Deep Space Network Antennas
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antenna deep space network dsn telemetry planetary↗
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Wind-induced dynamics of the deep space network antennas
Accurate spacecraft tracking by the NASA Deep Space Network (DSN) antennas must be assured during changing weather conditions. Wind disturbances are the main source of tracking errors. This paper presents the development of a wind force model, and simulations of wind-induced pointing errors of DSN antennas. The antenna model includes the antenna structure, the elevation and azimuth servos, and the tracking controller. Simulation results show that pointing errors due to wind gusts are of the same order as due to wind static pressure, and that (similarly to the static wind pressure) these errors satisfy the velocity quadratic law. The methodology presented is used for wind disturbance estimation, and for design of an antenna controller with wind disturbance rejection properties.
Gawronski, W.↗
Differential Ranging Experiments with NASA’s High-Gain Deep Space Network Antennas
Radar experiments are currently under way at the Goldstone Deep Space Communications Complex (GDSCC) to determine the range of cis-lunar objects using a single high-gain 34m antenna transmitting 20 kW at X-band wavelengths, and two 34m receivers located at the GDSCC. The 34m antennas of the Deep Space Network (DSN) have effective fields-of-view of approximately 1 milli-radians, hence predetermined pointing predicts with accuracies on the order of 1 milli-degree are required. The extremely accurate 1 nano-second timing distribution of the DSN network enables sub-meter level ranging in the radial direction, by measuring delays in phase-modulated pseudonoise (PN) echoes reflected from the target. In this paper we demonstrate experimental verification of the proposed techniques by post-processing radar reflections obtained from a decommissioned geo-synchronous satellite. The characteristics of correlation-based delay measurements obtained simultaneously with two different receiving antennas are described. These are the fundamental quantities used to form the “time difference of arrival” (TDOA) observables commonly used in GPS positioning algorithms, and the results interpreted in the context of hyperbolic position location that could be used in the future to monitor spacecraft in cis-lunar space.
Cheung, Kar-Ming↗
LQG and Feedforward Controllers for the Deep Space Network Antennas
The controller development and the tracking performance evaluation for NASA's Deep Space Network antenna are presented. A trajectory preprocessor, LQG (Linear Quadratic Gaussian) controller, feedforward controller, and their combination are designed, built, analyzed, and tested.
Deep Space Network (DSN)antennas feedforward contr↗
Azimuth Track Level Compensation to Reduce Blind Pointing Errors of the Deep Space Network Antennas
The Beam wave-guide antennas of the NASA Deep Space Network are -wheel and track- antennas.
Antenna↗
Sequential design of a linear quadratic controller for the deep space network antennas
A linear quadratic controller design procedure is proposed for the NASA/JPL Deep Space Network antennas. The procedure is developed for an antenna model divided into tracking and flexible subsystems. Controllers for the flexible and tracking parts are designed separately by adjusting the performance index weights. The effect of weights on system performance is a crucial property, and is analytically described in this paper. The procedure is illustrated with the control system design for the DSS 13 antenna.
Gawronski, Wodek↗
Torque Bias Profile for Improved Tracking of the Deep Space Network Antennas
Gearboxes and gears are part of the NASA Deep Space Network (DSN) antenna drives.
Antennas↗
Sequential design of a linear quadratic controller for the Deep Space Network antennas
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.
Gawronski, W.↗
Evolution of the large Deep Space Network antennas
The evolution of the largest antenna of the US NASA Deep Space Network (DSN) is described. The design, performance analysis, and measurement techniques, beginning with its initial 64-m operation at S-band (2295 MHz) in 1966 and continuing through the present ka-band (32-GHz) operation at 70 m, is described. Although their diameters and mountings differ, these parabolic antennas all employ a Cassegrainian feed system, and each antenna dish surface is constructed of precision-shaped perforated-aluminum panels that are secured to an open steel framework
Imbriale, William A.↗
Three-Dimensional Analysis of Deep Space Network Antenna Coverage
There is a need to understand NASA s Deep Space Network (DSN) coverage gaps and any limitations to provide redundant communication coverage for future deep space missions, especially for manned missions to Moon and Mars. The DSN antennas are required to provide continuous communication coverage for deep space flights, interplanetary missions, and deep space scientific observations. The DSN consists of ground antennas located at three sites: Goldstone in USA, Canberra in Australia, and Madrid in Spain. These locations are not separated by the exactly 120 degrees and some DSN antennas are located in the bowl-shaped mountainous terrain to shield against radiofrequency interference resulting in a coverage gap in the southern hemisphere for the current DSN architecture. To analyze the extent of this gap and other coverage limitations, simulations of the DSN architecture were performed. In addition to the physical properties of the DSN assets, the simulation incorporated communication forward link calculations and azimuth/elevation masks that constrain the effects of terrain for each DSN antenna. Analysis of the simulation data was performed to create coverage profiles with the receiver settings at a deep space altitudes ranging from 2 million to 10 million km and a spherical grid resolution of 0.25 degrees with respect to longitude and latitude. With the results of these simulations, two- and three-dimensional representations of the area without communication coverage and area with coverage were developed, showing the size and shape of the communication coverage gap projected in space. Also, the significance of this communication coverage gap is analyzed from the simulation data.
