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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Uncrewed Aerial Vehicles and Systems Support Safer Aeronautics Research

Area-I partnered with the NASA SBIR/STTR program to develop Uncrewed Aircraft Systems (UASs) that supported advanced aeronautics research. The company’s developments with NASA culminated in the Prototype-Technology Evaluator and Research Aircraft (PTERA), a versatile UAS enabling low-risk flight experiments that are safer than piloted tests and more dynamic than wind tunnel testing. From the initial seed funding from NASA, Area-I continued to hone its abilities in UAS development, finding success with the Department of Defense and leading to the company’s acquisition by Anduril Industries—a $4.6 billion defense technology company—in 2021.

Bruce R Cogan↗
NASA NTRS2020

CyberGAN: Generating High-fidelity Cybersecurity Data With Generative Adversarial Networks

Machine learning for cyber defense offers the promise of detecting adversarial activity against the ground data systems managing critical space assets. A fundamental challenge facing machine learning research in cybersecurity is the lack of high-fidelity, shareable datasets for robust evaluation and testing of machine learning-based solutions. High-fidelity, real-world datasets are necessary for reliable benchmarking of nominal system behavior and malicious activity. Unfortunately, such realistic datasets of both nominal and adversarial activity are rarely shared publicly by data owners due to security and privacy concerns. Besides, the available adversarial data is sparse, which makes training models on malicious activity much harder. This situation has impeded and continues to impede the research and successful adoption of machine learning methods for cyber defense. Researchers have dealt with this problem by generating data within a low-fidelity lab environment, using classified and thus unshareable datasets, or downloading low-fidelity public datasets made available by others. We propose an innovative solution to the problem by employing machine learning methods to generate high-fidelity data. Specifically, we propose the use of Generative Adversarial Networks (GANs) to generate high-fidelity data for cybersecurity purposes. GANs have found successful image processing and natural language applications, but have not yet been investigated for cyber data generation. Our proposed approach first involves training the `discriminator' network of the GAN with a sample of real-world data consisting of malicious and nominal samples. We then use the `generator' network to generate new high-fidelity data samples consisting of an appropriate mix of malicious and nominal activity. We demonstrate applications of our architecture by generating high-fidelity cybersecurity data containing both malicious and nominal samples. We thoroughly evaluate the fidelity of our generated data using heuristics and evaluate its usefulness for machine learning applications using three different datasets. Overall, our approach results in high-fidelity, shareable datasets.

Zhang, Yuening↗
NASA NTRSDate not supplied

Technology Gaps for Rapid Response Missions to Near-Earth Objects, Interstellar Objects, and Long-Period Comets

The last several years has seen the discovery of the first two interstellar objects, the first ever planetary defense mission, recommendation for a rapid reconnaissance planetary defense demonstration, and the continued emergence of a robust commercial small satellite industry. The present and near future also consist of emerging next-generation observatories including Vera Rubin and NEO Surveyor. These seemingly disparate events converge in the context of rapid response: a cross-cutting capability that would enable NASA and the international community to quickly discover and respond to an emerging target such as a near-Earth object (NEO), interstellar object (ISO), or long-period comet (LPC) either for rapid characterization of a potential threat, or revolutionary science that would inform early solar system formation and evolution. In late October 2022, subject matter experts gathered at the California Institute of Technology in a week-long workshop sponsored by the Keck Institute for Space Studies (KISS) to address enabling mission concepts for rapid response and key technology gaps (final report in progress). These experts concurred that the ability to respond on the order of a few months is necessary for rapid in-situ characterization of a NEO, ISO, or LPC, and that while there are several technology gaps, this capability could be realized in the near future.

