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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS1967

Doppler systems.

Doppler tracking systems as velocity measuring systems, including introduction methods for necessary position knowledge, one- and two-way systems analyses, etc

RANGE AND RANGE RATE TRACKING↗
NASA NTRS2006

UWB Two-Cluster AOA Tracking Prototype System Design

This presentation discusses a design effort for a prototype ultra-wideband (UWB) tracking system that is currently under development at NASA Johnson Space Center (JSC). The system is being studied for use in tracking of lunar/Mars rovers during early exploration missions when satellite navigation systems are not available. The UWB technology is exploited to implement the tracking system due to its properties such as fine time resolution, low power spectral density and multipath immunity. A two cluster prototype design using commercially available UWB radios is employed to implement the Angle of Arrival (AOA) tracking methodology in this design effort. In order to increase the tracking range, low noise amplifiers (LNA) and high gain horns are used at the receiving sides. Field tests were conducted jointly with the Science and Crew Operation Utility Testbed (SCOUT) vehicle near the Meteor Crater in Arizona to test the tracking capability for a moving target in an operational environment. These tests demonstrate that the UWB tracking system can co-exist with other on-board radio frequency (RF) communication systems (such as Global Positioning System (GPS), video, voice and telemetry systems), and that a tracking resolution less than 1% of the range can be achieved.

Ngo, Phong H.↗
NASA NTRS1988

Preliminary Orbit Determination System (PODS) for Tracking and Data Relay Satellite System (TDRSS)-tracked target Spacecraft using the homotopy continuation method

The Preliminary Orbit Determination System (PODS) provides early orbit determination capability in the Trajectory Computation and Orbital Products System (TCOPS) for a Tracking and Data Relay Satellite System (TDRSS)-tracked spacecraft. PODS computes a set of orbit states from an a priori estimate and six tracking measurements, consisting of any combination of TDRSS range and Doppler tracking measurements. PODS uses the homotopy continuation method to solve a set of nonlinear equations, and it is particularly effective for the case when the a priori estimate is not well known. Since range and Doppler measurements produce multiple states in PODS, a screening technique selects the desired state. PODS is executed in the TCOPS environment and can directly access all operational data sets. At the completion of the preliminary orbit determination, the PODS-generated state, along with additional tracking measurements, can be directly input to the differential correction (DC) process to generate an improved state. To validate the computational and operational capabilities of PODS, tests were performed using simulated TDRSS tracking measurements for the Cosmic Background Explorer (COBE) satellite and using real TDRSS measurements for the Earth Radiation Budget Satellite (ERBS) and the Solar Mesosphere Explorer (SME) spacecraft. The effects of various measurement combinations, varying arc lengths, and levels of degradation of the a priori state vector on the PODS solutions were considered.

Kirschner, S. M.↗
NASA NTRS1978

Solar tracking control system Sun Chaser

The solar tracking control system, Sun Chaser, a method of tracking the Sun in all types of weather conditions is described. The Sun Chaser follows the Sun from east to west in clear or cloudy weather, and resets itself to the east position after sundown in readiness for the next sunrise.

Scott, D. R.↗
NASA NTRS1980

Tracking and Data Relay Satellite System (TDRSS) Range and Doppler Tracking System Observation Measurement and Modeling

This document gives detailed descriptions of the tracking services, signal generation and processing, range and Doppler extraction, and the principles and procedures involved in modeling range and Doppler observations via the Tracking and Data Relay Satellite System (TDRSS). Major topics discussed include the following: TDRSS telecommunication services, functional description of the TDRSS, tracking signal generation and processing, range and Doppler observations modeling, angular observed and computed measurement algorithms, and tracking data transmission format.

Tracking and Data Relay Satellite System (TDRSS)↗
NASA NTRS1979

Solar tracking control system Sun Chaser

The solar tracking control system (Sun Chaser) is believed to be an improved method of tracking the Sun in all types of weather conditions. The Sun Chaser will follow the Sun from east to west in clear or cloudy weather, and reset itself to the east position after sundown in readiness for the next sunrise. A description of the Sun Chaser hardware and its operation together with results is presented.

Scott, D. R.↗
NASA NTRS1991

The spacecraft control laboratory experiment optical attitude measurement system

A stereo camera tracking system was developed to provide a near real-time measure of the position and attitude of the Spacecraft COntrol Laboratory Experiment (SCOLE). The SCOLE is a mockup of the shuttle-like vehicle with an attached flexible mast and (simulated) antenna, and was designed to provide a laboratory environment for the verification and testing of control laws for large flexible spacecraft. Actuators and sensors located on the shuttle and antenna sense the states of the spacecraft and allow the position and attitude to be controlled. The stereo camera tracking system which was developed consists of two position sensitive detector cameras which sense the locations of small infrared LEDs attached to the surface of the shuttle. Information on shuttle position and attitude is provided in six degrees-of-freedom. The design of this optical system, calibration, and tracking algorithm are described. The performance of the system is evaluated for yaw only.

