Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Sensor Fusion”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter

Current virtual environment and teleoperator applications are hampered by the need for an accurate, quick responding head-tracking system with a large working volume. Gyroscopic orientation sensors can overcome problems with jitter, latency, interference, line-of-sight obscurations, and limited range, but suffer from slow drift. Gravimetric inclinometers can detect attitude without drifting, but are slow and sensitive to transverse accelerations. This paper describes the design of a Kalman filter to integrate the data from these two types of sensors in order to achieve the excellent dynamic response of an inertial system without drift, and without the acceleration sensitivity of inclinometers.

Foxlin, Eric↗
NASA NTRS1996

Inertial Head-Tracker Sensor Fusion by a Complementary Separate-Bias Kalman Filter

Current virtual environment and teleoperator applications are hampered by the need for an accurate, quick-responding head-tracking system with a large working volume. Gyroscopic orientation sensors can overcome problems with jitter, latency, interference, line-of-sight obscurations, and limited range, but suffer from slow drift. Gravimetric inclinometers can detect attitude without drifting, but are slow and sensitive to transverse accelerations. This paper describes the design of a Kalman filter to integrate the data from these two types of sensors in order to achieve the excellent dynamic response of an inertial system without drift, and without the acceleration sensitivity of inclinometers.

Foxlin, Eric↗
NASA NTRS1990

A sensor fusion field experiment in forest ecosystem dynamics

The background of the Forest Ecosystem Dynamics field campaign is presented, a progress report on the analysis of the collected data and related modeling activities is provided, and plans for future experiments at different points in the phenological cycle are outlined. The ecological overview of the study site is presented, and attention is focused on forest stands, needles, and atmospheric measurements. Sensor deployment and thermal and microwave observations are discussed, along with two examples of the optical radiation measurements obtained during the experiment in support of radiative transfer modeling. Future activities pertaining to an archival system, synthetic aperture radar, carbon acquisition modeling, and upcoming field experiments are considered.

Smith, James A.↗
NASA NTRSDate not supplied

A Green’s Function Sensor Fusion Approach for Evaluating Spacecraft Entry Heating From on-Board Thermal Instrumentation

During atmospheric entry, distributed thermal measurements are critical to enable the evaluation of heat loads on spacecraft thermal protection systems (TPS). In recent space exploration missions, Schmidt-Boelter-type heat flux gauges have been integrated into the TPS alongside conventional temperature measurement instrumentation to measure total (convective and radiative) and radiative heat transfer rates1,2. While direct heat flux sensors (HFS) are able to provide valuable information detailing the thermal loads experienced by spacecraft, the interpretation of these measurements in unsteady, convective environments requires a correction factor to account for local heating augmentations at the cold wall HFS surface2,3. Current efforts to estimate cold wall correction factors, and thus recover the hot wall TPS heat flux, rely on time-marching computational fluid dynamics (CFD) simulations2. Simulation-based methods are susceptible to large uncertainties, however, as they require estimations of vehicle trajectory, gas kinetics, wall catalysis models, and other flight conditions as input parameters2,4. Furthermore, CFD simulations are computationally expensive and cannot efficiently survey all possible entry scenarios, exacerbating the uncertainty of reconstructed hot wall heat flux values. These drawbacks motivate the development of alternative hot wall heat flux reconstruction methods that are not reliant on CFD-based correction factors.

Kenneth McAfee↗
NASA NTRSDate not supplied

A Green’s Function Sensor Fusion Approach for Evaluating Spacecraft Entry Heating From on-Board Thermal Instrumentation