Technology gaps↗
NASA NTRSDate not supplied

Consequences of Asteroid Characterization on the State of Knowledge about Inferred Physical Properties and Impact Risk

Physical characteristics of Near-Earth Objects (NEOs) are essential inputs to planetary defense assessments. The size, density, and strength of an NEO are critical inputs to modeling behavior during atmospheric entry as well as assessing the risk of impact. Similarly, knowledge of the physical characteristics of an object are necessary to evaluate the probable result of a mitigation mission. Usually, these attributes cannot be directly measured, but increasingly sophisticated methods have been developed to infer physical properties from related measurements of asteroids, meteors, and/or meteorites. Fortuitously, some of these measurements have been obtained for enough NEOs to elucidate the distribution of values across the sampled population. However, the situation becomes more challenging when considering a specific asteroid, since it is unlikely that all the relevant measurements have been made for any given object. We have developed a Bayesian network that can combine available information about a particular NEO with knowledge of the larger population to infer probabilistic values and uncertainties for physical characteristics of interest. Distributions of asteroid population albedos, taxonomic classes, and macroporosities, along with meteorite density distributions and associations between taxonomic classes and meteorite classes, provide the default distributions for the network’s parameter nodes. The inference network links parameters for each virtual asteroid either deterministically or probabilistically as appropriate, and eliminates any unphysical combinations of parameters. Within the context of planetary defense, our Bayesian network can be used to constrain the ranges of likely impactor properties, which can subsequently reduce the uncertainty in modelling of atmospheric entry, mitigation efficacy, and impact risk assessment. When additional measurements become available for a specific object, the network incorporates those measurements to generate virtual asteroids with property distributions that are consistent with the measurements. We will use the 2023 PDC scenario to demonstrate how the inference network can be combined with plausible characterization measurements to refine the state of knowledge about likely combinations of physical parameters and the resulting impact risk.

risk assessment↗
NASA NTRSDate not supplied

Consequences of Asteroid Characterization on the State of Knowledge about Inferred Physical Properties and Impact Risk

Physical characteristics of Near-Earth Objects (NEOs) are essential inputs to planetary defense assessments. The size, density, and strength of an NEO are critical inputs to modeling behavior during atmospheric entry as well as assessing the risk of impact. Similarly, knowledge of the physical characteristics of an object are necessary to evaluate the probable result of a mitigation mission. Usually, these attributes cannot be directly measured, but increasingly sophisticated methods have been developed to infer physical properties from related measurements of asteroids, meteors, and/or meteorites. Fortuitously, some of these measurements have been obtained for enough NEOs to elucidate the distribution of values across the sampled population. However, the situation becomes more challenging when considering a specific asteroid, since it is unlikely that all the relevant measurements have been made for any given object. We have developed a Bayesian network that can combine available information about a particular NEO with knowledge of the larger population to infer probabilistic values and uncertainties for physical characteristics of interest. Distributions of asteroid population albedos, taxonomic classes, and macroporosities, along with meteorite density distributions and associations between taxonomic classes and meteorite classes, provide the default distributions for the network’s parameter nodes. The inference network links parameters for each virtual asteroid either deterministically or probabilistically as appropriate, and eliminates any unphysical combinations of parameters. Within the context of planetary defense, our Bayesian network can be used to constrain the ranges of likely impactor properties, which can subsequently reduce the uncertainty in modelling of atmospheric entry, mitigation efficacy, and impact risk assessment. When additional measurements become available for a specific object, the network incorporates those measurements to generate virtual asteroids with property distributions that are consistent with the measurements. We will use the 2023 PDC scenario to demonstrate how the inference network can be combined with plausible characterization measurements to refine the state of knowledge about likely combinations of physical parameters and the resulting impact risk.

risk assessment↗
NASA NTRS2023

Commercial Smallsat Data Acquisition Program: Airbus U.S. Synthetic Aperture Radar Quality Assessment Summary

