Long-range planning for the Deep Space Network
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DSN long range plan↗
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Long-range planning for the Deep Space Network
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DSN long range plan↗
Long-range planning for the Deep Space Network
Conduct of space exploration is undergoing a significant transformation. Initial reconnaissance missions are giving way to long duration observations with data-intensive instruments, in situ investigations and complex operations. To keep pace, a transformation in the Deep Space Network is in order.
DSN long range plan↗
Transforming the Deep Space Network into the Interplanetary Network
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long range plan↗
Transforming the Deep Space Network into the Interplanetary Network
Space exploration missions are undergoing a significant transformation as are the expectations of their scientific investigators and the public who participate in these great voyages of exploration.
DSN↗
Deep Space Mission Trend Analyses: A Briefing to the Next Generation EBRE Study Team
Determination of stakeholder needs for next generation implementations necessitates a multi ]pronged approach. . Future mission set analyses provide a lower gbound h for some of these needs. . Earth ]based analogies provide an upper gbound h for some of these needs. . Interpreting the results requires being mindful of both the near ]term contextual factors and long ]term factors that are in play. . In the context of last year fs analyses, the current budget environment, the potential Pu ]238 shortage, and SMD fs gsingle 34m only h policy may, collectively, create a future deep space mission set that, from a capacity and end ]to ]end link difficulty standpoint, is no more challenging than it is today. . Nonetheless, data rates and volumes continue to increase, suggesting capability and spectrum challenges ahead. These results agree with the results from the Earthbased analogies. . Emerging developments such as smallsats and distributed spacecraft could significantly change the capacity and end ]to ]end link difficulty picture.
future mission analysis↗
Long-range planning cost model for support of future space missions by the deep space network
A simple model is suggested to do long-range planning cost estimates for Deep Space Network (DSP) support of future space missions. The model estimates total DSN preparation costs and the annual distribution of these costs for long-range budgetary planning. The cost model is based on actual DSN preparation costs from four space missions: Galileo, Voyager (Uranus), Voyager (Neptune), and Magellan. The model was tested against the four projects and gave cost estimates that range from 18 percent above the actual total preparation costs of the projects to 25 percent below. The model was also compared to two other independent projects: Viking and Mariner Jupiter/Saturn (MJS later became Voyager). The model gave cost estimates that range from 2 percent (for Viking) to 10 percent (for MJS) below the actual total preparation costs of these missions.
Sherif, J. S.↗
Modeling preparation costs for space missions by using major cost drivers
A model is suggested for making long range planning cost estimates for Deep Space Network (DSN) support of future space missions. The model is a function of major mission cost drivers, such as maintenance and operations, downlink frequency upgrade, uplink frequency upgrade, telemetry upgrade, antenna gain/noise temperature, radiometric accuracy upgrade, radio science upgrade, and very long baseline interferometry. The model is derived from actual cost data from three space missions: Voyager (Uranus), Voyager (Neptune), and Magellan. The model allows one to estimate the total cost and the cost over time of a similar future space mission. The model was back tested against the three projects and gave cost estimates that range from 17 pct. below to 19 pct. above actual mission preparation costs. The model was also compared with two other independent projects: Mariner Jupiter/Saturn (MJS later became Voyager) and Viking. The model gave total preparation cost estimates that range from 15 pct. above to 4 pct. below actual total preparation costs for MJS and Viking, respectively.
