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All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Auto Tracking Antenna Platform

The present invention is an auto tracking antenna platform upon which multiple antenna elements can be mounted to track a common moving object. The antenna tracking platform generally comprises a bottom pedestal enclosing a rotary azimuth actuator for controlled-rotary motion about the single vertical (z) axis, and an upper multi-tier framework housing a horizontal antenna-mounting beam pivotally supported for rotation about a horizontal (x) axis, and a drive assembly for direct-drive rotation of the antenna-mounting bar. Antenna elements are mounted along the horizontal mounting bar and the feeds routed through the azimuth actuator. This enables the use of fiber optic rotary joints or slip rings to pass data and video, instead of RF (waveguide) rotary joints which are required to pass high power RF signals.

Sanner, Kurt G↗
NASA NTRS2018

Impairment of Human Ocular Tracking with Low-Dose Alcohol

Previous studies have documented adverse effects of alcohol on oculomotor performance. For example, moderate-dose alcohol (yielding a Blood Alcohol Concentration or BAC of 0.04-0.1%) has been shown to decrease steady-state pursuit gain (Fransson et al., 2010, Clin Neurophysiol, 121(12): 2134; Moser et al., 1998, J Neurol, 245(8): 542; Roche & King, 2010, Psychopharmacology, 212(1): 33), to increase saccade latency (Moser et al., 1998, J Neurol, 245(8): 542; Roche & King, 2010, Psychopharmacology, 212(1): 33), to decrease peak saccadic velocity (Fransson et al., 2010, Clin Neurophysiol, 121(12): 2134; Roche & King, 2010, Psychopharmacology, 212(1): 33), and to increase the frequency of catch-up saccades (Moser et al., 1998, J Neurol, 245(8): 542). Here, we administered two doses of ethanol on different days, yielding moderate (0.06%) and low (0.02%) levels of initial BAC, to examine the effects on human ocular tracking over BACs ranging from 0.00 to 0.07%. Twelve subjects (8 females) participated in a 5-day study. Three days of at-home measurements of daily activity and sleep were monitored, followed by two laboratory days where, ~5 hours after awakening, we administered one of the two possible single doses of alcohol. Using a previously published paradigm (Liston & Stone, 2014, J Vis, 14(14): 12), we measured oculomotor performance multiple times throughout the day with three pre-dosing baseline runs and bi-hourly post-dosing test runs until the subject recorded a BAC of 0.00% for two hours. BAC was measured before each run using an Alco-Sensor IV breathalyzer (Intoximeters, Inc., St. Louis, MO). For each of the oculometric measures, for each subject, we computed the within-subject % deviation for each test run from their baseline averaged across their three pre-dosing runs. We then averaged the data across subjects in 0.01% BAC bins. Finally, we used linear regression to compute the slope and x-intercept (threshold) of the mean binned % deviation as a function of BAC. We found that pursuit initiation was impaired at very low BAC levels, with significant (p < 0.002) linear trends in latency (+1.3%/0.01%BAC) and initial acceleration (-4.6%/0.01%BAC) with extrapolated absolute thresholds at or below 0.01% BAC. We also found that steady-state tracking was impaired showing significant (p < 0.002) linear trends in gain (- 3.8%/0.01%BAC) and catch-up saccade amplitude (+9.1%/0.01%BAC), again with extrapolated absolute thresholds around 0.01% BAC. We also found a significant (p < 0.02) increase in pursuit direction noise (+9.8%/0.01%BAC) with an extrapolated absolute threshold below 0.01% BAC. Many aspects of ocular tracking are impaired in a dose-dependent manner beginning at a BAC level around 0.01%, with significant effects at levels lower than previously reported and up to 8-times lower than the legal limit for driving in most states.

human performance↗
NASA NTRS2018

Analyzing the Relationship between Tracking and Covariance for Satellite Collision Avoidance

This presentation will display statistical analysis performed with over 150,000 Conjunction Data Messages (CDMs) received for the Earth Observing System (EOS) Aqua, Aura and Terra satellites within the three year period of March 2015 through February 2018. The analysis performed demonstrates the relationship between the covariance of a secondary object and the amount of tracks it receives, as a function of Time to Closest Approach (TCA). The examination indicates that higher tracking frequencies result in lower uncertainties inclusively from the time the data is screened up until TCA. Further investigation displays the impacts of how additional observations of secondary objects lower their position uncertainties and what period of time prior to TCA convergence of the covariance can be expected depending on how well the objects are tracked. Data points from several recent EOS High Interest Events (HIEs) are used to exemplify this analysis.

