SEARCH · Search NASA

Results for “Sensor Network”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,099 records · Page 61

All sources NASA NTRS DOE OSTI

NASA NTRS1986

The Vega balloons - A tool for studying atmosphere dynamics on Venus

The Vega balloon experiment, designed to measure the dynamics of the Venus atmosphere, comprised the balloons themselves, their gondolas with on-board sensors and radio transmitters, and the radio telescope network on the earth. The structures and the physical parameters of the balloon probe are described, together with the instruments on the gondola, designed for the measurements of the atmospheric pressure, temperature, and vertical wind flows, and illumination, as well as possible flashes of lightning. Consideration is also given to the formatting of the information flow for the individual parameters measured.

Kremnev, R. S.↗

NASA NTRS1991

Recent advances in the development and transfer of machine vision technologies for space

Recent work concerned with real-time machine vision is briefly reviewed. This work includes methodologies and techniques for optimal illumination, shape-from-shading of general (non-Lambertian) 3D surfaces, laser vision devices and technology, high level vision, sensor fusion, real-time computing, artificial neural network design and use, and motion estimation. Two new methods that are currently being developed for object recognition in clutter and for 3D attitude tracking based on line correspondence are discussed.

Defigueiredo, Rui J. P.↗

NASA NTRS2000

Evaluation of Fieldbus and OPC for Advanced Life Support

FOUNDATION(Tm) Fieldbus and OP(TM) (OLE(TM)for Process Control) technologies were integrated into an existing control system for a crop growth chamber at NASA Ames Research Center. FOUNDATION(TM) Fieldbus is a digital, bi-directional, multi-drop, serial communications network which functions essentially as a LAN for sensors. FOUNDATION(TM) Fieldbus is heterarchical, with publishers and subscribers of data performing complex control functions at low levels without centralized control and its associated overhead. OPC(TM) is a set of interfaces which replace proprietary drivers with a transparent means of exchanging data between the fieldbus and applications. The objectives were: (1) to integrate FOUNDATION(TM) Fieldbus into existing ALS hardware and determine its overall effectiveness and reliability and, (2) to quantify any savings produced by using fieldbus and OPC technologies. We encountered several problems with the FOUNDATION(TM) Fieldbus hardware chosen. Our hardware exposed 100 data for each channel of the fieldbus. The fieldbus configurator software used to program the fieldbus was simply not adequate. The fieldbus was also not inherently reliable. It lost its settings twice during our tests for unknown reasons. OPC also had issues. It did not function at all as supplied, requiring substitution of some of its components with those from other vendors. It would stop working after a fixed period of time. Certain database calls eventually lock the machine. Overall, we would not recommend FOUNDATION(TM) Fieldbus: it was too difficult to implement with little overall added value. It also seems unlikely that FOUNDATION(TM) Fieldbus will gain sufficient penetration into the laboratory instrument market to ever be cost effective for the ALS community. OPC had good reliability and performance once a stable installation was achieved. It allowed a rapid change to an alternative software strategy when our first strategy failed. It is a cost effective solution to distributed control systems development.

Boulanger, Richard P.↗

NASA NTRS2003

Airborne Oceanographic Lidar (AOL) (Global Carbon Cycle)

This bimonthly contractor progress report covers the operation, maintenance and data management of the Airborne Oceanographic Lidar and the Airborne Topographic Mapper. Monthly activities included: mission planning, sensor operation and calibration, data processing, data analysis, network development and maintenance and instrument maintenance engineering and fabrication.

Source record↗

NASA NTRS2006

Software Architecture of Sensor Data Distribution In Planetary Exploration

Data from mobile and stationary sensors will be vital in planetary surface exploration. The distribution and collection of sensor data in an ad-hoc wireless network presents a challenge. Irregular terrain, mobile nodes, new associations with access points and repeaters with stronger signals as the network reconfigures to adapt to new conditions, signal fade and hardware failures can cause: a) Data errors; b) Out of sequence packets; c) Duplicate packets; and d) Drop out periods (when node is not connected). To mitigate the effects of these impairments, a robust and reliable software architecture must be implemented. This architecture must also be tolerant of communications outages. This paper describes such a robust and reliable software infrastructure that meets the challenges of a distributed ad hoc network in a difficult environment and presents the results of actual field experiments testing the principles and actual code developed.

