Results for “ground and space data algorithms” — Page 2

NASA NTRS2010

Ground and Space Radar Volume Matching and Comparison Software

This software enables easy comparison of ground- and space-based radar observations. The software was initially designed to compare ground radar reflectivity from operational, ground based Sand C-band meteorological radars with comparable measurements from the Tropical Rainfall Measuring Mission (TRMM) satellite s Precipitation Radar (PR) instrument. The software is also applicable to other ground-based and space-based radars. The ground and space radar volume matching and comparison software was developed in response to requirements defined by the Ground Validation System (GVS) of Goddard s Global Precipitation Mission (GPM) project. This software innovation is specifically concerned with simplifying the comparison of ground- and spacebased radar measurements for the purpose of GPM algorithm and data product validation. This software is unique in that it provides an operational environment to routinely create comparison products, and uses a direct geometric approach to derive common volumes of space- and ground-based radar data. In this approach, spatially coincident volumes are defined by the intersection of individual space-based Precipitation Radar rays with the each of the conical elevation sweeps of the ground radar. Thus, the resampled volume elements of the space and ground radar reflectivity can be directly compared to one another.

Morris, Kenneth↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRS2010

Evaluation of Anomaly Detection Capability for Ground-Based Pre-Launch Shuttle Operations

This chapter will provide a thorough end-to-end description of the process for evaluation of three different data-driven algorithms for anomaly detection to select the best candidate for deployment as part of a suite of IVHM (Integrated Vehicle Health Management) technologies. These algorithms were deemed to be sufficiently mature enough to be considered viable candidates for deployment in support of the maiden launch of Ares I-X, the successor to the Space Shuttle for NASA's Constellation program. Data-driven algorithms are just one of three different types being deployed. The other two types of algorithms being deployed include a "nile-based" expert system, and a "model-based" system. Within these two categories, the deployable candidates have already been selected based upon qualitative factors such as flight heritage. For the rule-based system, SHINE (Spacecraft High-speed Inference Engine) has been selected for deployment, which is a component of BEAM (Beacon-based Exception Analysis for Multimissions), a patented technology developed at NASA's JPL (Jet Propulsion Laboratory) and serves to aid in the management and identification of operational modes. For the "model-based" system, a commercially available package developed by QSI (Qualtech Systems, Inc.), TEAMS (Testability Engineering and Maintenance System) has been selected for deployment to aid in diagnosis. In the context of this particular deployment, distinctions among the use of the terms "data-driven," "rule-based," and "model-based," can be found in. Although there are three different categories of algorithms that have been selected for deployment, our main focus in this chapter will be on the evaluation of three candidates for data-driven anomaly detection. These algorithms will be evaluated upon their capability for robustly detecting incipient faults or failures in the ground-based phase of pre-launch space shuttle operations, rather than based oil heritage as performed in previous studies. Robust detection will allow for the achievement of pre-specified minimum false alarm and/or missed detection rates in the selection of alert thresholds. All algorithms will also be optimized with respect to an aggregation of these same criteria. Our study relies upon the use of Shuttle data to act as was a proxy for and in preparation for application to Ares I-X data, which uses a very similar hardware platform for the subsystems that are being targeted (TVC - Thrust Vector Control subsystem for the SRB (Solid Rocket Booster)).

Martin, Rodney Alexander↗

NASA NTRS1983

Incorporating geometric and radiative effects into infrared scanning computer analysis

A NASA program, the SILTS experiment (Shuttle Infrared Leeside Temperature Sensing) will utilize an infrared scanning system mounted at the tip of the vertical stabilizer to remotely measure the surface temperature of the leeside of the Space Shuttle during entry from orbit. Scans of the fuselage and one wing will be made alternately. The experiment will correlate real full scale data to ground-based information. In order to quantitatively assess the temperature profile of the surface, an algorithm is required which incorporates the Space Shuttle shape, location of specific materials on the surface, and the measurement geometry between the camera and the surface. This paper will discuss the algorithm.