Quality assessment of the Airbus X-band Synthetic Aperture Radar (SAR) satellite products was conducted by the Commercial Smallsat Data Acquisition (CSDA) program’s radar subject matter experts, following the Joint NASA/ESA (European Space Agency) assessment draft guidelines. All three Airbus SAR spacecraft (TerraSAR-X, TanDEM-X, and PAZ) are based on the TerraSAR-X platform, and each have an active phased array antenna that is 4.8 x 0.7 m in the along-track and cross-track dimensions, respectively. TerraSAR-X and TanDEM-X are in a helical orbit, creating a bistatic imaging geometry, in addition to being capable of independent monostatic observations. The PAZ mission follows TerraSAR-X and TanDEM-X in the same 11-day orbit with a 5.5-day lag. TerraSAR-X and TanDEM-X are designed, developed, and operated through a Public-Private Partnership, while PAZ is a dual-use mission (civil and defense agencies), funded and owned by the Spanish Ministry of Defense and managed by Hisdesat (Hisdesat Servicios Estratégicos, S.A.), a Spanish private communications company. The assessment presented in this document is divided into two main parts: documentation review and the assessment of test datasets. The documentation review in sections 2.1 through 2.4 includes the assessment of the Airbus documentation provided to the CSDA evaluation team. The grading of these documents is given in columns 1-4 of the maturity matrix shown in section 1.1. Section 2.5 summarizes the evaluation performed by NASA using the data purchased through the CSDA program. The grading for this is given in the last column of the maturity matrix. Section 3 provides more detailed explanations on the methods and the results of the data analysis performed by NASA. Only the documents provided by Airbus for the evaluation were considered for the review. Additional documentation with more detailed description of the calibration and validation procedures may be available online but were not considered for this evaluation. The product information provided in the available documentation (RD-1, RD-2) and the product metadata together provided adequate information to work with the data. The product details in the metadata included the required information to work with the data in the common XML file format. Metrological traceability documentation was not provided to CSDA. All relevant characterization of the SAR system and data were provided, and the metadata include all relevant ancillary information. Documentation provided to CSDA included limited pre-flight and post-launch calibration information.

Batuhan Osmanoglu↗
NASA NTRSDate not supplied

Contracting Quality Early in the Lifecycle Using AS9145 Data Deliverables

Development schedules and a highly dynamic supply chain are a challenge to developers of complex systems produced at low volume. Flaws in designs, parts and materials availability problems, poor manufacturability, and a lack of knowledge about critical items and key process attributes can be realized well before traditional second-party quality assurance activities begin. Supplier audits and product inspections may have little mitigating effect once these foundational problems have been realized. Their impacts can be significant lifecycle disruption, cost overruns, inability to deliver to plan, and even project cancellation. AS9145, Requirements for Advanced Product Quality Planning and Production Part Approval Process, can be used to drive quality engineering practices into early development lifecycles to significantly reduce this late-cycle risk and to reduce the cost of quality overall. Since its initial publication in 2016, it has had very limited adoption by the DoD, no adoption by NASA, and sparse adoption in the aerospace and defense supply chain. A task group within the Aerospace Industries Association's (AIA) Joint Strategic Quality Council (JSQC) identified that both acquirers and suppliers see as AS9145 as a cost-adder and are hesitant to use it as an alternative to late-stage-heavy quality assurance approaches. A lack of prior use creates large capability gaps in request-for-proposal (RFP) teams, proposal teams, suppliers’ quality management systems (QMS), and in experienced personnel executing the early lifecycle approach. To create a more realizable on-ramp for using AS9145 in the space and defense sectors, the AIA JSQC task team created five deliverable requirements descriptions (DRDs) that can be used in a contract to begin to engage both parties in early lifecycle quality engineering and quality assurance activities, that reduce exposure to late-stage cost and schedule collapse due to unidentified risks in design, supply chain, and manufacturability. These DRDs drive the parties to engage in planning and analysis discussions early on to understand what production risks can be known and how they will focus resources based on safety criticality and the key elements of design and construction. The suppliers and acquirers who will produce the data and information required by the DRD will be able to incrementally evolve their QMS and the acquirer will incrementally be able to track and understand the benefits of cost shifting from late to early development phases. A white paper describing this approach and the five recommended DRDs will be published by the AIA in late 2024 or early 2025.