Sherif, J. S.↗
Modeling the Deep Space Network costs for future space missions by using major cost drivers
This paper develops a cost model to do long range planning cost estimates for Deep Space Network (DSN) support of future space missions. The model is a function of eight major mission cost drivers such as uplink frequency upgrade, telemetry upgrade, and antenna gain/noise temperature upgrade. This paper focuses on the costs required to modify and/or enhance the DSN to prepare for supporting future space missions. Two models are derived from actual cost data from three space missions: Voyager (Uranus), Voyager (Neptune), and Magellan. Model A makes it possible to estimate the total cost, and Model B makes it possible to estimate the time profile cost and total cost for DSN support of a similar future space mission. The models were back tested against the total cost and the time profile cost for DSN support of the three projects, and gave cost estimates which range from 17 percent below to 19 percent above actual costs for Model A, and 22.5 percent below to 17.5 percent above for Model B. The total cost Model A was also applied to estimate the total costs for DSN support of two other independent projects: Mariner Jupiter/Saturn (MJS later became Voyager), and Viking. This model gave total cost estimates for DSN support which range from 15 percent to 4 percent above actual total costs for MJS and Viking, respectively.
Remer, Donald S.↗
Estimating the Deep Space Network modification costs to prepare for future space missions by using major cost drivers
This paper develops a cost model to do long range planning cost estimates for Deep Space Network (DSN) support of future space missions. The paper focuses on the costs required to modify and/or enhance the DSN to prepare for future space missions. The model is a function of eight major mission cost drivers and estimates both the total cost and the annual costs of a similar future space mission. The model is derived from actual cost data from three space missions: Voyager (Uranus), Voyager (Neptune), and Magellan. Estimates derived from the model are tested against actual cost data for two independent missions, Viking and Mariner Jupiter/Saturn (MJS).
Remer, Donald S.↗
Mission and Assets Database
Mission and Assets Database (MADB) Version 1.0 is an SQL database system with a Web user interface to centralize information. The database stores flight project support resource requirements, view periods, antenna information, schedule, and forecast results for use in mid-range and long-term planning of Deep Space Network (DSN) assets.
Baldwin, John↗
Automating Mid- and Long-Range Scheduling for the NASA Deep Space Network
NASA has recently deployed a new mid-range scheduling system for the antennas of the Deep Space Network (DSN), called Service Scheduling Software, or S(sup 3). This system was designed and deployed as a modern web application containing a central scheduling database integrated with a collaborative environment, exploiting the same technologies as social web applications but applied to a space operations context. This is highly relevant to the DSN domain since the network schedule of operations is developed in a peer-to-peer negotiation process among all users of the DSN. These users represent not only NASA's deep space missions, but also international partners and ground-based science and calibration users. The initial implementation of S(sup 3) is complete and the system has been operational since July 2011. This paper describes some key aspects of the S(sup 3) system and on the challenges of modeling complex scheduling requirements and the ongoing extension of S(sup 3) to encompass long-range planning, downtime analysis, and forecasting, as the next step in developing a single integrated DSN scheduling tool suite to cover all time ranges.
Deep Space Network↗
Integrated Planning and Scheduling for NASA’s Deep Space Network – from Forecasting to Real-time
Over a period of several years, the software systems that plan and schedule the use of NASA’s Deep Space Network (DSN) for the projects it serves have been upgraded from a disparate set of decades-old software components, to an integrated suite covering long-range planning and forecasting, all the way to real-time scheduling. The most recent component of this suite is known as LAPS, for Loading Analysis and Planning Software, and is responsible for long-term planning and forecasting, including studies and analysis of new missions, changed mission requirements, downtime, and new or changed antenna capabilities. This paper discusses the architecture of LAPS and its interfaces with other elements of DSN planning and scheduling, its user interfaces, and some lessons learned from development and deployment.
