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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Radar range data signal enhancement tracker

The design, fabrication, and performance characteristics are described of two digital data signal enhancement filters which are capable of being inserted between the Space Shuttle Navigation Sensor outputs and the guidance computer. Commonality of interfaces has been stressed so that the filters may be evaluated through operation with simulated sensors or with actual prototype sensor hardware. The filters will provide both a smoothed range and range rate output. Different conceptual approaches are utilized for each filter. The first filter is based on a combination low pass nonrecursive filter and a cascaded simple average smoother for range and range rate, respectively. Filter number two is a tracking filter which is capable of following transient data of the type encountered during burn periods. A test simulator was also designed which generates typical shuttle navigation sensor data.

Source record↗
NASA NTRS1978

Synthetic Aperture Radar (SAR) data processing

The available and optimal methods for generating SAR imagery for NASA applications were identified. The SAR image quality and data processing requirements associated with these applications were studied. Mathematical operations and algorithms required to process sensor data into SAR imagery were defined. The architecture of SAR image formation processors was discussed, and technology necessary to implement the SAR data processors used in both general purpose and dedicated imaging systems was addressed.

Beckner, F. L.↗
NASA NTRS1978

An atlas of GEOS-3 radar altimeter data for data buoy comparison studies

An atlas is compiled to identify times and dates when GEOS-3 altimeter data was taken in the vicinity of data buoys. The time period covered is from GEOS launch to April 9, 1978. Two buffer areas are considered for each of the various buoy locations to determine the simultaneity of GEOS and buoy data. The two buffer areas are ? or - 0.25 degrees and ? or - 1 degree.

Stanley, H. R.↗
NASA NTRS1980

An atlas of 1977 and 1978 GEOS-3 radar altimeter data for tropical cyclone studies

All of the GEOS 3 satellite altimeter schedule information were collected with all of the available 1977 and 1978 tropical cyclone positional information. The time period covers from March 23, 1977 through Nov. 23, 1978. The geographical region includes all ocean area north of the equator divided into the following operational areas: the Atlantic area (which includes the Caribbean and Gulf of Mexico); the eastern Pacific area; the central and western Pacific area; and the Indian Ocean area. All available source material concerning tropical cyclones was collected. The date/time/location information was extracted for each disturbance. This information was compared with the GEOS 3 altimeter ON/OFF history information to determine the existence of any altimeter data close enough in both time and location to make the data potentially useful for further study (the very liberal criteria used was time less than 24 hours and location within 25 degrees). Geographic plots (cyclone versus GEOS 3 orbit track) were produced for all of the events found showing the approximate location of the cyclone and the GEOS 3 orbit traces for the full day.

Stanley, H. R.↗
NASA NTRS1982

Combined use of remote sensing and seismic observations to infer geologically recent crustal deformation, active faulting, and stress fields

Characteristic traits for earthquakes associated with strike-slip motion in Central California and the Salton Sea area, as revealed in ground based studies and LANDSAT imagery, were compared. The mapped lineaments are found to be oriented in several dominant directions. One direction is the same as the trend of the San Andreas fault. The other directions differ from area to area and may reflect the stresses of earlier geologic processes. The pattern of lineament orientations is significantly LANDSAT MSS data, SEASAT synthetic aperture radar data, and magnetic field data from the South Mountain area west of Gettysburg, Pennsylvania were registered to match each other in spatial position and merged. Pattern recognition techniques were applied to the composite data set to determine its utility in recognizing different rock types and structures in vegetated terrain around South Mountain. With the use of a texture algorithm to enhance geologic features, a classification of the entire area was made. A test of the correlation between SAR tone and texture, LANDSAT tone and texture, and magnetic field data revealed no tone or texture measures linking any two of the original data sets.

Alexander, S. S.↗
NASA NTRS1983

An exploitation of coregistered SIR-A, Seasat and Landsat images

Multispectral registration and classification of SIR-A, Seasat SAR, and Landsat MSS data is presented over two playas located in the northeastern Algerian Sahara. A supervised classification was made over six classes: salt, palm trees, dunes, limestones, gypsum and sand. The best classification is obtained by using all of the data. The images using radar only misclassify trees and salt, limestone and dunes, gypsum and dunes. Landsat only gives a good map but lacks the roughness information contained in the radar data. The Landsat/SIR-A combination gives a better classification than the Landsat/Seasat combination. Density number histograms computed within several classes on the Seasat and SIR-A data show the misclassification is mainly due to the Seasat data.

Rebillard, P.↗
NASA NTRS1992

Geologic process studies using Synthetic Aperture Radar (SAR) data

The use of SAR data to study geologic processes for better understanding of recent tectonic activity and climate change as well as the mitigation of geologic hazards and exploration for nonrenewable resources is discussed. The geologic processes that are particularly amenable to SAR-based data include volcanism; soil erosion, degradation, and redistribution; coastal erosion and inundation; glacier fluctuations; permafrost; and crustal motions. When SAR data are combined with data from other planned spaceborne sensors including ESA ERS, the Japanese Earth Resources Satellite, and the Canadian Radarsat, it will be possible to build a time-series view of temporal changes over many regions of earth.

Evans, Diane L.↗
NASA NTRS1994

Synthetic aperture radar signal data compression using block adaptive quantization

This paper describes the design and testing of an on-board SAR signal data compression algorithm for ESA's ENVISAT satellite. The Block Adaptive Quantization (BAQ) algorithm was selected, and optimized for the various operational modes of the ASAR instrument. A flexible BAQ scheme was developed which allows a selection of compression ratio/image quality trade-offs. Test results show the high quality of the SAR images processed from the reconstructed signal data, and the feasibility of on-board implementation using a single ASIC.

