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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Estimation of signal-to-noise - A new procedure applied to AVIRIS data

To make the best use of narrowband airborne visible/infrared imaging spectrometer (AVIRIS) data, an investigator needs to know the ratio of signal to random variability or noise (signal-to-noise ratio or SNR). The signal is land cover dependent and varies with both wavelength and atmospheric absorption; random noise comprises sensor noise and intrapixel variability (i.e., variability within a pixel). The three existing methods for estimating the SNR are inadequate, since typical laboratory methods inflate while dark current and image methods deflate the SNR. A new procedure is proposed called the geostatistical method. It is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intrapixel variability using the semi-variogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season and could be applied to remotely sensed data from broadband sensors.

Curran, Paul J.↗
NASA NTRS1984

Algorithm for astronomical, extended source, signal-to-noise radio calculations

An algorithm was developed to simulate the expected signal-to-noise ratio as a function of observation time in the charge coupled device detector plane of an optical telescope located outside the Earth's atmosphere for an extended, uniform astronomical source embedded in a uniform cosmic background. By choosing the appropriate input values, the expected extended source signal-to-noise ratios can be computed for the Hubble Space Telescope using the Wide Field/Planetary Camera science instrument.

Jayroe, R. R.↗
NASA NTRS1980

Measuring signal-to-noise ratio automatically

Automated method of measuring signal-to-noise ratio in digital communication channels is more precise and 100 times faster than previous methods used. Method based on bit-error-rate (B&R) measurement can be used with cable, microwave radio, or optical links.

Bergman, L. A.↗
NASA NTRS1995

Seamless data-range change using punctured convolutional codes for time-varying signal-to-noise ratios

In a time-varying signal-to-noise ration (SNR) environment, symbol rate is often changed to maximize data return. However, the symbol-rate change has some undesirable effects, such as changing the transmission bandwidth and perhaps causing the receiver symbol loop to lose lock temporarily, thus losing some data. In this article, we are proposing an alternate way of varying the data rate without changing the symbol rate and, therefore, the transmission bandwidth. The data rate change is achieved in a seamless fashion by puncturing the convolutionally encoded symbol stream to adapt to the changing SNR environment. We have also derived an exact expression to enumerate the number of distinct puncturing patterns. To demonstrate this seamless rate change capability, we searched for good puncturing patterns for the Galileo (14,1/4) convolutional code and changed the data rates by using the punctured codes to match the Galileo SNR profile of November 9, 1997. We show that this scheme reduces the symbol-rate changes from nine to two and provides a comparable data return in a day and a higher symbol SNR during most of the day.

Feria, Y.↗
NASA NTRS1995

Seamless Data-Rate Change Using Punctured Convolutional Codes for a Time-Varying Signal-to-Noise Ratio

In a time-varying signal-to-noise-ratio (SNR) environment, symbol rate is changed to maximize data return. However, the symbol-rate changes may cause the receiver symbol loop to lose lock, thus losing real-time data. We propose an alternate way of varying the data rate in a seamless fashion by puncturing the convolutionally encoded symbol stream and transmitting the punctured encoded symbols with a constant symbol rate. We systematically searched for good puncturing patterns for the Galileo (14,1/4) convolutional code and changed the data rates by using the punctured codes to match the Galileo SNR profile of November 9, 1997. We concluded that this scheme reduces the symbol-rate changes from 9 to 2 and provides a larger data return and a higher symbol SNR during most of the day.

SNR↗
NASA NTRS1995

Seamless Data-Rate Change Using Punctured Convolutional Codes for Time-Varying Signal-to-Noise Ratio

In a time-varying signal-to-noise (SNR) environment, symbol rate is often changed to maximize ata return. However, the symbol-rate change has some undesirable effects such as changing the ransmission bandwidth and perhaps causing the receiver symbol loop to lose lock temporarily, thus osing some data. In this article, we are proposing an alternate way of varying the data rate without hanging the symbol rate and therefore the transmission bandwidth. The data rate change is achieved n a seamless fashion by puncturing the convolutionally encoded symbol stream to adapt to the hanging SNR environment. We have also derived an exact expression to enumerate the number of nique puncturing patterns. To demonstrate this seamless rate-change capability, we searched for good uncturing patterns for the Galileo (14, 1/4) convolutional code and changed the data rates by using the unctured codes to match the Galileo SNR profile of November 9, 1997.

Seamless Data-Rate↗
NASA NTRS1986

Table look-up estimation of signal and noise parameters from quantized observables

A table look-up algorithm for estimating underlying signal and noise parameters from quantized observables is examined. A general mathematical model is developed, and a look-up table designed specifically for estimating parameters from four-bit quantized data is described. Estimator performance is evaluated both analytically and by means of numerical simulation, and an example is provided to illustrate the use of the look-up table for estimating signal-to-noise ratios commonly encountered in Voyager-type data.