Collision Avoidance↗
NASA NTRS2019

The Solar Neutron TRACking (SONTRAC) Instrument for the Detection of Fast Neutrons

The detection of fast neutrons has important applications in several fields including solar, Geospace and planetary physics. Neutrons are challenging to detect and measurements of them typically suffer from high background rates. High-energy neutrons (>50 MeV) pose even more challenges, because the traditional double-scatter technique based on a time-of-flight (ToF) measurement is limited by short flight paths and small detector sizes characteristic of small satellite platforms. It is now possible to perform high-energy neutron measurements inside a large monolithic detector by imaging the recoil proton tracks, thus eliminating the need for a measure of the time-of-flight. The concept is based on a spectrometer assembled from numerous thin hydrogenous scintillating fibers that allow ionization track imaging. Fine grained readout is now possible with arrays of1-mm pitch silicon photomultipliers (SiPMs). The Solar Neutron TRACking (SONTRAC) instrument, equipped with scintillating fibers readout with SiPMs sensors, provides high-resolution, fine grained, imaging of fast (between 20-200 MeV) neutron scatters in a compact, low-power design ideal for small satellite(and aircraft) platforms. We discuss below applications of this technology and performance characteristics of the prototype SONTRAC instrument.

de Nolfo, Georgia A.↗
NASA NTRS2020

Customer Avionics Interface Development and Analysis Development Activity Tracking System

The Customer Avionics Interface Development and Analysis (CAIDA) Development Activity (DA)Tracking System is a Microsoft Access Database that tracks, organizes, and analyzes data about DAs from the work management tool. The CAIDA DA Tracking System takes data imported from the work management system. Once in the system, the data is filtered to generate each DA’s Asset. From there, many different queries are run on the data and their results are imported into forms to create graphs to get and display a wide range of metrics. These graphs are automatically updated with each new data import and over time. They can be easily exported for use in presentations, documents, etc. Additionally, the system is highly customizable and can be added upon to include more data members, generate new graphs, and much more.

Tara Conti↗
NASA NTRS2018

Towards a Unified View of Modeling and Programming (ISoLA 2018 Track Introduction)

The article provides an introduction to the track: Towards a Unified View of Modeling and Programming, organized by the authors of this paper as part of ISoLA 2018: the 8th International Symposium On Leveraging Applications of Formal Methods, Verification and Validation. A total of 19 researchers were invited to present their views on the two questions: what are the commonalities between modeling and programming languages, and should we strive towards a unified view of modeling and programming? The idea behind the track, which is a continuation of a similar track at ISoLA 2016, emerged as a result of experiences gathered in the three fields: formal methods, model-based software engineering, and programming languages, and from the observation that these technologies share a large common part, to the extent where one may ask, does the following equation hold: modeling = programming?

Steffen, Bernhard↗
NASA NTRS2018

Synthetic Tracking on a Small Telescope

Synthetic tracking uses high speed (up to 10 Hz) low noise (<2e-) large format sensors ~16 Mpix along with a multi-vector shift/add algorithm that coadds multiple image frames to increase the signal to noise ratio (SNR) needed to detect (if present) multiple moving objects in the field of view (FOV). We published the application of synthetic tracking to look for asteroids in 2014 (Shao 2014), but recently have applied it more as well to Earth orbiting objects. We have begun testing the data processing graphical processing unit (GPU) array with a small telescope, a 28 cm Celestron RASA telescope and a low cost low noise 16 Mpix CMOS camera at a dark site in California. This system is now operational with a 2 sqdeg FOV and a limiting magnitude between ~16-17.5 stellar magnitudes (mag) depending on a number of observational parameters for short integration times. The instrument can be used to search for NEOs, where we use much longer integration times to get sensitivity ~ 20.5 mag (at new moon). Synthetic tracking provides significant improvements in both sensitivity and astrometric accuracy.