Myrick, D. L.↗

NASA NTRS1978

The Space Shuttle Ground Terminal Delta Modulation System

Shuttle voice processing uses a Variable Slope Delta Modulation scheme which is a variant of the ABATE algorithm. This paper summarizes the ground terminal subsystem which processes the multiplexed voice and telemetry data digital sequence in the downlink processing and digitizes the voice signal in the uplink processing. The Delta Modulation System (DMS) performance characteristics are given in terms of an intelligibility test score and in terms of time and frequency responses measured on a production system developed for NASA Goddard Space Flight Center. Implementation details of the encoder and decoder algorithms are given in terms of the circuit logic used to implement them.

Auger, R. L.↗

NASA NTRS1991

Advanced computing

Advanced concepts in hardware, software and algorithms are being pursued for application in next generation space computers and for ground based analysis of space data. The research program focuses on massively parallel computation and neural networks, as well as optical processing and optical networking which are discussed under photonics. Also included are theoretical programs in neural and nonlinear science, and device development for magnetic and ferroelectric memories.

Source record↗

NASA NTRS2001

Description of Selected Algorithms and Implementation Details of a Concept-Demonstration Aircraft VOrtex Spacing System (AVOSS)

A ground-based system has been developed to demonstrate the feasibility of automating the process of collecting relevant weather data, predicting wake vortex behavior from a data base of aircraft, prescribing safe wake vortex spacing criteria, estimating system benefit, and comparing predicted and observed wake vortex behavior. This report describes many of the system algorithms, features, limitations, and lessons learned, as well as suggested system improvements. The system has demonstrated concept feasibility and the potential for airport benefit. Significant opportunities exist however for improved system robustness and optimization. A condensed version of the development lab book is provided along with samples of key input and output file types. This report is intended to document the technical development process and system architecture, and to augment archived internal documents that provide detailed descriptions of software and file formats.

Hinton, David A.↗

NASA NTRS1994

Software-Implemented Fault Tolerance in Communications Systems

Software-implemented fault tolerance (SIFT) is used in many computer-based command, control, and communications (C(3)) systems to provide the nearly continuous availability that they require. In the communications subsystem of Space Station Alpha, SIFT algorithms are used to detect and recover from failures in the data and command link between the Station and its ground support. The paper presents a review of these algorithms and discusses how such techniques can be applied to similar systems found in applications such as manufacturing control, military communications, and programmable devices such as pacemakers. With support from the Tracking and Communication Division of NASA's Johnson Space Center, researchers at the University of Wyoming are developing a testbed for evaluating the effectiveness of these algorithms prior to their deployment. This testbed will be capable of simulating a variety of C(3) system failures and recording the response of the Space Station SIFT algorithms to these failures. The design of this testbed and the applicability of the approach in other environments is described.

Gantenbein, Rex E.↗

NASA NTRS1991

Ground-based PIV and numerical flow visualization results from the surface tension driven convection experiment

The Surface Tension Driven Convection Experiment (STDCE) is a Space Transportation System flight experiment to study both transient and steady thermocapillary fluid flows aboard the United States Microgravity Laboratory-1 (USML-1) Spacelab mission planned for June, 1992. One of the components of data collected during the experiment is a video record of the flow field. This qualitative data is then quantified using an all electric, two dimensional Particle Image Velocimetry (PIV) technique called Particle Displacement Tracking (PDT), which uses a simple space domain particle tracking algorithm. Results using the ground based STDCE hardware, with a radiant flux heating mode, and the PDT system are compared to numerical solutions obtained by solving the axisymmetric Navier Stokes equations with a deformable free surface. The PDT technique is successful in producing a velocity vector field and corresponding stream function from the raw video data which satisfactorily represents the physical flow. A numerical program is used to compute the velocity field and corresponding stream function under identical conditions. Both the PDT system and numerical results were compared to a streak photograph, used as a benchmark, with good correlation.

