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At least 1,135 records · Page 63

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NASA NTRS2016

Approach-Phase Precision Landing with Hazard Relative Navigation: Terrestrial Test Campaign Results of the Morpheus/ALHAT Project

The Morpheus Project began in late 2009 as an ambitious e ort code-named Project M to integrate three ongoing multi-center NASA technology developments: humanoid robotics, liquid oxygen/liquid methane (LOX/LCH4) propulsion and Autonomous Precision Landing and Hazard Avoidance Technology (ALHAT) into a single engineering demonstration mission to be own to the Moon by 2013. The humanoid robot e ort was redirected to a deploy- ment of Robonaut 2 on the International Space Station in February of 2011 while Morpheus continued as a terrestrial eld test project integrating the existing ALHAT Project's tech- nologies into a sub-orbital ight system using the world's rst LOX/LCH4 main propulsion and reaction control system fed from the same blowdown tanks. A series of 33 tethered tests with the Morpheus 1.0 vehicle and Morpheus 1.5 vehicle were conducted from April 2011 - December 2013 before successful, sustained free ights with the primary Vertical Testbed (VTB) navigation con guration began with Free Flight 3 on December 10, 2013. Over the course of the following 12 free ights and 3 tethered ights, components of the ALHAT navigation system were integrated into the Morpheus vehicle, operations, and ight control loop. The ALHAT navigation system was integrated and run concurrently with the VTB navigation system as a reference and fail-safe option in ight (see touchdown position esti- mate comparisons in Fig. 1). Flight testing completed with Free Flight 15 on December 15, 2014 with a completely autonomous Hazard Detection and Avoidance (HDA), integration of surface relative and Hazard Relative Navigation (HRN) measurements into the onboard dual-state inertial estimator Kalman lter software, and landing within 2 meters of the VTB GPS-based navigation solution at the safe landing site target. This paper describes the Mor- pheus joint VTB/ALHAT navigation architecture, the sensors utilized during the terrestrial ight campaign, issues resolved during testing, and the navigation results from the ight tests.

Crain, Timothy P.↗

NASA NTRS2017

Early Estimation of Solar Activity Cycle: Potential Capability and Limits

The variable solar magnetic activity known as the 11-year solar cycle has the longest history of solar observations. These cycles dramatically affect conditions in the heliosphere and the Earth's space environment. Our current understanding of the physical processes that make up global solar dynamics and the dynamo that generates the magnetic fields is sketchy, resulting in unrealistic descriptions in theoretical and numerical models of the solar cycles. The absence of long-term observations of solar interior dynamics and photospheric magnetic fields hinders development of accurate dynamo models and their calibration. In such situations, mathematical data assimilation methods provide an optimal approach for combining the available observational data and their uncertainties with theoretical models in order to estimate the state of the solar dynamo and predict future cycles. In this presentation, we will discuss the implementation and performance of an Ensemble Kalman Filter data assimilation method based on the Parker migratory dynamo model, complemented by the equation of magnetic helicity conservation and longterm sunspot data series. This approach has allowed us to reproduce the general properties of solar cycles and has already demonstrated a good predictive capability for the current cycle, 24. We will discuss further development of this approach, which includes a more sophisticated dynamo model, synoptic magnetogram data, and employs the DART Data Assimilation Research Testbed.

