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NASA NTRS1994

Evaluation of the TOPEX/POSEIDON dual-frequency ionosphere correction

The NASA altimeter on board TOPEX/POSEIDON exploits the difference in the delays of the Ku and C band radar pulses to estimate an ionosphere correction to the range measurement. The dependence of the ionosphere correction on ocean and satellite parameters is less than 1 cm. The standard deviation of the 1-s averaged ionosphere correction depends on the height of the ocean waves and ranges from 5 to 14 mm. The accuracy of the ionosphere correction is better than 1 cm at the 1 sigma confidence level. The ionosphere correction should be averaged over 140 km (20 s) along track in order to minimize its noise without sacrificing its accuracy. Ionosphere models must achieve an independent sample spacing of 900 km or less in order to allow a single-frequency altimeter to have an ionosphere correction comparable in accuracy to that of the NASA dual-frequency altimeter.

Imel, David A.↗

NASA NTRS1994

The 1994 International Geoscience and Remote Sensing Symposium (IGARSS 1994)

The papers presented at the symposium focus on remote sensing, particularly on global monitoring of the earth with emphasis on the solution of environmental problems. Topics discussed include remote sensing of clouds and earth troposphere, sea ice remote sensing, optical remote sensing, land monitoring and thermal sensing, atmospheric sounding and monitoring, atmospheric correction, and satellite imaging data. Other subject areas are ecosystems and vegetation monitoring; ocean winds and surface scattering; ocean waves, currents and bathymetry; satellite oceanography; SAR for remote sensing; neural nets application to remote sensing; geographical information systems; and electromagnetic wave propagation. Also discussed environmental monitoring using ERS-1; Topex/Poseidon results; spaceborne instruments; image processing and classification algorithms; and future space missions.

Source record↗

NASA NTRS2000

Salinity Measurements During the Gulf Stream Experiment

The salinity of the open ocean is important for understanding ocean circulation, for understanding energy exchange with the atmosphere and for improving models to predict weather and climate. Passive microwave sensors at L-band (1.4 GHz) operating from aircraft have demonstrated that salinity can be measured with sufficient accuracy (1 psu) to be scientifically meaningful in coastal waters. However, measuring salinity in the open ocean presents unresolved issues largely because of the much greater accuracy (approx. 0.1 psu) required to be scientifically viable. In the summer of 1999 a series of measurements called, The Gulf Stream Experiment, were conducted as part of research at the Goddard Space Flight Center to test the potential for passive microwave remote sensing of salinity in the open ocean. The measurements consisted of a compliment of airborne microwave instruments (radiometers and scatterometer) and ships and drifters for surface truth. The study area was a 200 km by 100 km rectangle about 250 km east of Delaware Bay between the continental shelf waters and north wall of the Gulf Stream. The primary passive instruments were the ESTAR radiometer (L-band, H-pol) and the SLFMR radiometer (L-band, V-pol). In addition, the compliment of instruments on the aircraft included a C-band radiometer (ACMR), an ocean wave scatterometer (ROWS) and an infrared radiometer. A GPS backscatter experiment was also part of the package. These instruments were mounted on the NASA P-3 Orion aircraft. Surface salinity measurements were provided by the RN Cape Henlopen and MN Oleander (thermosalinographs) plus salinity and temperature sensors on three surface drifters deployed from the RN Cape Henopen. The primary experiment period was August 26-September 2, 1999. During this period the salinity field within the study area consisted of a gradient on the order of 2-3 psu in the vicinity of the shelf break and a warm core ring with a gradient of 1-2 psu. Detailed maps were made with the airborne sensors on August 28 and 29 and on September 2 flights were made over the surface drifters to look for effects due to a change in surface roughness resulting from the passage of Hurricane Dennis. Preliminary results show a good agreement between the microwave measurements and ship measurements of salinity. The features of the brightness temperature maps correspond well with the features of the salinity field measured by the ship and drifters and a preliminary retrieval of salinity compares well with the ship data.

