The Deep Space Network
Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is presented.
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The deep space network
Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations is presented.
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Radio frequency interference protection of communications between the Deep Space Network and deep space flight projects
The increasing density of electrical and electronic circuits in Deep Space Station systems for computation, control, and numerous related functions has combined with the extension of system performance requirements calling for higher speed circuitry along with broader bandwidths. This has progressively increased the number of potential sources of radio frequency interference inside the stations. Also, the extension of spectrum usage both in power and frequency as well as the greater density of usage at all frequencies for national and international satellite communications, space research, Earth resource operations and defense, and particularly the huge expansion of airborne electronic warfare and electronic countermeasures operations in the Mojave area have greatly increased the potential number and severity of radio frequency interference incidents. The various facets of this problem and the efforts to eliminate or minimize the impact of interference on Deep Space Network support of deep space flight projects are described.
Johnston, D. W. H.↗
Long-range planning for the deep-space network.
Deep-Space Network /DSN/ planning, discussing support for lunar and planetary flight support and applied research applications
MANAGEMENT PLANNING↗
Implementation of an Antenna Array Signal Processing Breadboard for the Deep Space Network
The Deep Space Network Large Array will replace/augment 34 and 70 meter antenna assets. The array will mainly be used to support NASA's deep space telemetry, radio science, and navigation requirements. The array project will deploy three complexes in the western U.S., Australia, and European longitude each with 400 12m downlink antennas and a DSN central facility at JPL. THis facility will remotely conduct all real-time monitor and control for the network. Signal processing objectives include: provide a means to evaluate the performance of the Breadboard Array's antenna subsystem; design and build prototype hardware; demonstrate and evaluate proposed signal processing techniques; and gain experience with various technologies that may be used in the Large Array. Results are summarized..
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NASA Near Earth Network (NEN), Deep Space Network (DSN) and Space Network (SN) Support of CubeSat Communications
There has been a historical trend to increase capability and drive down the Size, Weight and Power (SWAP) of satellites and that trend continues today. Small satellites, including systems conforming to the CubeSat specification, because of their low launch and development costs, are enabling new concepts and capabilities for science investigations across multiple fields of interest to NASA. NASA scientists and engineers across many of NASAs Mission Directorates and Centers are developing exciting CubeSat concepts and welcome potential partnerships for CubeSat endeavors. From a communications and tracking point of view, small satellites including CubeSats are a challenge to coordinate because of existing small spacecraft constraints, such as limited SWAP and attitude control, low power, and the potential for high numbers of operational spacecraft. The NASA Space Communications and Navigation (SCaN) Programs Near Earth Network (NEN), Deep Space Network (DSN) and the Space Network (SN) are customer driven organizations that provide comprehensive communications services for space assets including data transport between a missions orbiting satellite and its Mission Operations Center (MOC). The NASA NEN consists of multiple ground antennas. The SN consists of a constellation of geosynchronous (Earth orbiting) relay satellites, named the Tracking and Data Relay Satellite System (TDRSS). The DSN currently makes available 13 antennas at its three tracking stations located around the world for interplanetary communication. The presentation will analyze how well these space communication networks are positioned to support the emerging small satellite and CubeSat market. Recognizing the potential support, the presentation will review the basic capabilities of the NEN, DSN and SN in the context of small satellites and will present information about NEN, DSN and SN-compatible flight radios and antenna development activities at the Goddard Space Flight Center (GSFC) and across industry. The presentation will review concepts on how the SN multiple access capability could help locate CubeSats and provide a low-latency early warning system. The presentation will also present how the DSN is evolving to maximize use of its assets for interplanetary CubeSats. The critical spectrum-related topics of available and appropriate frequency bands, licensing, and coordination will be reviewed. Other key considerations, such as standardization of radio frequency interfaces and flight and ground communications hardware systems, will be addressed as such standardization may reduce the amount of time and cost required to obtain frequency authorization and perform compatibility and end-to-end testing. Examples of standardization that exist today are the NASA NEN, DSN and SN systems which have published users guides and defined frequency bands for high data rate communication, as well as conformance to CCSDS standards. The workshop session will also seek input from the workshop participants to better understand the needs of small satellite systems and to identify key development activities and operational approaches necessary to enhance communication and navigation support using NASA's NEN, DSN and SN.
