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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Microwave Radiometers from 0.6 to 22 GHz for Juno, a Polar Orbiter around Jupiter

A compact radiometer instrument is under development at JPL for Juno, the next NASA New Frontiers mission, scheduled to launch in 2011. This instrument is called the MWR (MicroWave Radiometer), and its purpose is to measure the thermal emission from Jupiter's atmosphere at selected frequencies from 0.6 to 22 GHz. The objective is to measure the distributions and abundances of water and ammonia in Jupiter's atmosphere, with the goal of understanding the previously unobserved dynamics of the subcloud atmosphere, and to discriminate among models for planetary formation in our solar system. The MWR instrument is currently being developed to address these science questions for the Juno mission. As part of a deep space mission aboard a solar-powered spacecraft, MWR is designed to be compact, lightweight, and low power. The entire MWR instrument consists of six individual radiometer channels with approximately 4% bandwidth at 0.6, 1.25,2.6,5.2, 10,22 GHz operating in direct detection mode. Each radiometer channel has up to 80 dB of gain with a noise figure of several dB. The highest frequency channel uses a corrugated feedhorn and waveguide transmission lines, whereas all other channels use highly phase stable coaxial cables and either patch array or waveguide slot array antennas. Slot waveguide array antennas were chosen for the low loss at the next three highest frequencies and patch array antennas were implemented due to the mass constraint at the two lowest frequencies. The six radiometer channels receive their voltage supplies and control lines from an electronics unit that also provides the instrument communication interface to the Juno spacecraft. For calibration purposes each receiver has integrated noise diodes, a Dicke switch, and temperature sensors near each component that contributes to the noise figure. In addition, multiple sensors will be placed along the RF transmission lines and the antennas in order to measure temperature gradients. All antennas and RF transmission lines must withstand low temperatures and the harsh radiation environment surrounding Jupiter; the receivers and control electronics are protected by a radiation-shielding enclosure on the Juno spacecraft that also provides for a benign and stable operating temperature environment. This paper will focus on the concept of the MWR instrument and will present results of one breadboard receiver channel.

receiver↗
NASA NTRS2013

Development of a Prototype Algal Reactor for Removing CO2 from Cabin Air

Controlling carbon dioxide in spacecraft cabin air may be accomplished using algal photobioreactors (PBRs). The purpose of this project was to evaluate the use of a commercial microcontroller, the Arduino Mega 2560, for measuring key phot~ioreactor variables: dissolved oxygen, pH, temperature, light, and carbon dioxide. The Arduino platform is an opensource physical computing platform composed of a compact microcontroller board and a C++/C computer language (Arduino 1.0.5). The functionality of the Arduino platform can be expanded by the use of numerous add-ons or 'shields'. The Arduino Mega 2560 was equipped with the following shields: datalogger, BNC shield for reading pH sensor, a Mega Moto shield for controlling CO2 addition, as well as multiple sensors. The dissolved oxygen (DO) probe was calibrated using a nitrogen bubbling technique and the pH probe was calibrated via an Omega pH simulator. The PBR was constructed using a 2 L beaker, a 66 L box for addition of CO2, a micro porous membrane, a diaphragm pump, four 25 watt light bulbs, a MasterFiex speed controller, and a fan. The algae (wild type Synechocystis PCC6803) was grown in an aerated flask until the algae was dense enough to used in the main reactor. After the algae was grown, it was transferred to the 2 L beaker where CO2 consumption and O2 production was measured using the microcontroller sensor suite. The data was recorded via the datalogger and transferred to a computer for analysis.

