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Ka-band (32-GHz) downlink capability for deep space communications

The first quarter century of U.S. solar system exploration using unmanned spacecraft has involved progressively higher operating frequencies for deep space telemetry: L-band (960 MHz) in 1962 to S-band (2.3 GHz) in 1964 to X-band (8.4 GHZ) in 1977. The next logical frequency to develop for deep space is the Ka-band (32 GHz) for which a primary deep space allocation of 500 MHz between 31.8 to 32.3 GHz was established in 1979. The telecommunications capability was improved by a factor of 77 (18.9 dB) through the frequency changes from L-band to X-band. Another improvement factor of 14.5 (11.6 dB) can be achieved by going to Ka-band. Plans to develop and demonstrate Ka-band capability include the continued measurement of weather effects at Deep Space Network (DSN) sites, development of a prototype DSN ground antenna and supporting subsystems, augmentation of planned spacecraft with Ka-band beacons, and development of spacecraft prototype modules for future Ka-band transmitters. Plans for augmenting the DSN with Ka-band capability by 1995 were also developed. A companion set of articles describes the Ka-band performance and technology in greater detail.

Smith, J. G.↗

A Trilateration Scheme for Relative Positioning

We introduce a trilateration scheme that evaluates the 3-dimensional (3-D) relative position between a reference spacecraft and a target spacecraft using raw-range measurements from a distance baseline of known locations, which we call “anchors”. The anchors can be antennas of a ground-based network (e.g., Deep Space Network (DSN) or Near Earth Network (NEN) stations), or satellites of a spacebased network (e.g., global positioning system (GPS) or tracking and data relay satellite (TDRS)). We define raw-range as the range that includes all the systematic errors that occur during range measurements. A unique feature of this approach is that accurate relative position is derived from a “differencing function” of raw-range measurements of the reference spacecraft and target spacecraft, thereby eliminating most of the systematic errors, such as media effects, ephemeris errors, instrument delays, clock bias, etc. There can be an arbitrary number of target spacecraft, and relative positioning of target spacecraft with respect to the reference spacecraft can be done simultaneously. In this paper, we first assume an idealized system in which clocks on the reference and target spacecraft are synchronized, with clocks of the anchors synchronized as well. We develop a novel iterative algorithm that computes the relative position of the target spacecraft with respect to the reference spacecraft. We illustrate the relative positioning method using the scenario of a network of three ground stations (i.e., the anchors) at Goldstone, California, USA, Madrid, Spain, and Marlargue, Argentina tracking two spacecraft at geosynchronous orbit distance. We demonstrate that the algorithm converges to submeter accuracy in estimating the relative position, in the presence of random errors and systematic errors in raw-range measurements, and in the presence of angular errors in estimating the pointing vectors between the anchors and the reference spacecraft. Next, we relax the requirement of perfect time synchronization between spacecraft, and show that by using an additional anchor, one can estimate and remove the clock biases between the reference and target spacecraft. We add a ground station at Kourou to the above example of three ground stations of Goldstone, Madrid, and Marlargue, and demonstrate that the updated algorithm also converges to meter-level accuracy (submeter in some cases) in the presence of clock biases in addition to the random errors, systematic errors, and angular errors as shown in the above case. We compare this scheme with a similar trilateration scheme for relative positioning scheme first proposed by Montenbruck in 2002.

Cheung, Kar-Ming↗

Reengineering Deep Space Network Operations

Eight additional antennas are being added to NASA's Deep Space Network (DSN) at the same time that the budget is being decreased. Therefore, the DSN is reengineering its processes to operate more efficiently.

reengineering↗

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↗

Inter Continental, Inter-Agency Arraying - The Galileo Experience

Since November 1, 1996, the Galileo orbital tour of jupiter and its moons is routinely supported by a massive array of large antennas: two 70-meter antennas in GDSCC and CDSCC, a 64-meter antenna at Parkes, and additional 34-meter antennas at CDSCC. The scope of this array is unprevented, it operates daily in a routine fashion, inter-continentally, and employs antennas of several agenscies, namely two complexes of JPL's Deep Space Network (DSN) an CSIRO's Parkes facility. The Success of this routine arraying operation is the basis for the DSN's new array of 34-meter antennas that is being implementet at GDSCC.

