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The Deep Space Network Advanced Systems Program
The deep space network (DSN)--with its three complexes in Goldstone, California, Madrid, Spain, and Canberra, Australia--provides the resources to track and communicate with planetary and deep space missions. Each complex consists of an array of capabilities for tracking probes almost anywhere in the solar system. A number of innovative hardware, software and procedural tools are used for day-to-day operations at DSN complexes as well as at the network control at the Jet Propulsion Laboratory (JPL). Systems and technologies employed by the network include large-aperture antennas (34-m and 70-m), cryogenically cooled receivers, high-power transmitters, stable frequency and timing distribution assemblies, modulation and coding schemes, spacecraft transponders, radiometric tracking techniques, etc. The DSN operates at multiple frequencies, including the 2-GHz band, the 7/8-GHz band, and the 32/34-GHz band.
Space programs summary no. 37-44, volume III FOR the period January 1, 1967 to February 28, 1967. The deep space network
Tracking and navigation, communications R&D, ground support equipment, and computer programming for deep space network
Projected NASA/JPL deep space network capabilities in the 1970's.
Deep space network /DSN/ for navigational and communications support to NASA lunar and planetary space programs
Space programs summary no. 37-61, volume 2 for the period 1 November - 31 December 1969. The deep space network
Research and developments in Deep Space Network program
The deep space network
Summaries are given of Deep Space Network progress in flight project support, tracking and data acquisition research and technology, network engineering, hardware and software implementation, and operations.
Summary of DSN (Deep Space Network) reimbursable launch support
The Deep Space Network is providing ground support to space agencies of foreign governments as well as to NASA and other agencies of the Federal government which are involved in space activities. DSN funding for support of missions other than NASA are on either a cooperative or a reimbursable basis. Cooperative funding and support are accomplished in the same manner as NASA sponsored missions. Reimbursable launch funding and support methods are described.
Telemetry, tracking, and command consolidation in the Deep Space Network
Currently, in NASA's Deep Space Network (DSN), telemetry, tracking, and command (TT&C) functions are distributed between multiple subsystem computers. Control design of these subsystems did not consider the interaction necessary between the functions, which create opportunities for loss of data. Also, the current controller design can force the use of equipment that is not needed for the task at hand, to the detriment of others. As part of the Network Simplification Project (NSP), the TTC implementation has been re-examined, New telemetry and commanding equipment is being built, and the control of the TT&C functions is being consolidated into two controllers, Uplink and Downlink. The new equipment uses commercial components, as opposed to the custom built equipment it is replacing, which improves reliability and simplifies maintenance.
The Deep Space Network
The objectives, functions, and organization, of the Deep Space Network are summarized. Deep Space stations, ground communications, and network operations control capabilities are described. The network is designed for two-way communications with unmanned spacecraft traveling approximately 1600 km from earth to the farthest planets in the solar system. It has provided tracking and data acquisition support for the following projects: Ranger, Surveyor, Mariner, Pioneer, Apollo, Helios, Viking, and the Lunar Orbiter.
The deep space network
The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
The deep space network
The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
The deep space network
The objectives, functions, and organization of the Deep Space Network are summarized. Deep space station, ground communication, and network operations control capabilities are described.
The deep space network
The progress is reported of Deep Space Network (DSN) research in the following areas: (1) flight project support, (2) spacecraft/ground communications, (3) station control and operations technology, (4) network control and processing, and (5) deep space stations. A description of the DSN functions and facilities is included.
Automating Deep Space Network scheduling and conflict resolution
The Deep Space Network (DSN) is a central part of NASA's infrastructure for communicating with active space missions, from earth orbit to beyond the solar system. We describe our recent work in modeling the complexities of user requirements, and then scheduling and resolving conflicts on that basis. We emphasize our innovative use of background 'intelligent' assistants' that carry out search asynchrnously while the user is focusing on various aspects of the schedule.
Statistical porcess control in Deep Space Network operation
This report describes how the Deep Space Mission System (DSMS) Operations Program Office at the Jet Propulsion Laboratory's (EL) uses Statistical Process Control (SPC) to monitor performance and evaluate initiatives for improving processes on the National Aeronautics and Space Administration's (NASA) Deep Space Network (DSN).
Future Mission Trends and their Implications for the Deep Space Network
Planning for the upgrade and/or replacement of Deep Space Network (DSN) assets that typically operate for forty or more years necessitates understanding potential customer needs as far into the future as possible. This paper describes the methodology Deep Space Network (DSN) planners use to develop this understanding, some key future mission trends that have emerged from application of this methodology, and the implications of the trends for the DSN's future evolution. For NASA's current plans out to 2030, these trends suggest the need to accommodate: three times as many communication links, downlink rates two orders of magnitude greater than today's, uplink rates some four orders of magnitude greater, and end-to-end link difficulties two-to-three orders of magnitude greater. To meet these challenges, both DSN capacity and capability will need to increase.
Clocks and timing in the NASA Deep Space Network
A new timing system has been developed for the NASA Deep Space Network (DSN) and is currently in the final stages of integration, testing and implementation in all three DSN sites. The DSN is a distributed antenna network for deep space communication, whose facilities are continuously engaged in spacecraft tracking, Very Long Baseline Interferometry (VLBI) or Radio Astronomy activities. Its primary components consist of three Deep Space Communication Centers (DSCC) separated nearly equidistant around the Earth in California, USA; Spain; and Australia. Within each DSCC, synchronized, low jitter timing signals must be distributed to many users over distances of up to 30 kilometers. The design criteria for the timing system required state of the art stability and jitter performance, but also extremely high operability and reliability. This paper describes some of the key features and recent system performance data as measured both in the laboratory and the operational DSN.
Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation
Deep learning has exhibited remarkable results across diverse areas. To understand its success, substantial research has been directed towards its theoretical foundations. Nev- ertheless, the majority of these studies examine how well deep neural networks can model functions with uniform regularities. In this paper, we explore a different angle: how deep neural networks can adapt to varying degrees of smoothness in functions and nonuni- form data distributions across different locations and scales. More precisely, we focus on a broad class of functions defined by nonlinear tree-based approximation methods. This class encompasses a range of function types, such as functions with uniform regularities and discontinuous functions. We develop nonparametric approximation and estimation theories for this class using deep ReLU networks. Our results show that deep neural networks are adaptive to the nonuniform smoothness of functions and nonuniform data distributions at different locations and scales. We apply our results to several function classes, and derive the corresponding approximation and generalization errors. The validity of our results is demonstrated through numerical experiments.