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NASA NTRS2011

Optimal Tuner Selection for Kalman-Filter-Based Aircraft Engine Performance Estimation

An emerging approach in the field of aircraft engine controls and system health management is the inclusion of real-time, onboard models for the inflight estimation of engine performance variations. This technology, typically based on Kalman-filter concepts, enables the estimation of unmeasured engine performance parameters that can be directly utilized by controls, prognostics, and health-management applications. A challenge that complicates this practice is the fact that an aircraft engine s performance is affected by its level of degradation, generally described in terms of unmeasurable health parameters such as efficiencies and flow capacities related to each major engine module. Through Kalman-filter-based estimation techniques, the level of engine performance degradation can be estimated, given that there are at least as many sensors as health parameters to be estimated. However, in an aircraft engine, the number of sensors available is typically less than the number of health parameters, presenting an under-determined estimation problem. A common approach to address this shortcoming is to estimate a subset of the health parameters, referred to as model tuning parameters. The problem/objective is to optimally select the model tuning parameters to minimize Kalman-filterbased estimation error. A tuner selection technique has been developed that specifically addresses the under-determined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine that seeks to minimize the theoretical mean-squared estimation error of the Kalman filter. This approach can significantly reduce the error in onboard aircraft engine parameter estimation applications such as model-based diagnostic, controls, and life usage calculations. The advantage of the innovation is the significant reduction in estimation errors that it can provide relative to the conventional approach of selecting a subset of health parameters to serve as the model tuning parameter vector. Because this technique needs only to be performed during the system design process, it places no additional computation burden on the onboard Kalman filter implementation. The technique has been developed for aircraft engine onboard estimation applications, as this application typically presents an under-determined estimation problem. However, this generic technique could be applied to other industries using gas turbine engine technology.

Simon, Donald L.↗

NASA NTRSDate not supplied

Navigation Doppler Lidar Signal Processing Architecture and Algorithms

A coherent Doppler lidar has been developed to address the need for a high-performance, compact, and cost-effective velocity and altitude sensor onboard landing vehicles. One of the core subsystems within NDL is its signal processing architecture. The primary function of this subsystem is to digitize the analog output of the homodyned signal received from the photo-diode associated with each telescope, process this signal in the frequency domain, and then turn this information into velocity and range measurements. A secondary function of this subsystem is to setup and control the internal components within NDL and to enable the instrument to interface with external systems by receiving commands, sending telemetry, and synchronizing its measurements with external clocks and pulses. This paper will give an overview of the signal processing architecture within NDL. This will include a discussion on its fundamental processing components and the core signal processing algorithms. Command, telemetry and internal component controls will also be discussed as will how the signal processing architecture has also evolved over the years of development. We will discuss provisions for robust operation including Triple-Mode Redundancy (TMR) within NDL’s Field Programmable Gate Array (FPGA) and storage elements, a fault-tolerant boot process, and Error Detection and Correction (EDAC) on the Static Random-Access Memories (SRAMs). Finally, we conclude this paper with how this architecture could continue to evolve.

Doppler Lidar↗

NASA NTRS1974

Mariner IX optical navigation using Mars lit limb

Optical spacecraft navigation data, i.e., the lit limb TV image of Mars, acquired during the approach phase of the Mariner IX spacecraft to Mars, has been successfully demonstrated to augment the radio spacecraft tracking data. Accurate in-flight calibration of the TV instrument and the scan platform was performed by referencing stars and planets. Simulated real-time processing and the detailed postflight analyses of the onboard optical data have shown that planet limb data is an important data source in a far-encounter period for which other types of onboard optical measurements, e.g., natural satellite(s) of the target planet with star background, may not be available.

Jerath, N.↗

NASA NTRS1989

Consort 1 flight results - A synopsis

All six experiments performed onboard Consort 1, the first low gravity materials processing payload to be launched by a commercially licensed rocket in the U.S., are evaluated. The six experiments were carried out as planned, during approximately seven minutes of suborbital, low gravity flight and returned in excellent condition within four hour of launch. Nearly 150 physical samples supported by measurements and photographs made during the flight were taken for analysis. The rocket flight and payload configuration is described, along with experiment objectives, methods, results, and conclusions.