LeVine, D. M.↗

NASA NTRS2000

Objective Operational Utilization of Satellite Microwave Scatterometer Observations of Tropical Cyclones

This study has demonstrated that high-resolution scatterometer measurements in tropical cyclones and other high-marine surface wind regimes may be retrieved accurately for wind speeds up to about 35 mls (1-hour average at 10 m) when the scatterometer data are processed through a revised geophysical model function, and a spatial adaptive algorithm is applied which utilizes the fact that wind direction is so tightly constrained in tile inner core of severe marine storms that wind direction may be prescribed from conventional data. This potential is demonstrated through case studies with NSCAT data in a severe West Pacific Typhoon (Violet, 1996) and an intense North Atlantic hurricane (Lili, 1996). However, operational scatterometer winds from NSCAT and QuickScat in hurricanes and severe winter storms are biased low in winds above 25 m/s. We have developed an inverse model to specify the entire surface wind field about a tropical cyclone from operational QuickScat scatterometer measurements within 150 nm of a storm center with the restriction that only wind speeds up to 20 m/s are used until improved model function are introduced. The inverse model is used to specify the wind field over the entire life-cycle of Hurricane Floyd (1999) for use to drive an ocean wave model. The wind field compares very favorably with wind fields developed from the copious aircraft flight level winds obtained in this storm.

Cardone, Vincent J.↗

NASA NTRS2001

Passive Microwave Measurements of Salinity: The Gulf Stream Experiment

Passive microwave sensors at L-band (1.4 GHz) operating from aircraft have demonstrated that salinity can be measured with sufficient accuracy (I psu) to be scientifically meaningful in coastal waters. However, measuring salinity in the open ocean presents unresolved issues largely because of the much greater accuracy (approximately 0.2 psu) required of global maps to be scientifically viable. The development of a satellite microwave instrument to make global measurements of SSS (Sea Surface Salinity) is the focus of a joint JPL/GSFC/NASA ocean research program called Aquarius. In the summer of 1999 a series of measurements called, The Gulf Stream Experiment, were conducted as part of research at the Goddard Space Flight Center to test the potential for passive microwave remote sensing of salinity in the open ocean. The measurements consisted of airborne microwave instruments together with ships and drifters for surface truth. The study area was a 200 km by 100 km rectangle about 250 km east of Delaware Bay between the continental shelf waters and north wall of the Gulf Stream. The primary passive instruments were the ESTAR radiometer (L-band, H-pol) and the SLFMR radiometer (L-band, V-pol). In addition, the instruments on the aircraft included a C-band radiometer (ACMR), an ocean wave scatterometer (ROWS) and an infrared radiometer (for surface temperature). These instruments were mounted on the NASA P-3 Orion aircraft. Sea surface measurements consisted of thermosalinograph data provided by the R/V Cape Henlopen and the MN Oleander, and data from salinity and temperature sensors on three surface drifters deployed from the R/V Cape Henlopen. The primary experiment period was August 26-September 2, 1999. During this period the salinity field within the study area consisted of a gradient on the order of 2-3 psu in the vicinity of the shelf break and a warm core ring with a gradient of 1-2 psu. Detailed maps were made with the airborne sensors on August 28 and 29 and on September 2 flights were made over the surface drifters to look for effects due to a change in surface roughness resulting from the passage of Hurricane Dennis. Results show a good agreement between the microwave measurements and ship measurements of salinity. The features of the brightness temperature maps correspond well with the features of the salinity field measured by the ship and drifters and a preliminary retrieval of salinity compares well with the ship data.

LeVine, D. M.↗

NASA NTRS2007

Sun Glint and Sea Surface Salinity Remote Sensing

A new mission in space, called Aquarius/SAC-D, is being built to measure the salinity of the world's oceans. Salinity is an important parameter for understanding movement of the ocean water. This circulation results in the transportation of heat and is important for understanding climate and climate change. Measuring salinity from space requires precise instruments and a careful accounting for potential sources of error. One of these sources of error is radiation from the sun that is reflected from the ocean surface to the sensor in space. This paper examines this reflected radiation and presents an advanced model for describing this effect that includes the effects of ocean waves on the reflection.

