining technical and cost uncertainties. However, there do not appear to be any serious difficulties in upgrading the baseline DSN 70 m antenna network to operate efficiently in an improved configuration at 32 GHz (Ka-band). This upgrade can be achieved by a conventional mechanical upgrade or by a mechanical/electronic combination. An electronically compensated array feed system is technically feasible, although it needs to be modeled and demonstrated. Similarly, the mechanical upgrade requires the development and demonstration of panel actuators, sensors, and an optical surveying system.

Imbriale, W. A.↗
NASA NTRS1992

Operator assistant to support deep space network link monitor and control

Preparing the Deep Space Network (DSN) stations to support spacecraft missions (referred to as pre-cal, for pre-calibration) is currently an operator and time intensive activity. Operators are responsible for sending and monitoring several hundred operator directivities, messages, and warnings. Operator directives are used to configure and calibrate the various subsystems (antenna, receiver, etc.) necessary to establish a spacecraft link. Messages and warnings are issued by the subsystems upon completion of an operation, changes of status, or an anomalous condition. Some points of pre-cal are logically parallel. Significant time savings could be realized if the existing Link Monitor and Control system (LMC) could support the operator in exploiting the parallelism inherent in pre-cal activities. Currently, operators may work on the individual subsystems in parallel, however, the burden of monitoring these parallel operations resides solely with the operator. Messages, warnings, and directives are all presented as they are received; without being correlated to the event that triggered them. Pre-cal is essentially an overhead activity. During pre-cal, no mission is supported, and no other activity can be performed using the equipment in the link. Therefore, it is highly desirable to reduce pre-cal time as much as possible. One approach to do this, as well as to increase efficiency and reduce errors, is the LMC Operator Assistant (OA). The LMC OA prototype demonstrates an architecture which can be used in concert with the existing LMC to exploit parallelism in pre-cal operations while providing the operators with a true monitoring capability, situational awareness and positive control. This paper presents an overview of the LMC OA architecture and the results from initial prototyping and test activities.

Cooper, Lynne P.↗
NASA NTRS2016

Achieving Fast Operational Intelligence in NASA's Deep Space Network Through Complex Event Processing

NASA’s Deep Space Network (DSN) is a complex, global project, in which the expertise of human operators remain crucial for its successful operation. To find ways to save costs in operations and to improve its services, a number of modernization efforts are underway in the DSN. One such effort is a research and technology development task at the Jet Propulsion Laboratory that is investigating the use of complex event processing (CEP) for intelligent assessment of situations, trend analysis, and advanced automation. The technology leverages the significant business intelligence (BI) and data science advancements made in the enterprise industries over the last several years. The open source big data processing engine Apache SparkTM and the high-throughput, distributed messaging system Apache Kafka form the core of the DSN Complex Event Processing (DCEP) framework. This paper discusses the system engineering perspective of why achieving efficient, lower-cost operations in the DSN is a challenging problem, how the DCEP system handles the use cases that help realize intelligent operations, and how this solution fits into the overall model of the planned DSN Follow-the- Sun Operations (FtSO).

Choi, Joshua S.↗
NASA NTRS2020

Scheduling NASA's Deep Space Network: Priorities, Preferences, and Optimization

NASA's Deep Space Network (DSN) is the primary resource for communications and navigation for interplanetary space missions, for both NASA and partner agencies. Growth in mission demand, both in number of spacecraft and in data return, has led to increased loading levels on the network, and actual demand frequently exceeds network capacity. The DSN scheduling process involves peer-to-peer collaborative negotiation, which consumes significant time and resources in order to reach a baseline version of the schedule, and then to manage and agree to changes. Process delays are exacerbated by the high level of oversubscription experienced by the DSN: it is not unusual for the scheduling process to start with 20-40\% more requested time can be accommodated on the available antennas. The other NASA networks make use of a static mission priority list to address a similar problem: missions are ranked in priority order, then the schedule is populated by priority from highest to lowest. Such a mechanism would not work for DSN due to the heterogeneity of the mission set, and to the time-varying mission requirements with mission phase. This paper describes an alternative approach for the DSN that addresses key problems inherent in the current process --- oversubsubscription and how to "fairly'" reduce it to a manageable level. The main characteristics of the new approach are the use of loading-based limits based on balancing requested time, along with priorities and user preferences as the basis for optimization criteria that can be used by new algorithms.