Wessling, Francis C.↗

NASA NTRS1996

Technology Directions for the 21st Century, volume 1

For several decades, semiconductor device density and performance have been doubling about every 18 months (Moore's Law). With present photolithography techniques, this rate can continue for only about another 10 years. Continued improvement will need to rely on newer technologies. Transition from the current micron range for transistor size to the nanometer range will permit Moore's Law to operate well beyond 10 years. The technologies that will enable this extension include: single-electron transistors; quantum well devices; spin transistors; and nanotechnology and molecular engineering. Continuation of Moore's Law will rely on huge capital investments for manufacture as well as on new technologies. Much will depend on the fortunes of Intel, the premier chip manufacturer, which, in turn, depend on the development of mass-market applications and volume sales for chips of higher and higher density. The technology drivers are seen by different forecasters to include video/multimedia applications, digital signal processing, and business automation. Moore's Law will affect NASA in the areas of communications and space technology by reducing size and power requirements for data processing and data fusion functions to be performed onboard spacecraft. In addition, NASA will have the opportunity to be a pioneering contributor to nanotechnology research without incurring huge expenses.

Crimi, Giles F.↗

NASA NTRS1997

Snow Crystal Shape and Microwave Scattering

The radiometric calibration of SeaWiFS data includes a correction for the temperature dependence of the individual detector sensitivities. The detector temperature are measured by temperature sensors mounted on instrument focal planes. Processing of the temperature sensor output by an onboard instrument computer introduces a nonlinear response into the temperature data. This paper describes the calibration of the temperature sensor output and the computation of the temperature corrections for the radiometric calibration of the instrument.

Foster, J. L.↗

NASA NTRS2000

X-38 Experimental Controls Laws

X-38 Experimental Control Laws X-38 is a NASA JSC/DFRC experimental flight test program developing a series of prototypes for an International Space Station (ISS) Crew Return Vehicle, often called an ISS "lifeboat." X- 38 Vehicle 132 Free Flight 3, currently scheduled for the end of this month, will be the first flight test of a modem FCS architecture called Multi-Application Control-Honeywell (MACH), originally developed by the Honeywell Technology Center. MACH wraps classical P&I outer attitude loops around a modem dynamic inversion attitude rate loop. The dynamic inversion process requires that the flight computer have an onboard aircraft model of expected vehicle dynamics based upon the aerodynamic database. Dynamic inversion is computationally intensive, so some timing modifications were made to implement MACH on the slower flight computers of the subsonic test vehicles. In addition to linear stability margin analyses and high fidelity 6-DOF simulation, hardware-in-the-loop testing is used to verify the implementation of MACH and its robustness to aerodynamic and environmental uncertainties and disturbances.

Munday, Steve↗

NASA NTRS2006

Real Time On-line Space Research Laboratory Environment Monitoring with Off-line Trend and Prediction Analysis

One of the responsibilities of the NASA Glenn Principal Investigator Microgravity Services is to support NASA sponsored investigators in the area of reduced-gravity acceleration data analysis, interpretation and the monitoring of the reduced-gravity environment on-board various carriers. With the International Space Station currently operational, a significant amount of acceleration data is being down-linked and processed on ground for both the space station onboard environment characterization (and verification) and scientific experiments. Therefore, to help principal investigator teams monitor the acceleration level on-board the International Space Station to avoid undesirable impact on their experiment, when possible, the NASA Glenn Principal Investigator Microgravity Services developed an artificial intelligence monitoring system, which detects in near real time any change in the environment susceptible to affect onboard experiments. The main objective of the monitoring system is to help research teams identify the vibratory disturbances that are active at any instant of time onboard the International Space Station that might impact the environment in which their experiment is being conducted. The monitoring system allows any space research scientist, at any location and at any time, to see the current acceleration level on-board the Space Station via the World Wide Web. From the NASA Glenn s Exploration Systems Division web site, research scientists can see in near real time the active disturbances, such as pumps, fans, compressor, crew exercise, re-boost, extra-vehicular activity, etc., and decide whether or not to continue operating or stopping (or making note of such activity for later correlation with science results) their experiments based on the g-level associated with that specific event. A dynamic graphical display accessible via the World Wide Web shows the status of all the vibratory disturbance activities with their degree of confidence as well as their g-level contribution to the environment. The system can detect both known and unknown vibratory disturbance activities. It can also perform trend analysis and prediction by analyzing past data over many Increments of the space station for selected disturbance activities. This feature can be used to monitor the health of onboard mechanical systems to detect and prevent potential system failure as well as for use by research scientists during their science results analysis. Examples of both real time on-line vibratory disturbance detection and off-line trend analysis are presented in this paper. Several soft computing techniques such as Kohonen s Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.