Telecommunication↗
A history of the deep space network
The Deep Space Network (DSN) has been managed and operated by the Jet Propulsion Laboratory (JPL) under NASA contract ever since NASA was formed in late 1958. The Tracking and data acquisition tasks of the DSN are markedly different from those of the other NASA network, STDN. STDN, which is an amalgamation of the satellite tracking network (STADAN) and the Manned Space Flight Network (MSFN), is primarily concerned with supporting manned and unmanned earth satellites. In contrast, the DSN deals with spacecraft that are thousands to hundreds of millions of miles away. The radio signals from these distant craft are many orders of magnitude weaker than those from nearby satellites. Distance also makes precise radio location more difficult; and accurate trajectory data are vital to deep space navigation in the vicinities of the other planets of the solar system. In addition to tracking spacecraft and acquiring data from them, the DSN is required to transmit many thousands of commands to control the sophisticated planetary probes and interplanetary monitoring stations. To meet these demanding requirements, the DSN has been compelled to be in the forefront of technology.
Corliss, W. R.↗
Space Programs Summary No. 37-42, Volume III FOR the Period September 1, 1966 to October 31, 1966. Deep Space Network
The Deep Space Network (DSN), established by the NASA Office of Tracking and Data Acquisition, is under the system management and technical direction of JPL. The DSN is responsible for two-way communications with unmanned spacecraft travelling from approximately 10,000 miles from Earth to interplanetary distances. Tracking and data-handling equipment to support these missions is provided. Present facilities permit simultaneous control of a newly launched spacecraft and a second one already in flight. In preparation for the increased number of U.S. activities in space, a capability is being developed for simultaneous control of either two newly launched spacecraft plus two in flight, or four spacecraft in flight. Advanced communications techniques are being implemented to make possible obtaining data from, and tracking spacecraft to, planets as far out in space as Jupiter.
NAVIGATION AND GUIDANCE↗
The deep space network
A Deep Space Network progress report is presented dealing with in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations.
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The Deep Space Network
The Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operation is reported.
Source record↗
The scheduling of tracking times for interplanetary spacecraft on the Deep Space Network
The Deep Space Network (DSN) is a network of tracking stations, located throughout the globe, used to track spacecraft for NASA's interplanetary missions. This paper describes a computer program, DSNTRAK, which provides an optimum daily tracking schedule for the DSN given the view periods at each station for a mission set of n spacecraft, where n is between 2 and 6. The objective function is specified in terms of relative total daily tracking time requirements between the n spacecraft. Linear programming is used to maximize the total daily tracking time and determine an optimal daily tracking schedule consistent with DSN station capabilities. DSNTRAK is used as part of a procedure to provide DSN load forecasting information for proposed future NASA mission sets.
Webb, W. A.↗
The Deep Space Network
The Deep Space Network (DSN) is the largest and most sensitive scientific telecommunications and radio navigation network in the world. Its principal responsibilities are to support unmanned interplanetary spacecraft missions and to support radio and radar astronomy observations in the exploration of the solar system and the universe. The DSN facilities and capabilities as of January 1988 are described.
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Gravitational wave searches using the DSN (Deep Space Network)
The Deep Space Network Doppler spacecraft link is currently the only method available for broadband gravitational wave searches in the 0.01 to 0.001 Hz frequency range. The DSN's role in the worldwide search for gravitational waves is described by first summarizing from the literature current theoretical estimates of gravitational wave strengths and time scales from various astrophysical sources. Current and future detection schemes for ground based and space based detectors are then discussed. Past, present, and future planned or proposed gravitational wave experiments using DSN Doppler tracking are described. Lastly, some major technical challenges to improve gravitational wave sensitivities using the DSN are discussed.
