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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2015

A Heuristic Approach to Correlating ERAM Flight Data from Twenty Centers

Among its many other functions, the Federal Aviation Administration’s En Route Automation Modernization (ERAM) provides external systems with real-time air traffic data for flights in enroute airspace in the National Airspace System. It replaced the En Route Host computer and backup system used at 20 FAA Air Route Traffic Control Centers (Centers) nationwide. Among the new features of ERAM, its output data stream of flight plan and track data includes a unique identifier for a flight originating in any one of the 20 ERAM Centers. The unique identifier, called the Global Unique Flight Identifier (GUFI), is persistent across all the Centers that track the flight. However, certain factors make it difficult to correlate data using the GUFI. First, the value of the GUFI is only unique within a time window of seven days. Second, the GUFI is attached only to flight-plan related data messages. Finally, track positions reported by ERAM do not reference the GUFI. In order to correlate historical as well as real time flight-plan and position related ERAM data, an efficient, heuristic approach was developed, and a prototype was developed. The approach showed that the processing speed, through parallel processing, is sufficient to correlate ERAM data in real-time. As described in this paper, when there are multiple track positions reported from multiple Centers within a few seconds, each position is assigned with a weighted score to indicate the quality of the position relative to its last know position. The weighted score can be used to eliminate potentially duplicate track positions. The approach is database-agnostic, and can be implemented in a Big Data system such as an Apache Hadoop system, as well as in traditional database systems.

Correlating ERAM Flight Data↗
NASA NTRS1973

A gravitational field model for the earth.

Two models of the earth's gravitational field have been computed. The first, Goddard Earth Model 1 (GEM 1), has been derived from satellite tracking data. The second, Goddard Earth Model 2 (GEM 2), has been derived from a combination of satellite tracking and surface gravimetric data. The satellite data consisted primarily of optical data processed on 300 weekly orbital arcs for 25 close earth satellites. Surface gravity data were employed in the form of 5 x 5 deg mean free-air gravity anomalies providing about 70% world coverage. Station locations were obtained for 46 tracking sites by combining electronic, laser, and additional optical tracking data with the above satellite data. Analysis of the radial positions of these stations and a value of mean gravity on the geoid indicated a mean equatorial radius for the earth of about 6378.145 meters. Results of geopotential tests on satellite data not used in the solution show that better agreement was obtained with the GEM 1 and GEM 2 models than with the 1969 Smithsonian Standard Earth II model.

Smith, D. E.↗
NASA NTRS1972

Gravitational field models for the earth (GEM 1 and 2)

Two models of the earth's gravitational field have been computed at Goddard Space Flight Center. The first, Goddard Earth Model 1 (GEM 1), has been derived from satellite tracking data. The second, Goddard Earth Model 2 (GEM 2), has been derived from a combination of satellite tracking and surface gravimetric data. The geopotential models are represented in spherical harmonics complete to degree and order 16 for the combined solution and complete to degree and order 12 for the satellite solution. Both solutions include zonal terms to degree 21 and related satellite resonant coefficients to degree 22. The satellite data consisted primarily of optical data processed on 300 weekly orbital arcs for 25 close earth satellites. Surface gravity data were employed in the form of 5 deg x 5 deg mean free-air gravity anomalies providing about 70% world coverage. Station locations were obtained for 46 tracking sites by combining electronic, laser, and additional optical tracking data with the above satellite data. Analysis of the radial positions of these stations and a value of mean gravity on the geoid indicated a mean equatorial radius for the earth of about 6378145 meters. Results of geopotential tests on satellite data not used in the solution show that better agreement was obtained with the GEM 1 and GEM 2 models than with the 1969 Smithsonian Standard Earth 2 model.

