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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Highlights of Space Weather Services/Capabilities at NASA/GSFC Space Weather Center

The importance of space weather has been recognized world-wide. Our society depends increasingly on technological infrastructure, including the power grid as well as satellites used for communication and navigation. Such technologies, however, are vulnerable to space weather effects caused by the Sun's variability. NASA GSFC's Space Weather Center (SWC) (http://science.gsfc.nasa.gov//674/swx services/swx services.html) has developed space weather products/capabilities/services that not only respond to NASA's needs but also address broader interests by leveraging the latest scientific research results and state-of-the-art models hosted at the Community Coordinated Modeling Center (CCMC: http://ccmc.gsfc.nasa.gov). By combining forefront space weather science and models, employing an innovative and configurable dissemination system (iSWA.gsfc.nasa.gov), taking advantage of scientific expertise both in-house and from the broader community as well as fostering and actively participating in multilateral collaborations both nationally and internationally, NASA/GSFC space weather Center, as a sibling organization to CCMC, is poised to address NASA's space weather needs (and needs of various partners) and to help enhancing space weather forecasting capabilities collaboratively. With a large number of state-of-the-art physics-based models running in real-time covering the whole space weather domain, it offers predictive capabilities and a comprehensive view of space weather events throughout the solar system. In this paper, we will provide some highlights of our service products/capabilities. In particular, we will take the 23 January and the 27 January space weather events as examples to illustrate how we can use the iSWA system to track them in the interplanetary space and forecast their impacts.

Fok, Mei-Ching↗
NASA NTRS2012

Experimental Validation of Pulse Phase Tracking for X-Ray Pulsar Based

Pulsars are a form of variable celestial source that have shown to be usable as aids for autonomous, deep space navigation. Particularly those sources emitting in the X-ray band are ideal for navigation due to smaller detector sizes. In this paper X-ray photons arriving from a pulsar are modeled as a non-homogeneous Poisson process. The method of pulse phase tracking is then investigated as a technique to measure the radial distance traveled by a spacecraft over an observation interval. A maximum-likelihood phase estimator (MLE) is used for the case where the observed frequency signal is constant. For the varying signal frequency case, an algorithm is used in which the observation window is broken up into smaller blocks over which an MLE is used. The outputs of this phase estimation process were then looped through a digital phase-locked loop (DPLL) in order to reduce the errors and produce estimates of the doppler frequency. These phase tracking algorithms were tested both in a computer simulation environment and using the NASA Goddard Space flight Center X-ray Navigation Laboratory Testbed (GXLT). This provided an experimental validation with photons being emitted by a modulated X-ray source and detected by a silicon-drift detector. Models of the Crab pulsar and the pulsar B1821-24 were used in order to generate test scenarios. Three different simulated detector trajectories were used to be tracked by the phase tracking algorithm: a stationary case, one with constant velocity, and one with constant acceleration. All three were performed in one-dimension along the line of sight to the pulsar. The first two had a constant signal frequency and the third had a time varying frequency. All of the constant frequency cases were processed using the MLE, and it was shown that they tracked the initial phase within 0.15% for the simulations and 2.5% in the experiments, based on an average of ten runs. The MLE-DPLL cascade version of the phase tracking algorithm was used in the varying frequency case. This resulted in tracking of the phase and frequency by the DPLL outputs in both the simulation and experimental environments. The crab pulsar was experimentally tested with a trajectory with a higher acceleration. In this case the phase error tended toward zero as the observation extended to 250 seconds and the doppler frequency error tended to zero in under 100 seconds.

