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The use of radial basis function networks for fine pointing NASA's 70-meter deep space network antennas is described and evaluated.
Description, mission support, engineering projects, network operations, facilities, structures, and utilities of deep space network
The purpose of this paper is to highlight the current status and prospects for VLBI in the NASA Deep Space Network (DSN). Although the prime purpose of the DSN is to support spacecraft operations and space research in deep space, this unique facility is also used on a noninterference basis with flight projects to support ground-based science experiments. The DSN VLBI capabilities are an integral part of a number of space- and ground-based projects. They include support of experiments at major radio astronomy VLBI networks (e.g., VLBA, EVN, APT), space VLBI co-observing, as well as VLBI geodesy and astrometry programs. The paper will describe for the potential DSN VLBI users 1) DSN VLBI objectives, 2) the current organizational structure and 3) current and projected capabilities.
A new linear quadratic controller design procedure is proposed for the NASA/JPL Deep Space Network antennas. The antenna model is divided into a tracking subsystem and a flexible subsystem. Controllers for the flexible and tracking parts are designed separately by adjusting the performance index weights. Ad hoc weights are chosen for the tracking part of the controller and the weights of the flexible part are adjusted. Next, the gains of the tracking part are determined, followed by the flexible controller final tune-up. In addition, the controller for the flexible part is designed separately for each mode; thus the design procedure consists of weight adjustment for small-size subsystems. Since the controller gains are obtained by adjusting the performance index weights, determination of the weight effect on system performance is a crucial task. A method of determining this effect that allows an on-line improvement of the tracking performance is presented in this article. The procedure is illustrated with the control system design for the Deep Space Station (DSS)-13 antenna.
In the near future, the National Aeronautics and Space Administration (NASA) will returnhumansto the moon beginning the next era of human exploration. NASA’s Space Communications and Navigation (SCaN) program will play a vital role in establishing communications and navigation support to realize the ambitious goals of the Artemis program. SCaN’s overall lunar communications support plan will be covered in a separate 2023 SpaceOps paper: “NASA’s Communications and Navigation Architecture Plans to Support the Return to the Moon and a Sustainable Lunar Presence.” The four-point plan,as it currently stands, includes lunar relay services, a dedicated set of new ground stations, international partner contributions,and supportthrough the Deep Space Network (DSN)and associated upgrades. This paper will have a more granular focus on the DSN and NASA’s plans to upgrade and expand the network to be better suited for human spaceflight on and around the lunar surface. NASA’s Deep Space Network (DSN) will be a critical communications component for the upcoming lunar activities. There will be multiple spacecraft, using different bands, and some of those spacecraft will be transmitting and receiving using multiple bands, requiring DSN support of S-band (2 GHz), X-band (7 GHz up, 8 GHz down), and K-band (22.5 GHz up, 26 GHz down). Since there may be more than one spacecraft in the beamwidth of the DSN antennas, the DSN support will require an extension of the DSN’s capability to support multiple spacecraft using one antenna, expanding it to provide two simultaneous uplinks in the different bands at each antenna. Achieving this requires using new techniques for manufacturing the frequency selective surfaces, called dichroics, which steer the different frequency beams from and to the appropriate transmitting and receiving equipment, along with the addition of a new K-band uplink system. Additionally, due to the relative closeness of the moon from Earth (as opposed to the planetary missionsthe DSN supports daily), significantly higher data rates on both uplink and downlinkare requiredare possible and desirable by the lunar missions, specifically up to 20 Mbps on the uplink and 150 Mbps on the downlink, both using Low Density Parity Check (LDPC) error correcting codes. And, again due to the relative closeness of the moon, there is a need for low latency data delivery of the high rate downlink telemetry which requires a change in the current DSN paradigm of delivering higher rate data with higher latency.
A parametric software cost estimation model prepared for JPL deep space network (DSN) data systems implementation tasks is presented. The resource estimation model incorporates principles and data from a number of existing models, such as those of the General Research Corporation, Doty Associates, IBM (Walston-Felix), Rome Air Force Development Center, University of Maryland, and Rayleigh-Norden-Putnam. The model calibrates task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit JPL software lifecycle statistics. The estimation model output scales a standard DSN work breakdown structure skeleton, which is then input to a PERT/CPM system, producing a detailed schedule and resource budget for the project being planned.