Patel, Vrajen↗
NASA NTRS2006

Multiple Waveband Temperature Sensor (MWTS)

This slide presentation reviews the development of Multiple Waveband Temperature Sensor (MWTS). The MWTS project will result in a highly stable, monolithically integrated, high resolution infrared detector array sensor that records registered thermal imagery in four infrared wavebands to infer dynamic temperature profiles on a laser-irradiated ground target. An accurate surface temperature measurement of a target in extreme environments in a non-intrusive manner is required. The development challenge is to: determine optimum wavebands (suitable for target temperatures, nature of the targets and environments) to measure accurate target surface temperature independent of the emissivity, integrate simultaneously readable multiband Quantum Well Infrared Photodetectors (QWIPs) in a single monolithic focal plane array (FPA) sensor and to integrate the hardware/software and system calibration for remote temperature measurements. The charge was therefore to develop and demonstrate a multiband infrared imaging camera with the detectors simultaneously sensitive to multiple distinct color bands for front surface temperature measurements Wavelength ( m) measurements. Amongst the requirements are: that the measurement system will not affect target dynamics or response to the laser irradiation and that the simplest criterion for spectral band selection is to choose those practically feasible spectral bands that create the most contrast between the objects or scenes of interest in the expected environmental conditions. There is in the presentation a review of the modeling and simulation of multi-wave infrared temperature measurement and also a review of the detector development and QWIP capacities.

infrared photodector↗
NASA NTRS2017

Importance of Calibration/Validation Traceability for Multi-Sensor Imaging Spectrometry Applications

Knowledge of calibration traceability is essential for ensuring the quality of data products relying on multiple sensors and especially true for imaging spectrometers. The current work discusses the expected impact that imaging spectrometers have in ensuring radiometric traceability for both multispectral and hyperspectral products. The Climate Absolute Radiance and Refractivity Observatory Pathfinder mission is used to show the role that high-accuracy imaging spectrometers can play in understanding test sites used for vicarious calibration of sensors. The associated Solar, Lunar for Absolute Reflectance Imaging Spectroradiometer calibration demonstration system is used to illustrate recent advances in laboratory radiometric calibration approaches that will allow both the use of imaging spectrometers as calibration standards as well as to ensure the consistency of the multiple imaging spectrometers expected to be on orbit in the next decade.

imagin↗
NASA NTRS1989

Design and implementation of a multi-sensor robot system for printed circuit board insertion

The design and implementation of a robot system equipped with multiple sensors are described. The robotic assembly system couples the task sequence of the robot with information from different sensors, enabling the system to handle uncertainties encountered during task execution. The target task involves the insertion of a printed circuit board into a card cage. The sensors used are an overhead three-dimensional vision system, a fingertip cross-fire sensor, and a 6-DOF force/torque sensor. The vision system is used to locate the printed circuit boards and the insertion slots. Information provided by the cross-fire sensor is used to complement the information from the vision system in order to obtain a proper grasping location on the printed circuit board. A fuzzy-logic-based system is used to interpret the forces and torques generated by the force/torque sensor during the insertion process. The repeated success of this assembly task over many experimental trials verifies the modeling and controller assumptions.

Sood, Deepak↗
NASA NTRS2007

Magnetic Field Response Measurement Acquisition System

This paper presents a measurement acquisition method that alleviates many shortcomings of traditional measurement systems. The shortcomings are a finite number of measurement channels, weight penalty associated with measurements, electrical arcing, wire degradations due to wear or chemical decay and the logistics needed to add new sensors. Wire degradation has resulted in aircraft fatalities and critical space launches being delayed. The key to this method is the use of sensors designed as passive inductor-capacitor circuits that produce magnetic field responses. The response attributes correspond to states of physical properties for which the sensors measure. Power is wirelessly provided to the sensing element by using Faraday induction. A radio frequency antenna produces a time-varying magnetic field used to power the sensor and receive the magnetic field response of the sensor. An interrogation system for discerning changes in the sensor response frequency, resistance and amplitude has been developed and is presented herein. Multiple sensors can be interrogated using this method. The method eliminates the need for a data acquisition channel dedicated to each sensor. The method does not require the sensors to be near the acquisition hardware. Methods of developing magnetic field response sensors and the influence of key parameters on measurement acquisition are discussed. Examples of magnetic field response sensors and the respective measurement characterizations are presented. Implementation of this method on an aerospace system is discussed.

