Analog-digital data archive study final report, 30 jun. - 10 oct. 1964
Method for storing, recording, and retrieving telemetry data
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Method for storing, recording, and retrieving telemetry data
GSFC program in telemetry data archiving, discussing necessity of data accessing capability
A high-rate digital receiver (HRDR) implemented as a peripheral component interface (PCI) board has been developed as a prototype of compact, general-purpose, inexpensive, potentially mass-producible data-acquisition interfaces between telemetry systems and personal computers. The installation of this board in a personal computer together with an analog preprocessor enables the computer to function as a versatile, highrate telemetry-data-acquisition and demodulator system. The prototype HRDR PCI board can handle data at rates as high as 600 megabits per second, in a variety of telemetry formats, transmitted by diverse phase-modulation schemes that include binary phase-shift keying and various forms of quadrature phaseshift keying. Costing less than $25,000 (as of year 2003), the prototype HRDR PCI board supplants multiple racks of older equipment that, when new, cost over $500,000. Just as the development of standard network-interface chips has contributed to the proliferation of networked computers, it is anticipated that the development of standard chips based on the HRDR could contribute to reductions in size and cost and increases in performance of telemetry systems.
Since 1984 Marshall Space Flight Center was actively engaged in research and development concerning autonomous power systems. Much of the work in this domain has dealt with the development and application of knowledge-based or expert systems to perform tasks previously accomplished only through intensive human involvement. One such task is the health status monitoring of electrical power systems. Such monitoring is a manpower intensive task which is vital to mission success. The Hubble Space Telescope testbed and its associated Nickel Cadmium Battery Expert System (NICBES) were designated as the system on which the initial proof of concept for intelligent power system monitoing will be established. The key function performed by an engineer engaged in system monitoring is to analyze the raw telemetry data and identify from the whole only those elements which can be considered significant. This function requires engineering expertise on the functionality of the system, the mode of operation and the efficient and effective reading of the telemetry data. Application of this expertise to extract the significant components of the data is referred to as data reduction. Such a function possesses characteristics which make it a prime candidate for the application of knowledge-based systems' technologies. Such applications are investigated and recommendations are offered for the development of intelligent data reduction systems.
The role of Mission Evaluation Room (MER) engineers is to provide engineering support during Space Shuttle missions, for Space Shuttle systems. These engineers are concerned with ensuring that the systems for which they are responsible function reliably, and as intended. The MER is a central facility from which engineers may work, in fulfilling this obligation. Engineers participate in real-time monitoring of shuttle telemetry data and provide a variety of analyses associated with the operation of the shuttle. The Johnson Space Center's Automation and Robotics Division is working to transfer advances in intelligent systems technology to NASA's operational environment. Specifically, the MER Intelligent Diagnostic and Analysis System (MIDAS) project provides MER engineers with software to assist them with monitoring, filtering and analyzing Shuttle telemetry data, during and after Shuttle missions. MIDAS off-loads to computers and software, the tasks of data gathering, filtering, and analysis, and provides the engineers with information which is in a more concise and usable form needed to support decision making and engineering evaluation. Engineers are then able to concentrate on more difficult problems as they arise. This paper describes some, but not all of the applications that have been developed for MER engineers, under the MIDAS Project. The sampling described herewith was selected to show the range of tasks that engineers must perform for mission support, and to show the various levels of automation that have been applied to assist their efforts.
Traditional mission and vehicle management involves teams of highly trained specialists monitoring vehicle status and crew activities, responding rapidly to any anomalies encountered during operations. These teams work from the Mission Control Center and have access to engineering support teams with specialized expertise in International Space Station (ISS) subsystems. Integrated System Health Management (ISHM) applications can significantly augment these capabilities by providing enhanced monitoring, prognostic and diagnostic tools for critical decision support and mission management. The Intelligent Systems Division of NASA Ames Research Center is developing many prototype applications using model-based reasoning, data mining and simulation, working with Mission Control through the ISHM Testbed and Prototypes Project. This paper will briefly describe information technology that supports current mission management practice, and will extend this to a vision for future mission control workflow incorporating new ISHM applications. It will describe ISHM applications currently under development at NASA and will define technical approaches for implementing our vision of future human exploration mission management incorporating artificial intelligence and distributed web service architectures using specific examples. Several prototypes are under development, each highlighting a different computational approach. The ISStrider application allows in-depth analysis of Caution and Warning (C&W) events by correlating real-time telemetry with the logical fault trees used to define off-nominal events. The application uses live telemetry data and the Livingstone diagnostic inference engine to display the specific parameters and fault trees that generated the C&W event, allowing a flight controller to identify the root cause of the event from thousands of possibilities by simply navigating animated fault tree models on their workstation. SimStation models the functional power flow for the ISS Electrical Power System and can predict power balance for nominal and off-nominal conditions. SimStation uses realtime telemetry data to keep detailed computational physics models synchronized with actual ISS power system state. In the event of failure, the application can then rapidly diagnose root cause, predict future resource levels and even correlate technical documents relevant to the specific failure. These advanced computational models will allow better insight and more precise control of ISS subsystems, increasing safety margins by speeding up anomaly resolution and reducing,engineering team effort and cost. This technology will make operating ISS more efficient and is directly applicable to next-generation exploration missions and Crew Exploration Vehicles.