Lerch, F. J.↗
NASA NTRS1966

Pioneer project support

Pioneer project support - Doppler tracking data analysis, trajectory dispersions and initial tracking station acquisition, and gas leak effect on orbit

PIONEER SPACE PROBE↗
NASA NTRS1995

STS-70 Mission Highlights

The highlights of the STS-70 mission are presented in this video. The flight crew consisted of Cmdr. Tom Henricks, Pilot Kevin Kregel, Flight Engineer Nancy Currie, and Mission Specialists Dr. Don Thomas and Dr. Mary Ellen Weber. The mission's primary objective was the deployment of the 7th Tracking Data and Relay Satellite (TDRS), which will provide a communication, tracking, telemetry, data acquisition, and command services space-based network system essential to low Earth orbital spacecraft. Secondary mission objectives included activating and studying the Physiological and Anatomical Rodent Experiment/National Institutes of Health-Rodents (PARE/NIH-R), The Bioreactor Demonstration System (BDS), the Commercial Protein Crystal Growth (CPCG) studies, the Space Tissue Loss/National Institutes of Health-Cells (STL/NIH-C) experiment, the Biological Research in Canisters (BRIC) experiment, Shuttle Amateur Radio Experiment-2 (SAREX-2), the Visual Function Tester-4 (VFT-4), the Hand-Held, Earth Oriented, Real-Time, Cooperative, User-Friendly, Location-Targeting and Environmental System (HERCULES), the Microcapsules in Space-B (MIS-B) experiment, the Windows Experiment (WINDEX), the Radiation Monitoring Equipment-3 (RME-3), and the Military Applications of Ship Tracks (MAST) experiment. There was an in-orbit dedication ceremony by the spacecrew and the newly Integrated Mission Control Center to commemorate the Center's integration. The STS-70 mission was the first mission monitored by this new control center. Earth views included the Earth's atmosphere, a sunrise over the Earth's horizon, several views of various land masses, some B/W lightning shots, some cloud cover, and a tropical storm.

Source record↗
NASA NTRS2009

DSN Data Visualization Suite

The DSN Data Visualization Suite is a set of computer programs and reusable Application Programming Interfaces (APIs) that assist in the visualization and analysis of Deep Space Network (DSN) spacecraft-tracking data, which can include predicted and actual values of downlink frequencies, uplink frequencies, and antenna-pointing angles in various formats that can include tables of values and polynomial coefficients. The data can also include lists of antenna-pointing events, lists of antenna- limit events, and schedules of tracking activities. To date, analysis and correlation of these intricately related data before and after tracking have been difficult and time-consuming. The DSN Data Visualization Suite enables operators to quickly diagnose tracking-data problems before, during, and after tracking. The Suite provides interpolation on demand and plotting of DSN tracking data, correlation of all data on a given temporal point, and display of data with color coding configurable by users. The suite thereby enables rapid analysis of the data prior to transmission of the data to DSN control centers. At the control centers, the same suite enables operators to validate the data before committing the data to DSN subsystems. This software is also Web-enabled to afford its capabilities to international space agencies.

Bui, Bach X.↗
NASA NTRS2004

Southern Alaska Glaciers: Spatial and Temporal Variations in Ice Volume

Although temperate mountain glaciers comprise less than 1% of the glacier-covered area on Earth, they are important because they appear to be melting rapidly under present climatic conditions and, therefore, make significant contributions to rising sea level. In this study, we use ICESat observations made in the last 1.5 years of southern Alaska glaciers to estimate ice elevation profiles, ice surface slopes and roughness, and bi-annual and/or annual ice elevation changes. We report initial results from the near coastal region between Yakutat Bay and Cape Suckling that includes the Malaspina and Bering Glaciers. We show and interpret ice elevations changes across the lower reaches of the Bagley Ice Valley for the period between October 2003 and May 2004. In addition, we use off-nadir pointing observations to reference tracks over the Bering and Malaspina Glaciers in order to estimate annual ice elevation change. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Shuttle Radar Topography Mission (SRTM) derived DEMs are used to estimate across track regional slopes between ICESat data acquisitions. Although the distribution and quantity of ICESat elevation profiles with multiple, exact repeat data is currently limited in Alaska, individual ICESat data tracks, provide an accurate reference surface for comparison to other elevation data (e.g. ASTER and SRTM X- and C-band derived DEMs). Specifically we report the elevation change over the Malaspina Glacier's piedmont lobe between a DEM derived from SRTM C-band data acquired in Feb. 2000 and ICESat Laser #2b data from Feb.-March 2004. We also report use of ICESat elevation data to enhance ASTER derived absolute DEMs. Mountain glaciers generally have rougher surfaces and steeper regional slopes than the ice sheets for which the ICESat design was optimized. Therefore, rather than averaging ICESat observations over large regions or relying on crossovers, we are working with well-located ICESat footprint returns to estimate glacier ice elevations and surface characteristics. Additional information is included in the original extended abstract.