Woodard, Stanley E.↗
NASA NTRS2015

Desensitized Optimal Filtering and Sensor Fusion Toolkit

Analytical Mechanics Associates, Inc., has developed a software toolkit that filters and processes navigational data from multiple sensor sources. A key component of the toolkit is a trajectory optimization technique that reduces the sensitivity of Kalman filters with respect to model parameter uncertainties. The sensor fusion toolkit also integrates recent advances in adaptive Kalman and sigma-point filters for non-Gaussian problems with error statistics. This Phase II effort provides new filtering and sensor fusion techniques in a convenient package that can be used as a stand-alone application for ground support and/or onboard use. Its modular architecture enables ready integration with existing tools. A suite of sensor models and noise distribution as well as Monte Carlo analysis capability are included to enable statistical performance evaluations.

Karlgaard, Christopher D.↗
NASA NTRS2005

Coordinated Science Campaign Scheduling for Sensor Webs

Future Earth observing missions will study different aspects and interacting pieces of the Earth's eco-system. Scientists are designing increasingly complex, interdisciplinary campaigns to exploit the diverse capabilities of multiple Earth sensing assets. In addition, spacecraft platforms are being configured into clusters, trains, or other distributed organizations in order to improve either the quality or the coverage of observations. These simultaneous advances in the design of science campaigns and in the missions that will provide the sensing resources to support them offer new challenges in the coordination of data and operations that are not addressed by current practice. For example, the scheduling of scientific observations for satellites in low Earth orbit is currently conducted independently by each mission operations center. An absence of an information infrastructure to enable the scheduling of coordinated observations involving multiple sensors makes it difficult to execute campaigns involving multiple assets. This paper proposes a software architecture and describes a prototype system called DESOPS (Distributed Earth Science Observation Planning and Scheduling) that will address this deficiency.

Edgington, Will↗
DOE OSTI2023

Blockchain Empowered Provenance Framework for Sensor Identity Management and Data Flow Security in Fossil-based Power Plants

The overall goals of this project are divided into 3 important phases. First, a peer-to-peer SCADA network is to be established where identity profile of sensors from fossil-power plants will be stored in an immutable manner in order to verify data flow integrity in real time. Second, it is aimed to develop a Blockchain-based provenance platform to audit equipment operations along with data and process integrity violations. In this phase, various operational rules will be incorporated in the ledger on behalf of the operating sensor so its abnormal use can be easily detected. In addition, it is aimed to achieve provenance when the rate of transactions is exceedingly high, thus enabling to design a scalable provenance scheme that can work in real time when number of data collecting points or sensor units increase in fossil-based power plants. Finally, a working testbed will be developed to integrate the Blockchain platform with sensor-based fossil power plant comprising of Remote Terminal Units (RTU) and SCADA devices. Using multiple Raspberry-PIs mimicking the role of RTUs that are interfaced with multiple sensors devices will be deployed to serve as a critical component of fossil power plant and the performance metrics such as throughput, latency, bootstrapping time, etc., will be evaluated on the testbed.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS2016

Limb Correction of Polar-Orbiting Imagery for the Improved Interpretation of RGB Composites

Red-Green-Blue (RGB) composite imagery combines information from several spectral channels into one image to aid in the operational analysis of atmospheric processes. However, infrared channels are adversely affected by the limb effect, the result of an increase in optical path length of the absorbing atmosphere between the satellite and the earth as viewing zenith angle increases. This paper reviews a newly developed technique to quickly correct for limb effects in both clear and cloudy regions using latitudinally and seasonally varying limb correction coefficients for real-time applications. These limb correction coefficients account for the increase in optical path length in order to produce limb-corrected RGB composites. The improved utility of a limb-corrected Air Mass RGB composite from the application of this approach is demonstrated using Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. However, the limb correction can be applied to any polar-orbiting sensor infrared channels, provided the proper limb correction coefficients are calculated. Corrected RGB composites provide multiple advantages over uncorrected RGB composites, including increased confidence in the interpretation of RGB features, improved situational awareness for operational forecasters, and the ability to use RGB composites from multiple sensors jointly to increase the temporal frequency of observations.