Experimental↗
NASA NTRS2020

Cramer-Rao Lower Bound for SoOp-R-Based Root-Zone Soil Moisture Remote Sensing

Signals of opportunity (SoOp) reflectometry (SoOp-R) is a maturing field for geophysical remote sensing as evidenced by the growing number of airborne and spaceborne experiments. As this approach receives more attention, it is worth analyzing SoOp-R’s capabilities to retrieve subsurface soil moisture (SM) by leveraging communication and navigation satellite transmitters. In this research, the CRLB is used to identify the effects of variable SoOp-R parameters on the best achievable estimation error for root-zone soil moisture (RZSM). This study investigates the use of multiple frequency, polarization, and incidence angle measurement configurations on a two-layered dielectric profile. The results also detail the effects of variable SM conditions on the capability of SoOp-R systems to predict subsurface SM. The most prevalent observation is the importance of using at least two frequencies to limit uncertainties from subsurface SM estimates. If at least two frequencies are used, the CRLB of a profile is retrievable within the root-zone depending on the surface SM content as well as the number of independent measurements of the profile. For a depth of 30 cm, it is observed that a CRLB corresponding to 4% RZSM estimation accuracy is achievable with as few as 2 dual-frequency-based SoOp-R measurements. For this depth, increasing number of measurements provided by polarization and incidence angle allow for sensing of increasingly wet SM profile structures. This study, overall, details a methodology by which SoOp-R receiver system can be designed to achieve a desired CRLB using a trade-off study between the available measurements and SM profile.

Signals of Opportunity (SoOp)↗
NASA NTRS2002

The LWS Geospace Storm Investigations Exploring the Extremes of Space Weather

The Geospace mission of the Living With a Star program is a family of investigations focusing on the compelling science questions that advance our ability to specify, understand, and predict the societal impact of solar variance. Two key areas have been identified as combining both importance to society and potential for scientific progress: 1) characterization and understanding of the acceleration, global distribution, and variability of energetic electrons and ions in the inner magnetosphere, and 2) characterization and understanding of the ionosphere and irregularities that affect communications, navigation and radar systems. Under these broad categories specific science questions have emerged as the priority science objectives for the first Geospace Investigations: How and why do relativistic electrons in the outer zone and slot region vary during geomagnetic storms? How does the long- and short-term variability of the Sun affect the global-scale behavior of the ionospheric electron density and irregularities, especially during magnetic storms and at mid-latitudes? The first Geospace mission will attempt to answer these questions.

Source record↗
DOE OSTI2019

GPS-Free Navigation in Buildings - NA-241_2018_final_report_SBLibby_12_31_18

This project’s purpose is to evaluate candidate navigation solutions for GPS free inspection tools that can be used in tours of large building interiors. While the IAEA is currently using a system (ANPS – autonomous navigational and positioning sensor) that provides a satisfactory solution under ideal conditions for tours up to one hour, significant improvements for longer tours under variable conditions (also including the added features of on-board processing and time synchronization) are needed. In principle, next generation COTS portable inertial measurement unit (IMU) sensors with satisfactory accuracy, SWAP (size, weight, power), low error, and bias drift can provide sufficiently accurate dead reckoning navigation in a lengthy tour of a large building in the absence of GPS.

42 ENGINEERING↗
NASA NTRS1991

Combining GPS and VLBI earth-rotation data for improved universal time

The Deep Space Network (DSN) routinely measures Earth orientation in support of spacecraft tracking and navigation using very long-baseline interferometry (VLBI) with the deep-space tracking antennas. The variability of the most unpredictable Earth-orientation component, Universal Time 1 (UT1), is a major factor in determining the frequency with which the DSN measurements must be made. The installation of advanced Global Positioning System (GPS) receivers at the DSN sites and elsewhere may soon permit routine measurements of UT1 variation with significantly less dependence on the deep-space tracking antennas than is currently required. GPS and VLBI data from the DSN may be combined to generate a precise UT1 series, while simultaneously reducing the time and effort the DSN must spend on platform-parameter calibrations. This combination is not straightforward, however, and a strategy for the optimal combination of these data is presented and evaluated. It appears that, with the aid of GPS, the frequency of required VLBI measurements of Earth orientation could drop from twice weekly to once per month. More stringent real-time Earth orientation requirements possible in the future would demand significant improvements in both VLBI and GPS capabilities, however.

Freedman, A. P.↗
NASA NTRS2003

Detecting water hazards for autonomous off-road navigation

Detecting water hazards for autonomous, off-road navigation of unmanned ground vehicles is a largely unexplored problem. In this paper, we catalog environmental variables that affect the difficulty of this problem, including day vs. night operation, whether the water reflects sky or other terrain features, the size of the water body, and other factors. We briefly survey sensors that are applicable to detecting water hazards in each of these conditions. We then present analyses and results for water detection for four specific sensor cases: 1) using color image classification to recognize sky reflections in water during the day, 2) using ladar to detect the presence of water bodies and to measure their depth, 3) using short-wave infrared (SWIR) imagery to detect water bodies, as well as snow and ice, and 4) using mid-wave infrared (MWIR) imagery to recognize water bodies at night. For color imagery, we demonstrate solid results with a classifier that runs at nearly video rate on a 433 MHz processor. For ladar, we present a detailed propagation analysis that shows the limits of water body detection and depth estimation as a function of look ahead distance, water depth, and ladar wavelength. For SWIR and MWIR, we present sample imagery from a variety of data collections that illustrate the potential of these sensors. These results demonstrate significant progress on this problem.