Dinnat, Emmanuel P.↗

NASA NTRS2007

Multi-Antenna Radar Systems for Doppler Rain Measurements

Use of multiple-antenna radar systems aboard moving high-altitude platforms has been proposed for measuring rainfall. The basic principle of the proposed systems is a variant of that of along-track interferometric synthetic-aperture radar systems used previously to measure ocean waves and currents.

Durden, Stephen↗

NASA NTRS2005

Simulating Vibrations in a Complex Loaded Structure

The Dynamic Response Computation (DIRECT) computer program simulates vibrations induced in a complex structure by applied dynamic loads. Developed to enable rapid analysis of launch- and landing- induced vibrations and stresses in a space shuttle, DIRECT also can be used to analyze dynamic responses of other structures - for example, the response of a building to an earthquake, or the response of an oil-drilling platform and attached tanks to large ocean waves. For a space-shuttle simulation, the required input to DIRECT includes mathematical models of the space shuttle and its payloads, and a set of forcing functions that simulates launch and landing loads. DIRECT can accommodate multiple levels of payload attachment and substructure as well as nonlinear dynamic responses of structural interfaces. DIRECT combines the shuttle and payload models into a single structural model, to which the forcing functions are then applied. The resulting equations of motion are reduced to an optimum set and decoupled into a unique format for simulating dynamics. During the simulation, maximum vibrations, loads, and stresses are monitored and recorded for subsequent analysis to identify structural deficiencies in the shuttle and/or payloads.

Cao, Tim T.↗

NASA NTRS2014

APC: A New Code for Atmospheric Polarization Computations

A new polarized radiative transfer code Atmospheric Polarization Computations (APC) is described. The code is based on separation of the diffuse light field into anisotropic and smooth (regular) parts. The anisotropic part is computed analytically. The smooth regular part is computed numerically using the discrete ordinates method. Vertical stratification of the atmosphere, common types of bidirectional surface reflection and scattering by spherical particles or spheroids are included. A particular consideration is given to computation of the bidirectional polarization distribution function (BPDF) of the waved ocean surface.

surface reflection↗

NASA NTRS2012

Polarimetric Retrievals of Surface and Cirrus Clouds Properties in the Region Affected by the Deepwater Horizon Oil Spill

In 2010, the Goddard Institute for Space Studies (GISS) Research Scanning Polarimeter (RSP) performed several aerial surveys over the region affected by the oil spill caused by the explosion of the Deepwater Horizon offshore platform. The instrument was deployed on the NASA Langley B200 aircraft together with the High Spectral Resolution Lidar (HSRL), which provides information on the distribution of the aerosol layers beneath the aircraft, including an accurate estimate of aerosol optical depth. This work illustrates the merits of polarization measurements in detecting variations of ocean surface properties linked to the presence of an oil slick. In particular, we make use of the degree of linear polarization in the glint region, which is severely affected by variations in the refractive index but insensitive to the waviness of the water surface. Alterations in the surface optical properties are therefore expected to directly affect the polarization response of the RSP channel at 2264 nm, where both molecular and aerosol scattering are negligible and virtually all of the observed signal is generated via Fresnel reflection at the surface. The glint profile at this wavelength is fitted with a model which can optimally estimate refractive index, wind speed and direction, together with aircraft attitude variations affecting the viewing geometry. The retrieved refractive index markedly increases over oil-contaminated waters, while the apparent wind speed is significantly lower than in adjacent uncontaminated areas, suggesting that the slick dampens high-frequency components of the ocean wave spectrum. The constraint on surface reflectance provided by the short-wave infrared channels is a cornerstone of established procedures to retrieve atmospheric aerosol microphysical parameters based on the inversion of the RSP multispectral measurements. This retrieval, which benefits from the ancillary information provided by the HSRL, was in this specific case hampered by prohibitive variability in atmospheric conditions (very inhomogeneous aerosol distribution and cloud cover). Although the results presented for the surface are essentially unaffected, we discuss the results obtained by typing algorithms in sorting the complex mix of aerosol types, and show evidence of oriented ice in cirrus clouds present in the area. In this context, polarization measurements at 1880 nm were used to infer ice habit and cirrus optical depth, which was found in the subvisual/threshold-visible regime, confirming the utility of the aforementioned RSP channel for the remote sensing of even thin cold clouds.