Galileo↗

Comparison of Atmospheric Delay Statistics from Deep Space Network Arrays and Nearby Test Interferometers

Several techniques have been explored and demonstrated that allow for greater data return on space-to-ground links. Among these techniques, arraying several smaller diameter dish antennas together is one method used in several arenas. These arrays can achieve larger effective area and gain than are available from a single larger antenna. This technique is routinely used by the NASA Deep Space Network (DSN) at 8.4 GHz where the incoming signals are much weaker than those experienced by the near-Earth satellite community. When considering arraying at much higher frequencies such as 32 GHz deep-space Ka-band, the phase alignment of the individual antenna signals is significantly disrupted by atmospheric turbulence. Since 2012, several downlink array demonstrations have been conducted using 32 GHz carrier signals emitted by the deep space probes Cassini and Kepler. Site test interferometers (STIs) that receive signals from geostationary satellites have been deployed at all three DSN tracking complexes for long-term monitoring of atmospheric delay fluctuations. In a previous DSN array demonstration study involving the Cassini spacecraft, it was shown that statistics of the adjusted STI phase fluctuations matched the statistics of concurrent array demonstration phase fluctuations. These adjustments accounted for differences in antenna separation, elevation angle and spacecraft frequencies. The STI antenna separations were about 200 m and the DSN antenna separations were about 300 m. These adjustments made use of the thick-layer turbulence model that was applicable to the Goldstone desert climate during the summer months for which the data were acquired. In this paper, we report on the results of additional array demonstrations involving the Kepler spacecraft and compare the adjusted STI phase fluctuations with those seen by a nearby two-element array of 34 m diameter antennas tracking Kepler’s 32 GHz signal at the Goldstone, California and Madrid, Spain DSN sites. We also discuss results from a demonstration using an array over a longer 12.5 km baseline. The Cassini and Kepler array demonstrations were found to validate the long term statistics acquired from several years of STI data as well as the models used to adjust the statistics for the conditions of an array. These statistics represent reliable estimates of the phase fluctuations that would be seen by an array tracking a deep space signal after applying appropriate adjustments for a given array configuration, elevation angle profile and observing frequency.

Morabito, David D.↗

The Deep Space Network Array

This document is a viewgraph presentation that reviews the costs, and technological processing required to replace the current network of Deep Space Antennas. The concept of using an array for space communications is much less of a concern than the cost of implementing and operating such an array. Within the cost question, the cost uncertainty of the front-end components (repeated n-times) is of most importance. The activities at JPL have focused on both these aspects of the cost. A breadboard array of three antennas at JPL has been the vehicle to perform many investigations into the development of the new DSN. Several pictures of the antennas at JPL are shown.

Deep Space Network (DSN)↗

Frequency Domain Beamforming for a Deep Space Network Downlink Array

This paper describes a frequency domain beamformer to array up to 8 antennas of NASA's Deep Space Network currently in development. The objective of this array is to replace and enhance the capability of the DSN 70m antennas with multiple 34m antennas for telemetry, navigation and radio science use. The array will coherently combine the entire 500 MHz of usable bandwidth available to DSN receivers. A frequency domain beamforming architecture was chosen over a time domain based architecture to handle the large signal bandwidth and efficiently perform delay and phase calibration. The antennas of the DSN are spaced far enough apart that random atmospheric and phase variations between antennas need to be calibrated out on an ongoing basis in real-time. The calibration is done using measurements obtained from a correlator. This DSN Downlink Array expands upon a proof of concept breadboard array built previously to develop the technology and will become an operational asset of the Deep Space Network. Design parameters for frequency channelization, array calibration and delay corrections will be presented as well a method to efficiently calibrate the array for both wide and narrow bandwidth telemetry.

Filterbank↗

Feed-forward control upgrade of the deep space network antennas

In order to improve the accuracy of high-rate tracking of NASA's DSN antennas, the position-loop controller has been upgraded with a feed-forward loop. Conditions for perfect and approximate tracking with the feed-forward loop are presented. The feed-forward loop improves tracking performance and preserves wind disturbance rejection properties of the previous closed-loop system.