Jules, Kenol↗

NASA NTRS2009

Multilayer Cloud Detection with the MODIS Near-Infrared Water Vapor Absorption Band

Data Collection 5 processing for the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the NASA Earth Observing System EOS Terra and Aqua spacecraft includes an algorithm for detecting multilayered clouds in daytime. The main objective of this algorithm is to detect multilayered cloud scenes, specifically optically thin ice cloud overlying a lower-level water cloud, that presents difficulties for retrieving cloud effective radius using single layer plane-parallel cloud models. The algorithm uses the MODIS 0.94 micron water vapor band along with CO2 bands to obtain two above-cloud precipitable water retrievals, the difference of which, in conjunction with additional tests, provides a map of where multilayered clouds might potentially exist. The presence of a multilayered cloud results in a large difference in retrievals of above-cloud properties between the CO2 and the 0.94 micron methods. In this paper the MODIS multilayered cloud algorithm is described, results of using the algorithm over example scenes are shown, and global statistics for multilayered clouds as observed by MODIS are discussed. A theoretical study of the algorithm behavior for simulated multilayered clouds is also given. Results are compared to two other comparable passive imager methods. A set of standard cloudy atmospheric profiles developed during the course of this investigation is also presented. The results lead to the conclusion that the MODIS multilayer cloud detection algorithm has some skill in identifying multilayered clouds with different thermodynamic phases

Wind, Galina↗

NASA NTRSDate not supplied

Autonomous Navigation of a Lunar Relay Using GNSS and Other Measurements

Many of the highest priority destinations at the Moon lack a continuous view of Earth, such as the lunar poles or lunar far side. Exploration of these sites will require spacecraft in cislunar space to relay communications and provide position, navigation, and timing (PNT) services. Accurate knowledge of relay position, velocity, and time is essential to these services. This paper describes a concept for a PNT Instrument being developed for the Lunar Communications Relay and Navigation Systems (LCRNS) Project. The instrument is intended as a payload that would enable autonomous, on-board, real-time navigation and timing using Global Navigation Satellite System (GNSS), optical navigation, and one-way measurements from Earth-based ground stations. Hardware-in-the-loop simulations using flight software are used to realistically characterize performance on hardware platforms with a path to flight. These results provide preliminary validation of the proposed PNT Instrument, demonstrate the benefits of augmenting GNSS with other measurements, and serve as an insightful reference for the design of future lunar missions, including those that will operate within the LunaNet framework of standards. This instrument concept relies on several technologies developed at NASA Goddard Space Flight Center (GSFC). For GNSS observables, the instrument relies on the high-altitude NavCube 3 mini (NC3m) GNSS receiver specifically designed for cislunar applications. The autoNGC system, which consists of flight software and a hardware platform, is responsible for fusing the observables using its extended Kalman filter, the Goddard Enhanced Onboard Navigation System (GEONS). Optical navigation observables are processed within autoNGC (“autonomous Navigation, Guidance, and Control”) using the Goddard Image Analysis & Navigation Tool (GIANT) which is also responsible for simulating high-fidelity images for test and analysis. In addition to describing the PNT Instrument and its components, the paper will present predicted performance based on simulation results. As a baseline, it will present GNSS-only hardware-in-the loop results using a NC3m test unit to process Spirent-simulated GPS signals in a potential lunar relay trajectory: a 12-hour elliptical frozen lunar orbit (ELFO). GEONS then processes the GPS pseudorange and time differenced carrier phase measurements to estimate and propagate the relay state (position, velocity, and time). These results extend previously published work that showed preliminary ELFO performance. Previous work has shown the importance of other measurement types, so additional simulations are performed which augment GNSS with ground station observables and several methods of optical navigation, including celestial navigation, limb-finding (e.g., observations of the lunar horizon), and terrain relative navigation (TRN). TRN involves correlating simulated predicted images of the lunar surface with actual imagery; misalignments of landmarks identified in each image are translated into relay state updates. TRN is valuable as a measurement of the relay’s state relative to the Moon, especially during GNSS outages or after maneuvers. One-way Pseudorange and Doppler measurements from Earth-based ground stations are also simulated. The full set of observables is processed using autoNGC. These simulations make use of autoNGC and NC3m test units, a lab atomic clock, and a pulse-per-second (PPS) generation and distribution system. This combination of subsystems, and the hardware platforms used in this analysis, represents a PNT Instrument that could be flown on a lunar relay. Results from the hardware-in-the-loop simulations presented in this paper provide a preliminary assessment of the achievable navigation performance of this instrument concept. PNT Instrument performance is compared to the GPS-only performance, and a discussion is provided on the apparent merits and challenges of each measurement type.

