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The arduous task of tracking multiple flight software images, across redundant on-board processors, recorders and across time, has been automated and systematized via the use of a unified Flight Software Memory Tracker (FMT) Tool.
This paper presents systems modeling, simulation and implementation of Ka-band monopulse tracking feed for antennas in NASA/DSN ground stations.
The JPL Navigation and Mission Design Section has two related tasks: Mission Design: calculate a nominal trajectory which meets the diverse needs of the scientists on a particular mission. Locate deterministic maneuvers required to fly the nominal trajectory, while locating statistical maneuvers which correct errors in the actual trajectory. Navigation estimates the flight path of a spacecraft (orbit determination) using three fundamental data sets: ground based astrometric data; radio metric tracking data (Doppler, range).
Marshall Space Flight Center's (MSFC) Payload Operations Integration Center (POIC) for the International Space Station (ISS) uses a number of formal databases to manage and track flight plan changes, onboard and ground equipment anomalies, and other events. However, individual console positions encounter many action items and/or occurrences that don't fit neatly into the databases, and while console logs are comprehensive, manual or automated searches do not always yield consistent results. The Payload Communications Manager (PAYCOM) team, whose members speak directly with the ISS onboard crew with respect to NASA payload operations, has found a creative way to reformat a mandatory Daily Report to organize action items, standing reminders, significant events, and other comments. While the report keeps others appraised of PAYCOMs activities and issues of the moment, the format makes it easy to capture very brief summaries of the items in a "Roll Off Matrix", including start and stop dates, resolution, and possible applicability to future ops. The matrix provides accountability for all action items, gives direct insight into the issues surrounding various payloads and methods of dealing with them, yields indirect information on PAYCOM priorities and processes, and provides a roadmap that makes it easier to get back to extensive details if needed. This paper describes how the ISS PAYCOM Daily Report and Roll Off Matrix are organized, used, and inter-related to each other and the PAYCOM operations log. While the application is for a manned vehicle, the concepts could apply in a wide spectrum of operational settings.
A room temperature sapphire acoustics resonator incorporated into an oscillator represents a possible opportunity to improve on quartz ultrastable oscillator (USO) performance, which has been a staple for NASA missions since the inception of spaceflight. Where quartz technology is very mature and shows a performance improvement of perhaps 1 dB/decade, these sapphire acoustic resonators when integrated with matured quartz electronics could achieve a frequency stability improvement of 10 dB or more. As quartz oscillators are an essential element of nearly all types of frequency standards and reference systems, the success of MSAR would advance the development of frequency standards and systems for both groundbased and flight-based projects. Current quartz oscillator technology is limited by quartz mechanical Q. With a possible improvement of more than x 10 Q with sapphire acoustic modes, the stability limit of current quartz oscillators may be improved tenfold, to 10(exp -14) at 1 second. The electromagnetic modes of sapphire that were previously developed at JPL require cryogenic temperatures to achieve the high Q levels needed to achieve this stability level. However sapphire fs acoustic modes, which have not been used before in a high-stability oscillator, indicate the required Q values (as high as Q = 10(exp 8)) may be achieved at room temperature in the kHz range. Even though sapphire is not piezoelectric, such a high Q should allow electrostatic excitation of the acoustic modes with a combination of DC and AC voltages across a small sapphire disk (approximately equal to l mm thick). The first evaluations under this task will test predictions of an estimated input impedance of 10 kilohms at Q = 10(exp 8), and explore the Q values that can be realized in a smaller resonator, which has not been previously tested for acoustic modes. This initial Q measurement and excitation demonstration can be viewed similar to a transducer converting electrical energy to mechanical energy and back. Such an electrostatic tweeter type excitation of a mechanical resonator will be tested at 5 MHz. Finite element calculation will be applied to resonator design for the desired resonator frequency and optimum configuration. The experiment consists of the sapphire resonator sandwiched between parallel electrodes. A DC+AC voltage can be applied to generate a force to act on a sapphire resonator. With the frequency of the AC voltage tuned to the sapphire resonator frequency, a resonant condition occurs and the sapphire Q can be measured with a high-frequency impedance analyzer. To achieve high Q values, many experimental factors such as vacuum seal, gas damping effects, charge buildup on the sapphire surface, heat dissipation, sapphire anchoring, and the sapphire mounting configuration will need attention. The effects of these parameters will be calculated and folded into the resonator design. It is envisioned that the initial test configuration would allow for movable electrodes to check gap spacing dependency and verify the input impedance prediction. Quartz oscillators are key components in nearly all ground- and space-based communication, tracking, and radio science applications. They play a key role as local oscillators for atomic frequency standards and serve as flywheel oscillators or to improve phase noise in high performance frequency and timing distribution systems. With ultra-stable performance from one to three seconds, an Earth-orbit or moon-based MSAR can enhance available performance options for spacecraft due to elimination of atmospheric path degradation.