Kitiashvili, Irina N.↗

NASA NTRS2017

Using Data Assimilation Methods of Prediction of Solar Activity

The variable solar magnetic activity known as the 11-year solar cycle has the longest history of solar observations. These cycles dramatically affect conditions in the heliosphere and the Earth's space environment. Our current understanding of the physical processes that make up global solar dynamics and the dynamo that generates the magnetic fields is sketchy, resulting in unrealistic descriptions in theoretical and numerical models of the solar cycles. The absence of long-term observations of solar interior dynamics and photospheric magnetic fields hinders development of accurate dynamo models and their calibration. In such situations, mathematical data assimilation methods provide an optimal approach for combining the available observational data and their uncertainties with theoretical models in order to estimate the state of the solar dynamo and predict future cycles. In this presentation, we will discuss the implementation and performance of an Ensemble Kalman Filter data assimilation method based on the Parker migratory dynamo model, complemented by the equation of magnetic helicity conservation and long-term sunspot data series. This approach has allowed us to reproduce the general properties of solar cycles and has already demonstrated a good predictive capability for the current cycle, 24. We will discuss further development of this approach, which includes a more sophisticated dynamo model, synoptic magnetogram data, and employs the DART Data Assimilation Research Testbed.

Prediction↗

NASA NTRS2018

Output Feedback Adaptive Control of Non-Minimum Phase Systems Using Optimal Control Modification

This paper describes output feedback adaptive control approaches for non-minimum phase SISO systems with relative degree 1 and non-strictly positive real (SPR) MIMO systems with uniform relative degree 1 using the optimal control modification method. It is well-known that the standard model-reference adaptive control (MRAC) cannot be used to control non-SPR plants to track an ideal SPR reference model. Due to the ideal property of asymptotic tracking, MRAC attempts an unstable pole-zero cancellation which results in unbounded signals for non-minimum phase SISO systems. The optimal control modification can be used to prevent the unstable pole-zero cancellation which results in a stable adaptation of non-minimum phase SISO systems. However, the tracking performance using this approach could suffer if the unstable zero is located far away from the imaginary axis. The tracking performance can be recovered by using an observer-based output feedback adaptive control approach which uses a Luenberger observer design to estimate the state information of the plant. Instead of explicitly specifying an ideal SPR reference model, the reference model is established from the linear quadratic optimal control to account for the non-minimum phase behavior of the plant. With this non-minimum phase reference model, the observer-based output feedback adaptive control can maintain stability as well as tracking performance. However, in the presence of the mismatch between the SPR reference model and the non-minimum phase plant, the standard MRAC results in unbounded signals, whereas a stable adaptation can be achieved with the optimal control modification. An application of output feedback adaptive control for a flexible wing aircraft illustrates the approaches.

Adaptive Contro↗

NASA NTRS2019

Sextant Navigation on the International Space Station: A Human Space Exploration Demo

Astronauts on board the International Space Station (ISS) tested a hand-held sextant to demonstrate potential use on future human exploration missions such as Orion and Gateway. The investigation, designed to aid in the development of emergency navigation methods for future crewed spacecraft, took place from June-December 2018. A sextant provides manual capability to perform star/planet-limb sightings and estimate vehicle state during loss of communication or other contingencies. Its simplicity and independence from primary systems make it useful as an emergency survival backup or confirming measurement source. The concept of using a sextant has heritage in Gemini, Apollo, and Skylab. This paper discusses the instrument selection, flight certification, crew training, product development, experiment execution, and data analysis. Preflight training consisted of a hands-on session with the instrument and practice in a Cupola mock-up with star field projector dome. The experiment itself consisted of several sessions with sextant sightings in the ISS Cupola module by two crew members. Sightings were taken on star pairs, star/moon limb, and moon diameter. The sessions were designed to demonstrate star identification and acquisition, sighting stability, accuracy, and lunar sights. Results are presented which demonstrate sightings within the accuracy goal of 60 arcseconds, even in the presence of window refraction effects and minimal crew training. The crew members provided valuable feedback on sighting products and microgravity stability techniques.