Polarization↗

NASA NTRS2012

Aquarius Main Structure Configuration

The Aquarius/SAC-D Observatory is a joint US-Argentine mission to map the salinity at the ocean surface. This information is critical to improving our understanding of two major components of Earth's climate system - the water cycle and ocean circulation. By measuring ocean salinity from space, the Aquarius/SAC-D Mission will provide new insights into how the massive natural exchange of freshwater between the ocean, atmosphere and sea ice influences ocean circulation, weather and climate. Aquarius is the primary instrument on the SAC-D spacecraft. It consists of a Passive Microwave Radiometer to detect the surface emission that is used to obtain salinity and an Active Scatterometer to measure the ocean waves that affect the precision of the salinity measurement. The Aquarius Primary Structure houses instrument electronics, feed assemblies, and supports a deployable boom with a 2.5 m Reflector, and provides the structural interface to the SAC-D Spacecraft. The key challenge for the Aquarius main structure configuration is to satisfy the needs of component accommodations, ensuring that the instrument can meet all operational, pointing, environmental, and launch vehicle requirements. This paper describes the evolution of the Aquarius main structure configuration, the challenges of balancing the conflicting requirements, and the major configuration driving decisions and compromises.

Eremenko, Alexander↗

NASA NTRS2017

NeMO-Net: The Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 percent error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets. We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

NeMO-Net↗

NASA NTRS2017

A 3D Active Learning Application for NeMO-Net, the NASA Neural Multi-Modal Observation and Training Network for Global Coral Reef Assessment

NeMO-Net, the NASA neural multi-modal observation and training network for global coral reef assessment, is an open-source deep convolutional neural network and interactive active learning training software aiming to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology as well as mapping of spatial distribution. We present an interactive video game prototype for tablet and mobile devices where users interactively label morphology classifications over mm-scale 3D coral reef imagery captured using fluid lensing to create a dataset that will be used to train NeMO-Nets convolutional neural network. The application currently allows for users to classify preselected regions of coral in the Pacific and will be expanded to include additional regions captured using our NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as lower-resolution airborne remote sensing data from the ongoing NASA CORAL campaign. Active learning applications present a novel methodology for efficiently training large-scale Neural Networks wherein variances in identification can be rapidly mitigated against control data. NeMO-Net periodically checks users input against pre-classified coral imagery to gauge their accuracy and utilize in-game mechanics to provide classification training. Users actively communicate with a server and are requested to classify areas of coral for which other users had conflicting classifications and contribute their input to a larger database for ranking. In partnering with Mission Blue and IUCN, NeMO-Net leverages an international consortium of subject matter experts to classify areas of confusion identified by NeMO-Net and generate additional labels crucial for identifying decision boundary locations in coral reef assessment.