McHenry, Mike↗
NASA NTRS1999

CRV Escape Trajectories from the ISS

The Crew Return Vehicle (CRV) slated for use on the International Space Station (ISS) provides a safe return for up to seven crew members under various emergency conditions. One of the most demanding situations for executing the escape involves separating from a tumbling ISS Current requirements specify a maximum Root Sum Square (RSS) tumble rate of 2 degrees/second, with the additional requirement for an expedited departure from any ISS attitude. The design of a trajectory that ensures no re-contact with the ISS poses many challenges on the Guidance, Navigation, and Control (GN&C) system of the vehicle. To ensure no re-contact the trajectory design employs a two burn sequence, with the first burn preventing near-term collision and the second burn preventing far-field re-contact This presentation describes the approach used to design and to evaluate trajectories for CRV departure from the baselined location on the ISS Node 3 starboard. This approach involved performing a parametric search of selected control variables vital in escaping the tumbling ISS The presentation provides a candidate targeting methodology for escape using minimal information from available navigation devices, and presents the quantitative results from the analysis.

Foti, Tony M.↗
NASA NTRSDate not supplied

UAM Variable Separation

NASA’s Urban Air Mobility (UAM) Sub-Project is engaged in research to support the introduction of air taxis into the National Airspace System. Such operations will require a range of communication, navigation, and surveillance systems. Air vehicles for UAM are under development and will initially have human pilots. Separation from other aircraft, obstacles, and weather may be a pilot responsibility or provided by an operator’s ground-based systems. Eventually, air taxis may be flown from the ground or fly autonomously. The variable separation concept adds separation buffers to air taxis depending upon their situation or location.

aeronautics↗
NASA NTRS2023

What’s That Supposed to Mean? Capturing Micro-Behaviors in Teams

Future long-duration space exploration (LDSE) crews will require extensive coordination, cooperation, and team functioning as they face a myriad of challenges rooted in both taskwork and teamwork (Bell et al., 2015; Landon et al., 2018). While exposed to extreme conditions, crew members must navigate living and working together in prolonged confinement. Moreover, astronaut teams are becoming increasingly diverse, introducing significant variability in team composition. This increasing diversity, alongside traditional constraints of LDSE, introduces additional challenges into effective team functioning. To date, most methods for capturing team functioning rely on self-report measures. Such measures are prone to several limitations, including but not limited to social desirability bias, halo effect, and leniency effects (Trull & Ebner-Priemer, 2013), which skew data and limit nuanced understandings of phenomena at play. Self-report measures broadly capture team functioning, lending the nature of such methods to identifying underlying “macro”-behaviors (i.e., behaviors that are long-standing and last over time). However, team functioning is far more complex than a series of macro-behaviors, rendering reliance on self-report data deficient for accurate measurement. Recent research demonstrates the potential of alternative methods for capturing team functioning, such as speech and physiological data (Chaffin et al., 2017; Murray & Oertel, 2018). Consequently, these methods are more suitable for capturing micro-behaviors: brief, often unconscious expressions that affect the extent to which an individual feels included by others around them (Paletz et al., 2013). Micro-behaviors can be further classified into microaggressions (i.e., subtle, negative exchanges; Keller & Galgay, 2010) or micro-affirmations (i.e., subtle, positive exchanges; Kyte et al. 2020), both of which influence team functioning. Due to the subtle nature of micro-behaviors, contextual factors have a significant impact when determining if it is aggressive or affirmative. Additionally, several iterations of microbehaviors can have lingering effects on team interactions. For example, the use of “mm-hmm” by a crew member can function as both a micro-affirmation and micro-aggression. Specifically, it can be indication of active listening (i.e., micro-affirmation) or as an expression of annoyance (i.e., aggression) depending on the context in which it occurs. Auditory features (e.g., tone, frequency) can help delineate between the two forms; however, the contextual factors (e.g., previous interactions between team members, crew demographics) add a layer of complexity that render auditory features alone as insufficient to capture micro-behaviors. Consequently, this paper seeks to provide a novel approach in which multi-modal data (i.e., auditory features and contextual features) are used in a random-forest model to better identify distinguishing characteristics between micro-affirmations and micro-aggressions. In turn, detected micro-behaviors are used to predict team performance, thereby demonstrating the value of capturing micro-behaviors as supplemental data to macro-behaviors.