Turyshev, Slava G.↗
NASA NTRS2021

Detection and Tracking of Aircraft in the Far-Field from Small Unmanned Aerial Systems

Onboard far-field aircraft detection is needed for safe non-cooperative traffic mitigation in autonomous small Unmanned Aerial System (sUAS) operations. Machine vision systems, based on standard optics and visible light detectors, possess the ideal size, weight, and power (SWaP) requirements for sUAS. This work presents the design and analysis of a novel aircraft detection and tracking pipeline based on optical sensing alone. Key contributions of the work include a refined range inequality model based on sensing and detection with FAA well-clear separation assurance distances between aircraft in mind, a detector fusion method to maximize the benefit of two image detectors, and a comparative analysis of Linear Kalman-filtering and Extended Kalman-filtering to seek optimal tracking performance. The pipeline is evaluated offline against multiple intruder platforms, using two types of flight encounters: multirotor sUAS vs. fixed-wing sUAS and multirotor sUAS vs. general aviation(GA)plane. Analysis is restricted to the rate-limiting head-on and departing collision volume cases vertically separated for safety. Results indicate that it is feasible to use the proposed optical spatial-temporal tracking algorithm to provide adequate alerting time to prevent penetration of well-clear separation volumes for both sUAS and GA aircraft.

Unmanned Aerial System↗
NASA NTRS2020

Lunar Magnetic Field Models From Lunar Prospector and SELENE/Kaguya Along-Track Magnetic Field Gradients

We use L1-norm model regularization of |Br| component at the surface on magnetic monopoles bases and along-track magnetic field differences alone (without vector observations) to derive high quality global magnetic field models at the surface of the Moon. The practical advantages to this strategy are the following: monopoles are more stable at closer spacing in comparison to dipoles, improving spatial resolution; L1-norm model regularization leads to sparse models which may be appropriate for the Moon which has regions of localized magnetic field features; and along-track differences reduce the need for ad-hoc external field noise reduction strategies. We examine also the use of Lunar Prospector and SELENE/Kaguya magnetometer data, combined and separately, and find that the Lunar Prospector along-track vector field differences lead to surface field models that require weaker regularization and, hence, result in higher spatial resolution. Significantly higher spatial resolution (wavelengths of roughly 25–30 km) and higher amplitude surface magnetic fields can be derived over localized regions of high amplitude anomalies (due to their higher signal-to-noise ratio). These high-resolution field models are also compared with the results of Surface Vector Mapping approach of Tsunakawa et al. (2015, https://doi.org/10.1002/2014JE004785). Finally, the monopoles- as well as dipoles-based patterns of the Serenitatis high amplitude magnetic feature have characteristic textbook patterns of Br and Bθ component fields from a nearly vertically downwardly magnetized source region and it implies that the principal source of the anomaly was formed when the region was much closer to the north magnetic pole of the Moon.

D. Ravat↗
NASA NTRSDate not supplied

Saccade-Pursuit Coordination during Ocular Tracking across Different Impairment Sources