Nelson, S. J.↗
Advances in Ground Transmitters for the NASA Deep Space Network
The Deep Space Network (DSN), managed by the Jet Propulsion Laboratory for NASA, is equipped with multiple microwave transmitters ranging in average radiated power from 200 W to 400 kW. The transmitters are used for routine or emergency communication with spacecraft, for navigation, and for radio science tasks. The latest advances in transmitter engineering were implemented in a new generation of 20-kW dual-band transmitters developed for the DSN 34-m beam waveguide antennas. Innovations include additional X-band communication capability for near Earth missions, new control algorithms, automated calibration, improved and expanded computerized monitoring and diagnostics, reduced cabling, and improved maintainability. The innovations were very beneficial for the DSN 'overload' during the Mars 2003/2004 missions and will benefit other missions throughout the next decade. This paper describes the current design of the new transmitters and possible future developments.
microwaves↗
Time series comparisons in Deep Space Network
The Deep Space Network (DSN) is NASA’s international array of antennas that support interplanetary spacecraft missions. DSN provides radar and radio astronomy observations that enhance our understanding of the solar system and the larger universe. A track is a block of continuous multi-dimensional time series from the beginning to end of DSN communication with the target spacecraft, containing 129 monitor data items lasting several hours at a frequency of 0.2-1Hz. Monitor data on each track reports on the performance of specific spacecraft operations and the DSN itself. DSN is receiving signals from 32 spacecraft across the solar system. DSN has pressure to reduce costs while maintaining the quality of support for DSN mission users. DSN operators need to simultaneously monitor multiple tracks and identify anomalies in real time. DSN has seen that as the number of missions increases, the data that needs to be processed increases over time. In this project, we look at the last 8 years of data for analysis. Any anomaly in the track indicates a problem with either the spacecraft, DSN equipment, or weather conditions. DSN operators typically write “discrepancy reports” for further analysis. It is recognized that it would be quite helpful to identify 10 similar historical tracks out of the huge database to quickly find/match anomalies. This tool has three functions: (1) identification of the top 10 similar historical tracks, (2) detection of anomalies compared to the reference normal track, and (3) comparison of statistical differences between two given tracks. The requirements for these features were confirmed by survey responses from 21 DSN operators and engineers. The preliminary machine learning model has shown promising performance (AUC=0.92). We plan to increase the number of data sets and perform additional testing to improve performance further before its planned integration into the Track Visualizer to assist DSN field operators and engineers.
Rebbapragada, Umaa↗
The deep space network, volume iii. space programs summary, 1 may - 30 jun. 1965
Tracking stations engineering and operations, and communications engineering, research, and development for Deep Space Network
DEEP SPACE↗
Scheduling the NASA Deep Space Network with Deep Reinforcement Learning
With three complexes spread evenly across the Earth, NASA’s Deep Space Network (DSN) is the primary means of communications as well as a significant scientific instrument for dozens of active missions around the world. A rapidly rising number of spacecraft and increasingly complex scientific instruments with higher bandwidth requirements have resulted in demand that exceeds the network’s capacity across its 12 antennae. The existing DSN scheduling process operates on a rolling weekly basis and is time-consuming; for a given week, generation of the final baseline schedule of spacecraft tracking passes takes roughly 5 months from the initial requirements submission deadline, with several weeks of peer-to-peer negotiations in between. This paper proposes a deep reinforcement learning (RL) approach to generate candidate DSN schedules from mission requests and spacecraft ephemeris data with demonstrated capability to address real-world operational constraints. A deep RL agent is developed that takes mission requests for a given week as input, and interacts with a DSN scheduling environment to allocate tracks such that its reward signal is maximized. A comparison is made between an agent trained using Proximal Policy Optimization and its random, untrained counterpart. The results represent a proof-of-concept that, given a well-shaped reward signal, a deep RL agent can learn the complex heuristics used by experts to schedule the DSN. A trained agent can potentially be used to generate candidate schedules to bootstrap the scheduling process and thus reduce the turnaround cycle for DSN scheduling.
Wilson, Brian↗
Engineering Automated Operations for NASA's Deep Space Network
The NASA Deep Space Network is unique among space communication systems.
operations↗