Jeannette Plante↗
NASA NTRS1997

Vigilante: Ultrafast Smart Sensor for Target Recognition and Precision Tracking in a Simulated CMD Scenario

VIGILANTE is an ultrafast smart sensor testbed for generic Automatic Target Recognition (ATR) applications with a series of capability demonstration focussed on cruise missile defense (CMD). VIGILANTE's sensor/processor architecture is based on next-generation UV/visible/IR sensors and a tera-operations per second sugar-cube processor, as well as supporting airborne vehicle. Excellent results of efficient ATR methodologies that use an eigenvectors/neural network combination and feature-based precision tracking have been demonstrated in the laboratory environment.

TARGET RECOGNITION↗
NASA NTRS1996

Data Archival and Retrieval Enhancement (DARE) Metadata Modeling and Its User Interface

The Defense Nuclear Agency (DNA) has acquired terabytes of valuable data which need to be archived and effectively distributed to the entire nuclear weapons effects community and others...This paper describes the DARE (Data Archival and Retrieval Enhancement) metadata model and explains how it is used as a source for generating HyperText Markup Language (HTML)or Standard Generalized Markup Language (SGML) documents for access through web browsers such as Netscape.

The Defense Nuclear Agency DNA DARE Data Archival ↗
NASA NTRS1994

(abstract) Application of Neural Networks to Hyperspectral Unmixing

The emergence, in recent years, of hyperspectral sensors provides a tremendous opportunity for advancing the process of detailed and direct remote detection and identification from space of targets or surface materials. Such sensors exploit the uniqueness of the corresponding spectral reflectance signatures, which enables high resolution imaging spectrometer data to be processed on a pixel-by-pixel basis. This has implications both for defense-related applications (e.g., surveillance tasks) and in the civilian domain (e.g., for science applications). The purpose of this talk is to discuss a number of strong arguments that support neural networks as a choice for the generalized analysis (e.g., unmixing) of remotely sensed hyperspectral data.

signatures moving object detection surveillance co↗
NASA NTRS2015

BILLIARDS: A Demonstration Mission for Hundred-Meter Class Near-Earth Asteroid Disruption

Collisions from near-Earth asteroids (NEAs) have the potential to cause widespread harm to life on Earth. The hypervelocity nature of these collisions means that a relatively small asteroid (about a quartermile in diameter) could cause a global disaster. Proposed strategies for deflecting or disrupting such a threatening asteroid include detonation of a nuclear explosive device (NED) in close proximity to the asteroid, as well as intercepting the asteroid with a hypervelocity kinetic impactor. NEDs allow for the delivery of large amounts of energy to a NEA for a given mass launched from the Earth, but have not yet been developed or tested for use in deep space. They also present safety and political complications, and therefore may only be used when absolutely necessary. Kinetic impactors require a relatively simple spacecraft compared to NEDs, but also deliver a much lower energy for a given launch mass. To date, no demonstration mission has been conducted for either case, and such a demonstration mission must be conducted prior to the need to utilize them during an actual scenario to ensure that an established, proven system is available for planetary defense when the need arises. One method that has been proposed to deliver a kinetic impactor with impact energy approaching that of an NED is the "billiard-ball" approach. This approach would involve capturing an asteroid approximately ten meters in diameter with a relatively small spacecraft (compared to the launch mass of an equivalent direct kinetic impactor), and redirecting it into the path of an Earth-threatening asteroid. This would cause an impact which would disrupt the Earth-threatening asteroid or deflect it from its Earth-crossing trajectory. The BILLIARDS Project seeks to perform a demonstration of this mission concept in order to establish a protocol that can be used in the event of an impending Earth/asteroid collision. In order to accomplish this objective, the mission must (1) rendezvous with a small (less than 10m), NEA (hereinafter "Alpha"), (2) maneuver Alpha to a collision with a approx. 100 m NEA (hereinafter "Beta"), and (3) produce a detectable deflection or disruption of Beta. In addition to these primary objectives, the BILLIARDS project will contribute to the scientific understanding of the physical properties and collision dynamics of asteroids, and provide opportunities for international collaboration.