Traditional azimuth-elevation ("az-el") stages are used to point a variety of devices ranging from large optical telescopes to tank guns. Such a stage typically has an elevation stage having a horizontal axis mounted on an azimuth stage with a vertical axis. Both stages are often motorized. Optical communications transceivers often require two-axis motorized control, as when the communications link is between a ground station and an aircraft or satellite. In such applications, the traditional azimuth-elevation stage has two important drawbacks: a gimbal lock exclusion zone at zenith and susceptibility to pointing errors caused by backlash. Az-el stages in which the azimuth stage cannot rotate a full 360deg have the additional drawback of an azimuth exclusion zone. The diagonal-axes stage described here mitigates or eliminates all of these problems. Instead of one vertical axis and one horizontal axis, a diagonal-axes stage has two horizontal axes, both oriented at 45 to the trajectory of the target. For example, a ground station located on the equator tracking a satellite with an equatorial orbit would have one axis parallel to northeast and southwest, and the other axis parallel to northwest and southeast. The diagonal-axes stage is considerably less vulnerable to backlash. If it is correctly oriented, its axes rotate in only one direction during an overhead pass by a satellite. As a result, the effects of backlash may be inherently eliminated. If the gravity-induced torque on either axis changes during the pass, then backlash may become important during the part of the pass where the gravity torque, instead of opposing the motion of the stage, pushes the stage in the direction of motion. This can result in the loss of gear-to-gear contact in one or more stages of the gear reduction mechanism. In this case, a preload spring used to eliminate backlash need only be sufficiently strong to overcome the gravity torque, i.e. it need not overcome friction in the gear train. The diagonal-axes stage is not backlash-free for arbitrary target trajectories such as an aircraft might execute. If properly oriented for any particular satellite, however, it is backlash-free for all passes of that satellite, which will trace out parallel paths on the sky, and for all passes of any other satellite that are perpendicular to the first. It will also be backlash-free for some fraction of other satellite trajectories.
Autonomous orbit determination for departure stages of interplanetary trajectories is conducted by means of realistic radiometric observations between the departing spacecraft and a satellite orbiting the first lunar libration point. Linked Autonomous Interplanetary Satellite Orbit Navigation (LiAISON) is used to estimate the orbit solution. This paper uses high-fidelity simulations to explore the utilization of LiAISON in providing improved accuracy for interplanetary departure missions. The use of autonomous navigation to supplement current techniques for interplanetary spacecraft is assessed using comparisons with groundbased navigation. Results from simulations including the Mars Science Laboratory, Mars Exploration Rover, and Cassini are presented. It is shown that observations from a dedicated LiAISON navigation satellite could be used to supplement ground-based measurements and significantly improve tracking performance.
Autonomous orbit determination for departure stages of interplanetary trajectories is conducted by means of realistic radiometric observations between the departing spacecraft and a satellite orbiting the first lunar libration point. Linked Autonomous Interplanetary Satellite Orbit Navigation (LiAISON) is used to estimate the orbit solution. This paper uses high-fidelity simulations to explore the utilization of LiAISON in providing improved accuracy for interplanetary departure missions. The use of autonomous navigation to supplement current techniques for interplanetary spacecraft is assessed using comparisons with groundbased navigation. Results from simulations including the Mars Science Laboratory, Mars Exploration Rover, and Cassini are presented. It is shown that observations from a dedicated LiAISON navigation satellite could be used to supplement ground-based measurements and significantly improve tracking performance.