Lee, Charles↗

NASA NTRS2011

Attitude Estimation in Fractionated Spacecraft Cluster Systems

An attitude estimation was examined in fractioned free-flying spacecraft. Instead of a single, monolithic spacecraft, a fractionated free-flying spacecraft uses multiple spacecraft modules. These modules are connected only through wireless communication links and, potentially, wireless power links. The key advantage of this concept is the ability to respond to uncertainty. For example, if a single spacecraft module in the cluster fails, a new one can be launched at a lower cost and risk than would be incurred with onorbit servicing or replacement of the monolithic spacecraft. In order to create such a system, however, it is essential to know what the navigation capabilities of the fractionated system are as a function of the capabilities of the individual modules, and to have an algorithm that can perform estimation of the attitudes and relative positions of the modules with fractionated sensing capabilities. Looking specifically at fractionated attitude estimation with startrackers and optical relative attitude sensors, a set of mathematical tools has been developed that specify the set of sensors necessary to ensure that the attitude of the entire cluster ( cluster attitude ) can be observed. Also developed was a navigation filter that can estimate the cluster attitude if these conditions are satisfied. Each module in the cluster may have either a startracker, a relative attitude sensor, or both. An extended Kalman filter can be used to estimate the attitude of all modules. A range of estimation performances can be achieved depending on the sensors used and the topology of the sensing network.

Hadaegh, Fred Y.↗

NASA NTRS2011

Evaluation of Long-Range Lightning Detection Networks Using TRMM/LIS Observations

Recent advances in long-range lightning detection technologies have improved our understanding of thunderstorm evolution in the data sparse oceanic regions. Although the expansion and improvement of long-range lightning datasets have increased their applicability, these applications (e.g., data assimilation, atmospheric chemistry, and aviation weather hazards) require knowledge of the network detection capabilities. Toward this end, the present study evaluates data from the World Wide Lightning Location Network (WWLLN) using observations from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite. The study documents the WWLLN detection efficiency and location accuracy relative to LIS observations, describes the spatial variability in these performance metrics, and documents the characteristics of LIS flashes that are detected by WWLLN. Improved knowledge of the WWLLN detection capabilities will allow researchers, algorithm developers, and operational users to better prepare for the spatial and temporal coverage of the upcoming GOES-R Geostationary Lightning Mapper (GLM).

Rudlosky, Scott D.↗

NASA NTRS2012

Behavioral Mapless Navigation Using Rings

This paper presents work on the development and implementation of a novel approach to robotic navigation. In this system, map-building and localization for obstacle avoidance are discarded in favor of moment-by-moment behavioral processing of the sonar sensor data. To accomplish this, we developed a network of behaviors that communicate through the passing of rings, data structures that are similar in form to the sonar data itself and express the decisions of each behavior. Through the use of these rings, behaviors can moderate each other, conflicting impulses can be mediated, and designers can easily connect modules to create complex emergent navigational techniques. We discuss the development of a number of these modules and their successful use as a navigation system in the Trinity omnidirectional robot.

Monroe, Randall P.↗

NASA NTRS2014

Use of 2d-video Disdrometer to Derive Mean Density-size and Ze-SR Relations: Four Snow Cases from the Light Precipitation Validation Experiment

The application of the 2D-video disdrometer to measure fall speed and snow size distribution and to derive liquid equivalent snow rate, mean density-size and reflectivity-snow rate power law is described. Inversion of the methodology proposed by Böhm provides the pathway to use measured fall speed, area ratio and '3D' size measurement to estimate the mass of each particle. Four snow cases from the Light Precipitation Validation Experiment are analyzed with supporting data from other instruments such as Precipitation Occurrence Sensor System (POSS), Snow Video Imager (SVI), a network of seven snow gauges and three scanning C9 band radars. The radar-based snow accumulations using the 2DVD-derived Ze-SR relation are in good agreement with a network of seven snow gauges and outperform the accumulations derived from a climatological Ze-SR relation used by the Finnish Meteorological Institute (FMI). The normalized bias between radar-derived and gauge accumulation is reduced from 96% when using the fixed FMI relation to 28% when using the Ze-SR relations based on 2DVD data. The normalized standard error is also reduced significantly from 66% to 31%. For two of the days with widely different coefficients of the Ze-SR power law, the reflectivity structure showed significant differences in spatial variability. Liquid water path estimates from radiometric data also showed significant differences between the two cases. Examination of SVI particle images at the measurement site corroborated these differences in terms of unrimed versus rimed snow particles. The findings reported herein support the application of Böhm's methodology for deriving the mean density-size and Ze-SR power laws using data from 2D-video disdrometer.