satellite imagery↗
NASA NTRSDate not supplied

Adaptive Multi-Sensor Localization Information Fusion for Autonomous Urban Air Mobility Operations

An adaptive method is developed to iteratively fuse the information provided by multiple sensors to enable autonomous urban air mobility type operations. First, noisy and bias corrupted IMU readings are processed as soon as they arrive using kinematic equations represented in the vehicle's body frame. To correct the systems drift resulting from the integration, an information content measure is introduced to decide on the environment. For the cluttered environment the information provided by environmental sensors is counted as reliable and the drift correction as accurate. For the open space, the GPS data is counted as reliable, and the drift correction is done based on the GPS readings. The measurement noise effects are minimize using Iterated Extended Kalman Filter framework. The algorithm is implemented in the in-house developed FlightDeckz simulation environment using an IMU model, simulated video recorded from a camera mounted on the vehicle (for the purpose of this study, outside scenery was generated with XPlane), which flies in an urban environment, and GPS data generated from the environment's digital map.

Onboard Perception↗
DOE OSTI2021

Automatic Drift Correction through Nonlinear Sensing

For successful design and operation of advanced monitoring and control systems, engineers rely on high quality sensor signals that are simultaneously accurate, representative, voluminous, and timely. Unfortunately, sensor faults are common and lead to short-lived symptoms, such as outliers and spikes as well as long-lived symptoms, such as sensor drift. Sensor drift belongs to the category of incipient faults. These are particularly challenging to detect, diagnose, and correct as the time scales of these faults are typically longer than the time scales of the system dynamics that are of interest. Moreover, if sensor drift occurs as a result of exposure to measured medium, then it is likely that multiple sensors will exhibit similar drift rates, thus challenging fault management strategies based on redundancy. In this contribution, we present a first method that can handle this unique challenge.

Chowdhury, Dhruba↗
DOE OSTI2026

Automatic Calibration and Health Monitoring of Infrastructure Sensors

Smart transportation infrastructure relies on networks of heterogeneous sensors - cameras, radars, and lidars - continuously monitoring traffic conditions. However, executing the initial spatial calibration of multiple sensors and the subsequent health monitoring presents significant operational challenges. Environmental factors, mechanical vibrations, and gradual drift cause spatial misalignment, degrading fusion performance and tracking accuracy. Traditional calibration approaches require manual intervention with specialized targets or survey equipment, resulting in service interruptions and high maintenance costs. This work presents an automated framework for initial calibration and continuous health monitoring without human intervention or service disruption. Our approach addresses two critical problems: (1) detecting when sensors become miscalibrated during operation, and (2) automatically re-establishing spatial alignment using only operational traffic data. The health monitoring component analyzes measurement innovations - differences between sensor observations and predicted object states - to detect systematic biases indicative of calibration drift. By computing bias magnitude, directional consistency, and rejection rates, the system identifies miscalibrations as small as 0.5 meters. Unlike traditional methods requiring known calibration targets, our diagnostic operates continuously on live traffic observations, enabling early detection before fusion quality degrades. The automatic recalibration algorithm leverages overlapping sensor fields-of-view and temporal correlation of vehicle observations. Using graph-based optimization, the system automatically discovers which sensor pairs observe common regions, estimates pairwise spatial transformations using RANSAC-based robust estimation, and jointly optimizes all sensor poses through bundle adjustment. The framework handles practical deployment challenges, including different sensor sampling rates (1-10 Hz), varying installation positions, unknown orientations, and limited overlap regions (>10%). When approximate sensor positions are available from installation surveys (+/-1m accuracy), the algorithm additionally estimates sensor orientations, refining both position and rotation to sub-meter and sub-degree accuracy. We validate the framework on multi-hour traffic datasets from six heterogeneous sensors with sampling rates ranging from 1 Hz to 10 Hz. Results demonstrate successful calibration even with sparse overlap (<20%) and automatic detection of miscalibrations exceeding 0.8 meters. This work enables a "deploy-and-forget" sensor infrastructure that maintains calibration autonomously, reducing maintenance costs while improving tracking accuracy. The techniques generalize beyond transportation to any multi-sensor monitoring application requiring robust spatial alignment, including smart cities, industrial monitoring, and surveillance systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Assessment of Cloud Fraction Derived from a New Geostationary Satellite Cloud Retrieval Algorithm for CERES and Progress Towards Cross-platform Continuity