Purpose: Sleep loss and alcohol have been shown to impair smooth pursuit and its underlying visual motion processing in humans. This study examines the saccadic compensation for this poor pursuit. Methods: Using an established behavioral ocular-tracking paradigm (Liston & Stone, 2014, doi:10.1167/14.14.12), we examined the dose-response of ground lost (pursuit deficit integrated across our 300-ms steady-state tracking interval) and ground gained (increased saccadic response harnessed to compensate)across three separate studies – acute low-dose alcohol administration (LDA; n = 16 subjects), acute sleep loss(ASL; n = 12), and chronic sleep restriction (CSR; n = 12). We computed dose-responses as the linear regression slopes of ground lost and gained across treatment dose (% blood alcohol concentration [BAC] or hours awake). For the CSR study, we computed the mean effect for a single dose (5-hours nightly sleep for 1week). Results: For LDA, there was significantly increased ground lost (P < 0.001) and gained (P < 0.001) with increased %BAC. In addition, the dose-responses were not significantly different (P = 0.35), indicating effectively complete saccadic compensation due to significant increases in both saccadic rate (P < 0.05) and amplitude (P < 0.001). For ASL, there was significantly increased ground lost with time awake (P < 0.01),however ground gained was significantly lower (P < 0.001), indicating, at best, incomplete compensation due to a significant increase in saccadic rate (P < 0.001) but not amplitude (P = 0.10). With CSR, pursuit was again significantly impaired (P < 0.05), with saccadic rate significantly increased (P < 0.05) but, surprisingly, amplitude was significantly decreased (P < 0.05), effectively eliminating ground gained (P = 0.90). Conclusions: Our analyses show that LDA, ASL, and CSR affect tracking differently, suggesting the involvement of different brain pathways. With LDA, the effect appears largely due to the cortical impairment of visual motion processing with largely healthy brainstem and mid-brain responses (driving effective saccadic compensation). ASL and CSR however appear to affect both cortical and sub-cortical pathways, with at best partial saccadic compensation. Lastly, CSR is associated with an additional compromise due to a maladaptive decrease in saccade amplitude.

saccade↗
NASA NTRSDate not supplied

Dense Feature Tracking of Atmospheric Winds with Deep Optical Flow

Atmospheric winds are a key physical phenomenon impacting natural hazards, energy transport, ocean currents, large-scale circulation, and ecosystem fluxes. Observing winds is a complex process and presents a large gap in NASA’s Earth Observation System. Atmospheric motion vectors (AMVs) aim to fill this gap by making numerical estimates of cloud movement between sequences of multi-spectral satellite images, tracking clouds and water vapor. Recent imaging hardware and software advancements have enabled the use of numerical optical flow techniques to produce accurate and dense vector fields outperforming traditional methods. This work presents WindFlow as the first machine learning based system for feature tracking atmospheric motion using optical flow. Due to the lack of large-scale satellite-based observations, we leverage high-resolution numerical simulations from NASA's GEOS-5 Nature Run to perform supervised learning and transfer to satellite images. We demonstrate that our approach using deep learning based optical flow scales to ultra-high-resolution images of size 2881x5760 with less than 1 m/s bias and 2.5 m/s average error. Four network and learning architectures are compared and it is found that recurrent all-pairs field transforms (RAFT) produces the lowest errors on all metrics for wind speed and direction. Results on held out numerical outputs shows RAFT's good performance in each of the spatial, temporal, and physical dimensions. A comparison between WindFlow and an operational AMV product against rawinsonde observations show that RAFT transfers across simulations and thermal infrared satellite observations. This work shows that machine learning based optical flow is an efficient approach to generating robust feature tracking for AMVs consistently over large regions.

Atmospheric winds↗
NASA NTRSDate not supplied

Development and Application of NASA SPoRT’s DustTracker-AI Model for Real-Time Identification and Tracking of Dust in Geostationary Satellite Imagery