radar↗

NASA NTRS2018

Radar Measurements of the LEO Orbital Debris Environment

Access to space and the preservation of the near-Earth space environment is of critical significance. Increased interest in issues surrounding space traffic management and the continued assessment and discussion of orbital debris at the United Nations Committee on the Peaceful Uses of Outer Space (COPUOS) illustrates the significance of the topic of orbital debris. There are currently over 20,000 tracked objects in the publicly available satellite catalog on Space-Track.org. The catalog is maintained by the US Air Force Space Command using a network of optical and radar ground-based sensors and is believed to be complete for a characteristic size of 10 cm or larger in low Earth orbit (LEO). Based on the work of the NASA Orbital Debris Program Office (ODPO) over approximately the past 40 years it is understood that the small debris population (< 10 cm) increases by orders of magnitude as characteristic size decreases. With population estimates ranging from 500,000 to 1,000,000 small debris objects (> 5 mm) in orbit, it is currently not practical to track and maintain precision orbits on every object. Instead the NASA ODPO uses powerful ground-based radars to sample the low Earth Orbit (LEO) environment and assign approximate orbits to each detection. This poses an interesting signal processing challenge as we are trying to detect the smallest objects possible on the edge of the radar's sensitivity. For approximately the last 30 years, NASA ODPO has partnered with the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL) to utilize the Haystack Ultra-wideband Satellite Imaging Radar (HUSIR - formerly the Long-Range Imaging Radar or simply Haystack) and the Haystack Auxiliary (HAX) radar to collect orbital debris radar data. Additionally, the ODPO collaborates with the NASA Jet Propulsion Laboratory (JPL) to use the Goldstone Solar System Radar. The orbital debris detections from these radars serve as inputs for statistical risk models used by the human spaceflight and satellite communities to assess risk to spacecraft posed by orbital debris. In this paper, we will describe the history of orbital debris radar measurements conducted by NASA, provide an overview of current radar measurements techniques and facilities, discuss the signal processing software used for orbital debris measurements and the inference of debris size and orbital parameters from these measurements, and discuss how orbital debris radar measurements are validated for use in models that are used throughout the aerospace industry.

Blackwell, Chris↗

NASA NTRS2018

Lightning Enriched Global Precipitation Feature Database

The Tropical Rainfall Measurement Mission (TRMM) has provided a wealth of insight about lightning and precipitation in the tropics. However TRMM did not provide coverage outside the tropics and sub-tropics (i.e., beyond ±38°latitude), and hence it was unable to sample the lightning activity and precipitation features over a large fraction of mid-latitude continents and oceans, including extratropical cyclone storm tracks. The Global Precipitation Measurement (GPM) mission picks up where TRMM left off in that it provides information on precipitation features in the mid-and high latitudes (up to 65°N/S). However, GPM lacks a lightning instrument that can provide additional insights into mid-latitude thunderstorm activity and distribution. Hence we integrate observations from coincident the ISS Lightning Imaging Sensor (LIS) and the World Wide Lightning Location Network (WWLLN) observations with measurements from the GPM constellation of satellites, in particular to extend the existing GPM Precipitation Feature (PF) database so its data parameters are similar to that of the TRMM PF database (i.e., precipitation + lighting). Currently, WWLLN and ISS-LIS lightning have been collocated into precipitation features defined from GPM core satellite and constellation satellites observations.