Ben Ashman↗

NASA NTRS1998

LITE data processing

The Lidar In-space Technology Experiment (LITE) was a payload onboard the Space Shuttle Discovery as part of the September 1994 STS-64 mission. LITE successfully made both atmospheric and surface measurements at the 355 nm, 532 nm, and 1064 nm wavelengths [Winker et al., 1996]. At mission completion, nearly 45 Gbytes of data had been obtained. As the data were collected they were organized into three data sets. The largest of these data sets contained the single-shot full resolution digitized lidar signals for all three wavelengths. The other two data sets contained the instrument status data block (ISDB) and the quick look science data (QLSD). ISDB information was generated once per second and contained timing parameters and engineering data related to the health, status, and configuration of the instrument. The QLSD are 100shot averages of the 355 nm and 532 nm lidar signals and were generated at ten second intervals. As the LITE data were generated, they were output to low-rate and high-rate telemetry streams. The low-rate data contained the ISDB and QLSD. The lowrate data were transmitted to the ground by the Shuttle S-band system through the Tracking and Data Relay Satellite System (TDRSS). The high-rate data contained the full resolution digitized lidar signals for all three wavelengths, the ISDB, and QLSD. The high-rate data were transmitted in real time by the Ku-band system through TDRSS downlink to the LITE operations center at the Johnson Space Center (JSC). High-rate data were only obtained when the orbiter and TDRSS telemetry link coincided with the real-time operations of LITE. The low-rate data were backed up by a Shuttle recorder during periods when the telemetry link was unavailable, and the entire lowrate data stream was transferred to the ground over the course of the mission. A total of 53 hours of lowrate data and 45.5 hours of high-rate data were acquired. All of the LITE raw telemetry data have been archived at the NASA Langley Research Center (LaRC), and the LITE high-rate data have been processed to the level 1 data product. This paper describes the data processing steps required to convert the LITE raw, high-rate telemetry data to the LITE level 1 data product.

Powell, Kathleen A.↗

NASA NTRS2011

NASA Tech Briefs, January 2011

The topics include: 1) Distributed Aerodynamic Sensing and Processing Toolbox; 2) Collaborative Supervised Learning for Sensor Networks; 3) Hazard Detection Software for Lunar Landing; 4) Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme; 5) Network-Capable Application Process and Wireless Intelligent Sensors for ISHM; 6) Interface Supports Multiple Broadcast Transceivers for Flight Applications; 7) FPGA Sequencer for Radar Altimeter Applications; 8) Miniature Sapphire Acoustic Resonator - MSAR; 9) Process-Hardened, Multi-Analyte Sensor for Characterizing Rocket Plume Constituents; 10) SAD5 Stereo Correlation Line-Striping in an FPGA; 11) Hybrid Composite Cryogenic Tank Structure; 12) Nanoscale Deformable Optics; 13) Reliability-Based Design Optimization of a Composite Airframe Component; 14) Zinc Oxide Nanowire Interphase for Enhanced Lightweight Polymer Fiber Composites; 15) Plasma Igniter for Reliable Ignition of Combustion in Rocket Engines; 16) Wire Test Grip Fixture; 17) A Sub-Hertz, Low-Frequency Vibration Isolation Platform; 18) Carbon Nanofibers Synthesized on Selective Substrates for Nonvolatile Memory and 3D Electronics; 19) Nanoparticle/Polymer Nanocomposite Bond Coat or Coating; 20) High-Resolution Wind Measurements for Offshore Wind Energy Development; 21) Spring Tire; 22) Marsviewer 2008; 23) Mission Services Evolution Center Message Bus; 24) Major Constituents Analysis for the Vehicle Cabin Atmosphere Monitor; 25) Astronaut Health Participant Summary Application; 26) Adaption of the AMDIS Method to Flight Status on the VCAM Instrument; 27) Natural Language Interface for Safety Certification of Safety-Critical Software; 28) Cryogenic Caging for Science Instrumentation; 29) Wide-Range Neutron Detector for Space Nuclear Applications; 30) In Situ Guided Wave Structural Health Monitoring System; 31) Multiplexed Energy Coupler for Rotating Equipment; 32) Attitude Estimation in Fractionated Spacecraft Cluster Systems; 33) Full Piezoelectric Multilayer-Stacked Hybrid Actuation/Transduction Systems; 34) Active Flow Effectors for Noise and Separation Control; 35) Method and System for Temporal Filtering in Video Compression Systems; 36) Apparatus for Measuring Total Emissivity of Small, Low-Emissivity Samples; 37) Multiple-Zone Diffractive Optic Element for Laser Ranging Applications; 38) Simplified Architecture for Precise Aiming of a Deep-Space Communication Laser Transceiver; 39) Two-Photon-Absorption Scheme for Optical Beam Tracking; 40) High-Sensitivity, Broad-Range Vacuum Gauge Using Nanotubes for Micromachined Cavities; 41) Wide-Field Optic for Autonomous Acquisition of Laser Link; 42) Extracting Zero-Gravity Surface Figure of a Mirror; 43) Modeling Electromagnetic Scattering From Complex Inhomogeneous Objects; 44) Visual Object Recognition and Tracking of Tools; 45) Method for Implementing Optical Phase Adjustment; 46) Visual SLAM Using Variance Grid Maps; 47) Rapid Calculation of Spacecraft Trajectories Using Efficient Taylor Series Integration; 48) Efficient Kriging Algorithms; 49) Predicting Spacecraft Trajectories by the WeavEncke Method; 50) An Augmentation of G-Guidance Algorithms; 51) Comparison of Aircraft Icing Growth Assessment Software; 52) Silicon-Germanium Voltage-Controlled Oscillator at 105 GHz; 53) Estimation of Coriolis Force and Torque Acting on Ares-1; 54) Null Lens Assembly for X-Ray Mirror Segments; and 55) High-Precision Pulse Generator.

