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This study was conducted to support stability assessment for: (1) Tasks in lunar gravity (2) Exercises on a Vibration Isolation and Stabilization (VIS) system in microgravity. Stability assessment based on the dynamic feasibility criterion of whether the calculated position of the center of pressure (COP) falls within the base of support (BOS) which outlines the subject’s feet.
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Low altitude flight in urban areas is susceptible to degraded GNSS-based navigation system performance due to terrain interference with radio signals from orbital positioning satellites. Predictive navigation performance fidelity tools are needed a) in preflight planning to assist in the creation of safe flight paths and b) in-flight to provide contingency management agents with the navigation risk of proximal flight corridors. Two navigation fidelity prediction services are validated by comparison with over 6000 readings from GNSS sensors collected along a five-mile path through urban areas of Corpus Christi, Texas, on three dates in 2022. Predictions are based on satellite line of sight through 3D terrain data collected in 2018. Each service predicts a set of navigation fidelity metrics over a user-specified time period. One metric estimated by both is the number of visible satellites. A direct comparison of the number of predicted visible satellites with the number sensed by the receiver is used to validate the prediction services. Results show an exact match in the number of predicted satellites for 60% of the measurements, and a match within +/- 4 satellites for 95% of the measurements. As expected, agreement improves away from vertical blocking terrain. Most cases of mismatch are due a lower predicted count than measured (false negatives), and can be accounted for by receiver pickup of stray signals caused by multipath propagation. About 10% of mismatches are false positives and are mostly accounted for by foliage effects. The two services predict visibility of the same set of satellites 80% of the time, differ by two or less satellites 95% of the time, and can compute predictions for one hour of observations in one minute or less. Validation is analyzed statistically and in detailed case studies of selected observation times. The prediction services validated in this study run fast enough for preflight safety planning. The more stringent challenge of inflight navigation fidelity prediction for contingency management requires both a speedup of the current level of modeling and equally fast stray signal modeling.
- The Tropospheric Doppler Radar Wind Profiler (TDRWP) is a wind measurement instrument at NASA’s Kennedy Space Center, co-located on the United States Space Force’s (USSF) Eastern Range (ER) at the Cape Canaveral Space Force Station (CCAFS), that generates atmospheric wind profiles which are utilized in the design, certification and operations of various launch vehicles - NASA’s Space Launch System (SLS) - Commercial launch vehicles - NASA Crew-4 in April 2022 was first NASA crewed mission to use TDRWP - Composed of twenty antenna sectors and transmitter unit pairs which operate at 48 MHz - TDRWP data is required for day-of-launch structural margin and performance assessments
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Low altitude flight in urban areas is susceptible to degraded GNSS-based navigation system performance due to terrain interference with radio signals from orbital positioning satellites. Predictive navigation performance fidelity tools are needed a) in preflight planning to assist in the creation of safe flight paths and b) in-flight to provide contingency management agents with the navigation risk of proximal flight corridors. Two navigation fidelity prediction services are validated by comparison with over 6000 readings from GNSS sensors collected along a five-mile path through urban areas of Corpus Christi, Texas, on three dates in 2022. Predictions are based on satellite line of sight through 3D terrain data collected in 2018. Each service predicts a set of navigation fidelity metrics over a user-specified time period. One metric estimated by both is the number of visible satellites. A direct comparison of the number of predicted visible satellites with the number sensed by the receiver is used to validate the prediction services. Results show an exact match in the number of predicted satellites for 60% of the measurements, and a match within +/- 4 satellites for 95% of the measurements. As expected, agreement improves away from vertical blocking terrain. Most cases of mismatch are due a lower predicted count than measured (false negatives), and can be accounted for by receiver pickup of stray signals caused by multipath propagation. About 10% of mismatches are false positives and are mostly accounted for by foliage effects. The two services predict visibility of the same set of satellites 80% of the time, differ by two or less satellites 95% of the time, and can compute predictions for one hour of observations in one minute or less. Validation is analyzed statistically and in detailed case studies of selected observation times. The prediction services validated in this study run fast enough for preflight safety planning. The more stringent challenge of inflight navigation fidelity prediction for contingency management requires both a speedup of the current level of modeling and equally fast stray signal modeling.
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This presentation documents the progress made and challenges remaining in the development of NASA's 4D space-time mapping CAA code.
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The CubeSpark mission concept is being developed as a constellation of up to six satellites in low-Earth orbit (LEO) for high resolution 3D lightning mapping using optical and radio frequency (RF) sensors. Individual lightning VHF signals are simulated from Earth’s atmosphere through the ionosphere to each satellite, using their arrival times to reconstruct source locations. Here we present recent updates to these simulations based on improved ionospheric modeling, with a focus on the expected three-dimensional resolution. These studies include testing from the equator up to high latitudes, with varying vertical total electron content (vTEC), and using between one and six orbiting stations. In addition to the more robust ionosphere model, the constellation formation has also been updated to reduce its resulting errors and increase the effective range of VHF geolocation from space. The goals of CubeSpark include mapping thundercloud charge structure as well as lightning channel lengths relevant to climatology, meteorology, and more. These applications require location uncertainty less than 1-2 km in each dimension. This improved algorithm shows sufficient resolution up to high latitudes, including significantly larger areas having 3D resolution less than 1 km. Analysis of the distributions of biases in simulated arrival times has also revealed the unexpected relationship between the shape of those distributions and the resulting uncertainties. This work helps to refine our understanding of the sources of error in lightning geolocation and reinforces the potential for post-processing improvements in this and other similar systems.
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A novel method is suggested to reduce estimation error on metabolic workload during surface extravehicular activity (EVA) traverse.
When end-users are using multiple satellite images for a study the first step is often the assessment and rectification of geolocation offsets. We have developed an automated algorithm to calculate the geometric offsets between two satellite images. It splits both images into subset images (chips) and resamples them to a common resolution. Offsets are imposed on matching chips and the Pearson Cross Correlation (PCC) value calculated for each offset, where the best PCC gives the true offsets. With the true offsets corrected for, a quality of image registration is calculated, measurement uncertainty (MU). The MU threshold value is non-uniform across sensors because it depends on spectral characteristics of each sensor. Previously, the MU threshold was determined by manual inspection for each new sensor assessed. In this study, we automated MU threshold determination. 1/MU vs calculated offset approximates a Gaussian function in shape, see abstract image. A Gaussian function is fit to this dataset, and the root of the third derivative is calculated and applied as a quality threshold, removing the data of lower quality from the final assessment of image offsets. We apply this algorithm to commercial satellite data for proof of concept.
This presentation documents the progress made and challenges remaining in the development of NASA's 4D space-time mapping CAA code.