Vilnrotter, V. A.↗
NASA NTRS1977

Signal-to-noise ratios for stellar occultations by the rings of Uranus, 1977-1980

Approximate signal-to-noise ratios are calculated for 12 stellar occultations by the rings of Uranus during 1977-1980. Four of the stars are apparently bright enough to permit observation of the epsilon-ring occultations with a large telescope. For the best of these events, occultations by rings alpha through delta should also be observable with a large telescope, and epsilon-ring occultations should be detectable with smaller telescopes. Formulas for the signal-to-noise ratios are given to aid potential observers in evaluating the quality of the results they can expect to obtain with their own photometric equipment.

Elliot, J. L.↗
NASA NTRS1981

On the correction for quantization effects in signal-to-noise ratio estimation

In sampled data digital telemetry systems the signal to noise ratio (SNR) is typically derived as a function of the moments of the digitized input stream (e.g., the receiver output). This analog to digital conversion process is itself an additional noise source known as the quantization noise. Thus a digitally measured SNR will only approximately represent the SNR of the analog input signal. A procedure (Sheppard's corrections) for correcting moments of any order for this quantization effect is presented.

Howard, L.↗
NASA NTRS1994

A complex symbol signal-to-noise ratio estimator and its performance

This article presents an algorithm for estimating the signal-to-noise ratio (SNR) of signals that contain data on a downconverted suppressed carrier or the first harmonic of a square-wave subcarrier. This algorithm can be used to determine the performance of the full-spectrum combiner for the Galileo S-band (2.2- to 2.3-GHz) mission by measuring the input and output symbol SNR. A performance analysis of the algorithm shows that the estimator can estimate the complex symbol SNR using 10,000 symbols at a true symbol SNR of -5 dB with a mean of -4.9985 dB and a standard deviation of 0.2454 dB, and these analytical results are checked by simulations of 100 runs with a mean of -5.06 dB and a standard deviation of 0.2506 dB.

Feria, Y.↗
NASA NTRS1990

Exact closed-form expressions for the performance of the split-symbol moments estimator of signal-to-noise ratio

Previously, the performance of the split-symbol moments estimator (SSME) of signal-to-noise ratio (SNR) has been evaluated by means of approximate expressions for the estimator mean and variance. These are asymptotic formulas in the sense that they become accurate as the number of estimator samples gets large. Here, exact closed-form expressions are obtained for the same quantities. These expressions confirm the accuracy of the previously derived asymptotic results, and, unlike the asymptotic formulas, they are useful even when the number of samples is small. It is also shown that the conventional split-symbol estimator can be trivially scaled to form a signal-to-noise ratio estimator which is precisely unbiased (as long as the estimate is based on more than two split-symbols).

Dolinar, S.↗
NASA NTRS1977

Signal-to-noise ratio estimation in digital computer simulation of lowpass and bandpass systems with applications to analog and digital communications, volume 3

Techniques are developed to estimate power gain, delay, signal-to-noise ratio, and mean square error in digital computer simulations of lowpass and bandpass systems. The techniques are applied to analog and digital communications. The signal-to-noise ratio estimates are shown to be maximum likelihood estimates in additive white Gaussian noise. The methods are seen to be especially useful for digital communication systems where the mapping from the signal-to-noise ratio to the error probability can be obtained. Simulation results show the techniques developed to be accurate and quite versatile in evaluating the performance of many systems through digital computer simulation.

Tranter, W. H.↗
NASA NTRS1987

Optimal signal-to-noise in digital phase lock amplifiers

An expression for the optimal signal-to-noise ratio for linear phase lock amplifiers is derived. In its digital form the optimal method can be implemented by real-time signal processing techniques. The case of detector-noise-limited systems is analyzed in detail; it is found that the optimal technique can be used to eliminate effectively the detector's time constant from the set of parameters determining the overall efficiency. This has important implications for optical detection system design. Also reported is an experimental verification of the optimal method, thus establishing its practicality.

Doering, Charles R.↗
NASA NTRS1993

Symbol signal-to-noise ratio loss in square-wave subcarrier downconversion

This article presents the simulated results of the signal-to-noise ratio (SNR) loss in the process of a square-wave subcarrier down conversion. In a previous article, the SNR degradation was evaluated at the output of the down converter based on the signal and noise power change. Unlike in the previous article, the SNR loss is defined here as the difference between the actual and theoretical symbol SNR's for the same symbol-error rate at the output of the symbol matched filter. The results show that an average SNR loss of 0.3 dB can be achieved with tenth-order infinite impulse response (IIR) filters. This loss is a 0.2-dB increase over the SNR degradation in the previous analysis where neither the signal distortion nor the symbol detector was considered.

Feria, Y.↗