Kegege, Obadiah↗
Analysis of Near-field of Circular Aperture Antennas with Application to Study of High Intensity Radio Frequency (HIRF) Hazards to Aviation from JPL/NASA Deep Space Network Antennas
This work includes a simplified analysis of the radiated near to mid-field from JPL/NASA Deep Space Network (DSN) reflector antennas and uses an averaging technique over the main beam region and beyond for complying with FAA regulations in specific aviation environments. The work identifies areas that require special attention, including the implications of the very narrow beam of the DSN transmitters. The paper derives the maximum averaged power densities allowed and identifies zones where mitigation measures are required.
Antennas↗
An Array Feed Radial Basis Function Tracking System for NASA's Deep Space Network Antennas
The use of radial basis function networks for fine pointing NASA's 70-meter deep space network antennas is described and evaluated.
array feed radial NASA space network antennas↗
Design of the reduced LQG compensator for the Deep Space Network antennas
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.
Gawronski, W.↗
New Algorithms for Estimating Spacecraft Position Using Scanning Techniques for Deep Space Network Antennas
As more and more nonlinear estimation techniques become available, our interest is in finding out what performance improvement, if any, they can provide for practical nonlinear problems that have been traditionally solved using linear methods. In this paper we examine the problem of estimating spacecraft position using conical scan (conscan) for NASA's Deep Space Network antennas. We show that for additive disturbances on antenna power measurement, the problem can be transformed into a linear one, and we present a general solution to this problem, with the least square solution reported in literature as a special case. We also show that for additive disturbances on antenna position, the problem is a truly nonlinear one, and we present two approximate solutions based on linearization and Unscented Transformation respectively, and one 'exact' solution based on Markov Chain Monte Carlo (MCMC) method. Simulations show that, with the amount of data collected in practice, linear methods perform almost the same as MCMC methods. It is only when we artificially reduce the amount of collected data and increase the level of noise that nonlinear methods show significantly better accuracy than that achieved by linear methods, at the expense of more computation.
Metropolis-Hastings (MH)↗
Deep Space Network Antenna Monitoring Using Adaptive Time Series Methods and Hidden Markov Models
The Deep Space Network (DSN)(designed and operated by the Jet Propulsion Laboratory for the National Aeronautics and Space Administration (NASA) provides end-to-end telecommunication capabilities between earth and various interplanetary spacecraft throughout the solar system.
Antenna DSN↗
Identification of Abnormal System Noise Temperature Patterns in Deep Space Network Antennas Using Neural Network Trained Fuzzy Logic
This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.
pattern identification↗
Machine learning for Deep Space Network antenna motions detection
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.
Yi, Lin↗
Deep Space Network Antenna Logic Controller
The Antenna Logic Controller (ALC) software controls and monitors the motion control equipment of the 4,000-metric-ton structure of the Deep Space Network 70-meter antenna. This program coordinates the control of 42 hydraulic pumps, while monitoring several interlocks for personnel and equipment safety. Remote operation of the ALC runs via the Antenna Monitor & Control (AMC) computer, which orchestrates the tracking functions of the entire antenna. This software provides a graphical user interface for local control, monitoring, and identification of faults as well as, at a high level, providing for the digital control of the axis brakes so that the servo of the AMC may control the motion of the antenna. Specific functions of the ALC also include routines for startup in cold weather, controlled shutdown for both normal and fault situations, and pump switching on failure. The increased monitoring, the ability to trend key performance characteristics, the improved fault detection and recovery, the centralization of all control at a single panel, and the simplification of the user interface have all reduced the required workforce to run 70-meter antennas. The ALC also increases the antenna availability by reducing the time required to start up the antenna, to diagnose faults, and by providing additional insight into the performance of key parameters that aid in preventive maintenance to avoid key element failure. The ALC User Display (AUD) is a graphical user interface with hierarchical display structure, which provides high-level status information to the operation of the ALC, as well as detailed information for virtually all aspects of the ALC via drill-down displays. The operational status of an item, be it a function or assembly, is shown in the higher-level display. By pressing the item on the display screen, a new screen opens to show more detail of the function/assembly. Navigation tools and the map button allow immediate access to all screens.
Ahlstrom, Harlow↗