Sydney R. Begerowski↗
NASA NTRS2023

What’s That Supposed to Mean? Capturing Micro-Behaviors in Teams

Future long-duration space exploration (LDSE) crews will require extensive coordination, cooperation, and team functioning as they face a myriad of challenges rooted in both taskwork and teamwork (Bell et al., 2015; Landon et al., 2018). While exposed to extreme conditions, crew members must navigate living and working together in prolonged confinement. Moreover, astronaut teams are becoming increasingly diverse, introducing significant variability in team composition. This increasing diversity, alongside traditional constraints of LDSE, introduces additional challenges into effective team functioning. To date, most methods for capturing team functioning rely on self-report measures. Such measures are prone to several limitations, including but not limited to social desirability bias, halo effect, and leniency effects (Trull & Ebner-Priemer, 2013), which skew data and limit nuanced understandings of phenomena at play. Self-report measures broadly capture team functioning, lending the nature of such methods to identifying underlying “macro”-behaviors (i.e., behaviors that are long-standing and last over time). However, team functioning is far more complex than a series of macro-behaviors, rendering reliance on self-report data deficient for accurate measurement. Recent research demonstrates the potential of alternative methods for capturing team functioning, such as speech and physiological data (Chaffin et al., 2017; Murray & Oertel, 2018). Consequently, these methods are more suitable for capturing micro-behaviors: brief, often unconscious expressions that affect the extent to which an individual feels included by others around them (Paletz et al., 2013). Micro-behaviors can be further classified into micro-aggressions (i.e., subtle, negative exchanges; Keller & Galgay, 2010) or micro-affirmations (i.e., subtle, positive exchanges; Kyte et al. 2020), both of which influence team functioning. Due to the subtle nature of micro-behaviors, contextual factors have a significant impact when determining if it is aggressive or affirmative. Additionally, several iterations of micro-behaviors can have lingering effects on team interactions. For example, the use of “mm-hmm” by a crew member can function as both a micro-affirmation and micro-aggression. Specifically, it can be indication of active listening (i.e., micro-affirmation) or as an expression of annoyance (i.e., aggression) depending on the context in which it occurs. Auditory features (e.g., tone, frequency) can help delineate between the two forms; however, the contextual factors (e.g., previous interactions between team members, crew demographics) add a layer of complexity that render auditory features alone as insufficient to capture micro-behaviors. Consequently, this paper seeks to provide a novel approach in which multi-modal data (i.e., auditory features and contextual features) are used in a random-forest model to better identify distinguishing characteristics between micro-affirmations and micro-aggressions. In turn, detected micro-behaviors are used to predict team performance, thereby demonstrating the value of capturing micro-behaviors as supplemental data to macro-behaviors.

Sydney Begerowski↗
DOE OSTI2023

Shallow-Water Habitat in the Lower Columbia River Estuary: A Highly Altered System

Abstract Decreases in shallow-water habitat area (SWHA) in the Lower Columbia River and Estuary (LCRE) have adversely affected salmonid populations. We investigate the causes by hindcasting SWHA from 1928 to 2004, system-wide, based on daily higher high water (HHW) and system hypsometry. Physics-based regression models are used to represent HHW along the system as a function of river inflow, tides, and coastal processes, and hypsometry is used to estimate the associated SWHA. Scenario modeling is employed to attribute SWHA losses to levees, flow regulation, diversion, navigational development, and climate-induced hydrologic change, for subsidence scenarios of up to 2 m, and for 0.5 m fill. For zero subsidence, the system-wide annual-average loss of SWHA is 55 ± 5%, or 51 × 10 5 ha/year; levees have caused the largest decrease ( $${54}_{-14}^{+5}$$ 54 - 14 + 5 %, or ~ 50 × 10 5 ha/year). The loss in SWHA due to operation of the hydropower system is small, but spatially and seasonally variable. During the spring freshet critical to juvenile salmonids, the total SWHA loss was $${63}_{-3}^{+2}$$ 63 - 3 + 2 %, with the hydropower system causing losses of 5–16% (depending on subsidence). Climate change and navigation have caused SWHA losses of $${5}_{-5}^{+16}$$ 5 - 5 + 16 % and $${4}_{-6}^{+14}$$ 4 - 6 + 14 %, respectively, but with high spatial variability; irrigation impacts have been small. Uncertain subsidence causes most of the uncertainty in estimates; the sum of the individual factors exceeds the total loss, because factors interact. Any factor that reduces mean or peak flows (reservoirs, diversion, and climate change) or alters tides and along-channel slope (navigation) becomes more impactful as assumed historical elevations are increased to account for subsidence, while levees matter less.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Building Maps for Terrain Relative Navigation Using Blender: An Open-Source Approach