Source record↗

NASA NTRS1977

Satellite onboard feature classification using CCD's

In this paper discrete time analog signal processing applications of CCD's are discussed. The types of computations which can be performed in the CCD technology are discussed and compared with respect to their accuracy and required support hardware. The classification problem encountered in the reduction of multispectral data into features of terrestrial interest are decomposed into a CCD technology based hardware solution. The resulting system is readily implemented with existing and anticipated CCD devices. This system will be projected to spacecraft application.

Benz, H. F.↗

NASA NTRS2001

STS-105/Discovery/ISS 7A.1: Pre-Launch Activities, Launch, Orbit Activities and Landing

The crew of Space Shuttle Discovery on STS-105 is introduced at their pre-launch meal and at suit-up. The crew members include Commander Scott Horowitz, Pilot Rick Sturckow, and Mission Specialists Patrick Forrester and Daniel Barry, together with the Expedition 3 crew of the International Space Station (ISS). The Expedition 3 crew includes Commander Frank Culbertson, Soyuz Commander Vladimir Dezhurov, and Flight Engineer Mikhail Tyurin. When the astronauts depart for the launch pad in the Astrovan, their convoy is shown from above. Upon reaching the launch pad, they conduct a walk around of the shuttle, display signs for family members while being inspected in the White Room, and are strapped into their seats onboard Disciovery. The video includes footage of Discovery in the Orbiter Processing Facility, and some of the pre-launch procedures at the Launch Control Center are shown. The angles of launch replays include: TV-1, Beach Tracker, VAB, Pad A, Tower 1, UCS-15, Grandstand, OTV-70, Onboard, IGOR, and UCS-23. The moment of docking between Discovery and the ISS is shown from inside Discovery's cabin. While in orbit, the crew conducted extravehicular activities (EVAs) to attach an experiments container, and install handrails on the Destiny module of the ISS. The video shows the docking and unloading of the Leonardo Multipurpose Logistics Module (MPLM) onto the ISS. The deployment of a satellite from Discovery with the coast of the Gulf of Mexico in the background is shown. Cape Canaveral is also shown from space. Landing replays include VAB, Tower 1, mid-field, South End SLF, North End SLF, Tower 2, Playalinda DOAMS, UCS-23, and Pilot Point of View (PPOV). NASA Administrator Dan Goldin meets the crew upon landing and participates in their walk around of Discovery. The video concludes with a short speech by commander Horowitz.