The main purpose of this research is to study the spreading of the 2020 wildfire plumes in the San Francisco Bay Area. Last year’s fire plumes have caused severe impact on regional air quality and public health over large part of the west coast. We studied fire plumes with two datasets: aerosol optical depth (AOD) retrieved from MODIS sensor onboard two NASA satellites (Terra and Aqua), and surface PM2.5 measurements from US EPA. Satellite can monitor fire plumes from a top-down view, including active fire location, emission amount, spreading of the fire plume in both horizontal and vertical directions. EPA records air quality using the ground network of in-situ sensors. In general the two points of view are consistent with each other. But in the peak of the 2020 fire season, we found an episode where the AOD and PM2.5 are out-of-phase for two days. We explored the possible mechanism for this shift with available meteorological measurements, including both ground measurements and sounding data. By tracking the evolution of the sounding data, we concluded a heated near surface inversion layer might shield the region from aloft fire plumes for two days before they touch down and severely downgraded the air quality over the whole Bay area.
The main purpose of this research is to study the spreading of the 2020 wildfire plumes in the San Francisco Bay Area. Last year’s fire plumes have caused severe impact on regional air quality and public health over large part of the west coast. We studied fire plumes with two datasets: aerosol optical depth (AOD) retrieved from MODIS sensor onboard two NASA satellites (Terra and Aqua), and surface PM2.5 measurements from US Environmental Protection Agency (EPA). Satellite can monitor fire plumes from a top-down view, including active fire location, emission amount, spreading of the fire plume in both horizontal and vertical directions. EPA records air quality using the ground network of in-situ sensors. In general, the two points of view are consistent with each other. But in the peak of the 2020 fire season, we found an episode where the AOD and PM2.5 are out-of-phase for two days, in addition data from NASA satellite AERONET was use to corroborate AOD information for the episode, yielding similar results. Thereafter, we explored the possible mechanism for this shift with available meteorological measurements, including both ground measurements and sounding data. By tracking the evolution of the sounding data, we concluded a heated near surface inversion layer might shield the region from aloft fire plumes for two days before they touch down and severely downgraded the air quality over the whole Bay area.
Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.
Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.
Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.
Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.
NASA's intricate network, encompassing the Near Space Network (NSN) and Deep Space Network (DSN), plays a pivotal role in supporting an array of space missions. These range from those in Low Earth Orbit (LEO) and Geosynchronous Orbit (GEO), to the more distant Cislunar and Deep Space endeavors. To manage the vast communications needs, we utilize multiple ground stations globally, coupled with the Tracking and Data Relay Satellite System (TDRSS). The Mission Engagement Working Group (MEWG), within the Commercialization, Innovation, and Synergies (CIS) division, stands as the primary gateway for all Space Communications and Network (SCaN) communication and navigation requests. This includes not only NASA's internal missions but also extends to other governmental agencies and commercial sector endeavors. How does the MEWG Process Work? - Initial Contact : Clients initiate their interaction with the NSN by submitting their service requirements through a dedicated online portal. - Preliminary Assessment by MEWG: Upon submission, MEWG embarks on a primary screening of the request. This involves evaluating the client's identity and the foundational concept of their mission. - Detailed Analysis by the NSN Team: Parallelly, the NSN team conducts a comprehensive review of the service request. This often necessitates additional clarification from the requester, ensuring that the final assessment is both thorough and accurate. - Coordination & Streamlining: MEWG's overarching objective is to effectively log, classify, orchestrate, and guarantee that pertinent actions are delegated based on initial client interactions. Acting as the central hub for these primary contacts, MEWG ensures that each request is sufficiently detailed for an in-depth evaluation. - Feedback & Remediation: If a request is deemed unsuitable or lacking, MEWG doesn't merely reject it. Instead, the team discerns the reasons for the inadequacy and suggests potential rectification strategies. This approach ensures that feedback delivered to clients is precise, prompt, constructive, and actionable. Conclusion: This plenary presentation will detail the efforts of the MEWG resulting in a greatly streamlined and refined onboarding process for space communication Direct-To-Earth (DTE) and Space Relay (SR) support requests. By centralizing the preliminary interactions and assessments, we've reduced the complexity for clients, ensuring they engage with a singular, efficient, and responsive point of contact. This initiative, we believe, fortifies NASA's commitment to fostering effective and synergistic collaborations with its partners.