Sauber, J.↗
NASA NTRS2011

Orbit Determination of the SELENE Satellites Using Multi-Satellite Data Types and Evaluation of SELENE Gravity Field Models

The SELENE mission, consisting of three separate satellites that use different terrestrial-based tracking systems, presents a unique opportunity to evaluate the contribution of these tracking systems to orbit determination precision. The tracking data consist of four-way Doppler between the main orbiter and one of the two sub-satellites while the former is over the far side, and of same-beam differential VLBI tracking between the two sub-satellites. Laser altimeter data are also used for orbit determination. The contribution to orbit precision of these different data types is investigated through orbit overlap analysis. It is shown that using four-way and VLBI data improves orbit consistency for all satellites involved by reducing peak values in orbit overlap differences that exist when only standard two-way Doppler and range data are used. Including laser altimeter data improves the orbit precision of the SELENE main satellite further, resulting in very smooth total orbit errors at an average level of 18m. The multi-satellite data have also resulted in improved lunar gravity field models, which are assessed through orbit overlap analysis using Lunar Prospector tracking data. Improvements over a pre-SELENE model are shown to be mostly in the along-track and cross-track directions. Orbit overlap differences are at a level between 13 and 21 m with the SELENE models, depending on whether l-day data overlaps or I-day predictions are used.

Goossens, S.↗
NASA NTRS1999

Improved Gravitational Models of Mars from Radio Tracking of Mars Global Surveyor

After a long period of aerobraking, Mars Global Surveyor entered a low altitude, near-circular and polar orbit about Mars on February 4, 1999. Since that time, the spacecraft has been tracked routinely by the antennae of the Deep Space Network (DSN). The X band tracking data for the first time provide us: with uniform low-altitude coverage over the entire planet. In terms of both quality and geographic distribution, these data supersede the S Band tracking obtained by the Viking Orbiters and Mariner 9. We have used all available tracking obtained by Mars Global Surveyor to determine improved models; of the Man geopotential to 70 x 70 in spherical harmonies. The new models provide an order of magnitude improvement in both accuracy and detail. For instance, the Valles Marimeris canyon system clearly appears in the gravity anomaly maps: with the new model. In this paper, we discuss the derivation of these new models, and we evaluate their performance in terms of observed features and orbit quality. We also discuss the use of altimeter data from the Mars Orbiter Laser Altimeter (MOLA) in the form of altimeter crossovers, and how these data can contribute to the determination of geopotential models for Mars.

Lemoine, Frank G.↗
NASA NTRS2011

Methods of Optical Navigation

Optical navigation is the use of onboard imaging to aid in the determination of the spacecraft trajectory and of the targets' ephemerides. Opnav techniques provide a direct measurement of the direction from a spacecraft to target bodies. Opnav data thus complement both radiometric tracking data (for instance, Doppler and range) and the groundbased astrometry which is used to determine the a priori ephemeris of the targets. We present the geometry and camera models which form the mathematical basis for optical navigation and some of the image processing techniques by which one can extract the optical observables--that is, the sample and line coordinates of images--from pictures.

spacecraft navigation↗
NASA NTRS2022

The Mars 2020 Ground Data System Architecture

The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.