The NASA Short-term Prediction Research and Transition (SPoRT) Center developed the DustTracker-AI model for identifying and tracking dust in NASA/NOAA Geostationary Operational Environmental Satellite (GOES) imagery in a real-time framework. A training dataset consisting of day and night dust cases was gathered over the southwestern consisting of 115 distinct images and over a million dust pixels and 256 million no dust pixels. The dataset was separated into training (60%), testing (20%), and validation (20%). A simple random forest machine learning model was developed originally to overcome the problem of night-time dust detection and has been expanded to a comprehensive day/night model for dust identification and tracking. This physically-based machine-learning approach uses NASA/NOAA GOES-16 Advanced Baseline Imager infrared imagery as inputs to the model. The model probability of dust output achieves an Area-Under-Curve (AUC) of 0.97 with a standard deviation of 0.04 for dust cases. For images with dust present, the model correctly labels 85% of dust pixels for all dust images in the validation data set. In conjunction with developing the machine-learning model, the NASA Short-term Prediction Research and Transition Center (SPoRT) partnered with NOAA National Weather Service forecast offices to evaluate the model for utility in weather forecasting operations during the 2021 and 2023 late winter-spring seasons. Preliminary evaluation has indicated the majority of forecasters described the DustTracker-AI probabilities as having added confidence to interpreting the Dust RGB and other satellite products to objectively assess the dust extent and trends and increased the amount of time the dust plume could be tracked into the night as compared to use of the Dust RGB. More recently, SPoRT tested small scale events associated with thunderstorm outflow and burn scars to determine the model’s ability to capture local events. This presentation highlights design of the model, validation/evaluation of model performance, and example cases collected during end user product assessments.

Connor H Welch↗
NASA NTRS2022

Learning-Based State-Dependent Coefficient Form Task Space Tracking Control of Soft Robot

n this paper, a data-driven modeling and control framework is developed for task space control of a soft robot gripper which consists of four individual soft fingers. Each of the four fingers is modeled as a manipulator with high degrees of freedom. The corresponding task space dynamics of the manipulator are derived using a rigid-link approximation of the continuum manipulator. A neural network approach is used to learn the derived dynamics in State Dependent Coefficient (SDC) form. Using the learned SDC matrices, an asymptotically stable optimal closed-loop tracking controller which is based on solving the State Dependent Riccati Equation (SDRE) is derived. The model learning and trajectory tracking controller is implemented on an open source Soft Motion (SoMo) platform simulating the soft gripper motion and corresponding tracking results are presented.

Rounak Bhattacharya↗
NASA NTRS2024

LED Intensity Decay Particle Tracking Velocimetry

A particle tracking velocimetry (PTV) system is demonstrated that eliminates the need for expensive lasers and cameras, and encodes the particle tracks with a known intensity variation which allows for high-resolution particle velocity and directionality determination. Using a light-emitting diode (LED) as the light source significantly reduces cost and allows for eye-safe operation, in comparison to traditional laser systems. The intensity variation control is passive, being dictated by the capacitance discharge rate in the LED pulsing circuit. The decay rate can be adjusted with the LED circuit capacitors/resistors, while the duration of the decay can be adjusted with the LED trigger pulse width. Because the intensity decay illumination is controlled by the LED, a single long exposure camera image is used to acquire images of the particle streaks, and there is no need for a more complicated and costly double-pulsing camera. With the intensity coding of the light, a single-camera/ single-LED system can identify particles entering of leaving the illumination plane, but cannot discern the direction toward or away from the camera. By using a two-color system, three-dimensionality of the particle tracks can be determined.

Joshua M Weisberger↗
NASA NTRSDate not supplied

Analytical Calculation of the Energy Deposition Spectrum and Average Dose to A Spherical Target By Ion Tracks at All Impact Parameters

Microdosimetry is used to estimate quality factors for risk assessment in radiation protection [1] and to quantify relative biological effectiveness (RBE) for treatment planning of hadron radiation therapy [2]. Radiation physics has shown that energetic ions deposit energy in a complex manner: the track structure. The Local Effect Model (LEM) [3] is often used as an approximation to describe this complex track structure. We have previously calculated an analytical expression for the dose to a spherical target fully located in the penumbra of an ion track using the LEM [4]. In this work, the calculation has been extended to all impact parameters. Furthermore, using these analytical expressions, it was possible to obtain the theoretical energy deposition spectra to a sphere irradiated uniformly.