A persistent challenge for vision-based navigation systems that compare imagery to a reference map is generating high quality maps with similar lighting conditions. Image rendering software can be used to apply variable lighting to reference maps or to generate synthetic imagery for test trajectories. While many image rendering software packages are available, with several developed specifically for spaceflight applications, there are often limitations due to cost, image fidelity, or flexibility. In this paper, we demonstrate the use of an open-source image rendering software, Blender, for use in Terrain Relative Navigation (TRN) applications. A scene in Blender was generated based on elevation data and satellite imagery of the region of West Texas used by Blue Origin for the operation of their New Shepard suborbital rocket. The Blender scene was validated by reproducing imagery collected during a flight of New Shepard in October 2020 and was further used to generate reference maps for use by a TRN algorithm on a subsequent New Shepard flight in August 2021. The work was performed under the NASA Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) project, which is focused on technology advancement for precision landing and hazard avoidance. This work aims to lower the cost of entry and generally promote the adoption and advancement of vision-based navigation technologies.

Kyle W Smith↗
NASA NTRS2023

Extended Kalman Filter Performance on the Artemis-1 Mission

The Artemis Program is NASA’s campaign to explore the Moon and beyond. Artemis-1, the uncrewed exoLEO test flight of the Orion spacecraft, was completed in 2022. There were four navigation Extended Kalman Filters (EKFs) that are part of the Orion navigation system. The Atmospheric Extended Kalman Filter (ATMEKF) estimates the vehicle position, velocity, and attitude (referred to as the vehicle state) during the ascent and entry phases of flight. Once Orion is outside of Earths atmosphere, the Earth Orbit Extended Kalman Filter (EOEKF) and CisLunar Extended Kalman Filter (CLEKF) estimate the translational states, depending on the phase of flight, while the Attitude Extended Kalman Filter (ATTEKF) estimates the rotational state of the vehicle. The Kalman filters propagate the vehicle state forward in time using a combination of dynamics models and the output data from the Inertial Measurement Unit (IMU). The filters update the vehicle states and associated uncertainties, in the form of the covariance matrix, using pseudorange measurements from GPS (in ATMEKF/EOEKF), optical navigation measurements of the Earth or Moon (in CLEKF), and star tracker measurements (in ATTEKF). Simultaneously, the Kalman filters estimate error sources in the sensors, which are included in the state vectors as Exponentially Correlated Random Variables (ECRVs). This paper will summarize the performance of these filters during the Artemis-1 mission.

Artemis-1↗
NASA NTRS1992

Inertial navigation sensor integrated obstacle detection system

A system that incorporates inertial sensor information into optical flow computations to detect obstacles and to provide alternative navigational paths free from obstacles. The system is a maximally passive obstacle detection system that makes selective use of an active sensor. The active detection typically utilizes a laser. Passive sensor suite includes binocular stereo, motion stereo and variable fields-of-view. Optical flow computations involve extraction, derotation and matching of interest points from sequential frames of imagery, for range interpolation of the sensed scene, which in turn provides obstacle information for purposes of safe navigation.