Welch, Sharon S.↗
DOE OSTI2022

Aquatic organism tracking devices, systems and associated methods

Aquatic organism tracking devices, systems and associated methods are described. According to one aspect, an aquatic organism tracking device includes a housing, a transducer coupled with the housing and configured to transmit a data transmission externally of the housing and an aquatic organism associated with the tracking device, a programmable oscillator coupled with the housing, and wherein the programmable oscillator is configured to generate a clock signal having a selected one of a plurality of different frequencies, processing circuitry coupled with the housing and configured to receive the clock signal from the programmable oscillator and to execute a plurality of executable instructions according to the clock signal, a power source coupled with the housing and configured to store electrical energy, and wherein the processing circuitry is configured to control the provision of the electrical energy from the power source to the transducer to generate the data transmission as a result of the execution of the instructions.

Deng, Z. Daniel↗
NASA NTRS1969

An interferometer tracking radar system

Fine tuning acquisition and tracking interferometer radar system uses a first antenna array of at least three receiving antennas. Array includes a reference antenna, a coarse tuning antenna, and a fine tuning antenna aligned on a receiving axis. Short range rendezvous system provides increased position accuracy.

Broderick, R. F.↗
NASA NTRSDate not supplied

Developing Methods for Exercise System Kinematic Tracking

BACKGROUND How to quantify the load and forces produced by exercise equipment and their Vibration Isolation and Stabilization (VIS) platforms in-flight is an active area of investigation. Kinematic tracking paired with system modeling can provide insights as well as verification and validation of simulations used for system design and development. Traditional motion capture methods can require significant cost in equipment procurement and crew-time, but newer lessons learned can be leveraged [1]. The VIS systems of current and future exercise hardware on the International Space Station (ISS) such as the Cycle Ergometer with Vibration Isolation System (CEVIS) and the European Enhanced Exploration Exercise Device (E4D) are not currently outfitted with IMUs or similar measurement devices. Video-based methods would enable use of multi-purpose, crew-familiar flight equipment. An initial exploration of video-based solutions was performed utilizing 2-camera video from crew cycling on Teal-CEVIS on the ISS. METHODS AND RESULTS Our group has scoped a variety of video-based object tracking methods. To date, we have primarily investigated computer vision toolkits such as open CV. Techniques explored include key-point detection, background subtraction, region-of interest tracking, color-based tracking, tag masking and tracking, and corner detection. Although object-tracking and 6D pose estimation is a rich field, space applications are a unique problem that are challenging for existing software and toolkits. The majority of the existing object-tracking applications involve vehicles/pedestrians and household objects with simple backgrounds. We have identified the following features which pose particular challenges for on-station exercise equipment tracking: 1. Busy and visually cluttered background 2. Low-textured tracking object with relatively small motions 3. Occlusions and motion by human subject and loose, floating objects 4. Limited number of video cameras with no fixed global references 5. Limited ability to add tags, markers, or visual references to the tracking object 6. Lack of training data for Machine Learning (ML) algorithms CONCLUSION We will summarize the efficacy of techniques tested for a ground mock-trial and the on-station exercise trial. It is likely that human-in-loop feedback or a conglomerate of methods is required. ML-based methods, like those implemented for human body tracking [2], may still be a viable option, but more training data and validation is needed.

L Nilsson↗
NASA NTRSDate not supplied

Developing Methods for Exercise System Kinematics Tracking

BACKGROUND How to quantify the load and forces produced by exercise equipment and their Vibration Isolation and Stabilization (VIS) platforms in-flight is an active area of investigation. Kinematic tracking paired with system modeling can provide insights as well as verification and validation of simulations used for system design and development. Traditional motion capture methods can require significant cost in equipment procurement and crew-time, but newer lessons learned can be leveraged [1]. The VIS systems of current and future exercise hardware on the International Space Station (ISS) such as the Cycle Ergometer with Vibration Isolation System (CEVIS) and the European Enhanced Exploration Exercise Device (E4D) are not currently outfitted with IMUs or similar measurement devices. Video-based methods would enable use of multi-purpose, crew-familiar flight equipment. An initial exploration of video-based solutions was performed utilizing 2-camera video from crew cycling on Teal-CEVIS on the ISS. METHODS AND RESULTS Our group has scoped a variety of video-based object tracking methods. To date, we have primarily investigated computer vision toolkits such as openCV. Techniques explored include key-point detection, background subtraction, region-of interest tracking, color-based tracking, tag masking and tracking, and corner detection. Although object-tracking and 6D pose estimation is a rich field, space applications are a unique problem that are challenging for existing software and toolkits. The majority of the existing object-tracking applications involve vehicles/pedestrians and household objects with simple backgrounds. We have identified the following features which pose particular challenges for on-station exercise equipment tracking: Busy and visually cluttered background Low-textured tracking object with relatively small motions Occlusions and motion by human subject and loose, floating objects Limited number of video cameras with no fixed global references Limited ability to add tags, markers, or visual references to the tracking object Lack of training data for Machine Learning (ML) algorithms CONCLUSION We will summarize the efficacy of techniques tested for a ground mock-trial and the on-station exercise trial. It is likely that human-in-loop feedback or a conglomerate of methods is required. ML-based methods, like those implemented for human body tracking [2], may still be a viable option, but more training data and validation is needed.

L B Nilsson↗