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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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At least 19 records

The effect of partial coherence in receiving system noise temperature on array gain for telemetry and radio frequency carrier reception for receiving systems with unequal predetection signal-to-noise ratios

Signal to noise ratio improvement realized by arraying receiving systems or stations for coherent reception is reduced when some portion of the predetection noise is coherent in the array. This effect for arrayed receiving systems with unequal apertures including expected performance for selected planets of the solar system within the array beamwidth is examined.

Brockman, M. H.↗

Periodic variations in the signal-to-noise ratios of signals received from the ICE spacecraft

Data from the ICE probe to comet Giacobini-Zinner are analyzed to determine the effects of spacecraft rotation upon the signal to noise ratio (SNR) for the two channels of data. In addition, long-term variations from sources other than rotations are considered. Results include a pronounced SNR variation over a period of three seconds (one rotation) and a lesser effect over a two minute period (possibly due to the receiving antenna conscan).

Nadeau, T.↗

Precision of Spacecraft Doppler Tracking at Low Signal-to-Noise Ratios

The signal-to-noise ratio received at Earth is typically larger than 10 dB-Hz for radio tracking of a spacecraft in deep space, allowing a phase-locked loop to execute reliable carrier tracking. Recently, missions have been proposed to utilize low-gain antennas for Doppler tracking where the signal-to-noise ratio may be at the single-digit (dB-Hz) level. In this work, we discuss spacecraft Doppler tracking at these low signal-to-noise ratios through an analysis of thermal noise on the radio link, results from ground testing with the Deep Space Network, and demonstrations with an active spacecraft. We show that by utilizing an open-loop receiver to capture the carrier signal and by applying post-processing techniques, radio data with the signal-to-noise ratio as low as 4 dB-Hz can be used to derive Doppler data with precision sufficient for tracking a spacecraft in deep space.

D. R. Buccino↗

Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware

This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the noise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.

Johnson, Hans↗

Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware

This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the n oise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Signal-to-noise ratio determination circuit

A signal-to-noise ratio (SNR) determination of an input is described, having signal components within a given frequency range and noise components, without actual measurement of the noise components. Bandpass limiter having a constant signal plus noise output level is connected to the output of the first filter, the signal-to-noise ratio of the input to the bandpass limiter being linearly related to the dbm level of signal components at the output. Calibration is connected to the bandpass limiter and is responsive to the signal components at the output to derive the SNR of the input to the determination circuit. The SNR determination circuit is disclosed for use in a diversity receiver having a plurality of input channels.

Deerkoski, L. F.↗

Improving Signal-to-Noise Ratio (SNR) for Quantum Readout Signals Using Adaptive Hardware Filters on FPGA

This research presents an innovative approach to improving the Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals, utilizing FPGA-based adaptive filtering on the control board. Our solution aims to bolster quantum computations' reliability by providing a more accurate and precise way to obtain our readout traces and achieve a higher discernability between excitation states in real-time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Estimation of signal-to-noise ratios

Statistical method estimates signal-to-noise ratios in an observed random voltage, such as the output of a telemetry receiver. Signals from a distant transmitting source, overlaid by noise signals, are monitored continuously.

Couvillon, L. A., Jr.↗

Improving Signal Signal-to -Noise Ratio (SNR) for Quantum Readout Signals Using Adaptive Hardware Filters on FPGA

This research presents an innovative approach to improving theSignalSignal-to -Noise Ratio (SNR) in superconducting quantumcomputing readout signals, utilizing adaptive filtering on thecontrol board. Our solution FPGA FPGA-based aims to bolsterquantum computations' reliability by providing a more accurateand precise way to obtain our readout traces and achieve ahigher discernability between excitation states in real real-time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Estimating the signal-to-noise ratio of 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.↗

Increased accuracy and signal-to-noise ratio through recent improvements in infra-red video bolometer fabrication and calibration

The infra-red video bolometer (IRVB) is a diagnostic equipped with an infra-red camera that measures the total radiated power in thousands of lines of sight within a large field of view. Recently validated in MAST-U [Fderici et al., Rev. Sci. Instrum. 94, 033502 (2023)], it offers a high spatial resolution map of the radiated power in the divertor region, where large gradients are expected. The IRVB’s sensing element comprises a thin layer of high Z absorbing material, typically platinum, usually coated with carbon to reduce reflections [Peterson et al., Rev. Sci. Instrum. 79, 10E301 (2008)].Here, the possibility of using a relatively inert material such as titanium, is explored that can be produced in layers up to 1 μm compared to 2.5 μm for Pt and then coat it with Pt of the desired thickness (0.3 μm per side here) and carbon. This leads to a higher temperature signal (about 3 times) and better spatial resolution (about 4 times), resulting in higher accuracy in the measured power [Peterson et al., Rev. Sci. Instrum. 79, 10E301 (2008)]. This assembly is also expected to improve foil uniformity, as the Pt layer is obtained via deposition rather than mechanical processes [Mukai et al., Rev. Sci. Instrum. 87, 2014 (2016)].Given its multi-material composition, measuring the thermal properties of the foil assembly is vital. Various methods using a calibrated laser as a heat source have been developed, analyzing the temperature profile shape [Sano et al., Plasma and Fusion Res. 7, 2405039 (2012)] and [Mukai et al., Rev. Sci. Instrum. 89, 10E114 (2018)] or fitting the calculated laser power for different intensities and frequencies [Fderici et al., Rev. Sci. Instrum. 94, 033502 (2023)]. Here, a simpler approach is presented, which relies on analyzing the separate components of the foil heat equation for a single laser exposure in a given area. This can then be iterated over the entire foil to capture local deviations.

Magnetic Fusion Energy↗

Signal processing considerations for low signal to noise ratio laser Doppler and phase Doppler signals

The relative performance of current methods used for estimating the phase and the frequency in LDV and phase Doppler applications in low signal to noise ratio conditions is analyzed. These methods include the Fourier analysis and the correlation techniques. Three methods that use the correlation function for frequency and phase estimations are evaluated in terms of accuracy and speed of processing. These methods include: (1) the frequency estimation using zero crossings counting of the auto-correlation function, (2) the Blackman-Tukey method, and (3) the AutoRegressive method (AR). The relative performance of these methods is evaluated and compared with the Fourier analysis method which provides the optimum performance in terms of the Maximum Likelihood (ML) criteria.

Ibrahim, K. M.↗

Signal-to-noise ratio losses in full spectrum combining of signals with a downconverted subcarrier

This article presents the results of the signal-to-noise ratio loss in the process of full spectrum combining of signals with a downconverted subcarrier under imperfect conditions. These imperfect conditions not only include the misalignment of the carrier, the subcarrier, and the symbols, but they also include the nonideal filtering in the subcarrier downconversion process, the cutoff of the data bandwidth, and the distortion in signal waveform.

Feria, Y.↗