The Atmosphere Explorer Control System (AECS) was developed to provide automatic computer control of the Atmosphere Explorer spacecraft and experiments. The software performs several vital functions, such as issuing commands to the spacecraft and experiments, receiving and processing telemetry data, and allowing for extensive data processing by experiment analysis programs. The AECS was written for a 48K XEROX Data System Sigma 5 computer, and coexists in core with the XDS Real-time Batch Monitor (RBM) executive system. RBM is a flexible operating system designed for a real-time foreground/background environment, and hence is ideally suited for this application. Existing capabilities of RBM have been used as much as possible by AECS to minimize programming redundancy. The most important functions of the AECS are to send commands to the spacecraft and experiments, and to receive, process, and display telemetry data.
An Integrated Trending and Plotting System (ITPS) is a trending, analysis, and plotting system used by space missions to determine performance and status of spacecraft and its instruments. ITPS supports several NASA mission operational control centers providing engineers, ground controllers, and scientists with access to the entire spacecraft telemetry data archive for the life of the mission, and includes a secure Web component for remote access. FERMI/GLAST ITPS Release 5.0 features include the option to display dates (yyyy/ddd) instead of orbit numbers along orbital Long-Term Trend (LTT) plot axis, the ability to save statistics from daily production plots as image files, and removal of redundant edit/create Input Definition File (IDF) screens. Other features are a fix to address invalid packet lengths, a change in naming convention of image files in order to use in script, the ability to save all ITPS plot images (from Windows or the Web) as GIF or PNG format, the ability to specify ymin and ymax on plots where previously only the desired range could be specified, Web interface capability to plot IDFs that contain out-oforder page and plot numbers, and a fix to change all default file names to show yyyydddhhmmss time stamps instead of hhmmssdddyyyy. A Web interface capability sorts files based on modification date (with newest one at top), and the statistics block can be displayed via a Web interface. Via the Web, users can graphically view the volume of telemetry data from each day contained in the ITPS archive in the Web digest. The ITPS could be also used in nonspace fields that need to plot data or trend data, including financial and banking systems, aviation and transportation systems, healthcare and educational systems, sales and marketing, and housing and construction.
Abstract Studies of the effects of transmitters on wildlife often focus on survival. However, sublethal behavioral changes resulting from radio-marking have the potential to affect inferences from telemetry data and may vary based on individual and environmental characteristics. We used a long-term, multi-species tracking study of sea ducks to assess behavioral patterns at multiple temporal scales following implantation of intracoelomic satellite transmitters. We applied state-space models to assess short-term behavioral patterns in 476 individuals with implanted satellite transmitters, as well as comparing breeding site attendance and migratory phenology across multiple years after capture. In the short term, our results suggest an increase in dispersive behavior immediately following capture and transmitter implantation; however, behavior returned to seasonally average patterns within ~5 days after release. Over multiple years, we found that breeding site attendance by both males and females was depressed during the first breeding season after radio-marking relative to subsequent years, with larger relative decreases in breeding site attendance among males than females. We also found that spring and breeding migrations occurred later in the first year after radio-marking than in subsequent years. Across all behavioral effects, the severity of behavioral change often varied by species, sex, age, and capture season. We conclude that, although individuals appear to adjust relatively quickly (i.e. within 1 week) to implanted satellite transmitters, changes in breeding phenology may occur over the longer term and should be considered when analyzing and reporting telemetry data.
