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Telemetry data from the 'Copernicus' experiment stored on magnetic tape on the Ural-IX computer are transcribed to the internal memory of the IBM 360 computer. Operations involved in this process are described.
Design, operation, and applications of telemetry data frame readout display system
The recent advances in animal tracking technology have enabled the collection of a vast amount of in situ data regarding the movement of wildlife at high spatiotemporal resolution. These data are usually available at variable time resolutions and contains noise (error) originating from GPS fixes. Decoding movement characteristics, particularly of flying animals, from telemetry data while handling these factors is a challenging yet important task for conservation purposes. Typically, this task is broken into two subtasks: resampling, and model calibration. The resampling subtask converts the variable rate positional data into a constant time interval data, while the model calibration subtask uses the resampled data to tune time-invariant parameters of the proposed models. For telemetry data at high temporal resolutions (order of 1 second), it is very challenging to decouple noise from actual movements using interpolation-based resampling techniques. Any errors introduced during resampling can significantly alter the the calibration and prediction attributes of the movement model. We address this problem through a unified Bayesian state-space framework that can handle both the resampling and calibration tasks in a single step. In addition, we use the speed and heading of the bird from telemetry data to regularize the position information of the bird. We use a Kalman filtering approach to include these nonlinearly related motion parameters within the state space framework. We cross-validated to quantify how this inclusion affects the model performance in estimating true bird movements. The relationship between the true state of the bird and environmental and topographical covariates is then represented parametrically. These parameters are then tuned using stochastic sampling strategies like Markov Chain Monte Carlo (MCMC). We use the telemetry data collected from golden eagles in the western USA to demonstrate the applicability of this approach to build a predictive, probabilistic movement model. Our preliminary results show that this approach provides improved predictive performance in terms of capturing higher-order motion parameters such as angular and horizontal accelerations, which may have simpler and more direct relationships with environmental covariates than corresponding speeds. In this talk, we will demonstrate how this state-space approach benefits the prediction capabilities of a movement model in simulating golden eagle paths through a wind power plant in Wyoming given certain atmospheric conditions. The model outcomes are aimed at informing mitigation strategies that can minimize the potential for collisions of golden eagles with wind turbines.
The DSN discrepancy report system which records all outages of DSN data systems that occur during mission support operations is described. The recorded outages of the telemetry data system for 1981 through 1983 were tabulated and availability characteristics of the telemetry data system and several of its major elements, including the traveling wave maser (TWM) Assemblies were developed. Availability characteristics of the TWM assemblies are compared with those of other DSN subsystems and assemblies. It is found that for a three year period, the availability of the telemetry data system is 99.0%, and its mean time to restore service is 0.9 hours; the availability of the TWM assemblies is 99.83%, and their mean time to restore service is 2.5 hours.
This paper descibes a knowledge-based system for performing real-time monitoring and analysis of telemetry data from the NASA Hubble Space Telescope (HST). In order to handle asynchronous inputs and perform in real time the system consists of three or more separate processes, which run concurrently and communicate via a message passing scheme. The data management process gathers, compresses, and scales the incoming telemetry data befoe sending it to the other tasks. The inferencing process uses the incoming data to perform a real-time analysis of the state and health of the Space Telescope. The I/O process receives telemetry monitors from the data management process, updates its graphical displays in real time, and acts as the interface to the console operator. The three processes may run on the same or different computers. This system is currently under development and is being used to monitor testcases produced by the Bass Telemetry System in the Hardware/Software Integration Facility at Lockheed Missile and Space Co. in Sunnyvale, California.
Predicting raptor movements through a wind power plant under given atmospheric and topographical conditions is a crucial first step in the overall goal of quantifying the risk of turbine-related collisions and mortalities. Extracting behavioral traits of golden eagles (Aquila chrysaetos) from telemetry data requires the fusion of noisy and sparse movement data (location, heading, velocity) with a stochastic mathematical representation of the eagles' decision-making processes. In this study, we framed this problem in a Bayesian state-space framework where both observations and decision-making are assumed to be stochastic processes connected through hidden states (mode of flight, intent), and the unknown model parameters are assumed to be random variables that are calibrated using the available telemetry data. This framework allowed for rigorous consideration of underlying uncertainties while allowing for both data and prior biological knowledge to contribute to a probabilistic and predictive agent-based movement model. We implemented and applied the Bayesian framework to understand movement behavior of 23 GPS-tagged golden eagles travelling in the western US for years 2019 and 2020. Our preliminary findings show that the Bayesian state-space framework provides a robust inverse modeling apparatus to decode eagle behavioral characteristics from telemetry data. This study was primarily aimed at verifying and validating the framework with selected golden eagle tracks (both long- and short-ranged), with future research aimed at extending the framework to include multi-mode flight, consideration of atmospheric data and uplift mechanisms, eagle-to-eagle interaction, and eagle-to-turbine interaction.
Telemetry data processing problems noting error sources, probability distribution of counts in counting processes, sensor relay during motion in varying field, etc
Error probability in digital computer processing of PFM telemetry data
Transmission buffer overflow prevention in telemetry data compressors using adaptive queueing control system
Transmission buffer overflow prevention in telemetry data compressors using adaptive queueing control system
An engineering model of the Telemetry Data Processor monitoring and display system used by the Maneuverable Television (MTV) employed in remote inspections of the Space Shuttle has been developed, incorporating the MC 6809 microprocessor. The system acquires analog and digital signals from the MTV flyer and converts them into engineering units, formatting them in several pages according to their function. Upon request, a data page can be superimposed on the video signal from the MTV camera. In addition, the system monitors key parameters for out-of-limit conditions and flashes caution and warning messages on the selected page.
Probabilistic models and statistical methods in processing telemetry data with sequence of data and sync words
Explorer xvii real time pcm telemetry data processing and display test stand
Remote Access Multi-mission Processing and Analysis Ground Environment (RAMPAGE) is a Java-language server computer program that enables near-real-time display of spacecraft telemetry data on any authorized client computer that has access to the Internet and is equipped with Web-browser software. In addition to providing a variety of displays of the latest available telemetry data, RAMPAGE can deliver notification of an alarm by electronic mail. Subscribers can then use RAMPAGE displays to determine the state of the spacecraft and formulate a response to the alarm, if necessary. A user can query spacecraft mission data in either binary or comma-separated-value format by use of a Web form or a Practical Extraction and Reporting Language (PERL) script to automate the query process. RAMPAGE runs on Linux and Solaris server computers in the Ground Data System (GDS) of NASA's Jet Propulsion Laboratory and includes components designed specifically to make it compatible with legacy GDS software. The client/server architecture of RAMPAGE and the use of the Java programming language make it possible to utilize a variety of competitive server and client computers, thereby also helping to minimize costs.
Bias due to random bit errors that occur in PCM DATA during phases of operation of telemetry data processing system
Data from scientific and application satellites managed by the NASA Goddard Space Flight Center are acquired through the world-wide Spacecraft Tracking and Data Network (STDN). These data are forwarded to a central telemetry data processing facility whose primary objective is the timely provision of the data to users in a form suitable for their analysis. In a successful evolution, a satellite data processing facility must adapt to changing support requirements while operationally supporting active spacecraft. Advances in the technology of data transmission, data storage systems, and file management make it feasible to implement a third-generation system which will be able to satisfy experimenter requirements for the next ten years.
Quality control monitoring system for satellite telemetry data information systems
Precompression automatic wild point rejection from sampled telemetry data for data compressors evaluated by digital computer simulation