Source record↗

NASA NTRS2004

Autonomous Flight Safety System

Autonomous Flight Safety System (AFSS) is an independent flight safety system designed for small to medium sized expendable launch vehicles launching from or needing range safety protection while overlying relatively remote locations. AFSS replaces the need for a man-in-the-loop to make decisions for flight termination. AFSS could also serve as the prototype for an autonomous manned flight crew escape advisory system. AFSS utilizes onboard sensors and processors to emulate the human decision-making process using rule-based software logic and can dramatically reduce safety response time during critical launch phases. The Range Safety flight path nominal trajectory, its deviation allowances, limit zones and other flight safety rules are stored in the onboard computers. Position, velocity and attitude data obtained from onboard global positioning system (GPS) and inertial navigation system (INS) sensors are compared with these rules to determine the appropriate action to ensure that people and property are not jeopardized. The final system will be fully redundant and independent with multiple processors, sensors, and dead man switches to prevent inadvertent flight termination. AFSS is currently in Phase III which includes updated algorithms, integrated GPS/INS sensors, large scale simulation testing and initial aircraft flight testing.

Ferrell, Bob↗

NASA NTRS2009

Lunar Landing Trajectory Design for Onboard Hazard Detection and Avoidance

The Autonomous Landing and Hazard Avoidance Technology (ALHAT) Project is developing the software and hardware technology needed to support a safe and precise landing for the next generation of lunar missions. ALHAT provides this capability through terrain-relative navigation measurements to enhance global-scale precision, an onboard hazard detection system to select safe landing locations, and an Autonomous Guidance, Navigation, and Control (AGNC) capability to process these measurements and safely direct the vehicle to a landing location. This paper focuses on the key trajectory design issues relevant to providing an onboard Hazard Detection and Avoidance (HDA) capability for the lander. Hazard detection can be accomplished by the crew visually scanning the terrain through a window, a sensor system imaging the terrain, or some combination of both. For ALHAT, this hazard detection activity is provided by a sensor system, which either augments the crew s perception or entirely replaces the crew in the case of a robotic landing. Detecting hazards influences the trajectory design by requiring the proper perspective, range to the landing site, and sufficient time to view the terrain. Following this, the trajectory design must provide additional time to process this information and make a decision about where to safely land. During the final part of the HDA process, the trajectory design must provide sufficient margin to enable a hazard avoidance maneuver. In order to demonstrate the effects of these constraints on the landing trajectory, a tradespace of trajectory designs was created for the initial ALHAT Design Analysis Cycle (ALDAC-1) and each case evaluated with these HDA constraints active. The ALHAT analysis process, described in this paper, narrows down this tradespace and subsequently better defines the trajectory design needed to support onboard HDA. Future ALDACs will enhance this trajectory design by balancing these issues and others in an overall system design process.

Paschall, Steve↗

NASA NTRS2010

Processing AIRS Scientific Data Through Level 3

The Atmospheric Infra-Red Sounder (AIRS) Science Processing System (SPS) is a collection of computer programs, known as product generation executives (PGEs). The AIRS SPS PGEs are used for processing measurements received from the AIRS suite of infrared and microwave instruments orbiting the Earth onboard NASA's Aqua spacecraft. Early stages of the AIRS SPS development were described in a prior NASA Tech Briefs article: Initial Processing of Infrared Spectral Data (NPO-35243), Vol. 28, No. 11 (November 2004), page 39. In summary: Starting from Level 0 (representing raw AIRS data), the AIRS SPS PGEs and the data products they produce are identified by alphanumeric labels (1A, 1B, 2, and 3) representing successive stages or levels of processing. The previous NASA Tech Briefs article described processing through Level 2, the output of which comprises geo-located atmospheric data products such as temperature and humidity profiles among others. The AIRS Level 3 PGE samples selected information from the Level 2 standard products to produce a single global gridded product. One Level 3 product is generated for each day s collection of Level 2 data. In addition, daily Level 3 products are aggregated into two multiday products: an eight-day (half the orbital repeat cycle) product and monthly (calendar month) product.

Granger, Stephanie↗

DOE OSTI2026

VECTOR Phase 1 Dataset: CAV Trajectory and Energy Consumption Records

This dataset contains benchmark experimental data from Phase 1 of the VECTOR project, focusing on the energy impact of CAV hardware components. The dataset includes vehicle trajectory data (speed and position) and corresponding energy consumption records collected from a CAV platform equipped with lidar, cameras, onboard computation units, and communication modules. The primary objective is to quantify the baseline energy consumption attributable to sensing and computing systems, independent of any advanced cooperative control strategies. During experiments, the leading vehicle followed a predetermined velocity profile, and the following CAV mirrored this trajectory using a basic car-following control to ensure consistent driving behavior. This setup enables a reliable benchmark for assessing the energy cost introduced by onboard CDA hardware (e.g., lidar and GPU-based processing). The dataset is essential for evaluating energy baselines and supports future comparative studies involving additional cooperative strategies. ![system img](system.png) ![vector img](vector.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