Pline, Alexander D.↗

NASA NTRS1991

Ground-based PIV and numerical flow visualization results from the Surface Tension Driven Convection Experiment

The Surface Tension Driven Convection Experiment (STDCE) is a Space Transportation System flight experiment to study both transient and steady thermocapillary fluid flows aboard the United States Microgravity Laboratory-1 (USML-1) Spacelab mission planned for June, 1992. One of the components of data collected during the experiment is a video record of the flow field. This qualitative data is then quantified using an all electric, two dimensional Particle Image Velocimetry (PIV) technique called Particle Displacement Tracking (PDT), which uses a simple space domain particle tracking algorithm. Results using the ground based STDCE hardware, with a radiant flux heating mode, and the PDT system are compared to numerical solutions obtained by solving the axisymmetric Navier Stokes equations with a deformable free surface. The PDT technique is successful in producing a velocity vector field and corresponding stream function from the raw video data which satisfactorily represents the physical flow. A numerical program is used to compute the velocity field and corresponding stream function under identical conditions. Both the PDT system and numerical results were compared to a streak photograph, used as a benchmark, with good correlation.

Pline, Alexander D.↗

NASA NTRS1988

Atmospheric effect removal from space imagery

A method to derive a ground reflectance scene from space imagery by removing the contribution of atmospheric radiance was developed. The method utilizes a multiple scattering radiative transfer code to model the Earth-atmosphere system and converts the output into a set of quadratic coefficients which best fit the relationships between the upwelling radiance reaching the sensor and the lower boundary conditions of the ground reflectance. Pixel by pixel correction of the atmospheric effects is done by invoking these coefficients. The algorithm was applied to a number of Thematic Mapper and CZCS data to generate interesting ground scenes and was used to simulate space imagery from ground reflectance scenes.

Kim, Hongsuk H.↗

NASA NTRS1994

Evaluation of sensor placement algorithms for on-orbit identification of space platforms

Anticipating the construction of the international space station, on-orbit modal identification of space platforms through optimally placed accelerometers is an area of recent activity. Unwanted vibrations in the platform could affect the results of experiments which are planned. Therefore, it is important that sensors (accelerometers) be strategically placed to identify the amount and extent of these unwanted vibrations, and to validate the mathematical models used to predict the loads and dynamic response. Due to cost, installation, and data management issues, only a limited number of sensors will be available for placement. This work evaluates and compares four representative sensor placement algorithms for modal identification. Most of the sensor placement work to date has employed only numerical simulations for comparison. This work uses experimental data from a fully-instrumented truss structure which was one of a series of structures designed for research in dynamic scale model ground testing of large space structures at NASA Langley Research Center. Results from this comparison show that for this cantilevered structure, the algorithm based on Guyan reduction is rated slightly better than that based on Effective Independence.

Glassburn, Robin S.↗

NASA NTRS1992

A numerical approach to controller design with an application to a space structure test facility

An iterative numerical algorithm that improves feasible closed loop design criteria by updating the parameters of a linear controller is developed. The algorithm allows the use of experimentally derived data collected from the open loop plant. It eliminates the need for an accurate parametric model of the open loop system. Experimental results from the application of a controller designed for a large space structure ground test facility using the algorithm are presented.

Frazier, W. G.↗

NASA NTRSDate not supplied

Joint ESA-NASA Multi-Mission Algorithm and Analysis Platform (MAAP)