Kuduvalli, Gopinath↗
NASA NTRS1993

Preprocessing: Geocoding of AVIRIS data using navigation, engineering, DEM, and radar tracking system data

Remotely sensed data have geometric characteristics and representation which depend on the type of the acquisition system used. To correlate such data over large regions with other real world representation tools like conventional maps or Geographic Information System (GIS) for verification purposes, or for further treatment within different data sets, a coregistration has to be performed. In addition to the geometric characteristics of the sensor there are two other dominating factors which affect the geometry: the stability of the platform and the topography. There are two basic approaches for a geometric correction on a pixel-by-pixel basis: (1) A parametric approach using the location of the airplane and inertial navigation system data to simulate the observation geometry; and (2) a non-parametric approach using tie points or ground control points. It is well known that the non-parametric approach is not reliable enough for the unstable flight conditions of airborne systems, and is not satisfying in areas with significant topography, e.g. mountains and hills. The present work describes a parametric preprocessing procedure which corrects effects of flight line and attitude variation as well as topographic influences and is described in more detail by Meyer.

Meyer, Peter↗
NASA NTRS2001

A Study of Heavy Precipitation Events in Taiwan During 10-13 August, 1994: Mesoscale Model Simulations - Part 2

Several heavy precipitation episodes occurred over Taiwan from August 10 to 13, 1994. Precipitation patterns and characteristics are quite different between the precipitation events that occurred from August 10 and I I and from August 12 and 13. In Part I (Chen et al. 2001), the environmental situation and precipitation characteristics are analyzed using the EC/TOGA data, ground-based radar data, surface rainfall patterns, surface wind data, and upper air soundings. In this study (Part II), the Penn State/NCAR Mesoscale Model (MM5) is used to study the precipitation characteristics of these heavy precipitation events. Various physical processes (schemes) developed at NASA Goddard Space Flight Center (i.e., cloud microphysics scheme, radiative transfer model, and land-soil-vegetation surface model) have recently implemented into the MM5. These physical packages are described in the paper, Two way interactive nested grids are used with horizontal resolutions of 45, 15 and 5 km. The model results indicated that Cloud physics, land surface and radiation processes generally do not change the location (horizontal distribution) of heavy precipitation. The Goddard 3-class ice scheme produced more rainfall than the 2-class scheme. The Goddard multi-broad-band radiative transfer model reduced precipitation compared to a one-broad band (emissivity) radiation model. The Goddard land-soil-vegetation surface model also reduce the rainfall compared to a simple surface model in which the surface temperature is computed from a Surface energy budget following the "force-re store" method. However, model runs including all Goddard physical processes enhanced precipitation significantly for both cases. The results from these runs are in better agreement with observations. Despite improved simulations using different physical schemes, there are still some deficiencies in the model simulations. Some potential problems are discussed. Sensitivity tests (removing either terrain or radiative processes) are performed to identify the physical processes that determine the precipitation patterns and characteristics for heavy rainfall events. These sensitivity tests indicated that terrain can play a major role in determining the exact location for both precipitation events. The terrain can also play a major role in determining the intensity of precipitation for both events. However, it has a large impact on one event but a smaller one on the other. The radiative processes are also important for determining, the precipitation patterns for one case but. not the other. The radiative processes can also effect the total rainfall for both cases to different extents.

Tao, Wei Kuo↗
NASA NTRS2001

A study of heavy precipitation events in Taiwan during 10-13 August, 1994: mesoscale model simulations

Heavy rainfall occurred over the western side of Taiwan's complex terrain from August 10 to 13, 1994 after Typhoon Doug moved northward from the East China Sea into Taiwan and on towards the Yellow Sea. On August 10, most of the rainfall fell over sloped areas. The heaviest daily rainfall totals were in excess of 200 mm over southwestern as well as central Taiwan. However, not much rainfall occurred over northern Taiwan. The lack of rainfall over northern Taiwan also occurred on August 11, 12 and 13. The larger rainfall amounts shifted westward from the sloped areas on August 10 toward lower terrain on August 11. On August 12 and 13, most of the higher rainfall amounts were found over the coastal area in southwestern Taiwan. Notably, about 300 to 400 mm per day fell over the coastal area in southwest Taiwan on August 12 and 13. The distribution of rainfall amount was different on August 10 and 11 (termed as Case 1) compared to August 12 and 13 (termed as Case 2). The environmental situation and precipitation characteristics are analyzed using EC/TOGA data, ground-based radar data, surface rainfall patterns, surface wind data, and upper air soundings. Chen at al. (2001) also categorized the precipitation pattern into two types, propagating and quasi-stationary. For the propagating type of precipitation, rainrates increased or remained the same as systems went from the plains to mountainous regions. With the quasi-stationary type of precipitation, however, rainrates decreased as precipitation propagated across the plains and into the mountains. The focus of this study is to understand what causes the h1aher amounts of rainfall over Taiwan, and what factors influence where the higher amounts of rainfall will occur, over sloped areas or over coastal areas.

Tao, W.-K.↗
NASA NTRS1993

Proprocessing: Geocoding of AVIRIS Data Using Navigation, Engineering, DEM, and Radar Tracking System Data

Remotely sensed data have geometric characteristics and representation which depend on the type of the acquisition system used. To correlate such data over large regions with other real world representation lools like conventional maps or Geographic Information Systems (GIS) for verification purposes, or for further treatment within different data sets, a coregistration has to be performed.

topography stability of the platform sensor flight↗
NASA NTRS2018

Knowledge Discovery Process: Case Study of RNAV Adherence of Radar Track Data

This talk is an introduction to the knowledge discovery process, beginning with: identifying the problem, choosing data sources, matching the appropriate machine learning tools, and reviewing the results. The overview will be given in the context of an ongoing study that is assessing RNAV adherence of commercial aircraft in the national airspace.

Machine Learning↗