During atmospheric entry, distributed thermal measurements are critical to enable the evaluation of heat loads on spacecraft thermal protection systems (TPS). In recent space exploration missions, Schmidt-Boelter-type heat flux gauges have been integrated into the TPS alongside conventional temperature measurement instrumentation to measure total (convective and radiative) and radiative heat transfer rates1,2. While direct heat flux sensors (HFS) are able to provide valuable information detailing the thermal loads experienced by spacecraft, the interpretation of these measurements in unsteady, convective environments requires a correction factor to account for local heating augmentations at the cold wall HFS surface2,3. Current efforts to estimate cold wall correction factors, and thus recover the hot wall TPS heat flux, rely on time-marching computational fluid dynamics (CFD) simulations2. Simulation-based methods are susceptible to large uncertainties, however, as they require estimations of vehicle trajectory, gas kinetics, wall catalysis models, and other flight conditions as input parameters2,4. Furthermore, CFD simulations are computationally expensive and cannot efficiently survey all possible entry scenarios, exacerbating the uncertainty of reconstructed hot wall heat flux values. These drawbacks motivate the development of alternative hot wall heat flux reconstruction methods that are not reliant on CFD-based correction factors.

Kenneth McAfee↗
NASA NTRSDate not supplied

A Green’s Function Sensor Fusion Approach for Evaluating Spacecraft Entry Heating From on-Board Thermal Instrumentation

During atmospheric entry, distributed thermal measurements are critical to enable the evaluation of heat loads on spacecraft thermal protection systems (TPS). In recent space exploration missions, Schmidt-Boelter-type heat flux gauges have been integrated into the TPS alongside conventional temperature measurement instrumentation to measure total (convective and radiative) and radiative heat transfer rates1,2. While direct heat flux sensors (HFS) are able to provide valuable information detailing the thermal loads experienced by spacecraft, the interpretation of these measurements in unsteady, convective environments requires a correction factor to account for local heating augmentations at the cold wall HFS surface2,3. Current efforts to estimate cold wall correction factors, and thus recover the hot wall TPS heat flux, rely on time-marching computational fluid dynamics (CFD) simulations2. Simulation-based methods are susceptible to large uncertainties, however, as they require estimations of vehicle trajectory, gas kinetics, wall catalysis models, and other flight conditions as input parameters2,4. Furthermore, CFD simulations are computationally expensive and cannot efficiently survey all possible entry scenarios, exacerbating the uncertainty of reconstructed hot wall heat flux values. These drawbacks motivate the development of alternative hot wall heat flux reconstruction methods that are not reliant on CFD-based correction factors.

Kenneth McAfee↗
NASA NTRS1993

Sensor fusion for assured vision in space applications

By using emittance and reflectance radiation models, the effects of angle of observation, polarization, and spectral content are analyzed to characterize the geometrical and physical properties--reflectivity, emissivity, orientation, dielectric properties, and roughness--of a sensed surface. Based on this analysis, the use of microwave, infrared, and optical sensing is investigated to assure the perception of surfaces on a typical lunar outpost. Also, the concept of employing several sensors on a lunar outpost is explored. An approach for efficient hardware implementation of the fused sensor systems is discussed.

Collin, Marie-France↗
NASA NTRS2009

Wrap-Around Out-the-Window Sensor Fusion System

The Advanced Cockpit Evaluation System (ACES) includes communication, computing, and display subsystems, mounted in a van, that synthesize out-the-window views to approximate the views of the outside world as it would be seen from the cockpit of a crewed spacecraft, aircraft, or remote control of a ground vehicle or UAV (unmanned aerial vehicle). The system includes five flat-panel display units arranged approximately in a semicircle around an operator, like cockpit windows. The scene displayed on each panel represents the view through the corresponding cockpit window. Each display unit is driven by a personal computer equipped with a video-capture card that accepts live input from any of a variety of sensors (typically, visible and/or infrared video cameras). Software running in the computers blends the live video images with synthetic images that could be generated, for example, from heads-up-display outputs, waypoints, corridors, or from satellite photographs of the same geographic region. Data from a Global Positioning System receiver and an inertial navigation system aboard the remote vehicle are used by the ACES software to keep the synthetic and live views in registration. If the live image were to fail, the synthetic scenes could still be displayed to maintain situational awareness.