Resource availability is a key factor driving marine bird movements and distributions, but direct information on prey availability is difficult to obtain at relevant scales. We present novel methods for describing multi-scale trophic associations, combining movement analyses of marine birds with estimates of forage fish surface aggregations from digital aerial survey data and species occupancy from bottom trawl survey data. We analyzed satellite telemetry data from northern gannets Morus bassanus , red-throated loons Gavia stellata , and long-tailed ducks Clangula hyemalis in the US Atlantic during the non-breeding period. Using discrete-time hidden Markov models to distinguish area-restricted (i.e. putative foraging) from transit movements, we examined how environmental factors influence movement, and how forage fish species distributions and surface aggregations influence habitat use by gannets and loons that have greater dietary reliance. Our results suggest that chlorophyll a concentration significantly affected movement behavior across species, highlighting the importance of higher-productivity areas around estuaries during colder months when regional productivity is low. Though variable across species and seasons, spatial cross-correlation analysis revealed that herring species (Family Clupeidae), including Atlantic menhaden Brevoortia tyrannus , may be important resources; it also showed positive spatial correlations with forage fish aggregations. This suggests that prey patch dynamics and factors driving aggregation formation may be as important as species composition. However, spatial patterns were generally low (<0.3), suggesting a mismatch in spatiotemporal resolution, exemplifying the challenges in quantifying trophic relationships in marine systems. Disentangling predator-prey relationships is critical to understanding the mechanisms driving marine bird behavior in rapidly changing marine systems.
Signal conditioning for improved high resolution processing of PFM telemetry data
Noncoherent low power spacecraft communication system with 1 watt transmitted power and omnidirectional spacecraft antenna
The Ground/Orbiter Lasercomm Demonstration conducted between the Japanese Engineering Test Satellite (ETS-VI) and the ground station at JPL's Table Mountain Facility, Wrightwood, California, was the rst ground-to-space two-way optical communications experiment. Experiment objectives included validating the performance predictions of the optical link. Atmospheric attenuation and seeing measurements were made during the experiment, and data were analyzed. Downlink telemetry data recovered over the course of the experiment provided information on in-orbit performance of the ETS-VI's laser communications equipment. Biterror rates as low as 10 4 were measured on the uplink and 10 5 on the downlink. Measured signal powers agreed well with theoretical predictions.
The Mars Science Laboratory (MSL) Telecom Operations Team at the Jet Propulsion Laboratory (JPL) has implemented a machine learning system in order to automate the anomaly detection process as a part of daily operations. Machine learning enables reliable detection of anomalies in Telecom-related telemetry and automated reporting of Telecom subsystem status, resulting in an 90% reduction in team workload and improved anomaly detection reliability. At present, machine learning methods are used to detect: 1. Anomalous long-term trends in telemetry data 2. Anomalous time-domain evolution of telemetry values Both types of anomalies pose their own unique challenges that are addressed in different ways. In the first case, long term trending of daily minima, maximum, and mean telemetry values in temperatures, currents, voltages, and radio frequency (RF) power levels is used in addition to hard threshold safety checks to look for changes in long-term equipment health and performance. Long-term trending methods allow for ordinary seasonal variations in these quantities caused by temperature changes over the course of the Martian year while allowing operators to determine whether current performance remains in line with historical values from previous years. Changes in long-term trends can provide important insights into the health and status of the rover's on-board systems as well as valuable early warning if subtle degradation begins to take hold. But while trending of daily statistics is valuable, it does not detect anomalies in the short-term time evolution of data over the course of minutes or hours during a day, and this task is handled with short-term shape analysis. Principal components analysis (PCA) has been found to provide robust detection of short-term anomalies, and several examples of the use of PCA to detect actual anomalous events will be provided here. In using PCA, we use both the percentage of explained variance and also a log likelihood test on the PCA expansion coefficients to flag telemetry data for human review. Previous work in the field of spacecraft anomaly detection includes [1] for MSL and [2] for some other JPL missions.
The Miniature Infrared Data Acquisition and Telemetry (MIRDAT) Phase 1 study was performed to determine the technical and commercial feasibility of producing a miniaturized electro-optical telemetry system. This system acquires and transmits experimental data from aircraft scale models for realtime monitoring in wind tunnels. During the Phase 1 study, miniature prototype MIRDAT telemetry devices were constructed, successfully tested in the laboratory and delivered to the user for wind tunnel testing. A search was conducted for commercially available components and advanced hybrid techniques to further miniaturize the system during Phase 2 development. A design specification was generated from laboratory testing, user requirements and discussions with component manufacturers. Finally, a preliminary design of the proposed MIRDAT system was documented for Phase 2 development.
Hybrid cross correlation telemetry data processing system design for decoding pulse frequency modulation
Digital coding techniques for data telemetry links
Output format coding for telemetry data compressor channel and frame identification