Johnston, Mark D↗
NASA NTRS2006

Future Mission Trends and their Implications for the Deep Space Network

This viewgraph presentation discusses the direction of future missions and it's significance to the Deep Space Network. The topics include: 1) The Deep Space Network (DSN); 2) Past Missions Driving DSN Evolution; 3) The Changing Mission Paradigm; 4) Assessing Future Mission Needs; 5) Link Support Trends; 6) Downlink Rate Trends; 7) Uplink Rate Trends; 8) End-to-End Link Difficulty Trends; 9) Summary: Future Mission Trend Drivers; and 10) Conclusion: Implications for the DSN.

mission trends↗
NASA NTRS2018

Complexity-Based Link Assignment for NASA’s Deep Space Network for Follow-the-Sun Operations

NASA’s Deep Space Network (DSN) recently underwent a paradigm shift in its operations approach called Follow the Sun Operations (FtSO) in an effort to increase efficiency for forthcoming expansion of the network. This change requires each Deep Space Communications Complex (DSCC) to remotely control the other two complexes’ antennas during their local day shift, in contrast to locally controlling only their own antennas 24x7. Remote operations increases the workload of each complex during their day shift, specifically that of the Link Control Operators (LCOs), and presents a new challenge for planning and managing the distribution of responsibility for each link. A new DSN software assembly, the Link Complexity and Maintenance (LCM) software, was developed to support workload management for LCOs, as well as for planning site-local maintenance activities. The LCM deployment was a vital part of the transition to FtSO in November 2017. This paper discusses the architecture of LCM, its feature set, and lessons learned during its development and rollout.

Lee, Carlyn↗
NASA NTRS2005

7.3 Communications and Navigation

This presentation gives an overview of the networks NASA currently uses to support space communications and navigation, and the requirements for supporting future deep space missions, including manned lunar and Mars missions. The presentation addresses the Space Network, Deep Space Network, and Ground Network, why new support systems are needed, and the potential for catastrophic failure of aging antennas. Space communications and navigation are considered during Aerocapture, Entry, Descent and Landing (AEDL) only in order to precisely position, track and interact with the spacecraft at its destination (moon, Mars and Earth return) arrival. The presentation recommends a combined optical/radio frequency strategy for deep space communications.

Manning, Rob↗
NASA NTRS1981

The Deep Space Network: An instrument for radio science research

Doppler and ranging data routinely generated at the Deep Space Stations of the California Institute of Technology-Jet Propulsion Laboratory Deep Space Network serve as an excellent source of radio science information. Important radio science experiments based on Deep Space Network generated radio metric data have included confirmation of Einstein's Theory of Relativity, measurement of the masses and gravitational harmonics of the planets out to Saturn, and measurement of electron density distribution and turbulence in the solar corona. In response to an increased level of radio science requirements, the Deep Space Network chose in 1976 to implement a new radio science system, which was completed in late 1978. Key features include (1) highly phase stable open loop receivers, (2) reduction of recorded data bandwidth through use of programmed local oscillators, and (3) real time digitization and recording on computer compatible tape.

Renzetti, N. A.↗
NASA NTRS2002

A view of the future of NASA's Deep Space Network and associated systems

The current architecture of the Deep Space Network reflects its heritage of supporting past, and ongoing NASA missions. In the future, the size and character of the Agency's deep space mission fleet will significantly change. Consequently, the DSN must evolve to accomodate anticipated needs.

Deep Space Network operations↗
NASA NTRS2010

The Future of NASA's Deep Space Network and Applications to Planetary Probe Missions

NASA's Deep Space Network (DSN) has been an invaluable tool in the world's exploration of space. It has served the space-faring community for more than 45 years. The DSN has provided a primary communication pathway for planetary probes, either through direct- to-Earth links or through intermediate radio relays. In addition, its radiometric systems are critical to probe navigation and delivery to target. Finally, the radio link can also be used for direct scientific measurement of the target body ('radio science'). This paper will examine the special challenges in supporting planetary probe missions, the future evolution of the DSN and related spacecraft technology, the advantages and disadvantages of radio relay spacecraft, and the use of the DSN radio links for navigation and scientific measurements.

Deep Space Network (DSN)↗