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GPS/INS Sensor Fusion Using GPS Wind up Model
A method of stabilizing an inertial navigation system (INS), includes the steps of: receiving data from an inertial navigation system; and receiving a finite number of carrier phase observables using at least one GPS receiver from a plurality of GPS satellites; calculating a phase wind up correction; correcting at least one of the finite number of carrier phase observables using the phase wind up correction; and calculating a corrected IMU attitude or velocity or position using the corrected at least one of the finite number of carrier phase observables; and performing a step selected from the steps consisting of recording, reporting, or providing the corrected IMU attitude or velocity or position to another process that uses the corrected IMU attitude or velocity or position. A GPS stabilized inertial navigation system apparatus is also described.
Fusion of Satellite and Model Data to Study Hurricanes and Improve Forecasts: The JPL Tropical Cyclone Information System and Near Real Time Portal
No abstract available
GNSS Ephemeris with Graceful Degradation and Measurement Fusion
A method for providing an extended propagation ephemeris model for a satellite in Earth orbit, the method includes obtaining a satellite's orbital position over a first period of time, applying a least square estimation filter to determine coefficients defining osculating Keplarian orbital elements and harmonic perturbation parameters associated with a coordinate system defining an extended propagation ephemeris model that can be used to estimate the satellite's position during the first period, wherein the osculating Keplarian orbital elements include semi-major axis of the satellite (a), eccentricity of the satellite (e), inclination of the satellite (i), right ascension of ascending node of the satellite (.OMEGA.), true anomaly (.theta.*), and argument of periapsis (.omega.), applying the least square estimation filter to determine a dominant frequency of the true anomaly, and applying a Fourier transform to determine dominant frequencies of the harmonic perturbation parameters.
In-Situ and Remote-Sensing Data Fusion Using Machine Learning Techniques to Infer Urban and Fire Related Pollution Plumes
Airmass type characterization is key in understanding the relative contribution of various emission sources to atmospheric composition and air quality and can be useful in bottom-up model validation and emission inventories. However, classification of pollution plumes from space is often not trivial. Sub-orbital campaigns, such as SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) give us a unique opportunity to study atmospheric composition in detail, by using a vast suite of in-situ instruments for the detection of trace gases and aerosols. These measurements allow identification of spatial and temporal atmospheric composition changes due to various pollution plumes resulting from urban, biogenic and smoke emissions. Nevertheless, to transfer the knowledge gathered from such campaigns into a global spatial and temporal context, there is a need to develop workflow that can be applicable to measurements from space. In this work we rely on sub-orbital in-situ and total column remote sensing measurements of various pollution plumes taken aboard the NASA DC-8 during 2013 SEAC4RS campaign, linking them through a neural-network (NN) algorithm to allow inference of pollution plume types by input of columnar aerosol and trace-gas measurements. In particular, we use the 4STAR (Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research) airborne measurements of wavelength dependent aerosol optical depth (AOD), particle size proxies, O3, NO2 and water vapor to classify different pollution plumes. Our method relies on assigning a-priori ground-truth labeling to the various plumes, which include urban pollution, different fire types (i.e. forest and agriculture) and fire stage (i.e. fresh and aged) using cluster analysis of aerosol and trace-gases in-situ and auxiliary (e.g. trajectory) data and the training of a NN scheme to fit the best prediction parameters using 4STAR measurements as input. We explore our misclassification rates as related to our ground-truth labels, and with multi-layered pollution plume cases. The next step in our analysis is to optimize parameter selection for a scheme that can be applied to space-borne aerosol and trace-gas observation platforms such as OMI, and future geostationary satellites such as TEMPO and GEO-CAPE.
Overview of Fatigue and Damage Tolerance Performance of Powder Bed Fusion Alloy N07718
No abstract available
Structure from Motion Photogrammetry and Micro X-Ray Computed Tomography 3-D Reconstruction Data Fusion for Non-Destructive Conservation Documentation of Lunar Samples
Our team is developing a modern, cross-disciplinary approach to documentation and preservation of astromaterials, specifically lunar and meteorite samples stored at the Johnson Space Center (JSC) Lunar Sample Laboratory Facility. Apollo Lunar Sample 60639, collected as part of rake sample 60610 during the 3rd Extra-Vehicular Activity of the Apollo 16 mission in 1972, served as the first NASA-preserved lunar sample to be examined by our team in the development of a novel approach to internal and external sample visualization. Apollo Sample 60639 is classified as a breccia with a glass-coated side and pristine mare basalt and anorthosite clasts. The aim was to accurately register a 3-dimensional Micro X-Ray Computed Tomography (XCT)-derived internal composition data set and a Structure-From-Motion (SFM) Photogrammetry-derived high-fidelity, textured external polygonal model of Apollo Sample 60639. The developed process provided the means for accurate, comprehensive, non-destructive visualization of NASA's heritage lunar samples. The data products, to be ultimately served via an end-user web interface, will allow researchers and the public to interact with the unique heritage samples, providing a platform to "slice through" a photo-realistic rendering of a sample to analyze both its external visual and internal composition simultaneously.
Classification, Effects, and Prevention of Build Defects in Powder-Bed Fusion Printed Inconel 718
No abstract available
Evaluating Precipitation Observed in Complex Terrain During GPM Field Campaigns with the SIMBA Data-fusion Tool
No abstract available
The Land Surface Data Toolkit (LDT v7.2) - A Data Fusion Environment for Land Data Assimilation Systems
The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land-surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions and meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and data assimilation.
GEDI and TanDEM-X Fusion for 3D Forest Structure Parameter Retrieval
GEDI: Global Ecosystem Dynamics Investigation. Selected in late 2014 for $94 M (Class C mission). Multi-beam waveform lidar instrument. Deployed on International Space Station. Launch on SpaceX-17: Nov. 2018. Nominal 2 year mission length.