Highly stable frequency and timing standards are essential for deep-space missions and radio science. At the NASA Deep Space Network (DSN), these standards are distributed through a network of underground fiber cables to support several Goldstone antennas. Independently developed frequency-measuring instruments generate tremendous quantities of data to monitor and validate the antennas’ stringent frequency requirements. In this paper, we propose a lightweight processing tool capable of detecting disturbances on the frequency signal caused by DSN antenna motions. Our training data is sampled from the movement log of the antenna of interest and the generated data from the fiber optic metrology instrument linked to the antenna. We demonstrate that a convolutional neural network (CNN) model can achieve high accuracies on classifying instances of antenna movements and is an effective predictor when used iteratively on longer, variable stretches of metrology data. The simplicity, low training cost, and high accuracies of our model strongly suggest its efficacy in identifying and troubleshooting frequency disturbances caused by the antenna.
Our objective is to provide a framework of automated tools and techniques for reducing operational and maintenance costs in the NASA's Deep Space Network (DSN).
The space industry has seen an explosion in the number of operational SmallSats in Earth orbit, with a natural interest in extending SmallSat capabilities outside of low Earth orbit. As with larger missions, near-term deep-space SmallSats will rely on the Deep Space Network or similar facilities. Given the predicted growth in the number of deep space missions, effective use of DSN resources will be more critical than ever. Our investigation provides an initial survey of expected inner Solar System navigation performance for DSN radiometric data types, from two-way Doppler and ranging to one-way equivalents, including delta differential one-way range and alternative tracking strategies.
Mission support, advanced engineering, operations and systems analysis, and technical facilities programs related to Deep Space Network
A report on the utilization of the Deep Space Network during calendar year 1981 in support of all flight projects is presented. The network expended 63% of its total capability in support of Space Flight projects.
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.
A linear quadratic Gaussian (LQG) compensator design procedure is proposed for the NASA Deep Space Network (DSN) antennas. The procedure is based on two properties of antennas. It is shown that tracking and flexible motion of the DSN antennas are almost independent (the separation property), and that for the balanced LQG compensator its performance is evenly divided between the controller and the estimator (balancing property). As a consequence compensators for the flexible and tracking parts can be designed separately, and the compensator order is reduced significantly, which is important for implementation purposes. The approach is illustrated with the detailed compensator design for the DSS-13 antenna.
A next generation of 20kW dual-band transmitters was developed for NASA's Deep Space Network 34m Beam Waveguide antennas.
A 20-kW S- and X-Band transmitter was developed for NASA's Deep Space Network (DSN) 70-m antennas.
In this paper, therefore, we present an analysis of site selection for an optical deep space network as performed by the ODSN study group at JPL. Given a set of mission requirements, we illustrate how the high-level requirements, along with the properties of the atmospheric channel, can be used to determine the site selection and the architecture of an ODSN. Moreover, we characterize candidate sites for a global optical network and their possible suitability for global architectures such as the linear dispersed optical subnet (LDOS) and cluster optical subnet network (COS).
Spacecraft to be used in future missions supported by the Deep Space Network (DSN) will be
To enhance NASA's Deep Space Network (DSN), an effort is underway to improve network performance and simplify its operation and maintenance. This endeavor, known as the "Common Platform," has both short- and long-term objectives. The long-term work has not begun yet; however, the activity to realize the short-term goals has started. There are three goals for the long-term objective: 1. Convert the DSN into a digital network where signals are digitized at the output of the down converters at the antennas and are distributed via a digital IF switch to the processing platforms. 2. Employ a set of common hardware for signal processing applications, e.g., telemetry, tracking, radio science and Very Long Baseline Interferometry (VLBI). 3. Minimize in-house developments in favor of purchasing commercial off-the-shelf (COTS) equipment. The short-term goal is to develop a prototype of the above at NASA's experimental station known as DSS-13. This station consists of a 34m beam waveguide antenna with cryogenically cooled amplifiers capable of handling deep space research frequencies at S-, X-, and Ka-bands. Without the effort at DSS-13, the implementation of the long-term goal can potentially be risky because embarking on the modification of an operational network without prior preparations can, among other things, result in unwanted service interruptions. Not only are there technical challenges to address, full network implementation of the Common Platform concept includes significant cost uncertainties. Therefore, a limited implementation at DSS-13 will contribute to risk reduction. The benefits of employing common platforms for the DSN are lower cost and improved operations resulting from ease of maintenance and reduced number of spare parts. Increased flexibility for the user is another potential benefit. This paper will present the plans for DSS-13 implementation. It will discuss key issues such as the Common Platform architecture, choice of COTS equipment, and the standard for radio frequency (RF) to digital interface.