Precipitation↗

NASA NTRS2018

Adaptive Load Control of Flexible Aircraft Wings Using Fiber Optic Sensing

Over the past century aircraft wing design has transformed from the morphing wing used on the Wright Flyer to rigid wings with little to no shape-tailoring abilities. Modern day wings are designed to fly at a single trim condition and optimized to have a maximum aerodynamic efficiency at only this condition. Shape morphing wings on the other hand have the potential to undergo geometric changes allowing them to adapt to their mission profiles. Several flight demonstrations have been conducted over the decades using morphing-wing technologies. Active wing-twist control was demonstrated on the X-53 Active Aeroelastic Wing (AAW) research project by utilizing multiple leading- and trailing-edge control surfaces. Passive morphing technology has been demonstrated on the Rockwell RPRV-870 Highly Maneuverable Aircraft Technology (HiMAT) aircraft. In the current study, the wings of a small unmanned aerial system (sUAS) were modified to have segmented control surfaces (SCS). The modifications include segmenting the original wing control surfaces (one flap and one aileron per wing) into 44 individual sections, each section having its own independent servo control motor. The wings were also instrumented with a network of over 1800 fiber-optic strain sensors (on four sensing fibers distributed over the top and bottom surfaces of the wing) monitoring the strain response of the wing to aerodynamic loading. The SCS positions were manipulated in real time to modify the spanwise lift distribution of the wings on the sUAS. The change in the structural response of the wings caused by load redistribution was quantified by measuring the bending strains on the upper and lower wing surfaces using an on-board compact fiber-optic strain sensing (cFOSS) system. A feedback controller was developed to control the SCS positions using strain-based shape estimations from the Displacement Transfer Function (DTF). Post-processing of the strain data allowed for the transverse displacement distributions and load distributions to be compared for the conventional and segmented control surface cases using displacements and loads algorithms developed by Richards and Ko at AFRC (refs. 5-10). While the current study focused on the shifting of the spanwise aerodynamic loads as quantified by displacements, future applications for loads or displacement control might include active gust alleviation and flutter suppression.

Pena, Francisco↗

NASA NTRS2024

3D Scanning System to Assess Gravity-Dependent Body Shape Changes

The human body shows unique physiological and morphological changes when exposed to different gravity conditions, including muscle atrophy, fluid shift, spinal elongation, and body posture adjustments. Such changes need to be incorporated for human-system integration in the vehicle habitat, garment, and spacesuit designs, as inaccurate body measurements can result in suboptimal crew protection that can potentially decrease injury tolerance. However, the traditional linear measurements, such as stature, segment lengths or circumferences measured using a caliper or tape measure, often show limited consistency and are unable to capture the nonlinear characteristics of the human body. While 3D body scanning can provide significant advantages over linear measurements, the technologies have not been fully developed or customized for in-flight scanning. For ground laboratory use, several different scanner types are commercially available. However, in-flight scanning requires additional technical considerations, such as minimal scan time, simplified calibration, and sufficient capture volume. This work aims to develop a prototype 3-D body scanning system that is customized for in-flight use to scan crewmembers, with the configuration and performance optimized for detecting known gravity-dependent body shape and posture changes. A hardware system will be custom built using a network of commercial off-the-shelf 3D sensors. The sensor parameters and settings will be optimized to detect the targeted body shape changes, specifically using body manikins of which the shape and size are iteratively permuted to simulate the known changes by fluid shift and spinal elongation. The capture volume will be also matched for a range of body sizes from a 1st percentile female to 99th percentile male. Unintentional body motions or floating in microgravity will be also simulated and incorporated. A data acquisition software will be developed for efficient in-flight operations with minimal overhead and easy-to-use user interface. A simplified calibration procedure will be designed for robust scanning against frequent vibration or unexpected sensor position shifts. The system will be tested in the ground laboratory for accuracy and reliability against a reference scanning system. New anthropometry measurements will be also identified to sensitively capture the gravity dependent body shape changes, in addition to the traditional anthropometry measurements. A novel landmark-based technique will be tested for consistent anthropometry measurements across posture variations. If successfully developed and deployed, the new system is expected to provide previously unavailable body shape and size data from different gravitational environments, including 0-g, 1/6-g, and 1-g. Such data can improve suit fit, habitat design, exercise efficacy quantification and sizing of orthostatic intolerance garments.