NeMO=NET↗

NASA NTRS2017

NeMO-Net The Neural Multi-Modal Observation Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. Exploiting the fine-scaled features of these datasets, machine learning methods such as MAP, PCA, and SVM can not only accurately classify the living cover and morphology of these reef systems (below 8 error), but are also able to map the spectral space between airborne and satellite imagery, augmenting and improving the classification accuracy of previously low-resolution datasets.We are currently implementing NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive active learning and training software to accurately assess the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. NeMO-Net will be built upon the QGIS platform to ingest UAV, airborne and satellite datasets from various sources and sensor capabilities, and through data-fusion determine the coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. To achieve this, we will exploit virtual data augmentation, the use of semi-supervised learning, and active learning through a tablet platform allowing for users to manually train uncertain or difficult to classify datasets. The project will make use of Pythons extensive libraries for machine learning, as well as extending integration to GPU and High-End Computing Capability (HECC) on the Pleiades supercomputing cluster, located at NASA Ames. The project is being supported by NASAs Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Remote Sensin↗

NASA NTRS2019

Next-Generation Optical Sensing Technologies for Exploring Ocean Worlds - NASA FluidCam, MiDAR, and NeMO-Net

We highlight three emerging NASA optical technologies that enhance our ability to remotely sense, analyze, and explore ocean worlds–FluidCam and fluid lensing, MiDAR, and NeMO-Net. Fluid lensing is the first remote sensing technology capable of imaging through ocean waves without distortions in 3D at sub-cm resolutions. Fluid lensing and the purpose-built FluidCam CubeSat instruments have been used to provide refraction-corrected 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs). Results from repeat 2013 and 2016 airborne fluid lensing campaigns over coral reefs in American Samoa present a promising new tool for monitoring fine-scale ecological dynamics in shallow aquatic systems tens of square kilometers in area. MiDAR is a recently-patented active multispectral remote sensing and optical communications instrument which evolved from FluidCam. MiDAR is being tested on UAVs and autonomous underwater vehicles (AUVs) to remotely sense living and non-living structures in light-limited and analog planetary science environments. MiDAR illuminates targets with high-intensity narrowband structured optical radiation to measure an object’s spectral reflectance while simultaneously transmitting data. MiDAR is capable of remotely sensing reflectance at fine spatial and temporal scales, with a signal-to-noise ratio 10-10(exp 3) times higher than passive airborne and spaceborne remote sensing systems, enabling high-framerate multispectral sensing across the ultraviolet, visible, and near-infrared spectrum. Preliminary results from a 2018 mission to Guam show encouraging applications of MiDAR to imaging coral from airborne and underwater platforms whilst transmitting data across the air-water interface. Finally, we share NeMO-Net, the Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment. NeMO-Net is a machine learning technology under development that exploits high-resolution data from FluidCam and MiDAR for augmentation of low-resolution airborne and satellite remote sensing. NeMO-Net is intended to harmonize the growing diversity of 2D and 3D remote sensing with in situ data into a single open-source platform for assessing shallow marine ecosystems globally using active learning for citizen-science based training. Preliminary results from four-class Q17 coral classification have an accuracy of 94.4%. Together, these maturing technologies present promising scalable, practical, and cost-efficient innovations that address current observational and technological challenges in optical sensing of marine systems.

Ved Chirayath↗

NASA NTRS2020

An Introduction to the NASA GMAO Coupled Atmosphere-Ocean System - GEOS-S2S Version 3

Recently NASA's Global Modeling and Assimilation Office (GMAO) has developed a new Subseasonal to Seasonal Prediction system Version 3 (GEOS-S2S-3). This upgrade replaces the GEOS-S2S-2 which is NASA's current contribution to the North American Multi-Model Experiment seasonal prediction project (Kirtman et al., 2014). The main improvements for our S2S-3 system include 1) a higher resolution MOM5 (Griffies et al., 2005) ocean model (now 0.25o x 0.25o x 50 layers), 2) an improved atmospheric/ocean interface layer (Akella and Suarez, 2018), and 3) assimilation of a long-track satellite salinity into the ocean model (Hackert et al, 2019). Atmospheric forcing is provided by the NASA MERRA-2 reanalysis (Gelaro et al., 2017). Initialization for the ocean relies on the GMAO ocean reanalysis system which assimilates all available in situ temperature and salinity, satellite sea surface salinity, and sea level using the Local Ensemble Transform Kalman Filter (LETKF) implementation of (Penny et al., 2013) on a 5 day assimilation cycle with 20 fixed ensemble members.In this presentation, we will authenticate our new S2S-3 ocean reanalysis using standard GODAE validation metrics. For example, we will compare gridded fields of mean and standard deviation of the ocean reanalysis versus observed fields. We will show correlation/RMS of model versus observations and temperature and salinity mean profiles for the various basins and latitude bands. Basin-scale volume transports, such as the Atlantic Meridional Overturning Circulation and the Indonesian Throughflow will be validated. Equatorial ocean waves will be compared by decomposing sea level into Kelvin and Rossby components. For each of these metrics, we plan to validate the results and then compare our new S2S-3 against the current production version, S2S-2. Finally, we will compare 9-month seasonal forecasts initialized from these two systems for the tropical Pacific NINO3.4 region over the period 1981-present.