Asteroid↗
NASA NTRS2017

Studies of Short Time Response Options for Potentially Hazardous Objects: Current and Forthcoming Results

NASA's Goddard Space Flight Center (GSFC) and the National Nuclear Security Administration (NNSA), Department of Energy (DOE) National Laboratories, Lawrence Livermore National Laboratory (LLNL), Los Alamos National Laboratory(LANL), and Sandia National Laboratory (SNL) are collaborating on Planetary Defense Research. The research program is organized around three case studies: 1. Deflection of the Potentially Hazardous Asteroid (PHA) 101955 Bennu (1999 RQ36)[OSIRIS-REx mission target], 2. Deflection of the secondary member of the PHA 65803 Didymos (1996 GT) [DART mission target], 3. Deflection of a scaled-down version of the comet 67PChuryumov-Gerasimenko [Rosetta mission target]. NASAGSFC is providing astrodynamics and spacecraft mission design expertise, while NNSA, DOE, LLNL, LANL and SNL are providing expertise in modeling the effects of kinetic impactor spacecraft and nuclear explosive devices on the target objects.

Mission Design↗
NASA NTRS2023

Risk Assessment for Asteroid Impact Threat Scenarios

Asteroid impacts can cause a wide range of damage through multiple potential hazards, from localized blast waves or thermal radiation, to tsunami inundation, to global climatic effects. The level of risk posed by these hazards depends not only upon their extent and severity, but also upon the likelihood of the various damage ranges. Some consequences may be more moderate but very likely, while others may be unlikely but catastrophic. Evaluating the risk from these hazards involves substantial uncertainties across all aspects of the problem, including the properties of the asteroid itself, the specifics of its entry, and the complex high-energy damage physics involved. NASA’s Asteroid Threat Assessment Project performs Probabilistic Asteroid Impact Risk (PAIR) assessments that use fast-running entry and hazard models to evaluate millions of impact cases representing the distributions of these many uncertain parameters. This paper presents current probabilistic asteroid impact risk assessment modeling tools and approaches used for evaluating specific asteroid impact threat cases. We give an overview of the current PAIR model used to support impact threat scenarios and discuss some of the key applications of these assessment in supporting response decisions and planetary defense preparedness. We then present the results and key findings from the recent 2023 PDC hypothetical impact exercise as an example of the primary types of risk results and metrics being developed to inform and support those mitigation and response decisions.

SMD↗
NASA NTRS2024

JWST Sighting of Decameter Main-Belt Asteroids and View on Meteorite Sources

Asteroid discoveries are essential for planetary-defense efforts aiming to prevent impacts with Earth, including the more frequent megaton explosions from decameter impactors. While large asteroids (≥100 km) have remained in the main belt since their formation, small asteroids are commonly transported to the near-Earth object (NEO) population. However, due to the lack of direct observational constraints, their size-frequency distribution — which informs our understanding of the NEOs and the delivery of meteorite samples to Earth —varies significantly among models. Here, we report 138 detections of the smallest asteroids (⪆10 m) ever observed in the main belt, which were enabled by JWST’s infrared capabilities covering the asteroids’ emission peaks and synthetic tracking techniques. Despite small orbital arcs, we constrain the objects’ distances and phase angles using known asteroids as proxies, allowing us to derive sizes via radiometric techniques. Their size-frequency distribution exhibits a break at∼100 m (debiased cumulative slopes of q=−2.66±0.60 and−0.97±0.14 for diameters smaller and larger than∼100 m, respectively), suggestive of a population driven by collisional cascade. These asteroids were sampled from multiple asteroid families —most likely Nysa, Polana and Massalia — according to the geometry of pointings considered here. Through additional long-stare infrared observations, JWST is poised to serendipitously detect thousands of decameter-scale asteroids across the sky, probing individual asteroid families and the source regions of meteorites “in-situ”.

asteroid families↗