Bhanu, Bir↗
NASA NTRS2002

Automated Escape Guidance Algorithms for An Escape Vehicle

An escape vehicle was designed to provide an emergency evacuation for crew members living on a space station. For maximum escape capability, the escape vehicle needs to have the ability to safely evacuate a station in a contingency scenario such as an uncontrolled (e.g., tumbling) station. This emergency escape sequence will typically be divided into three events: The fust separation event (SEP1), the navigation reconstruction event, and the second separation event (SEP2). SEP1 is responsible for taking the spacecraft from its docking port to a distance greater than the maximum radius of the rotating station. The navigation reconstruction event takes place prior to the SEP2 event and establishes the orbital state to within the tolerance limits necessary for SEP2. The SEP2 event calculates and performs an avoidance burn to prevent station recontact during the next several orbits. This paper presents the tools and results for the whole separation sequence with an emphasis on the two separation events. The fust challenge includes collision avoidance during the escape sequence while the station is in an uncontrolled rotational state, with rotation rates of up to 2 degrees per second. The task of avoiding a collision may require the use of the Vehicle's de-orbit propulsion system for maximum thrust and minimum dwell time within the vicinity of the station vicinity. The thrust of the propulsion system is in a single direction, and can be controlled only by the attitude of the spacecraft. Escape algorithms based on a look-up table or analytical guidance can be implemented since the rotation rate and the angular momentum vector can be sensed onboard and a-priori knowledge of the position and relative orientation are available. In addition, crew intervention has been provided for in the event of unforeseen obstacles in the escape path. The purpose of the SEP2 burn is to avoid re-contact with the station over an extended period of time. Performing this maneuver properly requires knowledge of the orbital state, which is obtained during the navigation state reconstruction event. Since the direction of the delta-v of the SEPI maneuver is a random variable with respect to the Local Vertical Local Horizontal (LVLH) coordinate system, calculating the required SEP2 burn is a challenge. This problem was solved using a neural network as a model-free function approximation technique.

Flanary, Ronald↗
DOE OSTI2026

Data and script associated with “Shifts in Rain-Snow Partitioning Drive Faster Water Transit Times in the US Pacific Northwest”

This data package contains the data and code to use and run the Water Tracer enabled version of the Weather Research and Forecasting Hydrologic model (WT-WRF-Hydro) with the Sequential Precipitation Input Tagging (SPIT) framework. It is associated with the publication “Shifts in Rain-Snow Partitioning Drive Faster Water Transit Times in the US Pacific Northwest” published in Scientific Reports (Butler et al., 2026; https://doi.org/10.1038/s41598-026-46539-1). We use the Continental U.S. (CONUSII; Rasmussen et al., 2021) dataset to force the model with an historical climate (2006–2013) and a future climate (2086–2093) with a representative carbon pathway (RCP) 8.5 scenario. We use the model to calculate water transit times in five headwater catchments within the U.S. Pacific Northwest. We also show key hydrologic and environmental variables that affect water transit times and changes in the future. Finally, we use observed data to validate the model such as stream water isotopes, snowpack characteristics, and stream discharge. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. The data package consists of 11 folders: (1) "Figures" contains the exported figures used in the manuscript; (2) "Model_Isotope_Date" contains the WT-WRF-Hydro isotope date used in model validation; (3) “Model_Outputs_Future” contains the WT-WRF-Hydro future climate outputs; (4) “Model_Outputs_Historical” contains the WT-WRF-Hydro historical climate outputs; (5) “Model_Outputs_Weights_Areas” contains the WT-WRF-Hydro weights per catchment used to calculate water transit times and isotopes in stream water; (6) “MODIS_data_scripts” contains data used to validate snow conditions in the study area; (7) “Observed_Flow_Data” contains the observed streamflow data used in model validation; (8) “Observed_Isotope_Data” contains the observed stream water isotope data used in model validation; (9) “Scripts” contains the Python scripts used to general results and the figures; (10) “Statistic_Outputs” contains the water transit time statistical outputs reported in this manuscript; (11) “Validation_SNOTEL” contains the SNOTEL data used in model validation. The files in this data package have the following file extensions: .tif, .txt, .csv, .pdf, .py, .jpg, and .png.

American River↗
NASA NTRS1975

Time-controlled descent guidance in uncertain winds

A procedure has been developed for constructing a statistical model of the altitude-dependent mean wind profile from the historical record of wind measurements at particular locations. The model is constructed by fitting a Markov process, with altitude as the stage variable, to the historical wind data. The wind model, together with the aircraft dynamics and the error characteristics of the navigation system, are incorporated in the design of a state estimator, which gives the minimum variance estimate of the aircraft state and the wind vector. The state and wind estimates are used as inputs to a linear feedback law for guiding the aircraft along the nominal trajectory. An example design of a time-constrained (4D RNAV) descent guidance system is presented, showing tracking accuracy, control activity, and probability of arrival time with and without the wind estimator.

Menga, G.↗