Molod, Andrea↗

NASA NTRS2020

NeMO-Net – The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

We present NeMO-Net, the Srst open-source deep convolutional neural network (CNN) and interactive learning and training software aimed at assessing the present and past dynamics of coral reef ecosystems through habitat mapping into 10 biological and physical classes. Shallow marine systems, particularly coral reefs, are under significant pressures due to climate change, ocean acidification, and other anthropogenic pressures, leading to rapid, often devastating changes, in these fragile and diverse ecosystems. Historically, remote sensing of shallow marine habitats has been limited to meter-scale imagery due to the optical effects of ocean wave distortion, refraction, and optical attenuation. NeMO-Net combines 3D cm-scale distortion-free imagery captured using NASA FluidCam and Fluid lensing remote sensing technology with low resolution airborne and spaceborne datasets of varying spatial resolutions, spectral spaces, calibrations, and temporal cadence in a supercomputer-based machine learning framework. NeMO-Net augments and improves the benthic habitat classification accuracy of low-resolution datasets across large geographic ad temporal scales using high-resolution training data from FluidCam.NeMO-Net uses fully convolutional networks based upon ResNet and ReSneNet to perform semantic segmentation of remote sensing imagery of shallow marine systems captured by drones, aircraft, and satellites, including WorldView and Sentinel. Deep Laplacian Pyramid Super-Resolution Networks (LapSRN) alongside Domain Adversarial Neural Networks (DANNs) are used to reconstruct high resolution information from low resolution imagery, and to recognize domain-invariant features across datasets from multiple platforms to achieve high classification accuracies, overcoming inter-sensor spatial, spectral and temporal variations.Finally, we share our online active learning and citizen science platform, which allows users to provide interactive training data for NeMO-Net in 2D and 3D, integrated within a deep learning framework. We present results from the PaciSc Islands including Fiji, Guam and Peros Banhos 1 1 2 1 3 1 where 24-class classification accuracy exceeds 91%.

Chirayath, Ved↗

NASA NTRS2019

Dynamic Characterization of the Crew Module Uprighting System for NASA’s Orion Crew Module

The Orion Crew Module Uprighting System is a set of five airbag that are responsible for the uprighting of the crew module in the case of an inverted splashdown. A series of tests during the Underway Recovery Test 7 (URT-7) were run in preparation for the Artemis I mission, where the dynamic characterization of the CMUS in an ocean wave environment was performed. A Datawell Waverider DWR-G4 wave buoy was deployed to the characterize the wave environment during these tests. The heave measurements from this buoy were projected to the Orion Crew Module Buoyancy Test Article location by two different methods: (1) directly time-shifting the data, and (2) performing a frequency-domain, phase-shifting operation. Results demonstrate that the phase-shifting operation led to better correlation with the true crew module response to wave excitation as compared with the purely time-shifted method. Additionally, a novel approach to localize an object in a bidirectional wave field based on its heave response is presented and validated with URT- 7 data. Given a wave measurement device at a known location, one can estimate the relative distance to another object based solely off its heave response. Results show that if signals have sufficiently good correlation, this method can be used to estimate the relative separation between two objects in the same wave field.

Vertaska, Ivan Bertaska↗