SCIMP—A scanning interferometric multiplex photometer

1978 ◽  
Vol 56 (6) ◽  
pp. 681-686 ◽  
Author(s):  
G. G. Shepherd ◽  
A. J. Deans ◽  
Y. P. Neo
Keyword(s):  
Remote Sensing ◽  
Comparative Analysis ◽  
Signal To Noise Ratio ◽  
Interference Filter ◽  
Wavelength Shift ◽  
Spectral Elements ◽  
Signal To Noise ◽  
Airborne Remote Sensing ◽  
Noise Ratio ◽  
Rocket Measurements

An interference filter photometer concept is described in which equally-spaced spectral elements of equal width are generated. The method takes advantage of the wavelength shift of off-axis radiation transmitted by the filter, and is accomplished by the use of masks in the location of the field stop. This technique lends itself to multiplexing, using Fourier or Hadamard coding, but a direct spectral configuration is also possible. The advantages of the concept and a comparative analysis of signal-to-noise ratio are described. The technique has been employed in ground based airglow studies, airborne remote sensing, and rocket measurements of airglow and aurora.

Signal-to-Noise Ratio Enhancement of Spectra using Recursive Integration

1989 ◽  
Vol 43 (8) ◽  
pp. 1409-1413 ◽  
Author(s):  
Ron Williams
Keyword(s):  
Signal To Noise Ratio ◽  
Recursive Algorithm ◽  
Synthetic Data ◽  
Spectral Elements ◽  
Fourier Transform Spectrometer ◽  
Peak Shape ◽  
Signal Averaging ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Recursive Integration

A recursive algorithm independent of any functional peak shape is presented for determining optimal integration limits of spectral data from multiwavelength spectrometers. The resulting areas have significantly higher signal-to-noise ratios than the peak maxima. Signal-to-noise ratios are computed for synthetic data with both shot and white noise limitations. The algorithm is also applied to data from a Fourier transform spectrometer. For these data, integration of 25 adjacent spectral elements improves the signal-to-noise ratio as well as the signal averaging peak maxima from 25 successive spectra.

scholarly journals Comparative analysis of noise immunity of reception of nonlinear complex discrete signals with standard signals AFM-16 BPSK

Radiotekhnika ◽  
2020 ◽  
pp. 133-140
Author(s):  
S.G. Rassomakhin ◽  
A.A. Zamula ◽  
I.D. Gorbenko ◽  
Ho Tri Luc
Keyword(s):  
Comparative Analysis ◽  
Error Probability ◽  
Noise Immunity ◽  
Signal To Noise Ratio ◽  
Statistical Simulation ◽  
Gaussian Channel ◽  
Signal To Noise ◽  
Discrete Signals ◽  
Noise Ratio ◽  
Correlation Properties

The article shows that the solution to the problem of increasing the noise immunity (noise immunity and secrecy of functioning) of the ICS can be achieved using systems of nonlinear signals with improved ensemble, structural and correlation properties. Two classes of nonlinear complex discrete signals are considered: characteristic discrete signals (CDS) and cryptographic signals (CS). Methods for the synthesis of these signals are presented. The paper gives a statistical simulation model for studying the noise immunity of various classes of signals in the Gaussian channel. Using this model, estimates of the dependence of the error probability on the signal-to-noise ratio were obtained for various classes of signals, namely: CDS, KS and standard BPSK AFM-16 signals. It is shown that for the signal-to-noise ratio – 10 the error probability for the CDR is 4.6875e-06, for the CS is 3.515625e-06, and for the AFM-16 is 0.002025. Thus, the use of nonlinear complex discrete signals, in particular, CDS and KS, can significantly increase the noise immunity of signal reception in modern ICS. At the same time, taking into account the improved ensemble and structural properties of these nonlinear signals, it is possible to improve significantly the indicators of crypto- and imitation security of the systems functioning.

Error Analysis for Estimation of Trace Vapor Concentration Pathlength in Stack Plumes

2003 ◽  
Vol 57 (6) ◽  
pp. 614-621 ◽  
Author(s):  
Neal B. Gallagher ◽  
Barry M. Wise ◽  
David M. Sheen
Keyword(s):  
Remote Sensing ◽  
Error Analysis ◽  
Near Infrared ◽  
Signal To Noise Ratio ◽  
Chemical Vapor ◽  
Noise Model ◽  
Vapor Concentration ◽  
Signal To Noise ◽  
Sensing Applications ◽  
Noise Ratio

Near-infrared hyperspectral imaging is finding utility in remote sensing applications such as detection and quantification of chemical vapor effluents in stack plumes. Optimizing the sensing system or quantification algorithms is difficult because reference images are rarely well characterized. The present work uses a radiance model for a down-looking scene and a detailed noise model for dispersive and Fourier transform spectrometers to generate well-characterized synthetic data. These data were used with a classical least-squares-based estimator in an error analysis to obtain estimates of different sources of concentration-pathlength quantification error in the remote sensing problem. Contributions to the overall quantification error were the sum of individual error terms related to estimating the background, atmospheric corrections, plume temperature, and instrument signal-to-noise ratio. It was found that the quantification error depended strongly on errors in the background estimate and second-most on instrument signal-to-noise ratio. Decreases in net analyte signal (e.g., due to low analyte absorbance or increasing the number of analytes in the plume) led to increases in the quantification error as expected. These observations have implications on instrument design and strategies for quantification. The outlined approach could be used to estimate detection limits or perform variable selection for given sensing problems.

scholarly journals Fabry–Perot interferometer as a solar background noise suppressor: application to daytime lidar

2012 ◽  
Vol 55 (2) ◽  
Author(s):  
Karnam Raghunath ◽  
Karnam Ramesh ◽  
Sanama Narayana Reddy
Keyword(s):  
Background Noise ◽  
Noise Suppression ◽  
Performance Indicator ◽  
Signal To Noise Ratio ◽  
Interference Filter ◽  
Signal To Noise ◽  
Night Time ◽  
Fabry Perot ◽  
Noise Ratio ◽  
Identical Test

<p>Continuous atmospheric probing by a lidar is a requirement for many applications. However, due to high solar background noise during the daytime, lidar operations are mostly restricted to night-time. While many techniques are in practice, like reducing the receiver field of view, changing the view angle, introducing a narrow band Interference Filter (IF), these are applied to circumvent problems, rather than to suppress the noise. Using a Fabry-Perot interferometer as a narrow passband filter for solar background noise suppression is a known technique, and its potential is exploited in our system. An optical-fiber-coupled lidar system with its transmitter injection seeded was developed and has been operated during the daytime at Gadanki (13.6˚N, 79.2˚ E). The signal-to-noise ratio of the return signal is used as the performance indicator, to evaluate the improvements. Signal-to-noise ratios with and without the Fabry-Perot interferometer are measured with near identical test set-ups. The signal-to-noise ratio enhancement factor is ca. 4, in agreement with the theoretical value. The performance is compared when the receiver fields of view are changed.</p>

Sign in / Sign up

Export Citation Format

Share Document