Fox, Jeffrey↗
NASA NTRS1988

Sensor fusion of range and reflectance data for outdoor scene analysis

In recognizing objects in an outdoor scene, range and reflectance (or color) data provide complementary information. Results of experiments in recognizing outdoor scenes containing roads, trees, and cars are presented. The recognition program uses range and reflectance data obtained by a scanning laser range finder, as well as color data from a color TV camera. After segmentation of each image into primitive regions, models of objects are matched using various properties.

Kweon, In SO↗
NASA NTRS1987

Microwave and video sensor fusion for the shape extraction of 3D space objects

A new system for the fusion of optical image data and polarized radar scattering cross-sections is presented. By considering the scattering data in conjunction with image data, the problem of ambiguity can be reduced. Only a small part of the surface needs to be reconstructed from the radar cross-sections; the remaining portion is constrained by the optical image.

Shaw, Scott W.↗
NASA NTRS2005

Multi-Sensor Fusion and Enhancement for Object Detection

This was a quick &week effort to investigate the ability to detect changes along the flight path of an unmanned airborne vehicle (UAV) over time. Video was acquired by the UAV during several passes over the same terrain. Concurrently, GPS data and UAV attitude data were also acquired. The purpose of the research was to use information from all of these sources to detect if any change had occurred in the terrain encompassed by the flight path.

Rahman, Zia-Ur↗
NASA NTRS2013

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.

Williamson, Walton R.↗
NASA NTRS2018

A Survey of Methods for Computing Best Estimates of Endoatmospheric and Exoatmospheric Trajectories

Beginning with the mathematical prediction of planetary orbits in the early seventeenth century up through the most recent developments in sensor fusion methods, many techniques have emerged that can be employed on the problem of endo and exoatmospheric trajectory estimation. Although early methods were ad hoc, the twentieth century saw the emergence of many systematic approaches to estimation theory that produced a wealth of useful techniques. The broad genesis of estimation theory has resulted in an equally broad array of mathematical principles, methods and vocabulary. Among the fundamental ideas and methods that are briefly touched on are batch and sequential processing, smoothing, estimation, and prediction, sensor fusion, sensor fusion architectures, data association, Bayesian and non Bayesian filtering, the family of Kalman filters, models of the dynamics of the phases of a rocket's flight, and asynchronous, delayed, and asequent data. Along the way, a few trajectory estimation issues are addressed and much of the vocabulary is defined.

Bernard, William P.↗
NASA NTRS1992

Proposed evaluation framework for assessing operator performance with multisensor displays

Despite aggressive work on the development of sensor fusion algorithms and techniques, no formal evaluation procedures have been proposed. Based on existing integration models in the literature, an evaluation framework is developed to assess an operator's ability to use multisensor, or sensor fusion, displays. The proposed evaluation framework for evaluating the operator's ability to use such systems is a normative approach: The operator's performance with the sensor fusion display can be compared to the models' predictions based on the operator's performance when viewing the original sensor displays prior to fusion. This allows for the determination as to when a sensor fusion system leads to: 1) poorer performance than one of the original sensor displays (clearly an undesirable system in which the fused sensor system causes some distortion or interference); 2) better performance than with either single sensor system alone, but at a sub-optimal (compared to the model predictions) level; 3) optimal performance (compared to model predictions); or, 4) super-optimal performance, which may occur if the operator were able to use some highly diagnostic 'emergent features' in the sensor fusion display, which were unavailable in the original sensor displays. An experiment demonstrating the usefulness of the proposed evaluation framework is discussed.

Foyle, David C.↗
NASA NTRS1988

Fusion of radar and optical sensors for space robotic vision

Returned radar power estimates are used in an iterative procedure which generates successive approximations to the target shape in order to determine the shape of a 3-D surface. A simulation is shown which involves the reconstruction of an edge of a flat plate. Although this is a somewhat artificial example, it addresses the real problem of recovering edges of space objects lost in shadow or against a dark background. The results indicate that a microwave/optical sensor fusion system is possible, given sufficient computing power and accurate radar cross section measuring systems.

Shaw, Scott W.↗