The scientific community is faced with a need for greatly improved data sharing, analysis, visualization and advanced collaboration based firmly on open science principles. Recent and upcoming launches of new satellite missions with more complex and voluminous data, as well as the ever more urgent need to better understand the global carbon budget and related ecological processes, provided the immediate rational for the ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP). This highly collaborative joint project of ESA and NASA established a framework between ESA and NASA to share data, science algorithms and compute resources in order to foster and accelerate scientific research conducted by ESA and NASA EO data users. Presented to the public in October 2021, the current version of MAAP provides a common cloud-based platform with computing capabilities co-located with the data, a collaborative coding and analysis environment, and a set of interoperable tools and algorithms developed to support the estimation and visualization of global above-ground biomass. Data from the Global Ecosystem Dynamics Investigation (GEDI) mission on the International Space Station and the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) have been instrumental in the first products of MAAP including the first comprehensive map of Boreal above-ground Biomass and a current Global Biomass Harmonization Activity, but the platform is also being specifically designed to support the forthcoming ESA Biomass mission and incorporate data from the upcoming NASA-ISRO SAR (NISAR) mission. While these missions and the corresponding research which includes airborne, field, and calibration/validation data collection and analyses, provide a wealth of data and information relating to global biomass estimation, they also present data storing, processing and sharing challenges. The NISAR mission alone will produce about 80TB/day. These large data volumes present a challenge that would otherwise place accessibility limits on the scientific community and impact scientific progress. Other challenges being addressed by MAAP include: 1) Enabling researchers to easily discover, process, visualize and analyze large volumes of data from both agencies; 2) Providing a wide variety of data in the same coordinate reference frame to enable comparison, analysis, data evaluation, and data generation; 3) Providing a version-controlled science algorithm development environment that supports tools, co-located data and processing resources; and 4) Addressing intellectual property and sharing challenges related to collaborative algorithm development and sharing of data and algorithms. MAAP products can be explored on the MAAP Dashboard at https://earthdata.nasa.gov/maap-biomass or the joint platform entrance at scimaap.net. MAAP also can be accessed through individual NASA (https://maap-project.org) and ESA (https://esa-maap.org/) landing pages.

cloud computing↗

NASA NTRS1970

Navigation for space shuttle approach and landing using an inertial navigation system augmented by data from a precision ranging system or a microwave scan beam landing guidance system

A preliminary study has been made of the navigation performance which might be achieved for the high cross-range space shuttle orbiter during final approach and landing by using an optimally augmented inertial navigation system. Computed navigation accuracies are presented for an on-board inertial navigation system augmented (by means of an optimal filter algorithm) with data from two different ground navigation aids; a precision ranging system and a microwave scanning beam landing guidance system. These results show that augmentation with either type of ground navigation aid is capable of providing a navigation performance at touchdown which should be adequate for the space shuttle. In addition, adequate navigation performance for space shuttle landing is obtainable from the precision ranging system even with a complete dropout of precision range measurements as much as 100 seconds before touchdown.

Mcgee, L. A.↗

NASA NTRS1986

Estimation of vegetation cover at subpixel resolution using LANDSAT data

The present report summarizes the various approaches relevant to estimating canopy cover at subpixel resolution. The approaches are based on physical models of radiative transfer in non-homogeneous canopies and on empirical methods. The effects of vegetation shadows and topography are examined. Simple versions of the model are tested, using the Taos, New Mexico Study Area database. Emphasis has been placed on using relatively simple models requiring only one or two bands. Although most methods require some degree of ground truth, a two-band method is investigated whereby the percent cover can be estimated without ground truth by examining the limits of the data space. Future work is proposed which will incorporate additional surface parameters into the canopy cover algorithm, such as topography, leaf area, or shadows. The method involves deriving a probability density function for the percent canopy cover based on the joint probability density function of the observed radiances.

Jasinski, Michael F.↗

NASA NTRS1990

Artificial intelligence techniques for ground test monitoring of rocket engines

An expert system is being developed which can detect anomalies in Space Shuttle Main Engine (SSME) sensor data significantly earlier than the redline algorithm currently in use. The training of such an expert system focuses on two approaches which are based on low frequency and high frequency analyses of sensor data. Both approaches are being tested on data from SSME tests and their results compared with the findings of NASA and Rocketdyne experts. Prototype implementations have detected the presence of anomalies earlier than the redline algorithms that are in use currently. It therefore appears that these approaches have the potential of detecting anomalies early eneough to shut down the engine or take other corrective action before severe damage to the engine occurs.

Ali, Moonis↗