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Results for “SIGNAL TO NOISE RATIO”

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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

Heterodyne signal-to-noise ratios in acoustic mode scattering experiments

The relation between the signal to noise ratio (SNR) obtained in heterodyne detection of radiation scattered from acoustic modes in crystalline solids and the scattered spectral density function is studied. It is shown that in addition to the information provided by the measured frequency shifts and line widths, measurement of the SNR provides a determination of the absolute elasto-optical (Pockel's) constants. Examples are given for cubic crystals, and acceptable SNR values are obtained for scattering from thermally excited phonons at 10.6 microns, with no external perturbation of the sample necessary. The results indicate the special advantages of the method for the study of semiconductors.

Cochran, W. R.↗
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 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 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 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 NTRS1989

Degradation of signal-to-noise ratio due to amplitude distortion

The effect of filtering on the signal-to-noise ratio (SNR) of a coherently demodulated band-limited signal is determined in the presence of worst-case amplitude ripple. The problem is formulated as an optimizaton in the Hilbert space L2. The form of the worst-case amplitude ripple is specified, and the degradation in the SNR is derived in closed form. It is shown that, when the maximum passband amplitude ripple is 2Delta (peak-to-peak), the SNR is degraded by at most (1-Delta-squared), even when the ripple is unknown or uncompensated. For example, an SNR loss of less than 0.01 dB due to amplitude ripple can be assured by keeping the amplitude ripple under 0.42 dB.

Sadr, Ramin↗
NASA NTRS1977

Signal-to-noise ratio analysis and evaluation of the Hadamard imaging technique

The signal-to-noise ratio performance of the Hadamard imaging technique is analyzed and an experimental evaluation of a laboratory Hadamard imager is presented. A comparison between the performances of Hadamard and conventional imaging techniques shows that the Hadamard technique is superior only when the imaging objective lens is required to have an effective F (focus) number of about 2 or slower.

Jobson, D. J.↗
NASA NTRS2002

IKONOS Signal-to-Noise Ratio Estimation

This viewgraph presentation focuses on the differences in Signal-to-Noise Ratio (SNR) between IKONOS imagery with and without Modulation Transfer Function Correction (MTFC). The researchers used a simulated scene to evaluate the effects of MTFC on SNR. They also used four very uniform IKONOS scenes, two of Antarctica, one of Ivanpah, CA, and one of Mali to estimate SNR.

Zanoni, Vicki↗
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 NTRS1984

Signal-to-noise Ratio and Combiner Weight Estimation for Symbol Stream Combining

A method is presented for signal to noise ratio (SNR) and symbol stream combiner weight estimation. The SNR estimator employs absolute value moments as in an earlier method. The main contribution is that a new algorithm is derived for the combiner weight estimator to remove the large bias at low SNRs. The new algorithm is simulated to combine two independent symbol streams at various SNRs. As an example, the combining two symbol streams at SNRs of -1 dB and -7 dB, conbiner weight estimates using 1000 samples for the -1 dB stream and 10,000 samples for the -7 dB stream achieve an output SNR of -0.039 dB, which is just 0.012 dB below the theoretical limit achievable with perfect knowledge of the SNRs.

Vo, Q. D.↗
NASA NTRS1993

An operational method for estimating signal to noise ratios from data acquired with imaging spectrometers

A method, using the concept of local means and local standard deviations of small imaging blocks and using a box counting procedure, has been developed for unsupervised estimation of the average signal to noise ratios of images in which the noise is additive. The method has been applied to simulated images with Gaussian noise, to images acquired with the Airborne Visible Infrared Imaging Spectrometer and with the Geophysical and Environmental Research Imaging Spectrometer. The method is compared with other techniques for estimating signal to noise ratios from imaging data. It is believed that the method is generally applicable to images having many small homogeneous blocks. Because the method does not take into account interband effects, it is not appropriate to use the method to estimate signal to noise ratios from images having nonnegligible interband radiometric calibration errors at spatial scales less than or equal to the size of the small imaging blocks used in the noise estimation.

Gao, Bo-Cai↗
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.↗
NASA NTRS2015

Speech Intelligibility Advantages using an Acoustic Beamformer Display

A speech intelligibility test conforming to the Modified Rhyme Test of ANSI S3.2 "Method for Measuring the Intelligibility of Speech Over Communication Systems" was conducted using a prototype 12-channel acoustic beamformer system. The target speech material (signal) was identified against speech babble (noise), with calculated signal-noise ratios of 0, 5 and 10 dB. The signal was delivered at a fixed beam orientation of 135 deg (re 90 deg as the frontal direction of the array) and the noise at 135 deg (co-located) and 0 deg (separated). A significant improvement in intelligibility from 57% to 73% was found for spatial separation for the same signal-noise ratio (0 dB). Significant effects for improved intelligibility due to spatial separation were also found for higher signal-noise ratios (5 and 10 dB).

acoustic beamforming↗
NASA NTRS1987

Automated measurement of the bit-error rate as a function of signal-to-noise ratio for microwave communications systems

The performance of microwave systems and components for digital data transmission can be characterized by a plot of the bit-error rate as a function of the signal to noise ratio (or E sub b/E sub o). Methods for the efficient automated measurement of bit-error rates and signal-to-noise ratios, developed at NASA Lewis Research Center, are described. Noise measurement considerations and time requirements for measurement accuracy, as well as computer control and data processing methods, are discussed.

Kerczewski, Robert J.↗