K H Kim↗

NASA NTRS2024

3D Scanning System to Assess Gravity-Dependent Body Shape Changes

K H Kim↗

DOE OSTI2018

Autonomic Intelligent Cyber Sensor (AICS)

Explore the source record for details and available documents.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Hybridizing Classifiers and Collection Systems to Maximize Intelligence and Minimize Uncertainty in National Security Data Analytics Applications

There are numerous applications that combine data collected from sensors with machine-learning based classification models to predict the type of event or objects observed. Both the collection of the data itself and the classification models can be tuned for optimal performance, but we hypothesize that additional gains can be realized by jointly assessing both factors together. Through this research, we used a seismic event dataset and two neural network classification models that issued probabilistic predictions on each event to determine whether it was an earthquake or a quarry blast. Real world applications will have constraints on data collection, perhaps in terms of a budget for the number of sensors or on where, when, or how data can be collected. We mimicked such constraints by creating subnetworks of sensors with both size and locational constraints. We compare different methods of determining the set of sensors in each subnetwork in terms of their predictive accuracy and the number of events that they observe overall. Additionally, we take the classifiers into account, treating them both as black-box models and testing out various ways of combining predictions among models and among the set of sensors that observe any given event. We find that comparable overall performance can be seen with less than half the number of sensors in the full network. Additionally, a voting scheme that uses the average confidence across the sensors for a given event shows improved predictive accuracy across nearly all subnetworks. Lastly, locational constraints matter, but sometimes in unintuitive ways, as better-performing sensors may be chosen instead of the ones excluded based on location. This being a short-term research effort, we offer a lengthy discussion on interesting next-steps and ties to other ongoing research efforts that we did not have time to pursue. These include a detailed analysis of the subnetwork performance broken down by event type, specific location, and model confidence. This project also included a Campus Executive research partnership with Texas A&M University. Through this, we worked with a professor and student to study information gain for UAV routing. This was an alternative way of looking at the similar problem space that includes sensor operation for data collection and the resulting benefit to be gained from it. This work is described in an Appendix.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

A Risk-Informed Approach to Trustworthiness Assessment in Digital Twins-Based Autonomous Control

In autonomous control systems, digital twins (DTs) are used to perform diagnostic and prognostic functions. The trustworthiness of these DTs is dependent on quality and coverage of the training data, model accuracy and integrity of sensor data. This work introduces a methodology to determine the trustworthiness of a DT system given faulty sensor data using a risk informed approach. Bayesian Belief Networks (BBNs) are used to propagate uncertainties and determine the probability of trustable recommendations. The decision to trust the control action provided by the DT is based on the DT output, expert opinion, and severity of problems. The performance of DTs is reliant on the data they are trained on. When they encounter out of distribution data, the trustworthiness of the recommendations decreases. To address this issue, we include an expert component that provides input on sensor degradation. For this, we utilize a generative artificial intelligence (AI) model, such as Generative Pretrained Transformer (GPT). The GPT functions as an expert with broad knowledge. The GPT is fine-tuned to understand and discriminate sensor degradation scenarios using manufactured data. This methodology is demonstrated through a case study on a Nearly Autonomous Management and Control System (NAMAC) during a steady state scenario. Various sensor degradation types with different severity levels are considered. Degraded sensor data is processed by the DT system and the fine-tuned GPT. Finally, using the BBN, we combine the GPT information and the DT output with its sources of uncertainty. This provides an output regarding the trustworthiness of the DT recommendation.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2019

Robust Predictive Design of Field Measurements for Evapotranspiration Barriers Using Universal Multiple linear Regression

Surface barriers are normally installed to reduce downward water movement into contaminated zones. Specifically, evapotranspiration (ET) barriers are used to store water and release it, via ET, before it can percolate into an underlying waste zone. To assess the effectiveness of a surface barrier, we used an existing data set, model-simulated data, and a dimensionality reduction approach called universal multiple linear regression (uMLR) to optimize the required number of sensors in a 2-m thick surface barrier. To understand the usefulness of implementing predictive uMLR to accommodate multiple monitoring objectives, we compare several network designs, selected based on down-sampling of existing data, with a recommended sensor design based on model simulations performed without consideration of existing data. We further added consideration of “fuzzy” design, which allows more practical guidelines for field implementation of uMLR. We discovered that uMLR, combined with robust decision-making, provides a simple, flexible, and high-quality network design for monitoring the total water stored in a surface barrier across multiple uncertain conditions.

58 GEOSCIENCES↗