HIGH PERFORMANCE SATELLITE RANGING TECHNIQUE UTILIZING A FLEXIBLE RANGING SIGNAL WAVEFORM
KeywordsRanging signal bandwidth occupancy
Ranging signal acquisition time
Ranging accuracy and precision
Mutual interference with other uplink/downlink signals
Digital Signal Processing
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AbstractRange to an orbiting satellite from a ground reference point (ground station) can be determined by measuring the round trip time for a waveform transmitted to the satellite and returned to the ground station (Turnaround Ranging) and more recently by using the Global Positioning System (GPS). This paper first summarizes and compares the two approaches. The paper then describes and analyzes a new turn-around ranging system which uses a flexible ranging waveform that provides spectral compatibility with existing Military, NASA, and Commercial satellite uplink/downlink signals.
SponsorsInternational Foundation for Telemetering
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The Signal-to-Noise Ratio Estimation Techniques for PCM SignalsSos, John Y.; NASA/Goddard Space Flight Center (International Foundation for Telemetering, 1968-10)Reliable estimation of signal-to-noise (S/N) ratio in a demodulated PCM telemetry signal can be useful in evaluating the performance of the complete telemetry link, including its signal detection and data processing portions. This paper describes three potentially practical methods developed at Goddard Space Flight Center for estimating the S/N ratio in a PCM signal. One method referred to as "spectral null" method uses spectral characteristics of PCM' signals to estimate the S/N ratio, the other two use statistical properties of the signal, i.e., its mean value and variance. These two methods are known as "variance estimations and "null zone." The implementation of each method is discussed. The spectral null method takes the least amount of equipment, but is more difficult to calibrate and operate over a wide range of bit rates, than the other two systems. All three approaches, however, are uncomplicated enough to be included into almost any existing PCM data handling system. An analysis of the performance characteristics of each system is made. It is shown that the variance estimation method is the most versatile. It can reliably estimate the S/N ratio to within 1.5 db over a range of S/N ratios from 0 db to +10 db. (The S/N ratio is defined as the ratio of signal energy per bit/noise power density.) Under certain conditions all three methods can provide estimates to within 1 db, especially over a S/N ratio range from +3 db to +10 db.
Signal detection with random backgrounds and random signalsClarkson, Eric; Park, Subok (The University of Arizona., 2004)In this dissertation we explore theoretical and computational methods to investigate Bayesian ideal observers for performing signal-detection tasks. Object models are used to take into account object variability in image backgrounds and signals for the detection tasks. In particular, lumpy backgrounds (LBs) and Gaussian signals are used for various paradigms of signal-detection tasks. Simplified pinhole imaging systems in nuclear medicine are simulated for this work. Markov-chain Monte Carlo (MCMC) methods that estimate the ideal observer test statistic, the likelihood ratio, for signal-known-exactly (SKE) tasks, where signals are nonrandom, are employed. MCMC methods are extended to signal-known-statistically (SKS) tasks, where signals are random. Psychophysical studies for the SKE and SKS tasks using non-Gaussian and Gaussian distributed LBs are conducted. The performance of the Bayesian ideal observer, the human observer, and the channelized-Hotelling observer for the SKE and SKS tasks is compared. Human efficiencies for both the SKE tasks and SKS tasks are estimated. Also human efficiencies for non-Gaussian and Gaussian-distributed LBs are compared for the SKE tasks. Finally, the theory of the channelized-ideal observer (CIO) is introduced to approximate the performance of the ideal observer by the performance of the CIO in cases where the channel outputs of backgrounds and signals are non-Gaussian distributed. Computational approaches to estimate the CIO are investigated.