NASA's intricate network, encompassing the Near Space Network (NSN) and Deep Space Network (DSN), plays a pivotal role in supporting an array of space missions. These range from those in Low Earth Orbit (LEO) and Geosynchronous Orbit (GEO), to the more distant Cislunar and Deep Space endeavors. To manage the vast communications needs, we utilize multiple ground stations globally, coupled with the Tracking and Data Relay Satellite System (TDRSS). The Mission Engagement Working Group (MEWG), within the Commercialization, Innovation, and Synergies (CIS) division, stands as the primary gateway for all Space Communications and Network (SCaN) communication and navigation requests. This includes not only NASA's internal missions but also extends to other governmental agencies and commercial sector endeavors. How does the MEWG Process Work? - Initial Contact : Clients initiate their interaction with the NSN by submitting their service requirements through a dedicated online portal. - Preliminary Assessment by MEWG: Upon submission, MEWG embarks on a primary screening of the request. This involves evaluating the client's identity and the foundational concept of their mission. - Detailed Analysis by the NSN Team: Parallelly, the NSN team conducts a comprehensive review of the service request. This often necessitates additional clarification from the requester, ensuring that the final assessment is both thorough and accurate. - Coordination & Streamlining: MEWG's overarching objective is to effectively log, classify, orchestrate, and guarantee that pertinent actions are delegated based on initial client interactions. Acting as the central hub for these primary contacts, MEWG ensures that each request is sufficiently detailed for an in-depth evaluation. - Feedback & Remediation: If a request is deemed unsuitable or lacking, MEWG doesn't merely reject it. Instead, the team discerns the reasons for the inadequacy and suggests potential rectification strategies. This approach ensures that feedback delivered to clients is precise, prompt, constructive, and actionable. Conclusion: This plenary presentation will detail the efforts of the MEWG resulting in a greatly streamlined and refined onboarding process for space communication Direct-To-Earth (DTE) and Space Relay (SR) support requests. By centralizing the preliminary interactions and assessments, we've reduced the complexity for clients, ensuring they engage with a singular, efficient, and responsive point of contact. This initiative, we believe, fortifies NASA's commitment to fostering effective and synergistic collaborations with its partners.
The NREL Python Panel-Segmentation package is a toolkit that automates the process of extracting accurate and valuable metadata related to solar array installations, using publicly available Google Maps satellite imagery. Previously published work includes automated azimuth estimation for individual solar installations in satellite images. Our continued research focuses on automated detection and classification of solar installation mounting configuration (tracking or fixed-tilt; rooftop, ground, or carport). Specifically, a Faster-RCNN Resnet-50 feature pyramid network (FPN) model was trained and validated on 862 manually labeled satellite images. This model was used to perform object detection on satellite imagery, locating and classifying individual solar installations' mounting configuration and type. Model results showed a mean average precision score (mAP) of 77.79%, with the model strongest at detecting fixed-tilt ground mount and fixed-tilt carport installations. The object detection model and its outputs have been incorporated into the Panel-Segmentation package's automated metadata extraction pipeline, which returns the mounting configuration and azimuth for individual solar arrays in satellite imagery. The complete image data set with labels has been released on the U.S. Department of Energy (DOE) DuraMAT DataHub, to encourage further research in this area.
Typically for a rendezvous and docking scenario, only a single vehicle is considered the active vehicle. As a result, the target vehicle’s GN&C system is typically not a factor in the integrated performance analysis as it assumes a passive role. However, for upcoming NASA Artemis missions the active vehicle is both the chaser and target spacecraft. In addition, both vehicles are cooperating with one another, sharing telemetry data, and relying on data extracted from the GN&C system of the other. The process also includes a team of ground support personnel in mission control are tracking and monitoring each spacecraft uplinking state estimates and targeting solutions to support mission operations and enhance the onboard flight system performance. Being able to quickly analyze the impact of both vehicles with two different GN&C systems that also interact with the ground that does its own navigation and targeting uploads is critical. This paper outlines how to perform this rapid analysis using linear covariance techniques and applies them to a rendezvous scenario initiated in low lunar orbit and completed in a Near Rectilinear Halo Orbit (NRHO) representative of the NASA Artemis III mission.