Lad, Jigna↗
Automating Mid- and Long-Range Scheduling for NASA's Deep Space Network
NASA has recently deployed a new mid-range scheduling system for the antennas of the Deep Space Network (DSN), called Service Scheduling Software, or S(sup 3). This system is architected as a modern web application containing a central scheduling database integrated with a collaborative environment, exploiting the same technologies as social web applications but applied to a space operations context. This is highly relevant to the DSN domain since the network schedule of operations is developed in a peer-to-peer negotiation process among all users who utilize the DSN (representing 37 projects including international partners and ground-based science and calibration users). The initial implementation of S(sup 3) is complete and the system has been operational since July 2011. S(sup 3) has been used for negotiating schedules since April 2011, including the baseline schedules for three launching missions in late 2011. S(sup 3) supports a distributed scheduling model, in which changes can potentially be made by multiple users based on multiple schedule "workspaces" or versions of the schedule. This has led to several challenges in the design of the scheduling database, and of a change proposal workflow that allows users to concur with or to reject proposed schedule changes, and then counter-propose with alternative or additional suggested changes. This paper describes some key aspects of the S(sup 3) system and lessons learned from its operational deployment to date, focusing on the challenges of multi-user collaborative scheduling in a practical and mission-critical setting. We will also describe the ongoing project to extend S(sup 3) to encompass long-range planning, downtime analysis, and forecasting, as the next step in developing a single integrated DSN scheduling tool suite to cover all time ranges.
scheduling↗
DSN Resource Scheduling
TIGRAS is client-side software, which provides tracking-station equipment planning, allocation, and scheduling services to the DSMS (Deep Space Mission System). TIGRAS provides functions for schedulers to coordinate the DSN (Deep Space Network) antenna usage time and to resolve the resource usage conflicts among tracking passes, antenna calibrations, maintenance, and system testing activities. TIGRAS provides a fully integrated multi-pane graphical user interface for all scheduling operations. This is a great improvement over the legacy VAX VMS command line user interface. TIGRAS has the capability to handle all DSN resource scheduling aspects from long-range to real time. TIGRAS assists NASA mission operations for DSN tracking of station equipment resource request processes from long-range load forecasts (ten years or longer), to midrange, short-range, and real-time (less than one week) emergency tracking plan changes. TIGRAS can be operated by NASA mission operations worldwide to make schedule requests for the DSN station equipment.
Wang, Yeou-Fang↗
Scheduling with Automatic Resolution of Conflicts
DSN Requirement Scheduler is a computer program that automatically schedules, reschedules, and resolves conflicts for allocations of resources of NASA s Deep Space Network (DSN) on the basis of ever-changing project requirements for DSN services. As used here, resources signifies, primarily, DSN antennas, ancillary equipment, and times during which they are available. Examples of project-required DSN services include arraying, segmentation, very-long-baseline interferometry, and multiple spacecraft per aperture. Requirements can include periodic reservations of specific or optional resources during specific time intervals or within ranges specified in terms of starting times and durations. This program is built on the Automated Scheduling and Planning Environment (ASPEN) software system (aspects of which have been described in previous NASA Tech Briefs articles), with customization to reflect requirements and constraints involved in allocation of DSN resources. Unlike prior DSN-resource- scheduling programs that make single passes through the requirements and require human intervention to resolve conflicts, this program makes repeated passes in a continuing search for all possible allocations, provides a best-effort solution at any time, and presents alternative solutions among which users can choose.
Clement, Bradley↗
A model for analysis of TDA budget allocation
There is an ever-increasing need to achieve greater efficiency in the operation of the Deep Space Network (DSN), i.e., increased productivity at reduced cost. One of the tools used in the course of a planning workshop on this subject was a methodology for budget allocation applicable to long-range planning. This article presents a model for analysis of the TDA budget allocation. For the 1994 through 1999 period, the percentage of the total TDA budget allocated to capacity and capability is being cut in half, whereas the percentage spent on efficiency of delivery will be increasing.
Remer, D. S.↗
NASA deep space network operations planning and preparation
The responsibilities and structural organization of the Operations Planning Group of NASA Deep Space Network (DSN) Operations are outlined. The Operations Planning group establishes an early interface with a user's planning organization to educate the user on DSN capabilities and limitations for deep space tracking support. A team of one or two individuals works through all phases of the spacecraft launch and also provides planning and preparation for specific events such as planetary encounters. Coordinating interface is also provided for nonflight projects such as radio astronomy and VLBI experiments. The group is divided into a Long Range Support Planning element and a Near Term Operations Coordination element.
Jensen, W. N.↗