Exploration↗

NASA NTRS2019

NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment

Terrestrial hydrologic trends over the conterminous United States are estimated for 1980-47 2015 using the National Climate Assessment-Land Data Assimilation System (NCA-LDAS) reanalysis. NCA-LDAS employs the uncoupled Noah Version 3.3 land surface model at 0.125 degreesx0.125 degrees forced with NLDAS-2 meteorology, rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth and irrigation products. Mean annual trends are reported using the nonparametric Mann-Kendall test at p<0.1 significance. Results illustrate the interrelationship between regional gradients in forcing trends, and trends in other land energy and water stores and fluxes. Mean precipitation trends range from +3 to +9 mm/yr in the Upper Great Plains and Northeast to -1 to -9 in the West and South; net radiation flux trends range from +0.05 to +0.20 W/m(exp2)/yr in the East to -0.05 to -0.20 in the West; U.S.-wide temperature trends average about +0.03K/yr. Trends in soil moisture, snow cover, latent and sensible heat fluxes and runoff are consistent with forcings, contributing to increasing evaporative fraction trends from West to East. Evaluation of NCA-LDAS trends compared to independent data indicates mixed results. The RMSE of U.S.-wide trends in number of snow cover days improved from 3.13 to 2.89 days/yr while trend detection increased 11%; Trends in latent heat flux were hardly affected, RMSE decreasing only 0.17 to 0.16 W/m(exp 2)/yr, while trend detection increased 2%; NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm/yr while trend detection was unaffected. Analysis also indicated that NCA-LDAS exhibits relatively more skill in low precipitation station density areas, suggesting there are limits to the effectiveness of satellite data assimilation in densely gaged regions. Overall, NCA-LDAS demonstrates capability for quantifying physically consistent, U.S. hydrologic climate trends over the satellite era.

Snow depth↗

NASA NTRS2019

Fusion of Visible and Thermal-Infrared Imagery for SLAM for Landing on Icy Moons

This paper addresses the problem of localization for landing on the surface of icy moons, like Europa or Enceladus. Due to the possibility of specular reflection as well as high bulk albedo, icy surfaces present new challenges that make traditional vision-based navigation systems relying on visible imagery unreliable. We propose augmenting visible light cameras with a thermal-infrared camera using inverse-depth parameterized monocular EKF-SLAM to address problems arising from the appearance of icy moons. Results were obtained from a novel procedural Europa surface simulation which models the appearance and the thermal properties simultaneously from physically-based methods. In this framework, we show that thermal features improve localization by 23% on average when compared to a visible camera. Moreover, fusing both sensing modalities increases the improvement in localization to 31% on average, compared to using a visible light camera alone.

thermal vision↗

NASA NTRS2017

Using Data Assimilation Methods for Prediction of Solar Activity

The variability of solar magnetic activity known as the 11-‐year solar cycles has the longest history of observations. These solar cycles dramatically affect conditions in the heliosphere and the Earth’s space environment. Our current understanding of the physical processes that make up global solar dynamics and the dynamo that generates the magnetic fields is sketchy, resulting in unrealistic descriptions in theoretical and numerical models of the solar cycles. The absence of long-‐term observations of solar interior dynamics and photospheric magnetic fields hinders development of accurate dynamo models and their calibration. In such situations, mathematical data assimilation methods provide an optimal approach for combining the available observational data and their uncertainties with theoretical models in order to estimate the state of the solar dynamo and predict future cycles. In this presentation, we will discuss the implementation and performance of an Ensemble Kalman Filter data assimilation method based on the Parker migratory dynamo model complemented by the equation of magnetic helicity conservation and long-‐term sunspot data series. This approach has allowed us to reproduce the general properties of the solar cycles and has already demonstrated a good predictive capability for the current cycle, 24. We will discuss further development of this approach, which includes a more sophisticated dynamo model, synoptic magnetogram data, and employs the DART Data Assimilation Research Testbed.