For over two decades The Clouds and the Earth’s Radiant Energy System (CERES) project has produced long-term records of top-of-atmosphere (TOA) and surface irradiances for detecting changes in the Earth’s radiation budget and advancing understanding of how clouds contribute to those changes. Accurate characterization of the spatial and temporal distributions of clouds are a critical component for producing CERES datasets. The CERES Cloud Working Group (CWG) derives cloud properties from both geostationary (GEO) and low-Earth orbit (LEO) satellite sensors in order to provide complete global coverage at hourly temporal resolution. However, the use of multiple sensors to provide this spatiotemporal coverage throughout a long-term record presents some challenges, because the various sensors generally have different spectral band characteristics (e.g., spectral band width and response) and spatial resolution. These differences can result in spatial artifacts at the coverage boundary between two sensors or temporal artifacts when one sensor replaces another in the record. For the upcoming Edition 5 release of CERES products, the CWG is developing cloud retrieval algorithms which utilize only spectral bands common to most modern passive satellite radiometers and account for differences in spectral width and response. The goal with this approach is to provide global cloud properties for CERES with greater cross-platform consistency than the previous Edition 4 products and thus mitigate artifacts which are evident at the interface of two sensors. This study focuses on retrieval of total cloud fraction from various GEO sensors, and we use Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations to assess the accuracy of total cloud fraction derived from the various sensors. The Edition 5 cloud mask algorithm depends on estimates of cloud-free TOA spectral radiances to differentiate clear and cloudy conditions, and CALIOP is used to assess the accuracy of those estimates when cloud-free conditions are indeed observed.

CALIOP↗
NASA NTRS2005

Sensor Webs: Autonomous Rapid Response to Monitor Transient Science Events

To better understand how physical phenomena, such as volcanic eruptions, evolve over time, multiple sensor observations over the duration of the event are required. Using sensor web approaches that integrate original detections by in-situ sensors and global-coverage, lower-resolution, on-orbit assets with automated rapid response observations from high resolution sensors, more observations of significant events can be made with increased temporal, spatial, and spectral resolution. This paper describes experiments using Earth Observing 1 (EO-1) along with other space and ground assets to implement progressive mission autonomy to identify, locate and image with high resolution instruments phenomena such as wildfires, volcanoes, floods and ice breakup. The software that plans, schedules and controls the various satellite assets are used to form ad hoc constellations which enable collaborative autonomous image collections triggered by transient phenomena. This software is both flight and ground based and works in concert to run all of the required assets cohesively and includes software that is model-based, artificial intelligence software.

Mandl, Dan↗
NASA NTRS2003

Optimization Of Ocean Color Algorithms: Application To Satellite And In Situ Data Merging