I Plante↗
NASA NTRSDate not supplied

New Tools for Automating Arcjet Sample Recession Tracking and Analysis

Arcjet Computer Vision (arcjetCV) has been significantly upgraded to enhance accuracy and performance in tracking material recession and shock-material standoff in test videos. These improvements include integrating new machine learning models, developing a specialized edge detection class, and incorporating a more comprehensive training dataset. These upgrades have refined the software’s ability to automate time-resolved recession tracking, making it more precise and reliable for analyzing complex physical processes. In parallel, a new tool called STARscan (Spatial Targeting and Alignment Rig for Scanning) is being developed to capture detailed 3D surface data before and after testing. By comparing these pre- and post-test scans with arcjetCV’s automated video analysis results, users can achieve a more comprehensive assessment of material recession. This method enables cross-validation of results, improving confidence in the analysis of tested materials. The expanded capabilities of arcjetCV have been successfully demonstrated on videos from various facilities, including the NASA Ames arcjets, UIUC’s PlasmatronX, and the VKI Plasmatron. It has been adopted as a new standard for in-situ recession tracking by the Mars Sample Return Project and Orion. ArcjetCV’s improved efficiency and accuracy are critical for reducing testing uncertainties and validating heatshield material performance under extreme conditions. The software’s user-friendly graphical interface ensures ease of use, enabling seamless processing and precise analysis of arcjet videos, providing deeper insights into material behavior in hypersonic environments. ArcjetCV is now available on both PyPI and Conda, allowing easy installation via "pip install arcjetCV" or through the Conda package manager, ensuring broad accessibility and streamlined deployment for users across various platforms.

Ablation↗
NASA NTRS2024

Lightning and Radar Measures of Mixed Phase Updraft Variability in Tracked Storms During the TRACER Field Campaign in Houston, Texas

Properties of 7488 thunderstorms are summarized for June-September 2022 during the TRACER field campaign Houston, Texas using polarimetric weather radar and VHF 3D lightning mapping array data. Automated tracking of storms linked each instrument’s measurements to a data-defined, time-evolving storm footprint. Within each storm, the depth and magnitude of episodic columns of radar differential reflectivity and specific differential phase quantified the prevalence of updrafts that activated mixed-phase precipitation pathways. Lightning measurements further distinguished the degree of rimed precipitation formation: the fraction of tracks with lightning varied from day to day, and cells with lightning had stronger polarimetric columns. Track-level correlation of lightning flash rate with radar polarimetric measures had substantial spread, showing that lightning provides an additional signal of mixed-phase precipitation processes that can complement future studies of thermodynamic and aerosol controls on cloud microphysics in the Houston region.

TRACER field campaign↗
DOE OSTI2021

Evaluation of Models and Measurements to Estimate Solar Radiation for 1-Axis Tracking Modules at NREL's SRRL: Preprint

Solar radiation reaching photovoltaic (PV) modules on a 1-axis tracking system can be measured by reference cells or thermopiles. The former is often biased due to the reflection of solar radiation by the glass cover of the PV. The uncertainty can be moderated by applying a correction factor, as a function of solar incident angle and the refractive index of the glass, to the measurement. On the other hand, solar radiation on the inclined PV panels can be computed by transposition models using global horizontal irradiance (GHI) observations from thermopiles. This study examines the models and measurements to estimate solar radiation for 1-axis tracking modules at National Renewable Energy Laboratory’s (NREL’s) Solar Radiation Research Laboratory (SRRL). The 1-minute plane-of-array (POA) irradiances in 2019 are computed using the observed GHIs and a transposition model developed by Perez et al. The POA irradiances are compared with the observation by an IMT reference cell and a Kipp & Zonen CM Pyranometer 22 (CMP22) thermopile. For SRRL’s 1-axis tracking system with the annual solar energy of 2323.9 kWh/m2, the POA irradiance is overestimated by ~70 kWh/m2 using the transposition model. This bias is reduced by more than 50% using the IMT measurements calibrated by a correction factor for a PV surface of antireflection coated glass.

41 EE - Solar Energy Technologies Office (EE-4S)↗