Solar Activity↗

NASA NTRS2020

Development of a Supervisory Tool for Fault Detection and Diagnosis of DC Electric Power Systems with the Application of Deep Space Vehicles

This dissertation formulates the problem of fault detection and diagnosis of DC electric power systems for the application of autonomous spacecraft. The ability to accurately identify and isolate failures in the electrical power system is critical to ensure the reliability of a spacecraft. This problem becomes more pronounced during deep space missions that lack the ability to monitor from ground control. The current state of electrical power system fault supervision is insufficient to guarantee highly reliable and robust operation. To solve this issue, a combination of model-based and rules-based techniques are used in a hierarchical framework to improve the diagnostic performance of the spacecraft electrical power system. Noise, disturbances, and modeling errors are considered in the design of the method. Practical considerations related to the hardware and software are discussed for the flight application. A wide array of failure types are simulated in a series of experiments to assess the functionality of the design. The experiments showed that the methods used improved the diagnostic capability of the autonomous system while taking into account the limitations attributed to flight software requirements. The significance of this study is to provide a framework capable of advanced diagnostics of an electrical power system with little to no interaction from a human operator.

Fault Detection and Diagnosis↗

NASA NTRS2021

Southern Idaho Health & Air Quality II: Evaluating Atmospheric Mixing Height Estimations in the Western United States

Explore the source record for details and available documents.

Dean Berkowitz↗

NASA NTRS2019

NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment

Terrestrial hydrologic trends over the conterminous United States are estimated for 1980–2015 using the National Climate Assessment Land Data Assimilation System (NCA-LDAS) reanalysis. NCA-LDAS employs the uncoupled Noah version 3.3 land surface model at 0.125° 3 0.125° forced with NLDAS-2 meteorology, rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth, and irrigation products. Mean annual trends are reported using the nonparametric Mann–Kendall test at p < 0.1 significance. Results illustrate the interrelationship between regional gradients in forcing trends and trends in other land energy and water stores and fluxes. Mean precipitation trends range from +3 to +9 mm/yr in the upper Great Plains and Northeast to -1 to -9 mm/yr in the West and South, net radiation flux trends range from +0.05 to +0.20 W/sq. m yr in the East to -0.05 to -0.20 W/sq. m yr in the West, and U.S.-wide temperature trends average about +0.03K/yr. Trends in soil moisture, snow cover, latent and sensible heat fluxes, and runoff are consistent with forcings, contributing to increasing evaporative fraction trends from west to east. Evaluation of NCA-LDAS trends compared to independent data indicates mixed results. The RMSE of U.S.-wide trends in number of snow cover days improved from 3.13 to 2.89 days/yr while trend detection increased 11%. Trends in latent heat flux were hardly affected, with RMSE decreasing only from 0.17 to 0.16 W/sq. m yr, while trend detection increased 2%. NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm/yr while trend detection was unaffected. Analysis also indicated that NCA-LDAS exhibits relatively more skill in low precipitation station density areas, suggesting there are limits to the effectiveness of satellite data assimilation in densely gauged regions. Overall, NCA-LDAS demonstrates capability for quantifying physically consistent, U.S. hydrologic climate trends over the satellite era.

Michael F. Jasinski↗

NASA NTRS2023

Fault Detection and Diagnosis in Spacecraft Electrical Power Systems

The ability to accurately identify and isolate failures in the electrical power system (EPS) is critical to ensure the reliability of spacecraft. This paper proposes a novel solution to the problem of fault detection and diagnosis in direct current (DC) electric power systems for spacecraft. Autonomous operation becomes essential during deep space missions that lack the ability to monitor and control the spacecraft from ground locations. The current state of EPS fault supervision is insufficient to guarantee highly reliable operation. To solve this issue, a combination of model-based and knowledge-based techniques are used in a hierarchical framework to improve the diagnostic performance of the system. Noise, disturbances, and modeling errors are considered in the design of the fault detection system. Practical considerations related to spacecraft flight hardware and software are accounted for in the system design for flight applications. To assess the functionality of the design, a wide array of failures are simulated in a series of experiments. The experiments showed that the technique improved the capability of the autonomous system by increasing the number of fault types diagnosed. The significance of this study is to provide a framework capable of advanced diagnostics of an EPS with little to no interaction from human operators.