Gawronski, W.↗

Experimental Evaluation of the "Polished Panel Optical Receiver" Concept on the Deep Space Network's 34 Meter Antenna

The potential development of large aperture ground-based "photon bucket" optical receivers for deep space communications has received considerable attention recently. One approach currently under investigation proposes to polish the aluminum reflector panels of 34-meter microwave antennas to high reflectance, and accept the relatively large spotsize generated by even state-of-the-art polished aluminum panels. Here we describe the experimental effort currently underway at the Deep Space Network (DSN) Goldstone Communications Complex in California, to test and verify these concepts in a realistic operational environment. A custom designed aluminum panel has been mounted on the 34 meter research antenna at Deep-Space Station 13 (DSS-13), and a remotely controlled CCD camera with a large CCD sensor in a weather-proof container has been installed next to the subreflector, pointed directly at the custom polished panel. Using the planet Jupiter as the optical point-source, the point-spread function (PSF) generated by the polished panel has been characterized, the array data processed to determine the center of the intensity distribution, and expected communications performance of the proposed polished panel optical receiver has been evaluated.

photon counting detection↗

Pros and Cons of Using Arrays of Small Antennas Versus Large Single Dish Antennas for the Deep Space Network

This paper briefly describes pros and cons of using arrays of small antennas instead of large single dish antennas for spacecraft telemetry, command, and tracking (TT and C) - communications and navigation (C and N) - and science support that the Deep Space Network (DSN) normally provides. It considers functionality and performance aspects, mainly for TT and C, though it also considers science. It only briefly comments on the cost aspects that seem to favor arrays of small antennas over large single antennas, at least for receiving (downlinks).

Deep Space Network↗

Compliance with High-Intensity Radiated Fields Regulations - Emitter's Perspective

NASA's Deep Space Network (DSN) uses high-power transmitters on its large antennas to communicate with spacecraft of NASA and its partner agencies. The prime reflectors of the DSN antennas are parabolic, at 34m and 70m in diameter. The DSN transmitters radiate Continuous Wave (CW) signals at 20 kW - 500 kW at X-band and S-band frequencies. The combination of antenna reflector size and high frequency results in a very narrow beam with extensive oscillating near-field pattern. Another unique feature of the DSN antennas is that they (and the radiated beam) move mostly at very slow sidereal rate, essentially identical in magnitude and at the opposite direction of Earth rotation.The DSN is in the process of revamping its documentation to provide analysis of the High Intensity Radiation Fields (HIRF) environment resulting from radio frequency radiation from DSN antennas for comparison to FAA regulations regarding certification of HIRF protection as outlined in the FAA regulations on HIRF protection for aircraft electrical and electronic systems (Title 14, Code of Federal Regulations (14 CFR) [section sign][section sign] 23.1308, 25.1317, 27.1317, and 29.1317).This paper presents work done at JPL, in consultation with the FAA. The work includes analysis of the radiated field structure created by the unique DSN emitters (combination of transmitters and antennas) and comparing it to the fields defined in the environments in the FAA regulations. The paper identifies areas that required special attention, including the implications of the very narrow beam of the DSN emitters and the sidereal rate motion. The paper derives the maximum emitter power allowed without mitigation and the mitigation zones, where required.Finally, the paper presents summary of the results of the analyses of the DSN emitters and the resulting DSN process documentation.

DSN emitters↗

The Venus Balloon Project

On June 11 and 15, 1985, two instrumental balloons were released from the Soviet VEGA 1 and VEGA 2 spacecraft and deployed in the atmosphere of Venus. The VEGA probes flew by the planet on their way to a rendezvous with comet Halley in March 1986. Drifting with the wind at altitudes of 54 km, the balloons traveled one-third of the way around the planet during their 46-hour lifetimes. Sensors on-board the gondolas made periodic measurements of pressure, temperature, vertical wind velocity, cloud particle density, ambient light level, and frequency of lightning. The data were transmitted to Earth and received at the Deep Space Network (DSN) 64-m stations and at several large antennas in the USSR. Approximately 95 percent of the telemetry data were successfully decoded at the DSN complexes and in the Soviet Union, and were provided to the international science team for analysis. Very Long Baseline Interferometry (VLBI) data were acquired by 20 radio observatories around the world for the purpose of monitoring the Venus winds. The DSN 64-m subnet was part of a 15-station VLBI network organized by the Centre National d'Etudes Spatiales (CNES) of France. In addition, five antennas of the Soviet network participated. VLBI data from the CNES network are currently being processed at the Jet Propulsion Laboratory.