The objective of our program is to develop and validate a procedure for ocean color data merging which is one of the major goals of the SIMBIOS project (McClain et al., 1995). The need for a merging capability is dictated by the fact that since the launch of MODIS on the Terra platform and over the next decade, several global ocean color missions from various space agencies are or will be operational simultaneously. The apparent redundancy in simultaneous ocean color missions can actually be exploited to various benefits. The most obvious benefit is improved coverage (Gregg et al., 1998; Gregg & Woodward, 1998). The patchy and uneven daily coverage from any single sensor can be improved by using a combination of sensors. Beside improved coverage of the global ocean the merging of ocean color data should also result in new, improved, more diverse and better data products with lower uncertainties. Ultimately, ocean color data merging should result in the development of a unified, scientific quality, ocean color time series, from SeaWiFS to NPOESS and beyond. Various approaches can be used for ocean color data merging and several have been tested within the frame of the SIMBIOS program (see e.g. Kwiatkowska & Fargion, 2003, Franz et al., 2003). As part of the SIMBIOS Program, we have developed a merging method for ocean color data. Conversely to other methods our approach does not combine end-products like the subsurface chlorophyll concentration (chl) from different sensors to generate a unified product. Instead, our procedure uses the normalized waterleaving radiances (LwN( )) from single or multiple sensors and uses them in the inversion of a semianalytical ocean color model that allows the retrieval of several ocean color variables simultaneously. Beside ensuring simultaneity and consistency of the retrievals (all products are derived from a single algorithm), this model-based approach has various benefits over techniques that blend end-products (e.g. chlorophyll): 1) it works with single or multiple data sources regardless of their specific bands, 2) it exploits band redundancies and band differences, 3) it accounts for uncertainties in the LwN( ) data and, 4) it provides uncertainty estimates for the retrieved variables.

Maritorena, Stephane↗
NASA NTRS1991

A cost-effective intelligent robotic system with dual-arm dexterous coordination and real-time vision

Dexterous coordination of manipulators based on the use of redundant degrees of freedom, multiple sensors, and built-in robot intelligence represents a critical breakthrough in development of advanced manufacturing technology. A cost-effective approach for achieving this new generation of robotics has been made possible by the unprecedented growth of the latest microcomputer and network systems. The resulting flexible automation offers the opportunity to improve the product quality, increase the reliability of the manufacturing process, and augment the production procedures for optimizing the utilization of the robotic system. Moreover, the Advanced Robotic System (ARS) is modular in design and can be upgraded by closely following technological advancements as they occur in various fields. This approach to manufacturing automation enhances the financial justification and ensures the long-term profitability and most efficient implementation of robotic technology. The new system also addresses a broad spectrum of manufacturing demand and has the potential to address both complex jobs as well as highly labor-intensive tasks. The ARS prototype employs the decomposed optimization technique in spatial planning. This technique is implemented to the framework of the sensor-actuator network to establish the general-purpose geometric reasoning system. The development computer system is a multiple microcomputer network system, which provides the architecture for executing the modular network computing algorithms. The knowledge-based approach used in both the robot vision subsystem and the manipulation control subsystems results in the real-time image processing vision-based capability. The vision-based task environment analysis capability and the responsive motion capability are under the command of the local intelligence centers. An array of ultrasonic, proximity, and optoelectronic sensors is used for path planning. The ARS currently has 18 degrees of freedom made up by two articulated arms, one movable robot head, and two charged coupled device (CCD) cameras for producing the stereoscopic views, and articulated cylindrical-type lower body, and an optional mobile base. A functional prototype is demonstrated.

Marzwell, Neville I.↗
DOE OSTI2025

Thermomagnetic generators for ultra-low-grade marine thermal energy harvesting

Low-grade thermal gradients in marine environments represent an underexploited energy source for autonomous sensing and monitoring. Converting such small temperature differences into usable electrical power remains a key challenge for ocean-deployed systems. We present a deployable thermomagnetic generator thoroughly characterized for marine-relevant energy harvesting. The device powers an internet-connected sensor and harvests ultra-low temperature differences akin to those at the ocean surface. It draws heat from water and rejects it to ambient air, operating optimally at a temperature difference (ΔT) of ~7.5 °C. Laboratory prototypes generated up to 6.7 mW at ΔT ~ 10 °C with gentle airflow (~1 m s -1 ). A separate controlled wave-tank demonstration validated stable operation and sensor powering under marine-like boundary conditions. Given its voltage and power margins, the generator could sustain multiple sensor nodes. Scalability and material assessments identify modular deployment and non-rare-earth alternatives as pathways toward practical marine energy harvesting and low-grade waste-heat recovery.

16 TIDAL AND WAVE POWER↗