Autonomous Power Systems↗

NASA NTRSDate not supplied

Hazard Boresight Relative Navigation for Safe Lunar Landing

In the area of planetary landing, hazard detection and avoidance is the act of driving a vehicle to a safe landing area using onboard resources. A hazard detection sensor is used to scan the terrain and these measurements are evaluated to determine where the safe landing sites are located. The selected site is generally not the same as the nominal target, so the vehicle must divert to the new site. This activity involves the interaction between several components, including a suite of onboard GNC algorithms that work together to efficiently choose and divert to a new site. This paper presents the Hazard Boresight Relative Navigation concept, which is a method that provides a common interface between the hazard scan, safe-site selection algorithm, size of the target-relative landing ellipse, divert offset distance and guidance targeting algorithm. After the safe-site is selected from the hazard scan, the original inertial target is replaced with a vehicle-relative target, which is initialized by a measurement from the hazard scan. The new target-relative position state is estimated over time in the navigation filter, and is fed to the guidance algorithm to perform the divert maneuver. In addition to detailing the Hazard Boresight Relative Navigation concept, this paper also presents some general landing terms that can be used in the greater discussion, as well as analysis on how to estimate and predict the vehicle footprint dispersion ellipse during flight, which is used in the safe-site selection algorithm.

Stephen R. Steffes↗

NASA NTRSDate not supplied

Hazard Boresight Relative Navigation for Safe Lunar Landing

Stephen Steffes↗

NASA NTRS2024

ResORR: A Globally Scalable and Satellite Data-Driven Algorithm for River Flow Regulation Due to Reservoir Operations

We propose a globally scalable algorithm, ResORR (Reservoir Operations driven River Regulation), to predict regulated river flow and tested it over the heavily regulated basin of the Cumberland River in the US. ResORR was found able to model regulated river flow due to upstream reservoir operations of the Cumberland River. Over a mountainous basin dominated by high rainfall, ResORR was effective in capturing extreme flooding modified by upstream hydropower dam operations. On average, ResORR improved regulated river flow simulation by more than 50% across all performance metrics when compared to a hydrologic model without a regulation module. ResORR is a timely software algorithm for understanding human regulation of surface water as satellite-estimated reservoir state is expected to improve globally with the recently launched Surface Water and Ocean Topography (SWOT) mission.

River Regulation↗

NASA NTRS2024

High Spectral Resolution Lidar – Generation 2 (HSRL-2) Retrievals of Ocean Surface Wind Speed: Methodology and Evaluation

Ocean surface wind speed (i.e., wind speed 10 m above sea level) is a critical parameter used by atmospheric models to estimate the state of the marine atmospheric boundary layer (MABL). Accurate surface wind speed measurements in diverse locations are required to improve characterization of MABL dynamics and assess how models simulate large-scale phenomena related to climate change and global weather patterns. To provide these measurements, this study introduces and evaluates a new surface wind speed data product from the NASA Langley Research Center nadir-viewing High Spectral Resolution Lidar – generation 2 (HSRL-2) using data collected as part of the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) mission. The HSRL-2 can directly measure vertically resolved aerosol backscatter and extinction profiles without additional constraints or assumptions, enabling the instrument to accurately derive atmospheric attenuation and directly determine surface reflectance (i.e., surface backscatter). Also, the high horizontal spatial resolution of the HSRL-2 retrievals (0.5 s or ∼ 75 m along track) allows the instrument to probe the fine-scale spatial variability in surface wind speeds over time along the flight track and over breaks in broken cloud fields. A rigorous evaluation of these retrievals is performed by comparing coincident HSRL-2 and National Center for Atmospheric Research (NCAR) Airborne Vertical Atmosphere Profiling System (AVAPS) dropsonde data, owing to the joint deployment of these two instruments on the ACTIVATE King Air aircraft. These comparisons show correlations of 0.89, slopes of 1.04 and 1.17, and y intercepts of −0.13 and −1.05 m s−1 for linear and bisector regressions, respectively, and the overall accuracy is calculated to be 0.15 ± 1.80 m s−1. It is also shown that the dropsonde surface wind speed data most closely follow the HSRL-2 distribution of wave slope variance using the distribution proposed by Hu et al. (2008) rather than the ones proposed by Cox and Munk (1954) and Wu (1990) for surface wind speeds below 7 m s−1, with this category comprising most of the ACTIVATE data set. The retrievals are then evaluated separately for surface wind speeds below 7 m s−1 and between 7 and 13.3 m s−1 and show that the HSRL-2 retrieves surface wind speeds with a bias of ∼ 0.5 m s−1 and an error of ∼ 1.5 m s−1, a finding not apparent in the cumulative comparisons. Also, it is shown that the HSRL-2 retrievals are more accurate in the summer (−0.18 ± 1.52 m s−1) than in the winter (0.63 ± 2.07 m s−1), but the HSRL-2 is still able to make numerous (N=236) accurate retrievals in the winter. Overall, this study highlights the abilities and assesses the performance of the HSRL-2 surface wind speed retrievals, and it is hoped that further evaluation of these retrievals will be performed using other airborne and satellite data sets.