Stelzried, C. T.↗

Conceptual communications system design in the 25.25-27.5 and 37.0-40.5 GHz frequency bands

Future space applications are likely to rely heavily on Ka-band frequencies (20-40 GHz) for communications traffic. Many space research activities are now conducted using S-band and X-band frequencies, which are becoming congested and require a degree of pre-coordination. In addition to providing relief from frequency congestion, Ka-band technologies offer potential size, weight, and power savings when compared to lower frequency bands. The use of the 37.0-37.5 and 40.0-40.5 GHz bands for future planetary missions was recently approved at the 1992 World Administrative Radio Conference (WARC-92). WARC-92 also allocated the band 25.25-27.5 GHz to the Intersatellite Service on a primary basis to accommodate Data Relay Satellite return link requirements. Intersatellite links are defined to be between artificial satellites and thus a communication link with the surface of a planetary body, such as the moon, and a relay satellite orbiting that body are not permitted in this frequency band. This report provides information about preliminary communications system concepts for forward and return links for earth-Mars and earth-lunar links using the 37.0-37.5 (return link) and 40.0-40.5 (forward link) GHz frequency bands. In this study we concentrate primarily on a conceptual system for communications between earth and a single lunar surface terminal (LST), and between earth and a single Mars surface terminal (MST). Due to large space losses, these links have the most stringent link requirements for an overall interplanetary system. The earth ground station is assumed to be the Deep Space Network (DSN) using either 34 meter or 70 meter antennas. We also develop preliminary communications concepts for a space-to-space system operating at near 26 GHz. Space-to-space applications can encompass a variety of operating conditions, and we consider several 'typical' scenarios described in more detail later in this report. Among these scenarios are vehicle-to-vehicle communications, vehicle-to-geosyncronous satellite (GEO) communications, and GEO-to-GEO communications. Additional details about both the interplanetary and space-to-space communications systems are provided in an 'expanded' final report which has been submitted to the Tracking and Communications Division (TCD) at the NASA Johnson Space Center.

Thompson, Michael W.↗

Orbit Determination for the Lunar Reconnaissance Orbiter Using an Extended Kalman Filter

Since launch, the FDF has performed daily OD for LRO using the Goddard Trajectory Determination System (GTDS). GTDS is a batch least-squares (BLS) estimator. The tracking data arc for OD is 36 hours. Current operational OD uses 200 x 200 lunar gravity, solid lunar tides, solar radiation pressure (SRP) using a spherical spacecraft area model, and point mass gravity for the Earth, Sun, and Jupiter. LRO tracking data consists of range and range-rate measurements from: Universal Space Network (USN) stations in Sweden, Germany, Australia, and Hawaii. A NASA antenna at White Sands, New Mexico (WS1S). NASA Deep Space Network (DSN) stations. DSN data was sparse and not included in this study. Tracking is predominantly (50) from WS1S. The OD accuracy requirements are: Definitive ephemeris accuracy of 500 meters total position root-mean-squared (RMS) and18 meters radial RMS. Predicted orbit accuracy less than 800 meters root sum squared (RSS) over an 84-hour prediction span.

LRO↗

Modeling the Atmospheric Phase Effects of a Digital Antenna Array Communications System

In an antenna array system such as that used in the Deep Space Network (DSN) for satellite communication, it is often necessary to account for the effects due to the atmosphere. Typically, the atmosphere induces amplitude and phase fluctuations on the transmitted downlink signal that invalidate the assumed stationarity of the signal model. The degree to which these perturbations affect the stationarity of the model depends both on parameters of the atmosphere, including wind speed and turbulence strength, and on parameters of the communication system, such as the sampling rate used. In this article, we focus on modeling the atmospheric phase fluctuations in a digital antenna array communications system. Based on a continuous-time statistical model for the atmospheric phase effects, we show how to obtain a related discrete-time model based on sampling the continuous-time process. The effects of the nonstationarity of the resulting signal model are investigated using the sample matrix inversion (SMI) algorithm for minimum mean-squared error (MMSE) equalization of the received signal

Tkacenko, A.↗

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↗

A common-aperture X- and S-band four-function feedcone

Williams and Withington (1979) have considered a prototype X-S-band feedhorn which enabled simultaneous X- and S-band reception from a Cassegrain antenna. This feedhorn has quite successfully demonstrated an alternate method to the standard Deep Space Network (DSN) system of multiple subreflectors and dichroic plate for dual-band reception. In connection with a Network Consolidation Program, involving centralized control of existing antennas and construction of new reflector antennas, a second-generation feedhorn/combiner was conceived to show that this common-aperture feedhorn system was capable of performing all necessary functions the DSN would be called upon to perform with existing and future X-S-band spacecraft. Attention is given to the feedhorn concept, the combiner concept, the first and the second generation of the horn, Sand X-band tuning, and planned capabilities. The feedhorn greatly extends the state of the art in DSN performance and will enhance DSN capabilities in the future.

Withington, J. R.↗