Lopez-Roig, Reynaldo↗
NASA NTRS2014

A User Guide for Smoothing Air Traffic Radar Data

Matlab software was written to provide smoothing of radar tracking data to simulate ADS-B (Automatic Dependent Surveillance-Broadcast) data in order to test a tactical conflict probe. The probe, called TSAFE (Tactical Separation-Assured Flight Environment), is designed to handle air-traffic conflicts left undetected or unresolved when loss-of-separation is predicted to occur within approximately two minutes. The data stream that is down-linked from an aircraft equipped with an ADS-B system would include accurate GPS-derived position and velocity information at sample rates of 1 Hz. Nation-wide ADS-B equipage (mandated by 2020) should improve surveillance accuracy and TSAFE performance. Currently, position data are provided by Center radar (nominal 12-sec samples) and Terminal radar (nominal 4.8-sec samples). Aircraft ground speed and ground track are estimated using real-time filtering, causing lags up to 60 sec, compromising performance of a tactical resolution tool. Offline smoothing of radar data reduces wild-point errors, provides a sample rate as high as 1 Hz, and yields more accurate and lag-free estimates of ground speed, ground track, and climb rate. Until full ADS-B implementation is available, smoothed radar data should provide reasonable track estimates for testing TSAFE in an ADS-B-like environment. An example illustrates the smoothing of radar data and shows a comparison of smoothed-radar and ADS-B tracking. This document is intended to serve as a guide for using the smoothing software.

Center/Tracon Radar↗
NASA NTRS1973

Mars gravity field via the short data arcs

Short arc reduction of satellite Mars tracking data shows that: (1) There is one large gravity high covering the region of Nix Olympica and the three peaks to the east (about 110 deg longitude). It has an amplitude of 50 milligals at 2200-km altitude and implies a surface mass anomaly times greater than any on earth; (2) there are no large negative gravity anomalies comparable to the positive; and (3) the large 3000-km canyon seems to originate in a gravity high and end in a gravity low.

Sjogren, W. L.↗
NASA NTRS2010

Preliminary Design of the Guidance, Navigation, and Control System of the Altair Lunar Lander

Guidance, Navigation, and Control (GN&C) is the measurement and control of spacecraft position, velocity, and attitude in support of mission objectives. This paper provides an overview of a preliminary design of the GN&C system of the Lunar Lander Altair. Key functions performed by the GN&C system in various mission phases will first be described. A set of placeholder GN&C sensors that is needed to support these functions is next described. To meet Crew safety requirements, there must be high degrees of redundancy in the selected sensor configuration. Two sets of thrusters, one on the Ascent Module (AM) and the other on the Descent Module (DM), will be used by the GN&C system. The DM thrusters will be used, among other purposes, to perform course correction burns during the Trans-lunar Coast. The AM thrusters will be used, among other purposes, to perform precise angular and translational controls of the ascent module in order to dock the ascent module with Orion. Navigation is the process of measurement and control of the spacecraft's "state" (both the position and velocity vectors of the spacecraft). Tracking data from the Earth-Based Ground System (tracking antennas) as well as data from onboard optical sensors will be used to estimate the vehicle state. A driving navigation requirement is to land Altair on the Moon with a landing accuracy that is better than 1 km (radial 95%). Preliminary performance of the Altair GN&C design, relative to this and other navigation requirements, will be given. Guidance is the onboard process that uses the estimated state vector, crew inputs, and pre-computed reference trajectories to guide both the rotational and the translational motions of the spacecraft during powered flight phases. Design objectives of reference trajectories for various mission phases vary. For example, the reference trajectory for the descent "approach" phase (the last 3-4 minutes before touchdown) will sacrifice fuel utilization efficiency in order to provide landing site visibility for both the crew and the terrain hazard detection sensor system. One output of Guidance is the steering angle commands sent to the 2 degree-of-freedom (dof) gimbal actuation system of the descent engine. The engine gimbal actuation system is controlled by a Thrust Vector Control algorithm that is designed taking into account the large quantities of sloshing liquids in tanks mounted on Altair. In this early design phase of Altair, the GN&C system is described only briefly in this paper and the emphasis is on the GN&C architecture (that is still evolving). Multiple companion papers will provide details that are related to navigation, optical navigation, guidance, fuel sloshing, rendezvous and docking, machine-pilot interactions, and others. The similarities and differences of GN&C designs for Lunar and Mars landers are briefly compared.

Lee, Allan Y.↗