Sanja Dmitrovic↗

NASA NTRSDate not supplied

Steady Surface Pressure Measurement via the Lifetime Method With High-Speed Cameras in NASA's Unitary Plan Wind Tunnel

High spatial resolution measurement of steady surface pressure via pressure-sensitive paint at NASA Ames has traditionally relied on specialized cameras equipped to accumulate charge over multiple exposures, whereas measurement of the fluctuating component of pressure uses an altogether separate set of high-speed cameras. To reduce complexity of installation, data acquisition, and processing, we have implemented methods to use a single set of commercial off-the-shelf cameras to produce both steady and unsteady pressure measurements. Both imaging systems were installed in the 11-by 11-foot NASA Ames Unitary Plan Wind Tunnel and acquired images of a scaled model of the Space Launch System Block 1B Crew and Cargo configurations over a variety of flow conditions. This work focuses on methods for data acquisition, processing, and calibration to produce steady-state pressure estimates on the surface of the wind tunnel model based on the lifetime method. It will then compare the steady solutions produced by the legacy and high-speed imaging systems.

Pressure Sensitive Paint↗

NASA NTRSDate not supplied

Vision-Based Distributed Sensing at Vertiports for Advanced Air Mobility and Urban Air Mobility Approach and Landing

Advanced Air Mobility (AAM) encompasses a broad vision for air transportation, including Urban Air Mobility (UAM) as a subset. AAM aims to create a more connected and efficient transportation network across various geographical settings. However, navigating AAM aircraft in GPS-denied or degraded environments during approach and landing is challenging. Traditional vision aids like glideslopes and localizers are limited in vertiport environments due to narrow beam constraints and reduced landing angle options. This paper addresses the need for accurate navigation solutions at vertiports by proposing a vision-based distributed sensing (VIDIS) system utilizing cameras with bundle adjustment to assist incoming AAM aircraft during approach and landing while monitoring surface movements to enhance safety and efficiency. Key focus areas for current and future vertiport developers include identifying suitable sensor types and infrastructure standards to support AAM operations and including vertiport markings as vision-based navigation aids. The proposed system offers a novel approach to overcoming navigation challenges in AAM operations, particularly in urban settings where traditional aids may be insufficient. Preliminary simulation results with distributed cameras demonstrate promising outcomes for implementing bundle adjustment techniques to enhance vision-based navigation solutions at vertiports. Generating waypoint-based trajectories via waypoint integration using explicit guidance synthesis (WINGS) creates smooth AAM trajectories for landing at vertiports by using the current waypoint's terminal conditions as the initial conditions for the next waypoint. Combining bundle adjustment's ground-based solution of vertiport features with WINGS, Coplanar Pose from Orthography and Scaling with Iterations (COPOSIT), and an extended Kalman filter (EKF) estimates the state of an incoming aircraft during approach and landing at vertiports. Future work includes testing VIDIS in a high-fidelity simulation and with real-world data.

Distributed sensing↗